Repository: wbsth/cs50ai Branch: master Commit: b97b10ad1b39 Files: 49 Total size: 1.4 MB Directory structure: gitextract_o39wik8g/ ├── .gitignore ├── README.md ├── week0/ │ ├── degrees/ │ │ ├── degrees.py │ │ ├── small/ │ │ │ ├── movies.csv │ │ │ ├── people.csv │ │ │ └── stars.csv │ │ └── util.py │ └── tictactoe/ │ ├── requirements.txt │ ├── runner.py │ └── tictactoe.py ├── week1/ │ ├── knights/ │ │ ├── logic.py │ │ └── puzzle.py │ └── minesweeper/ │ ├── minesweeper.py │ ├── requirements.txt │ └── runner.py ├── week2/ │ ├── heredity/ │ │ └── heredity.py │ └── pagerank/ │ └── pagerank.py ├── week3/ │ └── crossword/ │ ├── crossword.py │ ├── data/ │ │ ├── structure0.txt │ │ ├── structure1.txt │ │ ├── structure2.txt │ │ ├── words0.txt │ │ ├── words1.txt │ │ └── words2.txt │ └── generate.py ├── week4/ │ ├── nim/ │ │ ├── nim.py │ │ └── play.py │ └── shopping/ │ ├── shopping.csv │ └── shopping.py ├── week5/ │ └── traffic/ │ └── traffic.py └── week6/ ├── parser/ │ ├── parser.py │ └── sentences/ │ ├── 1.txt │ ├── 10.txt │ ├── 2.txt │ ├── 3.txt │ ├── 4.txt │ ├── 5.txt │ ├── 6.txt │ ├── 7.txt │ ├── 8.txt │ ├── 9.txt │ └── test.txt └── questions/ ├── corpus/ │ ├── artificial_intelligence.txt │ ├── machine_learning.txt │ ├── natural_language_processing.txt │ ├── neural_network.txt │ ├── probability.txt │ └── python.txt └── questions.py ================================================ FILE CONTENTS ================================================ ================================================ FILE: .gitignore ================================================ *.zip *.pyc __pycache__/ .venv/ venv/ .idea/ */degrees/large/ ================================================ FILE: README.md ================================================ # CS50 AI Mine solutions for CS50's Introduction to Artificial Intelligence with Python course *Warning : before visiting this repo files, please read about [CS50's Academic Honesty rules](https://cs50.harvard.edu/college/2021/fall/syllabus/#academic-honesty)*. ## Includes: * Quizzes answers * Projects solutions ## Course info: * __Name:__ CS50's Introduction to Artificial Intelligence with Python * __University:__ Harvard University * __WWW:__ https://cs50.harvard.edu/ai/2020/ ================================================ FILE: week0/degrees/degrees.py ================================================ import csv import sys from util import Node, StackFrontier, QueueFrontier # Maps names to a set of corresponding person_ids names = {} # Maps person_ids to a dictionary of: name, birth, movies (a set of movie_ids) people = {} # Maps movie_ids to a dictionary of: title, year, stars (a set of person_ids) movies = {} def load_data(directory): """ Load data from CSV files into memory. """ # Load people with open(f"{directory}/people.csv", encoding="utf-8") as f: reader = csv.DictReader(f) for row in reader: people[row["id"]] = { "name": row["name"], "birth": row["birth"], "movies": set() } if row["name"].lower() not in names: names[row["name"].lower()] = {row["id"]} else: names[row["name"].lower()].add(row["id"]) # Load movies with open(f"{directory}/movies.csv", encoding="utf-8") as f: reader = csv.DictReader(f) for row in reader: movies[row["id"]] = { "title": row["title"], "year": row["year"], "stars": set() } # Load stars with open(f"{directory}/stars.csv", encoding="utf-8") as f: reader = csv.DictReader(f) for row in reader: try: people[row["person_id"]]["movies"].add(row["movie_id"]) movies[row["movie_id"]]["stars"].add(row["person_id"]) except KeyError: pass def main(): if len(sys.argv) > 2: sys.exit("Usage: python degrees.py [directory]") directory = sys.argv[1] if len(sys.argv) == 2 else "large" # Load data from files into memory print("Loading data...") load_data(directory) print("Data loaded.") source = person_id_for_name(input("Name: ")) if source is None: sys.exit("Person not found.") target = person_id_for_name(input("Name: ")) if target is None: sys.exit("Person not found.") path = shortest_path(source, target) if path is None: print("Not connected.") else: degrees = len(path) print(f"{degrees} degrees of separation.") path = [(None, source)] + path for i in range(degrees): person1 = people[path[i][1]]["name"] person2 = people[path[i + 1][1]]["name"] movie = movies[path[i + 1][0]]["title"] print(f"{i + 1}: {person1} and {person2} starred in {movie}") def shortest_path(source, target): """ Returns the shortest list of (movie_id, person_id) pairs that connect the source to the target. If no possible path, returns None. """ start = Node(state=source, parent=None, action=None) frontier = QueueFrontier() frontier.add(start) explored = set() while True: if frontier.empty(): return None node = frontier.remove() if node.state == target: rt = [] while node.parent is not None: rt.append((node.action, node.state)) node = node.parent rt.reverse() return rt explored.add(node.state) # find neighbours neighbours = neighbors_for_person(node.state) # add neighbours to frontier for action, state in neighbours: if not frontier.contains_state(state) and state not in explored: child = Node(state=state, parent=node, action=action) if child.state == target: rt = [] while child.parent is not None: rt.append((child.action, child.state)) child = child.parent rt.reverse() return rt frontier.add(child) def person_id_for_name(name): """ Returns the IMDB id for a person's name, resolving ambiguities as needed. """ person_ids = list(names.get(name.lower(), set())) if len(person_ids) == 0: return None elif len(person_ids) > 1: print(f"Which '{name}'?") for person_id in person_ids: person = people[person_id] name = person["name"] birth = person["birth"] print(f"ID: {person_id}, Name: {name}, Birth: {birth}") try: person_id = input("Intended Person ID: ") if person_id in person_ids: return person_id except ValueError: pass return None else: return person_ids[0] def neighbors_for_person(person_id): """ Returns (movie_id, person_id) pairs for people who starred with a given person. """ movie_ids = people[person_id]["movies"] neighbors = set() for movie_id in movie_ids: for person_id in movies[movie_id]["stars"]: neighbors.add((movie_id, person_id)) return neighbors if __name__ == "__main__": main() ================================================ FILE: week0/degrees/small/movies.csv ================================================ id,title,year 112384,"Apollo 13",1995 104257,"A Few Good Men",1992 109830,"Forrest Gump",1994 93779,"The Princess Bride",1987 95953,"Rain Man",1988 ================================================ FILE: week0/degrees/small/people.csv ================================================ id,name,birth 102,"Kevin Bacon",1958 129,"Tom Cruise",1962 144,"Cary Elwes",1962 158,"Tom Hanks",1956 1597,"Mandy Patinkin",1952 163,"Dustin Hoffman",1937 1697,"Chris Sarandon",1942 193,"Demi Moore",1962 197,"Jack Nicholson",1937 200,"Bill Paxton",1955 398,"Sally Field",1946 420,"Valeria Golino",1965 596520,"Gerald R. Molen",1935 641,"Gary Sinise",1955 705,"Robin Wright",1966 914612,"Emma Watson",1990 ================================================ FILE: week0/degrees/small/stars.csv ================================================ person_id,movie_id 102,104257 102,112384 129,104257 129,95953 144,93779 158,109830 158,112384 1597,93779 163,95953 1697,93779 193,104257 197,104257 200,112384 398,109830 420,95953 596520,95953 641,109830 641,112384 705,109830 705,93779 ================================================ FILE: week0/degrees/util.py ================================================ class Node(): def __init__(self, state, parent, action): self.state = state self.parent = parent self.action = action class StackFrontier(): def __init__(self): self.frontier = [] def add(self, node): self.frontier.append(node) def contains_state(self, state): return any(node.state == state for node in self.frontier) def empty(self): return len(self.frontier) == 0 def remove(self): if self.empty(): raise Exception("empty frontier") else: node = self.frontier[-1] self.frontier = self.frontier[:-1] return node class QueueFrontier(StackFrontier): def remove(self): if self.empty(): raise Exception("empty frontier") else: node = self.frontier[0] self.frontier = self.frontier[1:] return node ================================================ FILE: week0/tictactoe/requirements.txt ================================================ pygame ================================================ FILE: week0/tictactoe/runner.py ================================================ import pygame import sys import time import tictactoe as ttt pygame.init() size = width, height = 600, 400 # Colors black = (0, 0, 0) white = (255, 255, 255) screen = pygame.display.set_mode(size) mediumFont = pygame.font.Font("OpenSans-Regular.ttf", 28) largeFont = pygame.font.Font("OpenSans-Regular.ttf", 40) moveFont = pygame.font.Font("OpenSans-Regular.ttf", 60) user = None board = ttt.initial_state() ai_turn = False while True: for event in pygame.event.get(): if event.type == pygame.QUIT: sys.exit() screen.fill(black) # Let user choose a player. if user is None: # Draw title title = largeFont.render("Play Tic-Tac-Toe", True, white) titleRect = title.get_rect() titleRect.center = ((width / 2), 50) screen.blit(title, titleRect) # Draw buttons playXButton = pygame.Rect((width / 8), (height / 2), width / 4, 50) playX = mediumFont.render("Play as X", True, black) playXRect = playX.get_rect() playXRect.center = playXButton.center pygame.draw.rect(screen, white, playXButton) screen.blit(playX, playXRect) playOButton = pygame.Rect(5 * (width / 8), (height / 2), width / 4, 50) playO = mediumFont.render("Play as O", True, black) playORect = playO.get_rect() playORect.center = playOButton.center pygame.draw.rect(screen, white, playOButton) screen.blit(playO, playORect) # Check if button is clicked click, _, _ = pygame.mouse.get_pressed() if click == 1: mouse = pygame.mouse.get_pos() if playXButton.collidepoint(mouse): time.sleep(0.2) user = ttt.X elif playOButton.collidepoint(mouse): time.sleep(0.2) user = ttt.O else: # Draw game board tile_size = 80 tile_origin = (width / 2 - (1.5 * tile_size), height / 2 - (1.5 * tile_size)) tiles = [] for i in range(3): row = [] for j in range(3): rect = pygame.Rect( tile_origin[0] + j * tile_size, tile_origin[1] + i * tile_size, tile_size, tile_size ) pygame.draw.rect(screen, white, rect, 3) if board[i][j] != ttt.EMPTY: move = moveFont.render(board[i][j], True, white) moveRect = move.get_rect() moveRect.center = rect.center screen.blit(move, moveRect) row.append(rect) tiles.append(row) game_over = ttt.terminal(board) player = ttt.player(board) # Show title if game_over: winner = ttt.winner(board) if winner is None: title = f"Game Over: Tie." else: title = f"Game Over: {winner} wins." elif user == player: title = f"Play as {user}" else: title = f"Computer thinking..." title = largeFont.render(title, True, white) titleRect = title.get_rect() titleRect.center = ((width / 2), 30) screen.blit(title, titleRect) # Check for AI move if user != player and not game_over: if ai_turn: time.sleep(0.5) move = ttt.minimax(board) board = ttt.result(board, move) ai_turn = False else: ai_turn = True # Check for a user move click, _, _ = pygame.mouse.get_pressed() if click == 1 and user == player and not game_over: mouse = pygame.mouse.get_pos() for i in range(3): for j in range(3): if (board[i][j] == ttt.EMPTY and tiles[i][j].collidepoint(mouse)): board = ttt.result(board, (i, j)) if game_over: againButton = pygame.Rect(width / 3, height - 65, width / 3, 50) again = mediumFont.render("Play Again", True, black) againRect = again.get_rect() againRect.center = againButton.center pygame.draw.rect(screen, white, againButton) screen.blit(again, againRect) click, _, _ = pygame.mouse.get_pressed() if click == 1: mouse = pygame.mouse.get_pos() if againButton.collidepoint(mouse): time.sleep(0.2) user = None board = ttt.initial_state() ai_turn = False pygame.display.flip() ================================================ FILE: week0/tictactoe/tictactoe.py ================================================ """ Tic Tac Toe Player """ import math from copy import deepcopy X = "X" O = "O" EMPTY = None def initial_state(): """ Returns starting state of the board. """ return [[EMPTY, EMPTY, EMPTY], [EMPTY, EMPTY, EMPTY], [EMPTY, EMPTY, EMPTY]] def player(board): """ Returns player who has the next turn on a board. """ Xcount = 0 Ocount = 0 for row in board: Xcount += row.count(X) Ocount += row.count(O) if Xcount <= Ocount: return X else: return O def actions(board): """ Returns set of all possible actions (i, j) available on the board. """ possible_moves = set() for row_index, row in enumerate(board): for column_index, item in enumerate(row): if item == None: possible_moves.add((row_index, column_index)) return possible_moves def result(board, action): """ Returns the board that results from making move (i, j) on the board. """ player_move = player(board) new_board = deepcopy(board) i, j = action if board[i][j] != None: raise Exception else: new_board[i][j] = player_move return new_board def winner(board): """ Returns the winner of the game, if there is one. """ for player in (X, O): # check vertical for row in board: if row == [player] * 3: return player # check horizontal for i in range(3): column = [board[x][i] for x in range(3)] if column == [player] * 3: return player # check diagonal if [board[i][i] for i in range(0, 3)] == [player] * 3: return player elif [board[i][~i] for i in range(0, 3)] == [player] * 3: return player return None def terminal(board): """ Returns True if game is over, False otherwise. """ # game is won by one of the players if winner(board) != None: return True # moves still possible for row in board: if EMPTY in row: return False # no possible moves return True def utility(board): """ Returns 1 if X has won the game, -1 if O has won, 0 otherwise. """ win_player = winner(board) if win_player == X: return 1 elif win_player == O: return -1 else: return 0 def minimax(board): """ Returns the optimal action for the current player on the board. """ def max_value(board): optimal_move = () if terminal(board): return utility(board), optimal_move else: v = -5 for action in actions(board): minval = min_value(result(board, action))[0] if minval > v: v = minval optimal_move = action return v, optimal_move def min_value(board): optimal_move = () if terminal(board): return utility(board), optimal_move else: v = 5 for action in actions(board): maxval = max_value(result(board, action))[0] if maxval < v: v = maxval optimal_move = action return v, optimal_move curr_player = player(board) if terminal(board): return None if curr_player == X: return max_value(board)[1] else: return min_value(board)[1] ================================================ FILE: week1/knights/logic.py ================================================ import itertools class Sentence(): def evaluate(self, model): """Evaluates the logical sentence.""" raise Exception("nothing to evaluate") def formula(self): """Returns string formula representing logical sentence.""" return "" def symbols(self): """Returns a set of all symbols in the logical sentence.""" return set() @classmethod def validate(cls, sentence): if not isinstance(sentence, Sentence): raise TypeError("must be a logical sentence") @classmethod def parenthesize(cls, s): """Parenthesizes an expression if not already parenthesized.""" def balanced(s): """Checks if a string has balanced parentheses.""" count = 0 for c in s: if c == "(": count += 1 elif c == ")": if count <= 0: return False count -= 1 return count == 0 if not len(s) or s.isalpha() or ( s[0] == "(" and s[-1] == ")" and balanced(s[1:-1]) ): return s else: return f"({s})" class Symbol(Sentence): def __init__(self, name): self.name = name def __eq__(self, other): return isinstance(other, Symbol) and self.name == other.name def __hash__(self): return hash(("symbol", self.name)) def __repr__(self): return self.name def evaluate(self, model): try: return bool(model[self.name]) except KeyError: raise Exception(f"variable {self.name} not in model") def formula(self): return self.name def symbols(self): return {self.name} class Not(Sentence): def __init__(self, operand): Sentence.validate(operand) self.operand = operand def __eq__(self, other): return isinstance(other, Not) and self.operand == other.operand def __hash__(self): return hash(("not", hash(self.operand))) def __repr__(self): return f"Not({self.operand})" def evaluate(self, model): return not self.operand.evaluate(model) def formula(self): return "¬" + Sentence.parenthesize(self.operand.formula()) def symbols(self): return self.operand.symbols() class And(Sentence): def __init__(self, *conjuncts): for conjunct in conjuncts: Sentence.validate(conjunct) self.conjuncts = list(conjuncts) def __eq__(self, other): return isinstance(other, And) and self.conjuncts == other.conjuncts def __hash__(self): return hash( ("and", tuple(hash(conjunct) for conjunct in self.conjuncts)) ) def __repr__(self): conjunctions = ", ".join( [str(conjunct) for conjunct in self.conjuncts] ) return f"And({conjunctions})" def add(self, conjunct): Sentence.validate(conjunct) self.conjuncts.append(conjunct) def evaluate(self, model): return all(conjunct.evaluate(model) for conjunct in self.conjuncts) def formula(self): if len(self.conjuncts) == 1: return self.conjuncts[0].formula() return " ∧ ".join([Sentence.parenthesize(conjunct.formula()) for conjunct in self.conjuncts]) def symbols(self): return set.union(*[conjunct.symbols() for conjunct in self.conjuncts]) class Or(Sentence): def __init__(self, *disjuncts): for disjunct in disjuncts: Sentence.validate(disjunct) self.disjuncts = list(disjuncts) def __eq__(self, other): return isinstance(other, Or) and self.disjuncts == other.disjuncts def __hash__(self): return hash( ("or", tuple(hash(disjunct) for disjunct in self.disjuncts)) ) def __repr__(self): disjuncts = ", ".join([str(disjunct) for disjunct in self.disjuncts]) return f"Or({disjuncts})" def evaluate(self, model): return any(disjunct.evaluate(model) for disjunct in self.disjuncts) def formula(self): if len(self.disjuncts) == 1: return self.disjuncts[0].formula() return " ∨ ".join([Sentence.parenthesize(disjunct.formula()) for disjunct in self.disjuncts]) def symbols(self): return set.union(*[disjunct.symbols() for disjunct in self.disjuncts]) class Implication(Sentence): def __init__(self, antecedent, consequent): Sentence.validate(antecedent) Sentence.validate(consequent) self.antecedent = antecedent self.consequent = consequent def __eq__(self, other): return (isinstance(other, Implication) and self.antecedent == other.antecedent and self.consequent == other.consequent) def __hash__(self): return hash(("implies", hash(self.antecedent), hash(self.consequent))) def __repr__(self): return f"Implication({self.antecedent}, {self.consequent})" def evaluate(self, model): return ((not self.antecedent.evaluate(model)) or self.consequent.evaluate(model)) def formula(self): antecedent = Sentence.parenthesize(self.antecedent.formula()) consequent = Sentence.parenthesize(self.consequent.formula()) return f"{antecedent} => {consequent}" def symbols(self): return set.union(self.antecedent.symbols(), self.consequent.symbols()) class Biconditional(Sentence): def __init__(self, left, right): Sentence.validate(left) Sentence.validate(right) self.left = left self.right = right def __eq__(self, other): return (isinstance(other, Biconditional) and self.left == other.left and self.right == other.right) def __hash__(self): return hash(("biconditional", hash(self.left), hash(self.right))) def __repr__(self): return f"Biconditional({self.left}, {self.right})" def evaluate(self, model): return ((self.left.evaluate(model) and self.right.evaluate(model)) or (not self.left.evaluate(model) and not self.right.evaluate(model))) def formula(self): left = Sentence.parenthesize(str(self.left)) right = Sentence.parenthesize(str(self.right)) return f"{left} <=> {right}" def symbols(self): return set.union(self.left.symbols(), self.right.symbols()) def model_check(knowledge, query): """Checks if knowledge base entails query.""" def check_all(knowledge, query, symbols, model): """Checks if knowledge base entails query, given a particular model.""" # If model has an assignment for each symbol if not symbols: # If knowledge base is true in model, then query must also be true if knowledge.evaluate(model): return query.evaluate(model) return True else: # Choose one of the remaining unused symbols remaining = symbols.copy() p = remaining.pop() # Create a model where the symbol is true model_true = model.copy() model_true[p] = True # Create a model where the symbol is false model_false = model.copy() model_false[p] = False # Ensure entailment holds in both models return (check_all(knowledge, query, remaining, model_true) and check_all(knowledge, query, remaining, model_false)) # Get all symbols in both knowledge and query symbols = set.union(knowledge.symbols(), query.symbols()) # Check that knowledge entails query return check_all(knowledge, query, symbols, dict()) ================================================ FILE: week1/knights/puzzle.py ================================================ from logic import * AKnight = Symbol("A is a Knight") AKnave = Symbol("A is a Knave") BKnight = Symbol("B is a Knight") BKnave = Symbol("B is a Knave") CKnight = Symbol("C is a Knight") CKnave = Symbol("C is a Knave") # Puzzle 0 # A says "I am both a knight and a knave." knowledge0 = And( Not(And(AKnight, AKnave)), # cannot be knight and knave at the same time Or(AKnight, AKnave), # will be Knight or Knave Implication(AKnight, And(AKnight, AKnave)), Implication(AKnave, Not(And(AKnight, AKnave))) ) # Puzzle 1 # A says "We are both knaves." # B says nothing. knowledge1 = And( Not(And(AKnight, AKnave)), # cannot be knight and knave at the same time Or(AKnight, AKnave), # will be Knight or Knave Not(And(BKnight, BKnave)), # cannot be knight and knave at the same time Or(BKnight, BKnave), # will be Knight or Knave # Or(And(AKnight, BKnave), And(AKnave, BKnight)), # A and B can't be the same figure Implication(AKnight, And(AKnave, BKnave)), Implication(AKnave, Not(And(AKnave, BKnave))) ) # Puzzle 2 # A says "We are the same kind." # B says "We are of different kinds." knowledge2 = And( Not(And(AKnight, AKnave)), # cannot be knight and knave at the same time Or(AKnight, AKnave), # will be Knight or Knave Not(And(BKnight, BKnave)), # cannot be knight and knave at the same time Or(BKnight, BKnave), # will be Knight or Knave # Or(And(AKnight, BKnave), And(AKnave, BKnight)), # A and B can't be the same figure Implication(AKnight, And(AKnight, BKnight)), Implication(AKnave, Not(And(AKnave, BKnave))), Implication(BKnight, And(BKnight, AKnave)), Implication(BKnave, Not(And(BKnave, AKnight))) ) # Puzzle 3 # A says either "I am a knight." or "I am a knave.", but you don't know which. # B says "A said 'I am a knave'." # B says "C is a knave." # C says "A is a knight." knowledge3 = And( Not(And(AKnight, AKnave)), # A cannot be knight and knave at the same time Or(AKnight, AKnave), # A will be Knight or Knave Not(And(BKnight, BKnave)), # B cannot be knight and knave at the same time Or(BKnight, BKnave), # B will be Knight or Knave Not(And(CKnight, CKnave)), # C cannot be knight and knave at the same time Or(CKnight, CKnave), # C will be Knight or Knave # A says either "I am a knight." or "I am a knave.", but you don't know which. Or( # "I am a knight." And( Implication(AKnight, AKnight), Implication(AKnave, Not(AKnight)) ), # "I am a knave." And( Implication(AKnight, AKnave), Implication(AKnave, Not(AKnave)) ) ), Not(And( # "I am a knight." And( Implication(AKnight, AKnight), Implication(AKnave, Not(AKnight)) ), # "I am a knave." And( Implication(AKnight, AKnave), Implication(AKnave, Not(AKnave)) ) )), # B says "A said 'I am a knave'." Implication(BKnight, And( Implication(AKnight, AKnave), Implication(AKnave, Not(AKnave)) )), Implication(BKnave, Not(And( Implication(AKnight, AKnave), Implication(AKnave, Not(AKnave)) ))), # B says "C is a knave." Implication(BKnight, CKnave), Implication(BKnave, Not(CKnave)), # C says "A is a knight." Implication(CKnight, AKnight), Implication(CKnave, Not(AKnight)) ) def main(): symbols = [AKnight, AKnave, BKnight, BKnave, CKnight, CKnave] puzzles = [ ("Puzzle 0", knowledge0), ("Puzzle 1", knowledge1), ("Puzzle 2", knowledge2), ("Puzzle 3", knowledge3) ] for puzzle, knowledge in puzzles: print(puzzle) if len(knowledge.conjuncts) == 0: print(" Not yet implemented.") else: for symbol in symbols: if model_check(knowledge, symbol): print(f" {symbol}") if __name__ == "__main__": main() ================================================ FILE: week1/minesweeper/minesweeper.py ================================================ import itertools import random import copy class Minesweeper(): """ Minesweeper game representation """ def __init__(self, height=8, width=8, mines=8): # Set initial width, height, and number of mines self.height = height self.width = width self.mines = set() # Initialize an empty field with no mines self.board = [] for i in range(self.height): row = [] for j in range(self.width): row.append(False) self.board.append(row) # Add mines randomly while len(self.mines) != mines: i = random.randrange(height) j = random.randrange(width) if not self.board[i][j]: self.mines.add((i, j)) self.board[i][j] = True # At first, player has found no mines self.mines_found = set() def print(self): """ Prints a text-based representation of where mines are located. """ for i in range(self.height): print("--" * self.width + "-") for j in range(self.width): if self.board[i][j]: print("|X", end="") else: print("| ", end="") print("|") print("--" * self.width + "-") def is_mine(self, cell): i, j = cell return self.board[i][j] def nearby_mines(self, cell): """ Returns the number of mines that are within one row and column of a given cell, not including the cell itself. """ # Keep count of nearby mines count = 0 # Loop over all cells within one row and column for i in range(cell[0] - 1, cell[0] + 2): for j in range(cell[1] - 1, cell[1] + 2): # Ignore the cell itself if (i, j) == cell: continue # Update count if cell in bounds and is mine if 0 <= i < self.height and 0 <= j < self.width: if self.board[i][j]: count += 1 return count def won(self): """ Checks if all mines have been flagged. """ return self.mines_found == self.mines class Sentence(): """ Logical statement about a Minesweeper game A sentence consists of a set of board cells, and a count of the number of those cells which are mines. """ def __init__(self, cells, count): self.cells = set(cells) self.count = count def __eq__(self, other): return self.cells == other.cells and self.count == other.count def __str__(self): return f"{self.cells} = {self.count}" def known_mines(self): """ Returns the set of all cells in self.cells known to be mines. """ if self.count == len(self.cells): return self.cells def known_safes(self): """ Returns the set of all cells in self.cells known to be safe. """ if self.count == 0: return self.cells def mark_mine(self, cell): """ Updates internal knowledge representation given the fact that a cell is known to be a mine. """ if cell in self.cells: self.cells.remove(cell) self.count -= 1 else: pass def mark_safe(self, cell): """ Updates internal knowledge representation given the fact that a cell is known to be safe. """ if cell in self.cells: self.cells.remove(cell) else: pass class MinesweeperAI(): """ Minesweeper game player """ def __init__(self, height=8, width=8): # Set initial height and width self.height = height self.width = width # Keep track of which cells have been clicked on self.moves_made = set() # Keep track of cells known to be safe or mines self.mines = set() self.safes = set() # List of sentences about the game known to be true self.knowledge = [] def mark_mine(self, cell): """ Marks a cell as a mine, and updates all knowledge to mark that cell as a mine as well. """ self.mines.add(cell) for sentence in self.knowledge: sentence.mark_mine(cell) def mark_safe(self, cell): """ Marks a cell as safe, and updates all knowledge to mark that cell as safe as well. """ self.safes.add(cell) for sentence in self.knowledge: sentence.mark_safe(cell) def add_knowledge(self, cell, count): """ Called when the Minesweeper board tells us, for a given safe cell, how many neighboring cells have mines in them. This function should: 1) mark the cell as a move that has been made 2) mark the cell as safe 3) add a new sentence to the AI's knowledge base based on the value of `cell` and `count` 4) mark any additional cells as safe or as mines if it can be concluded based on the AI's knowledge base 5) add any new sentences to the AI's knowledge base if they can be inferred from existing knowledge """ # mark the cell as one of the moves made in the game self.moves_made.add(cell) # mark the cell as a safe cell, updating any sequences that contain the cell as well self.mark_safe(cell) # add new sentence to AI knowledge base based on value of cell and count cells = set() count_cpy = copy.deepcopy(count) close_cells = self.return_close_cells(cell) # returns neighbour cells for cl in close_cells: if cl in self.mines: count_cpy -= 1 if cl not in self.mines | self.safes: cells.add(cl) # only add cells that are of unknown state new_sentence = Sentence(cells, count_cpy) # prepare new sentence if len(new_sentence.cells) > 0: # add that sentence to knowledge only if it is not empty self.knowledge.append(new_sentence) # print(f"Adding new sentence: {new_sentence}") # # print("Printing knowledge:") # for sent in self.knowledge: # # print(sent) # check sentences for new cells that could be marked as safe or as mine self.check_knowledge() # print(f"Safe cells: {self.safes - self.moves_made}") # print(f"Mine cells: {self.mines}") # print("------------") self.extra_inference() def return_close_cells(self, cell): """ returns cell that are 1 cell away from cell passed in arg """ # returns cells close to arg cell by 1 cell close_cells = set() for rows in range(self.height): for columns in range(self.width): if abs(cell[0] - rows) <= 1 and abs(cell[1] - columns) <= 1 and (rows, columns) != cell: close_cells.add((rows, columns)) return close_cells def check_knowledge(self): """ check knowledge for new safes and mines, updates knowledge if possible """ # copies the knowledge to operate on copy knowledge_copy = copy.deepcopy(self.knowledge) # iterates through sentences for sentence in knowledge_copy: if len(sentence.cells) == 0: try: self.knowledge.remove(sentence) except ValueError: pass # check for possible mines and safes mines = sentence.known_mines() safes = sentence.known_safes() # update knowledge if mine or safe was found if mines: for mine in mines: # print(f"Marking {mine} as mine") self.mark_mine(mine) self.check_knowledge() if safes: for safe in safes: # print(f"Marking {safe} as safe") self.mark_safe(safe) self.check_knowledge() def extra_inference(self): """ update knowledge based on inference """ # iterate through pairs of sentences for sentence1 in self.knowledge: for sentence2 in self.knowledge: # check if sentence 1 is subset of sentence 2 if sentence1.cells.issubset(sentence2.cells): new_cells = sentence2.cells - sentence1.cells new_count = sentence2.count - sentence1.count new_sentence = Sentence(new_cells, new_count) mines = new_sentence.known_mines() safes = new_sentence.known_safes() if mines: for mine in mines: # print(f"Used inference to mark mine: {mine}") # print(f"FinalSen: {new_sentence}") # print(f"Sent1: {sent1copy}") # print(f"Sent2: {sent2copy}") self.mark_mine(mine) if safes: for safe in safes: # print(f"Used inference to mark safe: {safe}") # print(f"FinalSen: {new_sentence}") # print(f"Sent1: {sent1copy}") # print(f"Sent2: {sent2copy}") self.mark_safe(safe) def make_safe_move(self): """ Returns a safe cell to choose on the Minesweeper board. The move must be known to be safe, and not already a move that has been made. This function may use the knowledge in self.mines, self.safes and self.moves_made, but should not modify any of those values. """ for i in self.safes - self.moves_made: # choose first safe cell that wasn't picked before # print(f"Making {i} move") return i return None def make_random_move(self): """ Returns a move to make on the Minesweeper board. Should choose randomly among cells that: 1) have not already been chosen, and 2) are not known to be mines """ maxmoves = self.width * self.height while maxmoves > 0: maxmoves -= 1 row = random.randrange(self.height) column = random.randrange(self.width) if (row, column) not in self.moves_made | self.mines: return (row, column) return None ================================================ FILE: week1/minesweeper/requirements.txt ================================================ pygame ================================================ FILE: week1/minesweeper/runner.py ================================================ import pygame import sys import time from minesweeper import Minesweeper, MinesweeperAI HEIGHT = 8 WIDTH = 8 MINES = 8 # Colors BLACK = (0, 0, 0) GRAY = (180, 180, 180) WHITE = (255, 255, 255) # Create game pygame.init() size = width, height = 600, 400 screen = pygame.display.set_mode(size) # Fonts OPEN_SANS = "assets/fonts/OpenSans-Regular.ttf" smallFont = pygame.font.Font(OPEN_SANS, 20) mediumFont = pygame.font.Font(OPEN_SANS, 28) largeFont = pygame.font.Font(OPEN_SANS, 40) # Compute board size BOARD_PADDING = 20 board_width = ((2 / 3) * width) - (BOARD_PADDING * 2) board_height = height - (BOARD_PADDING * 2) cell_size = int(min(board_width / WIDTH, board_height / HEIGHT)) board_origin = (BOARD_PADDING, BOARD_PADDING) # Add images flag = pygame.image.load("assets/images/flag.png") flag = pygame.transform.scale(flag, (cell_size, cell_size)) mine = pygame.image.load("assets/images/mine.png") mine = pygame.transform.scale(mine, (cell_size, cell_size)) # Create game and AI agent game = Minesweeper(height=HEIGHT, width=WIDTH, mines=MINES) ai = MinesweeperAI(height=HEIGHT, width=WIDTH) # Keep track of revealed cells, flagged cells, and if a mine was hit revealed = set() flags = set() lost = False # Show instructions initially instructions = True while True: # Check if game quit for event in pygame.event.get(): if event.type == pygame.QUIT: sys.exit() screen.fill(BLACK) # Show game instructions if instructions: # Title title = largeFont.render("Play Minesweeper", True, WHITE) titleRect = title.get_rect() titleRect.center = ((width / 2), 50) screen.blit(title, titleRect) # Rules rules = [ "Click a cell to reveal it.", "Right-click a cell to mark it as a mine.", "Mark all mines successfully to win!" ] for i, rule in enumerate(rules): line = smallFont.render(rule, True, WHITE) lineRect = line.get_rect() lineRect.center = ((width / 2), 150 + 30 * i) screen.blit(line, lineRect) # Play game button buttonRect = pygame.Rect((width / 4), (3 / 4) * height, width / 2, 50) buttonText = mediumFont.render("Play Game", True, BLACK) buttonTextRect = buttonText.get_rect() buttonTextRect.center = buttonRect.center pygame.draw.rect(screen, WHITE, buttonRect) screen.blit(buttonText, buttonTextRect) # Check if play button clicked click, _, _ = pygame.mouse.get_pressed() if click == 1: mouse = pygame.mouse.get_pos() if buttonRect.collidepoint(mouse): instructions = False time.sleep(0.3) pygame.display.flip() continue # Draw board cells = [] for i in range(HEIGHT): row = [] for j in range(WIDTH): # Draw rectangle for cell rect = pygame.Rect( board_origin[0] + j * cell_size, board_origin[1] + i * cell_size, cell_size, cell_size ) pygame.draw.rect(screen, GRAY, rect) pygame.draw.rect(screen, WHITE, rect, 3) # Add a mine, flag, or number if needed if game.is_mine((i, j)) and lost: screen.blit(mine, rect) elif (i, j) in flags: screen.blit(flag, rect) elif (i, j) in revealed: neighbors = smallFont.render( str(game.nearby_mines((i, j))), True, BLACK ) neighborsTextRect = neighbors.get_rect() neighborsTextRect.center = rect.center screen.blit(neighbors, neighborsTextRect) row.append(rect) cells.append(row) # AI Move button aiButton = pygame.Rect( (2 / 3) * width + BOARD_PADDING, (1 / 3) * height - 50, (width / 3) - BOARD_PADDING * 2, 50 ) buttonText = mediumFont.render("AI Move", True, BLACK) buttonRect = buttonText.get_rect() buttonRect.center = aiButton.center pygame.draw.rect(screen, WHITE, aiButton) screen.blit(buttonText, buttonRect) # Reset button resetButton = pygame.Rect( (2 / 3) * width + BOARD_PADDING, (1 / 3) * height + 20, (width / 3) - BOARD_PADDING * 2, 50 ) buttonText = mediumFont.render("Reset", True, BLACK) buttonRect = buttonText.get_rect() buttonRect.center = resetButton.center pygame.draw.rect(screen, WHITE, resetButton) screen.blit(buttonText, buttonRect) # Display text text = "Lost" if lost else "Won" if game.mines == flags else "" text = mediumFont.render(text, True, WHITE) textRect = text.get_rect() textRect.center = ((5 / 6) * width, (2 / 3) * height) screen.blit(text, textRect) move = None left, _, right = pygame.mouse.get_pressed() # Check for a right-click to toggle flagging if right == 1 and not lost: mouse = pygame.mouse.get_pos() for i in range(HEIGHT): for j in range(WIDTH): if cells[i][j].collidepoint(mouse) and (i, j) not in revealed: if (i, j) in flags: flags.remove((i, j)) else: flags.add((i, j)) time.sleep(0.2) elif left == 1: mouse = pygame.mouse.get_pos() # If AI button clicked, make an AI move if aiButton.collidepoint(mouse) and not lost: move = ai.make_safe_move() if move is None: move = ai.make_random_move() if move is None: flags = ai.mines.copy() print("No moves left to make.") else: print("No known safe moves, AI making random move.") else: print("AI making safe move.") time.sleep(0.2) # Reset game state elif resetButton.collidepoint(mouse): game = Minesweeper(height=HEIGHT, width=WIDTH, mines=MINES) ai = MinesweeperAI(height=HEIGHT, width=WIDTH) revealed = set() flags = set() lost = False continue # User-made move elif not lost: for i in range(HEIGHT): for j in range(WIDTH): if (cells[i][j].collidepoint(mouse) and (i, j) not in flags and (i, j) not in revealed): move = (i, j) # Make move and update AI knowledge if move: if game.is_mine(move): lost = True else: nearby = game.nearby_mines(move) revealed.add(move) ai.add_knowledge(move, nearby) pygame.display.flip() ================================================ FILE: week2/heredity/heredity.py ================================================ import csv import itertools import sys PROBS = { # Unconditional probabilities for having gene "gene": { 2: 0.01, 1: 0.03, 0: 0.96 }, "trait": { # Probability of trait given two copies of gene 2: { True: 0.65, False: 0.35 }, # Probability of trait given one copy of gene 1: { True: 0.56, False: 0.44 }, # Probability of trait given no gene 0: { True: 0.01, False: 0.99 } }, # Mutation probability "mutation": 0.01 } def main(): # Check for proper usage if len(sys.argv) != 2: sys.exit("Usage: python heredity.py data.csv") people = load_data(sys.argv[1]) # Keep track of gene and trait probabilities for each person probabilities = { person: { "gene": { 2: 0, 1: 0, 0: 0 }, "trait": { True: 0, False: 0 } } for person in people } # Loop over all sets of people who might have the trait names = set(people) for have_trait in powerset(names): # Check if current set of people violates known information fails_evidence = any( (people[person]["trait"] is not None and people[person]["trait"] != (person in have_trait)) for person in names ) if fails_evidence: continue # Loop over all sets of people who might have the gene for one_gene in powerset(names): for two_genes in powerset(names - one_gene): # Update probabilities with new joint probability p = joint_probability(people, one_gene, two_genes, have_trait) update(probabilities, one_gene, two_genes, have_trait, p) # Ensure probabilities sum to 1 normalize(probabilities) # Print results for person in people: print(f"{person}:") for field in probabilities[person]: print(f" {field.capitalize()}:") for value in probabilities[person][field]: p = probabilities[person][field][value] print(f" {value}: {p:.4f}") def load_data(filename): """ Load gene and trait data from a file into a dictionary. File assumed to be a CSV containing fields name, mother, father, trait. mother, father must both be blank, or both be valid names in the CSV. trait should be 0 or 1 if trait is known, blank otherwise. """ data = dict() with open(filename) as f: reader = csv.DictReader(f) for row in reader: name = row["name"] data[name] = { "name": name, "mother": row["mother"] or None, "father": row["father"] or None, "trait": (True if row["trait"] == "1" else False if row["trait"] == "0" else None) } return data def powerset(s): """ Return a list of all possible subsets of set s. """ s = list(s) return [ set(s) for s in itertools.chain.from_iterable( itertools.combinations(s, r) for r in range(len(s) + 1) ) ] def joint_probability(people, one_gene, two_genes, have_trait): """ Compute and return a joint probability. The probability returned should be the probability that * everyone in set `one_gene` has one copy of the gene, and * everyone in set `two_genes` has two copies of the gene, and * everyone not in `one_gene` or `two_gene` does not have the gene, and * everyone in set `have_trait` has the trait, and * everyone not in set` have_trait` does not have the trait. """ probability = 1 for person in people: gene_number = 1 if person in one_gene else 2 if person in two_genes else 0 trait = True if person in have_trait else False gene_numb_prop = PROBS['gene'][gene_number] trait_prop = PROBS['trait'][gene_number][trait] if people[person]['mother'] is None: # no parents, use probability distribution probability *= gene_numb_prop * trait_prop else: # info about parents is available mother = people[person]['mother'] father = people[person]['father'] percentages = {} for ppl in [mother, father]: number = 1 if ppl in one_gene else 2 if ppl in two_genes else 0 perc = 0 + PROBS['mutation'] if number == 0 else 0.5 if number == 1 else 1 - PROBS['mutation'] percentages[ppl] = perc if gene_number == 0: # 0, none of parents gave gene probability *= (1 - percentages[mother]) * (1 - percentages[father]) elif gene_number == 1: # 1, one of parents gave gene probability *= (1 - percentages[mother]) * percentages[father] + percentages[mother] * (1 - percentages[father]) else: # 2, both of parents gave gene probability *= percentages[mother] * percentages[father] probability *= trait_prop return probability def update(probabilities, one_gene, two_genes, have_trait, p): """ Add to `probabilities` a new joint probability `p`. Each person should have their "gene" and "trait" distributions updated. Which value for each distribution is updated depends on whether the person is in `have_gene` and `have_trait`, respectively. """ for person in probabilities: gene_number = 1 if person in one_gene else 2 if person in two_genes else 0 probabilities[person]["gene"][gene_number] += p probabilities[person]["trait"][person in have_trait] += p def normalize(probabilities): """ Update `probabilities` such that each probability distribution is normalized (i.e., sums to 1, with relative proportions the same). """ normalized = probabilities.copy() for person in probabilities: for typ in ['gene', 'trait']: summed = sum(probabilities[person][typ].values()) for category in probabilities[person][typ]: val = probabilities[person][typ][category] normalized_val = val / summed normalized[person][typ][category] = normalized_val return normalized if __name__ == "__main__": main() ================================================ FILE: week2/pagerank/pagerank.py ================================================ import os import random import re import sys DAMPING = 0.85 SAMPLES = 10000 def main(): if len(sys.argv) != 2: sys.exit("Usage: python pagerank.py corpus") corpus = crawl(sys.argv[1]) ranks = sample_pagerank(corpus, DAMPING, SAMPLES) print(f"PageRank Results from Sampling (n = {SAMPLES})") for page in sorted(ranks): print(f" {page}: {ranks[page]:.4f}") ranks = iterate_pagerank(corpus, DAMPING) print(f"PageRank Results from Iteration") for page in sorted(ranks): print(f" {page}: {ranks[page]:.4f}") def crawl(directory): """ Parse a directory of HTML pages and check for links to other pages. Return a dictionary where each key is a page, and values are a list of all other pages in the corpus that are linked to by the page. """ pages = dict() # Extract all links from HTML files for filename in os.listdir(directory): if not filename.endswith(".html"): continue with open(os.path.join(directory, filename)) as f: contents = f.read() links = re.findall(r"]*?)href=\"([^\"]*)\"", contents) pages[filename] = set(links) - {filename} # Only include links to other pages in the corpus for filename in pages: pages[filename] = set( link for link in pages[filename] if link in pages ) return pages def transition_model(corpus, page, damping_factor): """ Return a probability distribution over which page to visit next, given a current page. With probability `damping_factor`, choose a link at random linked to by `page`. With probability `1 - damping_factor`, choose a link at random chosen from all pages in the corpus. """ prop_dist = {} # check if page has outgoing links dict_len = len(corpus.keys()) pages_len = len(corpus[page]) if len(corpus[page]) < 1: # no outgoing pages, choosing randomly from all possible pages for key in corpus.keys(): prop_dist[key] = 1 / dict_len else: # there are outgoing pages, calculating distribution random_factor = (1 - damping_factor) / dict_len even_factor = damping_factor / pages_len for key in corpus.keys(): if key not in corpus[page]: prop_dist[key] = random_factor else: prop_dist[key] = even_factor + random_factor return prop_dist def sample_pagerank(corpus, damping_factor, n): """ Return PageRank values for each page by sampling `n` pages according to transition model, starting with a page at random. Return a dictionary where keys are page names, and values are their estimated PageRank value (a value between 0 and 1). All PageRank values should sum to 1. """ # prepare dictionary with number of samples == 0 samples_dict = corpus.copy() for i in samples_dict: samples_dict[i] = 0 sample = None # itearting n times for _ in range(n): if sample: # previous sample is available, choosing using transition model dist = transition_model(corpus, sample, damping_factor) dist_lst = list(dist.keys()) dist_weights = [dist[i] for i in dist] sample = random.choices(dist_lst, dist_weights, k=1)[0] else: # no previous sample, choosing randomly sample = random.choice(list(corpus.keys())) # count each sample samples_dict[sample] += 1 # turn sample count to percentage for item in samples_dict: samples_dict[item] /= n return samples_dict def iterate_pagerank(corpus, damping_factor): """ Return PageRank values for each page by iteratively updating PageRank values until convergence. Return a dictionary where keys are page names, and values are their estimated PageRank value (a value between 0 and 1). All PageRank values should sum to 1. """ pages_number = len(corpus) old_dict = {} new_dict = {} # assigning each page a rank of 1/n, where n is total number of pages in the corpus for page in corpus: old_dict[page] = 1 / pages_number # repeatedly calculating new rank values basing on all of the current rank values while True: for page in corpus: temp = 0 for linking_page in corpus: # check if page links to our page if page in corpus[linking_page]: temp += (old_dict[linking_page] / len(corpus[linking_page])) # if page has no links, interpret it as having one link for every other page if len(corpus[linking_page]) == 0: temp += (old_dict[linking_page]) / len(corpus) temp *= damping_factor temp += (1 - damping_factor) / pages_number new_dict[page] = temp difference = max([abs(new_dict[x] - old_dict[x]) for x in old_dict]) if difference < 0.001: break else: old_dict = new_dict.copy() return old_dict if __name__ == "__main__": main() ================================================ FILE: week3/crossword/crossword.py ================================================ class Variable(): ACROSS = "across" DOWN = "down" def __init__(self, i, j, direction, length): """Create a new variable with starting point, direction, and length.""" self.i = i self.j = j self.direction = direction self.length = length self.cells = [] for k in range(self.length): self.cells.append( (self.i + (k if self.direction == Variable.DOWN else 0), self.j + (k if self.direction == Variable.ACROSS else 0)) ) def __hash__(self): return hash((self.i, self.j, self.direction, self.length)) def __eq__(self, other): return ( (self.i == other.i) and (self.j == other.j) and (self.direction == other.direction) and (self.length == other.length) ) def __str__(self): return f"({self.i}, {self.j}) {self.direction} : {self.length}" def __repr__(self): direction = repr(self.direction) return f"Variable({self.i}, {self.j}, {direction}, {self.length})" class Crossword(): def __init__(self, structure_file, words_file): # Determine structure of crossword with open(structure_file) as f: contents = f.read().splitlines() self.height = len(contents) self.width = max(len(line) for line in contents) self.structure = [] for i in range(self.height): row = [] for j in range(self.width): if j >= len(contents[i]): row.append(False) elif contents[i][j] == "_": row.append(True) else: row.append(False) self.structure.append(row) # Save vocabulary list with open(words_file) as f: self.words = set(f.read().upper().splitlines()) # Determine variable set self.variables = set() for i in range(self.height): for j in range(self.width): # Vertical words starts_word = ( self.structure[i][j] and (i == 0 or not self.structure[i - 1][j]) ) if starts_word: length = 1 for k in range(i + 1, self.height): if self.structure[k][j]: length += 1 else: break if length > 1: self.variables.add(Variable( i=i, j=j, direction=Variable.DOWN, length=length )) # Horizontal words starts_word = ( self.structure[i][j] and (j == 0 or not self.structure[i][j - 1]) ) if starts_word: length = 1 for k in range(j + 1, self.width): if self.structure[i][k]: length += 1 else: break if length > 1: self.variables.add(Variable( i=i, j=j, direction=Variable.ACROSS, length=length )) # Compute overlaps for each word # For any pair of variables v1, v2, their overlap is either: # None, if the two variables do not overlap; or # (i, j), where v1's ith character overlaps v2's jth character self.overlaps = dict() for v1 in self.variables: for v2 in self.variables: if v1 == v2: continue cells1 = v1.cells cells2 = v2.cells intersection = set(cells1).intersection(cells2) if not intersection: self.overlaps[v1, v2] = None else: intersection = intersection.pop() self.overlaps[v1, v2] = ( cells1.index(intersection), cells2.index(intersection) ) def neighbors(self, var): """Given a variable, return set of overlapping variables.""" return set( v for v in self.variables if v != var and self.overlaps[v, var] ) ================================================ FILE: week3/crossword/data/structure0.txt ================================================ #___# #_##_ #_##_ #_##_ #____ ================================================ FILE: week3/crossword/data/structure1.txt ================================================ ############## #######_####_# #____________# #_#####_####_# #_##_____###_# #_#####_####_# #_###______#_# #######_####_# ############## ================================================ FILE: week3/crossword/data/structure2.txt ================================================ ######_ ____##_ _##____ _##_##_ _##_##_ #___##_ ================================================ FILE: week3/crossword/data/words0.txt ================================================ one two three four five six seven eight nine ten ================================================ FILE: week3/crossword/data/words1.txt ================================================ adversarial alpha arc artificial bayes beta bit breadth byte classification classify condition constraint create depth distribution end false graph heuristic infer inference initial intelligence knowledge language learning line logic loss markov minimax network neural node optimization probability proposition prune reason recurrent regression resolution resolve satisfaction search sine start true truth uncertainty ================================================ FILE: week3/crossword/data/words2.txt ================================================ a abandon ability able abortion about above abroad absence absolute absolutely absorb abuse academic accept access accident accompany accomplish according account accurate accuse achieve achievement acid acknowledge acquire across act action active activist activity actor actress actual actually ad adapt add addition additional address adequate adjust adjustment administration administrator admire admission admit adolescent adopt adult advance advanced advantage adventure advertising advice advise adviser advocate affair affect afford 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vs vulnerable wage wait wake walk wall wander want war warm warn warning wash waste watch water wave way we weak wealth wealthy weapon wear weather wedding week weekend weekly weigh weight welcome welfare well west western wet what whatever wheel when whenever where whereas whether which while whisper white who whole whom whose why wide widely widespread wife wild will willing win wind window wine wing winner winter wipe wire wisdom wise wish with withdraw within without witness woman wonder wonderful wood wooden word work worker working works workshop world worried worry worth would wound wrap write writer writing wrong yard yeah year yell yellow yes yesterday yet yield you young your yours yourself youth zone ================================================ FILE: week3/crossword/generate.py ================================================ import sys import copy from crossword import * class CrosswordCreator(): def __init__(self, crossword): """ Create new CSP crossword generate. """ self.crossword = crossword self.domains = { var: self.crossword.words.copy() for var in self.crossword.variables } def letter_grid(self, assignment): """ Return 2D array representing a given assignment. """ letters = [ [None for _ in range(self.crossword.width)] for _ in range(self.crossword.height) ] for variable, word in assignment.items(): direction = variable.direction for k in range(len(word)): i = variable.i + (k if direction == Variable.DOWN else 0) j = variable.j + (k if direction == Variable.ACROSS else 0) letters[i][j] = word[k] return letters def print(self, assignment): """ Print crossword assignment to the terminal. """ letters = self.letter_grid(assignment) for i in range(self.crossword.height): for j in range(self.crossword.width): if self.crossword.structure[i][j]: print(letters[i][j] or " ", end="") else: print("█", end="") print() def save(self, assignment, filename): """ Save crossword assignment to an image file. """ from PIL import Image, ImageDraw, ImageFont cell_size = 100 cell_border = 2 interior_size = cell_size - 2 * cell_border letters = self.letter_grid(assignment) # Create a blank canvas img = Image.new( "RGBA", (self.crossword.width * cell_size, self.crossword.height * cell_size), "black" ) font = ImageFont.truetype("assets/fonts/OpenSans-Regular.ttf", 80) draw = ImageDraw.Draw(img) for i in range(self.crossword.height): for j in range(self.crossword.width): rect = [ (j * cell_size + cell_border, i * cell_size + cell_border), ((j + 1) * cell_size - cell_border, (i + 1) * cell_size - cell_border) ] if self.crossword.structure[i][j]: draw.rectangle(rect, fill="white") if letters[i][j]: w, h = draw.textsize(letters[i][j], font=font) draw.text( (rect[0][0] + ((interior_size - w) / 2), rect[0][1] + ((interior_size - h) / 2) - 10), letters[i][j], fill="black", font=font ) img.save(filename) def solve(self): """ Enforce node and arc consistency, and then solve the CSP. """ self.enforce_node_consistency() self.ac3() return self.backtrack(dict()) def enforce_node_consistency(self): """ Update `self.domains` such that each variable is node-consistent. (Remove any values that are inconsistent with a variable's unary constraints; in this case, the length of the word.) """ # do the deep copy of domains domain_copy = copy.deepcopy(self.domains) # iterate through domains copy for variable in domain_copy: # getting the variable length length = variable.length # iterate through words in domain for word in domain_copy[variable]: if len(word) != length: # if length of the word doesn't fit variable, delete it from # the original domain (not copy) self.domains[variable].remove(word) def revise(self, x, y): """ Make variable `x` arc consistent with variable `y`. To do so, remove values from `self.domains[x]` for which there is no possible corresponding value for `y` in `self.domains[y]`. Return True if a revision was made to the domain of `x`; return False if no revision was made. """ # getting x and y ovelapping cells, unpack cords to variables xoverlap, yoverlap = self.crossword.overlaps[x, y] # make variable describing if revision was made revision_made = False # making domains copy domains_copy = copy.deepcopy(self.domains) # if overlap occurs if xoverlap: # iterate through words in x's domain for xword in domains_copy[x]: matched_value = False # iterate through words in y's domain for yword in self.domains[y]: # if x's word and y's word have same letter in overlapping position if xword[xoverlap] == yword[yoverlap]: matched_value = True break # no need to check rest of y's words for that x if matched_value: continue # if x and y was matched, proceed with another x else: self.domains[x].remove(xword) # no matching y's word to x, removing word from domain revision_made = True # return bolean if revision was made return revision_made def ac3(self, arcs=None): """ Update `self.domains` such that each variable is arc consistent. If `arcs` is None, begin with initial list of all arcs in the problem. Otherwise, use `arcs` as the initial list of arcs to make consistent. Return True if arc consistency is enforced and no domains are empty; return False if one or more domains end up empty. """ if not arcs: # no arcs provided, start with an initial queue of all of the arcs in the problem queue = [] # populating queue for variable1 in self.domains: for variable2 in self.crossword.neighbors(variable1): if self.crossword.overlaps[variable1, variable2] is not None: queue.append((variable1, variable2)) while len(queue) > 0: x, y = queue.pop(0) if self.revise(x, y): if len(self.domains[x]) == 0: return False for neighbour in self.crossword.neighbors(x): if neighbour != y: queue.append((neighbour, x)) return True def assignment_complete(self, assignment): """ Return True if `assignment` is complete (i.e., assigns a value to each crossword variable); return False otherwise. """ for variable in self.domains: if variable not in assignment: return False return True def consistent(self, assignment): """ Return True if `assignment` is consistent (i.e., words fit in crossword puzzle without conflicting characters); return False otherwise. """ # all values are distinct, every value is the correct length, # and there are no conflicts between neighboring variables. # check if all values are distinct words = [*assignment.values()] if len(words) != len(set(words)): return False # check if every value is the correct length for variable in assignment: if variable.length != len(assignment[variable]): return False # check if there are any conflicts between neighbouring variables for variable in assignment: for neighbour in self.crossword.neighbors(variable): if neighbour in assignment: x, y = self.crossword.overlaps[variable, neighbour] if assignment[variable][x] != assignment[neighbour][y]: return False # all cases checked, no conflicts, can return True return True def order_domain_values(self, var, assignment): """ Return a list of values in the domain of `var`, in order by the number of values they rule out for neighboring variables. The first value in the list, for example, should be the one that rules out the fewest values among the neighbors of `var`. """ # make temporary dict for holding values word_dict = {} # getting neighbours of var neighbours = self.crossword.neighbors(var) # iterating through var's words for word in self.domains[var]: eliminated = 0 for neighbour in neighbours: # don't count if neighbor has already assigned value if neighbour in assignment: continue else: # calculate overlap between two variables xoverlap, yoverlap = self.crossword.overlaps[var, neighbour] for neighbour_word in self.domains[neighbour]: # iterate through neighbour's words, check for eliminate ones if word[xoverlap] != neighbour_word[yoverlap]: eliminated += 1 # add eliminated neighbour's words to temporary dict word_dict[word] = eliminated # sort variables dictionary by number of eliminated neighbour values sorted_dict = {k: v for k, v in sorted(word_dict.items(), key=lambda item: item[1])} return [*sorted_dict] def select_unassigned_variable(self, assignment): """ Return an unassigned variable not already part of `assignment`. Choose the variable with the minimum number of remaining values in its domain. If there is a tie, choose the variable with the highest degree. If there is a tie, any of the tied variables are acceptable return values. """ choice_dict = {} # iterating through variables in domains for variable in self.domains: # iterating through variables in assignment if variable not in assignment: # if variable is not yet in assigment, add it to temp dict choice_dict[variable] = self.domains[variable] # make list of variables sorted by number of remaining values sorted_list = [v for v, k in sorted(choice_dict.items(), key=lambda item:len(item[1]))] # return variable with the minimum number of remaining values return sorted_list[0] def backtrack(self, assignment): """ Using Backtracking Search, take as input a partial assignment for the crossword and return a complete assignment if possible to do so. `assignment` is a mapping from variables (keys) to words (values). If no assignment is possible, return None. """ # if assignment is already ready if len(assignment) == len(self.domains): return assignment # selecting one of unassigned variables variable = self.select_unassigned_variable(assignment) # iterating through words in that variable for value in self.domains[variable]: # making assignment copy, with updated variable value assignment_copy = assignment.copy() assignment_copy[variable] = value # checking for consistency, getting result of that new assignment backtrack if self.consistent(assignment_copy): result = self.backtrack(assignment_copy) if result is not None: return result return None def main(): # Check usage if len(sys.argv) not in [3, 4]: sys.exit("Usage: python generate.py structure words [output]") # Parse command-line arguments structure = sys.argv[1] words = sys.argv[2] output = sys.argv[3] if len(sys.argv) == 4 else None # Generate crossword crossword = Crossword(structure, words) creator = CrosswordCreator(crossword) assignment = creator.solve() # Print result if assignment is None: print("No solution.") else: creator.print(assignment) if output: creator.save(assignment, output) if __name__ == "__main__": main() ================================================ FILE: week4/nim/nim.py ================================================ import math import random import time class Nim(): def __init__(self, initial=[1, 3, 5, 7]): """ Initialize game board. Each game board has - `piles`: a list of how many elements remain in each pile - `player`: 0 or 1 to indicate which player's turn - `winner`: None, 0, or 1 to indicate who the winner is """ self.piles = initial.copy() self.player = 0 self.winner = None @classmethod def available_actions(cls, piles): """ Nim.available_actions(piles) takes a `piles` list as input and returns all of the available actions `(i, j)` in that state. Action `(i, j)` represents the action of removing `j` items from pile `i` (where piles are 0-indexed). """ actions = set() for i, pile in enumerate(piles): for j in range(1, pile + 1): actions.add((i, j)) return actions @classmethod def other_player(cls, player): """ Nim.other_player(player) returns the player that is not `player`. Assumes `player` is either 0 or 1. """ return 0 if player == 1 else 1 def switch_player(self): """ Switch the current player to the other player. """ self.player = Nim.other_player(self.player) def move(self, action): """ Make the move `action` for the current player. `action` must be a tuple `(i, j)`. """ pile, count = action # Check for errors if self.winner is not None: raise Exception("Game already won") elif pile < 0 or pile >= len(self.piles): raise Exception("Invalid pile") elif count < 1 or count > self.piles[pile]: raise Exception("Invalid number of objects") # Update pile self.piles[pile] -= count self.switch_player() # Check for a winner if all(pile == 0 for pile in self.piles): self.winner = self.player class NimAI(): def __init__(self, alpha=0.5, epsilon=0.1): """ Initialize AI with an empty Q-learning dictionary, an alpha (learning) rate, and an epsilon rate. The Q-learning dictionary maps `(state, action)` pairs to a Q-value (a number). - `state` is a tuple of remaining piles, e.g. (1, 1, 4, 4) - `action` is a tuple `(i, j)` for an action """ self.q = dict() self.alpha = alpha self.epsilon = epsilon def update(self, old_state, action, new_state, reward): """ Update Q-learning model, given an old state, an action taken in that state, a new resulting state, and the reward received from taking that action. """ old = self.get_q_value(old_state, action) best_future = self.best_future_reward(new_state) self.update_q_value(old_state, action, old, reward, best_future) def get_q_value(self, state, action): """ Return the Q-value for the state `state` and the action `action`. If no Q-value exists yet in `self.q`, return 0. """ try: return self.q[tuple(state), action] except KeyError: return 0 def update_q_value(self, state, action, old_q, reward, future_rewards): """ Update the Q-value for the state `state` and the action `action` given the previous Q-value `old_q`, a current reward `reward`, and an estiamte of future rewards `future_rewards`. Use the formula: Q(s, a) <- old value estimate + alpha * (new value estimate - old value estimate) where `old value estimate` is the previous Q-value, `alpha` is the learning rate, and `new value estimate` is the sum of the current reward and estimated future rewards. """ new_q = old_q + self.alpha * ((reward + future_rewards) - old_q) self.q[tuple(state), action] = new_q def best_future_reward(self, state): """ Given a state `state`, consider all possible `(state, action)` pairs available in that state and return the maximum of all of their Q-values. Use 0 as the Q-value if a `(state, action)` pair has no Q-value in `self.q`. If there are no available actions in `state`, return 0. """ max_reward = 0 for sta, q in self.q.items(): if sta[0] == state and q > max_reward: max_reward = q return max_reward def choose_action(self, state, epsilon=True): """ Given a state `state`, return an action `(i, j)` to take. If `epsilon` is `False`, then return the best action available in the state (the one with the highest Q-value, using 0 for pairs that have no Q-values). If `epsilon` is `True`, then with probability `self.epsilon` choose a random available action, otherwise choose the best action available. If multiple actions have the same Q-value, any of those options is an acceptable return value. """ max_reward = 0 best_action = None available_moves = Nim.available_actions(state) for move in available_moves: try: q = self.q[tuple(state), move] except KeyError: q = 0 if q > max_reward: max_reward = q best_action = move if max_reward == 0: return random.choice(tuple(available_moves)) if not epsilon: return best_action else: if random.random() < self.epsilon: return random.choice(tuple(available_moves)) else: return best_action def train(n): """ Train an AI by playing `n` games against itself. """ player = NimAI() # Play n games for i in range(n): print(f"Playing training game {i + 1}") game = Nim() # Keep track of last move made by either player last = { 0: {"state": None, "action": None}, 1: {"state": None, "action": None} } # Game loop while True: # Keep track of current state and action state = game.piles.copy() action = player.choose_action(game.piles) # Keep track of last state and action last[game.player]["state"] = state last[game.player]["action"] = action # Make move game.move(action) new_state = game.piles.copy() # When game is over, update Q values with rewards if game.winner is not None: player.update(state, action, new_state, -1) player.update( last[game.player]["state"], last[game.player]["action"], new_state, 1 ) break # If game is continuing, no rewards yet elif last[game.player]["state"] is not None: player.update( last[game.player]["state"], last[game.player]["action"], new_state, 0 ) print("Done training") # Return the trained AI return player def play(ai, human_player=None): """ Play human game against the AI. `human_player` can be set to 0 or 1 to specify whether human player moves first or second. """ # If no player order set, choose human's order randomly if human_player is None: human_player = random.randint(0, 1) # Create new game game = Nim() # Game loop while True: # Print contents of piles print() print("Piles:") for i, pile in enumerate(game.piles): print(f"Pile {i}: {pile}") print() # Compute available actions available_actions = Nim.available_actions(game.piles) time.sleep(1) # Let human make a move if game.player == human_player: print("Your Turn") while True: pile = int(input("Choose Pile: ")) count = int(input("Choose Count: ")) if (pile, count) in available_actions: break print("Invalid move, try again.") # Have AI make a move else: print("AI's Turn") pile, count = ai.choose_action(game.piles, epsilon=False) print(f"AI chose to take {count} from pile {pile}.") # Make move game.move((pile, count)) # Check for winner if game.winner is not None: print() print("GAME OVER") winner = "Human" if game.winner == human_player else "AI" print(f"Winner is {winner}") return ================================================ FILE: week4/nim/play.py ================================================ from nim import train, play ai = train(10000) play(ai) ================================================ FILE: week4/shopping/shopping.csv ================================================ Administrative,Administrative_Duration,Informational,Informational_Duration,ProductRelated,ProductRelated_Duration,BounceRates,ExitRates,PageValues,SpecialDay,Month,OperatingSystems,Browser,Region,TrafficType,VisitorType,Weekend,Revenue 0,0,0,0,1,0,0.2,0.2,0,0,Feb,1,1,1,1,Returning_Visitor,FALSE,FALSE 0,0,0,0,2,64,0,0.1,0,0,Feb,2,2,1,2,Returning_Visitor,FALSE,FALSE 0,0,0,0,1,0,0.2,0.2,0,0,Feb,4,1,9,3,Returning_Visitor,FALSE,FALSE 0,0,0,0,2,2.666666667,0.05,0.14,0,0,Feb,3,2,2,4,Returning_Visitor,FALSE,FALSE 0,0,0,0,10,627.5,0.02,0.05,0,0,Feb,3,3,1,4,Returning_Visitor,TRUE,FALSE 0,0,0,0,19,154.2166667,0.015789474,0.024561404,0,0,Feb,2,2,1,3,Returning_Visitor,FALSE,FALSE 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0,0,0,0,1,0,0.2,0.2,0,0,Feb,2,2,2,3,Returning_Visitor,FALSE,FALSE 0,0,1,19,10,852,0,0.009090909,0,0,Feb,2,2,3,6,Returning_Visitor,FALSE,FALSE 0,0,0,0,3,780,0.066666667,0.066666667,0,0.4,Feb,2,4,1,3,Returning_Visitor,FALSE,FALSE 0,0,0,0,3,1038,0,0.033333333,0,0,Feb,2,2,1,1,Returning_Visitor,FALSE,FALSE 0,0,0,0,4,573,0,0.05,0,0,Feb,2,2,3,3,Returning_Visitor,FALSE,FALSE 0,0,0,0,28,1231.233333,0.007142857,0.030357143,0,0,Feb,1,1,4,3,Returning_Visitor,FALSE,FALSE 0,0,0,0,11,316.6666667,0,0.027272727,0,0.8,Feb,2,2,2,1,Returning_Visitor,FALSE,FALSE 0,0,0,0,20,873.7365079,0.0075,0.028166667,0,0,Feb,1,2,1,1,Returning_Visitor,FALSE,FALSE 0,0,0,0,1,0,0.2,0.2,0,0.4,Feb,2,2,3,4,Returning_Visitor,FALSE,FALSE 0,0,0,0,4,16,0.1,0.15,0,0,Feb,2,5,5,1,Returning_Visitor,FALSE,FALSE 0,0,0,0,19,615,0,0.026315789,0,0.2,Feb,2,2,3,3,Returning_Visitor,FALSE,FALSE 0,0,0,0,9,261.75,0,0.026666667,0,0,Feb,2,6,1,1,Returning_Visitor,FALSE,FALSE 0,0,0,0,21,615.8831169,0.033333333,0.04993081,0,0,Feb,3,2,2,1,Returning_Visitor,TRUE,FALSE 0,0,0,0,1,0,0.2,0.2,0,0,Feb,3,3,3,2,Returning_Visitor,TRUE,FALSE 0,0,0,0,6,117.5,0,0.033333333,0,0.8,Feb,1,1,1,3,Returning_Visitor,TRUE,FALSE 0,0,0,0,1,0,0.2,0.2,0,0,Feb,1,1,1,3,Returning_Visitor,FALSE,FALSE 0,0,0,0,1,0,0.2,0.2,0,0,Feb,3,2,3,3,Returning_Visitor,FALSE,FALSE 0,0,0,0,23,501.6666667,0.008695652,0.01884058,0,0.6,Feb,1,1,4,2,Returning_Visitor,FALSE,FALSE 2,58,2,22,31,829.1666667,0.03030303,0.040606061,0,0,Feb,1,1,1,1,Returning_Visitor,TRUE,FALSE 1,9.5,0,0,23,1223,0.015217391,0.012463768,0,0,Feb,1,1,1,2,Returning_Visitor,TRUE,FALSE 0,0,0,0,6,467,0,0.016666667,0,0.4,Feb,1,1,3,3,Returning_Visitor,FALSE,FALSE 0,0,0,0,23,737.1666667,0.008695652,0.011594203,0,0.6,Feb,2,2,1,3,Returning_Visitor,FALSE,FALSE 0,0,0,0,12,129,0,0.033333333,0,0,Feb,1,1,3,2,Returning_Visitor,FALSE,FALSE 0,0,0,0,5,63,0,0.04,0,0,Feb,2,2,1,3,Returning_Visitor,FALSE,FALSE 0,0,0,0,5,125,0.086666667,0.166666667,0,0,Feb,2,2,1,3,Returning_Visitor,FALSE,FALSE 0,0,0,0,11,684.3333333,0.018181818,0.063636364,0,0,Feb,2,4,6,1,Returning_Visitor,FALSE,FALSE 0,0,0,0,20,665.4722222,0,0.0145,0,0.8,Feb,3,2,7,4,Returning_Visitor,TRUE,FALSE 1,0,0,0,15,487.5,0,0.019166667,0,0.8,Feb,1,1,2,4,Returning_Visitor,TRUE,FALSE 0,0,0,0,17,437.1666667,0.011111111,0.033333333,0,0,Feb,2,4,1,3,Returning_Visitor,FALSE,FALSE 0,0,0,0,3,55,0.066666667,0.1,0,0.6,Feb,1,1,1,3,Returning_Visitor,FALSE,FALSE 0,0,0,0,1,0,0.2,0.2,0,0.8,Feb,1,1,1,2,Returning_Visitor,TRUE,FALSE 5,41.3,0,0,24,446.9277778,0,0.008602151,0,1,Feb,2,2,8,1,Returning_Visitor,TRUE,FALSE 3,87,0,0,10,320.5,0,0.015384615,0,0,Feb,1,1,2,3,Returning_Visitor,FALSE,FALSE 0,0,0,0,7,666,0,0.014285714,0,0,Feb,1,1,6,2,Returning_Visitor,FALSE,FALSE 1,0,0,0,21,405.7095238,0.010743802,0.047002635,0,0,Feb,1,1,1,4,Returning_Visitor,FALSE,FALSE 0,0,0,0,1,0,0.2,0.2,0,0,Feb,3,2,3,3,Returning_Visitor,FALSE,FALSE 0,0,0,0,6,163.25,0.033333333,0.033333333,0,0.6,Feb,1,1,6,2,Returning_Visitor,FALSE,FALSE 0,0,0,0,17,284.25,0,0.011764706,0,0,Feb,3,2,6,2,Returning_Visitor,TRUE,FALSE 0,0,0,0,1,0,0.2,0.2,0,0.6,Feb,4,2,1,3,Returning_Visitor,FALSE,FALSE 0,0,0,0,1,0,0.2,0.2,0,0,Feb,2,4,1,1,Returning_Visitor,FALSE,FALSE 0,0,0,0,8,260,0.008333333,0.025,0,0.8,Feb,3,2,1,3,Returning_Visitor,TRUE,FALSE 0,0,16,1210.397619,5,279.8571429,0.003174603,0.012764202,0,0,Mar,2,2,1,8,Returning_Visitor,FALSE,FALSE 0,0,0,0,20,927.45,0.011111111,0.027248677,8.000740741,0,Mar,1,1,3,1,Returning_Visitor,FALSE,FALSE 2,9,0,0,50,836.8,0,0.006339869,0,0,Mar,2,2,3,2,Returning_Visitor,FALSE,FALSE 10,293.7782051,2,153,96,3283.166739,0.001960784,0.013509414,0,0,Mar,3,2,6,2,Returning_Visitor,TRUE,FALSE 9,111.5,1,48.5,49,1868.819697,0,0.020708874,1.706014966,0,Mar,2,2,7,2,Returning_Visitor,FALSE,TRUE 3,47,1,51,68,3008.124108,0.007142857,0.016727891,46.53017511,0,Mar,2,2,1,1,Returning_Visitor,FALSE,FALSE 0,0,0,0,2,0,0.2,0.2,0,0,Mar,1,1,1,3,Returning_Visitor,FALSE,FALSE 3,1226,5,3,24,3230.25,0.036190476,0.096,0,0,Mar,2,2,1,2,Returning_Visitor,FALSE,FALSE 3,52,0,0,9,319,0,0.02,0,0,Mar,1,1,1,1,Returning_Visitor,FALSE,FALSE 0,0,0,0,3,42,0,0.066666667,0,0,Mar,2,2,3,1,Returning_Visitor,FALSE,FALSE 0,0,0,0,22,354.3333333,0.009090909,0.057662338,0,0,Mar,1,1,7,2,Returning_Visitor,TRUE,FALSE 0,0,0,0,98,3556.61241,0.002061856,0.010173465,0,0,Mar,1,1,1,3,Returning_Visitor,FALSE,FALSE 2,56,1,144,67,2563.783333,0,0.005797101,19.34265017,0,Mar,2,2,4,2,New_Visitor,FALSE,TRUE 3,112.9607843,0,0,13,3014.018519,0.013068182,0.06140625,0,0,Mar,2,2,1,2,Returning_Visitor,FALSE,FALSE 0,0,0,0,17,840.2333333,0,0.001666667,109.176,0,Mar,2,2,9,2,New_Visitor,FALSE,TRUE 3,94,2,125,55,1970.844805,0,0.001724138,96.25511582,0,Mar,2,4,1,2,New_Visitor,TRUE,TRUE 1,32,0,0,50,2867,0,0.004,153.4432478,0,Mar,2,2,7,8,Returning_Visitor,TRUE,TRUE 0,0,0,0,5,43,0,0.04,0,0,Mar,1,1,1,9,Returning_Visitor,TRUE,FALSE 5,218,0,0,13,284.5,0,0.004166667,0,0,Mar,1,1,1,2,New_Visitor,FALSE,FALSE 0,0,0,0,3,133.5,0,0.088888889,0,0,Mar,1,2,1,8,Returning_Visitor,FALSE,FALSE 1,119,0,0,12,297.6666667,0,0.008333333,0,0,Mar,3,2,1,10,Returning_Visitor,FALSE,FALSE 3,281,0,0,16,453.75,0,0.005263158,0,0,Mar,2,2,5,2,New_Visitor,FALSE,FALSE 1,18,0,0,16,1331.75,0,0.0125,33.79956701,0,Mar,2,5,2,3,New_Visitor,FALSE,TRUE 2,40,0,0,5,558.5,0,0.028571429,0,0,Mar,2,2,7,2,New_Visitor,TRUE,FALSE 3,107,0,0,4,145.8333333,0,0.016666667,0,0,Mar,3,2,7,1,New_Visitor,FALSE,FALSE 0,0,0,0,8,731,0,0.04375,0,0,Mar,2,4,1,3,Returning_Visitor,FALSE,FALSE 2,49,1,127,4,188,0.033333333,0.0375,0,0,Mar,3,2,3,2,Returning_Visitor,FALSE,FALSE 0,0,0,0,14,565.3333333,0.037619048,0.04952381,0,0,Mar,2,6,3,1,Returning_Visitor,FALSE,FALSE 0,0,0,0,2,50,0,0.05,0,0,Mar,3,2,8,11,Returning_Visitor,FALSE,FALSE 4,57,0,0,7,591,0.006666667,0.033333333,0,0,Mar,1,2,6,6,Returning_Visitor,TRUE,FALSE 2,2,3,261,10,281,0,0.042857143,0,0,Mar,2,2,1,2,Returning_Visitor,FALSE,FALSE 2,123,2,306.3333333,18,483.8333333,0,0.02,0,0,Mar,2,2,4,1,Returning_Visitor,FALSE,FALSE 2,118,0,0,4,42,0,0.066666667,0,0,Mar,1,2,7,3,Returning_Visitor,FALSE,FALSE 0,0,0,0,35,1192.386111,0.005714286,0.028571429,0,0,Mar,2,4,2,2,Returning_Visitor,FALSE,FALSE 0,0,0,0,12,198,0.016666667,0.075,0,0,Mar,2,2,3,2,Returning_Visitor,FALSE,FALSE 0,0,0,0,3,15,0,0.066666667,0,0,Mar,2,4,3,12,Returning_Visitor,FALSE,FALSE 2,38,0,0,14,643,0,0.013333333,35.0928,0,Mar,2,2,5,1,Returning_Visitor,FALSE,TRUE 0,0,0,0,5,23,0.08,0.12,0,0,Mar,2,2,1,1,Returning_Visitor,FALSE,FALSE 0,0,0,0,4,14,0.1,0.15,0,0,Mar,3,2,1,1,Returning_Visitor,FALSE,FALSE 1,54,0,0,6,210.5,0,0.02,0,0,Mar,2,2,1,8,New_Visitor,FALSE,FALSE 1,993,0,0,28,1529.30467,0.001481481,0.009150327,0,0,Mar,3,2,4,2,Returning_Visitor,FALSE,FALSE 0,0,0,0,2,19,0,0.1,0,0,Mar,2,2,3,11,Returning_Visitor,TRUE,FALSE 5,74.25,2,84,37,1599.083333,0,0.006578947,0,0,Mar,2,2,4,1,Returning_Visitor,FALSE,FALSE 0,0,0,0,9,102.5,0,0.022222222,0,0,Mar,2,2,1,3,Returning_Visitor,FALSE,FALSE 0,0,0,0,16,2337,0,0.013333333,17.76266667,0,Mar,2,2,1,1,Returning_Visitor,FALSE,TRUE 0,0,0,0,19,1883.5,0.010526316,0.021052632,0,0,Mar,2,2,1,3,Returning_Visitor,FALSE,FALSE 2,32,0,0,50,1245.3,0.004,0.020666667,7.146260888,0,Mar,2,2,7,9,Returning_Visitor,TRUE,FALSE 6,666.8333333,0,0,22,1674.583333,0,0.014666667,0,0,Mar,2,2,3,2,Returning_Visitor,FALSE,FALSE 0,0,0,0,9,132,0.011111111,0.033333333,0,0,Mar,4,1,1,3,Returning_Visitor,TRUE,FALSE 1,73,1,740,50,1434.255128,0,0.019230769,22.03276923,0,Mar,2,2,3,2,Returning_Visitor,TRUE,FALSE 2,154,0,0,18,367.25,0.005263158,0.044210526,0,0,Mar,3,2,2,1,Returning_Visitor,TRUE,FALSE 3,49.33333333,5,303.25,7,148.5,0,0.014081633,0,0,Mar,3,2,2,2,Returning_Visitor,FALSE,FALSE 3,118.5,0,0,29,1482.210784,0.006896552,0.010600255,0,0,Mar,3,2,1,8,Returning_Visitor,TRUE,FALSE 6,153.5,0,0,34,1048.492063,0.011578947,0.032225685,0,0,Mar,2,6,1,1,Returning_Visitor,TRUE,FALSE 0,0,0,0,9,478,0,0.011111111,0,0,Mar,3,2,2,3,Returning_Visitor,TRUE,FALSE 0,0,0,0,16,980.25,0,0.04,0,0,Mar,2,2,4,3,Returning_Visitor,FALSE,FALSE 8,72.25,1,21.5,51,1124.693074,0.022807018,0.036550087,0,0,Mar,2,2,2,2,Returning_Visitor,FALSE,FALSE 2,24.5,0,0,22,353.1666667,0.018181818,0.031818182,0,0,Mar,2,5,1,3,Returning_Visitor,FALSE,FALSE 1,0,0,0,18,1489.083333,0.023529412,0.046470588,0,0,Mar,2,2,8,1,Returning_Visitor,FALSE,FALSE 0,0,1,1220,28,2389.333333,0.016666667,0.030833333,0,0,Mar,2,2,1,3,Returning_Visitor,TRUE,FALSE 0,0,0,0,7,753,0,0.00952381,0,0,Mar,2,10,1,2,Returning_Visitor,FALSE,FALSE 0,0,0,0,31,688.1666667,0,0.019354839,0,0,Mar,4,1,1,3,Returning_Visitor,FALSE,FALSE 0,0,0,0,14,811,0.015384615,0.041025641,0,0,Mar,2,2,8,1,Returning_Visitor,FALSE,FALSE 1,22,0,0,9,415.25,0.033333333,0.048148148,0,0,Mar,3,3,1,1,Returning_Visitor,FALSE,FALSE 16,155.6313131,0,0,32,908.013456,0.005,0.01805276,4.368515556,0,Mar,2,2,3,2,Returning_Visitor,FALSE,TRUE 1,21.33333333,1,235,71,2753.378571,0.012676056,0.015201207,0,0,Mar,3,2,2,1,Returning_Visitor,TRUE,FALSE 2,191,1,100,46,2452.582143,0.007246377,0.022253906,0,0,Mar,3,2,9,3,Returning_Visitor,TRUE,FALSE 0,0,0,0,18,5188.5,0,0.05,0,0,Mar,2,5,1,1,Returning_Visitor,FALSE,FALSE 0,0,0,0,2,0,0.2,0.2,0,0,Mar,1,1,1,1,Returning_Visitor,FALSE,FALSE 0,0,0,0,5,57,0.08,0.12,0,0,Mar,2,5,2,1,Returning_Visitor,FALSE,FALSE 0,0,0,0,2,8,0,0.1,0,0,Mar,2,2,5,1,Returning_Visitor,TRUE,FALSE 4,96.5,0,0,19,1430.659091,0.01,0.025666667,0,0,Mar,1,1,1,1,Returning_Visitor,FALSE,FALSE 1,13,0,0,2,20,0,0.05,0,0,Mar,3,2,3,2,Returning_Visitor,FALSE,FALSE 5,1388.75,0,0,4,1373.75,0,0.02,0,0,Mar,1,1,7,8,Returning_Visitor,FALSE,FALSE 0,0,0,0,45,844.25,0,0.01037037,0,0,Mar,1,1,3,1,Returning_Visitor,FALSE,FALSE 0,0,1,0,7,188,0,0.033333333,0,0,Mar,2,2,1,8,New_Visitor,FALSE,FALSE 3,51,0,0,14,527.375,0.015555556,0.033862434,0,0,Mar,7,1,6,1,Returning_Visitor,TRUE,FALSE 0,0,0,0,1,0,0.2,0.2,0,0,Mar,3,3,5,2,Returning_Visitor,FALSE,FALSE 1,158,1,64,29,1916.761905,0.006451613,0.018709677,8.772473118,0,Mar,2,2,3,2,Returning_Visitor,TRUE,TRUE 0,0,0,0,9,116.3333333,0,0.066666667,0,0,Mar,2,2,1,1,Returning_Visitor,FALSE,FALSE 0,0,0,0,30,397,0,0.02,0,0,Mar,2,2,7,2,Returning_Visitor,FALSE,FALSE 0,0,0,0,63,5220.083333,0,0.003329865,51.46331148,0,Mar,2,5,3,1,Returning_Visitor,TRUE,TRUE 0,0,0,0,20,219.3333333,0,0.011666667,0,0,Mar,1,8,7,2,Returning_Visitor,FALSE,FALSE 0,0,0,0,5,74,0,0.04,0,0,Mar,2,2,1,10,Returning_Visitor,FALSE,FALSE 0,0,0,0,15,1559.6,0,0.033846154,0,0,Mar,2,4,1,1,Returning_Visitor,FALSE,FALSE 0,0,0,0,6,236.5,0,0.016666667,0,0,Mar,1,1,1,3,Returning_Visitor,TRUE,FALSE 1,10,0,0,14,348.6666667,0.035714286,0.053571429,0,0,Mar,3,2,1,1,Returning_Visitor,TRUE,FALSE 0,0,0,0,1,0,0.2,0.2,0,0,Mar,2,5,1,1,Returning_Visitor,FALSE,FALSE 1,28,1,47,13,224.5,0,0.014285714,0,0,Mar,1,1,1,8,New_Visitor,FALSE,FALSE 4,27,0,0,42,601.7666667,0,0.004545455,0,0,Mar,2,5,3,3,Returning_Visitor,FALSE,FALSE 0,0,0,0,5,75.5,0.02,0.013333333,0,0,Mar,1,1,2,9,Returning_Visitor,TRUE,FALSE 0,0,0,0,2,153,0,0.05,0,0,Mar,2,5,6,1,Returning_Visitor,FALSE,FALSE 4,15,0,0,10,197.2,0,0.016666667,38.99,0,Mar,1,1,1,3,Returning_Visitor,TRUE,TRUE 0,0,0,0,18,3658.5,0.016666667,0.05,0,0,Mar,4,2,9,1,Returning_Visitor,FALSE,FALSE 0,0,0,0,5,1162.5,0,0.025,0,0,Mar,2,2,2,2,Returning_Visitor,FALSE,FALSE 0,0,0,0,32,556.6,0.014583333,0.039583333,0,0,Mar,2,2,3,1,Returning_Visitor,FALSE,FALSE 0,0,0,0,3,41,0,0.033333333,0,0,Mar,2,2,1,3,Returning_Visitor,TRUE,FALSE 0,0,0,0,11,300.0714286,0,0.02,53.9892,0,Mar,2,2,1,11,Returning_Visitor,TRUE,TRUE 13,1249.809524,4,205,36,1626.625433,0.010638298,0.026186255,6.831792392,0,Mar,3,2,2,3,Returning_Visitor,FALSE,FALSE 0,0,0,0,6,69.25,0,0.044444444,0,0,Mar,2,2,4,3,Returning_Visitor,FALSE,FALSE 2,390,0,0,10,1169.1,0.02,0.05,0,0,Mar,3,9,9,2,Returning_Visitor,FALSE,FALSE 0,0,0,0,8,402,0,0.038095238,0,0,Mar,2,2,2,1,Returning_Visitor,FALSE,FALSE 0,0,0,0,1,0,0.2,0.2,0,0,Mar,2,2,1,1,Returning_Visitor,FALSE,FALSE 0,0,0,0,35,3324.333333,0.011428571,0.024761905,0,0,Mar,2,2,1,1,Returning_Visitor,FALSE,FALSE 11,215.5238095,1,70,87,3037.499952,0.002150538,0.007722385,0,0,Mar,2,2,5,1,Returning_Visitor,FALSE,FALSE 0,0,0,0,4,59,0,0.05,0,0,Mar,1,1,1,10,Returning_Visitor,FALSE,FALSE 1,11,0,0,51,1059.253472,0.010588235,0.022643039,0,0,Mar,3,2,6,3,Returning_Visitor,FALSE,FALSE 2,20,0,0,7,720,0,0.011111111,0,0,Mar,2,2,3,6,Returning_Visitor,FALSE,FALSE 0,0,0,0,40,905.4856061,0.0135,0.03747619,0,0,Mar,3,2,2,1,Returning_Visitor,FALSE,FALSE 0,0,0,0,1,0,0.2,0.2,0,0,Mar,1,1,1,1,Returning_Visitor,TRUE,FALSE 2,8,1,14.33333333,14,603.6666667,0,0.0125,44.67925,0,Mar,2,2,1,7,Returning_Visitor,FALSE,TRUE 0,0,0,0,13,328.9166667,0,0.014285714,59.79014286,0,Mar,3,2,1,3,Returning_Visitor,FALSE,TRUE 0,0,0,0,33,778.8166667,0,0.022424242,0,0,Mar,2,5,1,1,Returning_Visitor,FALSE,FALSE 4,171.25,0,0,22,851.5595238,0.004545455,0.031907308,0,0,Mar,3,2,1,1,Returning_Visitor,FALSE,FALSE 0,0,0,0,1,0,0.2,0.2,0,0,Mar,1,1,8,1,Returning_Visitor,FALSE,FALSE 0,0,0,0,3,227.5,0,0.022222222,0,0,Mar,2,2,1,3,Returning_Visitor,FALSE,FALSE 5,227.3333333,0,0,17,528.9,0,0.002272727,0,0,Mar,1,1,1,9,Returning_Visitor,TRUE,FALSE 0,0,0,0,15,872.8333333,0,0.026666667,0,0,Mar,2,2,2,3,Returning_Visitor,TRUE,FALSE 0,0,0,0,2,0,0.2,0.2,0,0,Mar,3,2,3,1,Returning_Visitor,FALSE,FALSE 0,0,0,0,35,835.0666667,0.012571429,0.044761905,0,0,Mar,2,2,6,1,Returning_Visitor,FALSE,FALSE 0,0,0,0,4,82.5,0,0.05,0,0,Mar,3,2,1,3,Returning_Visitor,FALSE,FALSE 0,0,1,0,1,13,0,0.1,0,0,Mar,3,2,3,1,Returning_Visitor,FALSE,FALSE 0,0,0,0,2,9,0,0.1,0,0,Mar,2,2,1,3,Returning_Visitor,TRUE,FALSE 0,0,0,0,2,7,0,0.1,0,0,Mar,2,2,1,1,Returning_Visitor,FALSE,FALSE 0,0,0,0,12,94.5,0.033333333,0.025555556,0,0,Mar,3,2,4,11,Returning_Visitor,FALSE,FALSE 5,86,0,0,23,267.3,0,0.016,0,0,Mar,1,1,4,9,Returning_Visitor,TRUE,FALSE 0,0,0,0,29,3312.5,0.013793103,0.028965517,0,0,Mar,2,2,1,13,Returning_Visitor,FALSE,FALSE 0,0,0,0,3,75,0,0.066666667,0,0,Mar,2,5,1,3,Returning_Visitor,TRUE,FALSE 2,7,0,0,10,75,0.05,0.11,0,0,Mar,2,2,1,1,Returning_Visitor,FALSE,FALSE 0,0,1,24,27,1547.5,0.003703704,0.014814815,0,0,Mar,3,2,1,1,Returning_Visitor,FALSE,FALSE 7,186.9761905,2,40,35,1276.157071,0,0.008681765,0,0,Mar,1,1,6,2,Returning_Visitor,FALSE,FALSE 3,138,2,136,54,2631.1,0,0.00969697,0,0,Mar,1,2,1,2,Returning_Visitor,FALSE,FALSE 0,0,0,0,2,96,0,0.1,0,0,Mar,4,1,1,1,Returning_Visitor,FALSE,FALSE 2,374,0,0,3,1370,0,0.025,0,0,Mar,2,2,1,8,Returning_Visitor,FALSE,FALSE 2,61,0,0,5,97,0,0.04,0,0,Mar,2,2,1,1,Returning_Visitor,FALSE,FALSE 1,14,0,0,46,1380.166667,0,0.01037037,0,0,Mar,4,1,1,1,Returning_Visitor,FALSE,FALSE 1,38,0,0,23,1483.6,0.022916667,0.038373016,7.103020833,0,Mar,3,2,6,1,Returning_Visitor,FALSE,TRUE 0,0,0,0,5,156,0,0.04,0,0,Mar,2,2,4,1,Returning_Visitor,FALSE,FALSE 0,0,0,0,10,240.75,0,0.02,0,0,Mar,2,2,9,2,Returning_Visitor,FALSE,FALSE 2,96,0,0,37,1238.5,0.005128205,0.016410256,0,0,Mar,2,2,1,2,New_Visitor,FALSE,FALSE 6,615.25,0,0,68,2524.719048,0,0.000175593,0,0,Mar,1,2,6,1,Returning_Visitor,FALSE,FALSE 4,111,0,0,4,54,0,0.028571429,0,0,Mar,2,2,1,1,Returning_Visitor,FALSE,FALSE 2,147,0,0,2,35,0,0.05,0,0,Mar,2,2,1,2,New_Visitor,FALSE,FALSE 0,0,0,0,15,175.9166667,0.013333333,0.026666667,0,0,Mar,1,1,1,1,Returning_Visitor,TRUE,FALSE 3,53.16666667,0,0,11,263.6,0,0.003571429,0,0,Mar,3,2,1,9,Returning_Visitor,FALSE,FALSE 2,58,0,0,7,101.8,0,0.011111111,0,0,Mar,3,2,8,2,New_Visitor,FALSE,FALSE 0,0,0,0,1,0,0.2,0.2,0,0,Mar,3,2,3,1,Returning_Visitor,FALSE,FALSE 3,48.5,0,0,55,1042.242857,0,0.002222222,0,0,Mar,4,2,1,2,New_Visitor,FALSE,FALSE 1,21,3,116,21,613.6666667,0.008333333,0.023611111,0,0,Mar,1,2,1,1,Returning_Visitor,FALSE,FALSE 4,115.3333333,0,0,64,1588.944444,0.003076923,0.009213675,0,0,Mar,2,2,1,1,Returning_Visitor,FALSE,FALSE 0,0,1,929,75,4105.666667,0.002631579,0.017324561,0,0,Mar,2,2,3,1,Returning_Visitor,FALSE,FALSE 1,8,0,0,14,269.5,0,0.019047619,0,0,Mar,2,2,3,1,Returning_Visitor,FALSE,FALSE 1,32.28571429,0,0,8,1236.2,0,0.028571429,0,0,Mar,3,2,1,3,Returning_Visitor,FALSE,FALSE 0,0,0,0,49,1384.016667,0,0.011904762,0,0,Mar,2,2,1,2,Returning_Visitor,FALSE,FALSE 0,0,0,0,4,52,0,0.025,0,0,Mar,2,2,1,2,Returning_Visitor,FALSE,FALSE 1,11,0,0,29,664.75,0,0.005057471,0,0,Mar,3,2,1,2,Returning_Visitor,FALSE,FALSE 0,0,0,0,7,64,0,0.014285714,0,0,Mar,2,2,1,3,Returning_Visitor,FALSE,FALSE 0,0,0,0,1,0,0.2,0.2,0,0,Mar,2,2,4,1,Returning_Visitor,FALSE,FALSE 0,0,0,0,8,326.5,0.1,0.125,0,0,Mar,1,1,1,3,Returning_Visitor,FALSE,FALSE 0,0,0,0,25,1690.977778,0.008,0.037333333,0,0,Mar,1,1,8,1,Returning_Visitor,FALSE,FALSE 0,0,0,0,1,0,0.2,0.2,0,0,Mar,2,2,8,1,Returning_Visitor,FALSE,FALSE 4,277.75,0,0,3,178.75,0,0.008,0,0,Mar,3,2,3,8,New_Visitor,FALSE,FALSE 0,0,0,0,12,407,0,0.033333333,0,0,Mar,2,2,4,3,Returning_Visitor,FALSE,FALSE 6,241.1666667,0,0,39,1513.566667,0.005128205,0.021367521,0,0,Mar,1,1,1,3,Returning_Visitor,FALSE,FALSE 3,64,0,0,24,832.0909091,0,0.009876543,0,0,Mar,2,10,1,3,New_Visitor,FALSE,FALSE 1,25.5,0,0,12,250.8333333,0,0.016666667,0,0,Mar,3,2,2,1,Returning_Visitor,TRUE,FALSE 2,64,0,0,15,565.6666667,0,0.00625,0,0,Mar,2,2,1,1,New_Visitor,FALSE,FALSE 0,0,0,0,10,194,0.02,0.04,0,0,Mar,2,5,7,1,Returning_Visitor,FALSE,FALSE 8,130,1,18,19,298,0,0.016666667,19.66818546,0,Mar,2,2,3,1,Returning_Visitor,TRUE,FALSE 9,79,1,155.5,55,1743.066667,0,0.004371585,0,0,Mar,2,2,6,2,Returning_Visitor,FALSE,FALSE 3,84,0,0,27,655.9166667,0,0.011111111,0,0,Mar,2,2,2,1,Returning_Visitor,FALSE,FALSE 0,0,1,9,30,1992.833333,0,0.003333333,9.227000967,0,Mar,2,4,1,3,Returning_Visitor,FALSE,TRUE 0,0,0,0,3,0,0.2,0.2,0,0,Mar,2,2,4,1,Returning_Visitor,FALSE,FALSE 0,0,0,0,24,308.8635531,0,0.028030303,0,0,Mar,2,6,2,2,Returning_Visitor,FALSE,FALSE 3,41,0,0,46,2749.666667,0,0.005555556,0,0,Mar,2,2,1,2,Returning_Visitor,FALSE,FALSE 0,0,0,0,1,0,0.2,0.2,0,0,Mar,3,2,1,1,Returning_Visitor,FALSE,FALSE 0,0,0,0,25,812,0,0.008,0,0,Mar,2,2,1,1,Returning_Visitor,FALSE,FALSE 0,0,0,0,12,166,0.028571429,0.071428571,0,0,Mar,2,2,1,1,Returning_Visitor,TRUE,FALSE 1,14,0,0,4,65,0,0.05,0,0,Mar,2,4,3,1,Returning_Visitor,FALSE,FALSE 4,86,0,0,46,2450.688095,0.027083333,0.047905093,0,0,Mar,1,1,3,3,Returning_Visitor,FALSE,FALSE 0,0,0,0,10,226.8333333,0,0.01,0,0,Mar,1,1,1,2,Returning_Visitor,TRUE,FALSE 3,618,0,0,9,341,0,0.007692308,34.43601877,0,Mar,2,2,9,3,New_Visitor,FALSE,TRUE 0,0,0,0,13,226,0,0.053846154,0,0,Mar,2,2,4,3,Returning_Visitor,TRUE,FALSE 1,11.88888889,0,0,111,3510.879903,9.83E-05,0.009438427,12.01656549,0,Mar,3,2,5,2,Returning_Visitor,FALSE,FALSE 5,51.5,0,0,2,6,0,0.033333333,0,0,Mar,2,5,2,1,Returning_Visitor,FALSE,FALSE 2,7,0,0,27,559.4,0.014814815,0.030452675,0,0,Mar,2,4,4,3,Returning_Visitor,TRUE,FALSE 8,269,0,0,33,1109.883333,0.013888889,0.027742165,0,0,Mar,1,1,4,1,Returning_Visitor,FALSE,FALSE 0,0,0,0,5,239,0,0.04,0,0,Mar,2,2,9,1,Returning_Visitor,FALSE,FALSE 2,44.5,0,0,7,212.1666667,0,0.00625,0,0,Mar,3,2,1,8,New_Visitor,TRUE,FALSE 1,3,14,1165.166667,81,2820.952419,0.008897485,0.029431012,0,0,Mar,2,2,3,2,Returning_Visitor,FALSE,FALSE 0,0,0,0,6,44,0.066666667,0.1,0,0,Mar,2,2,3,1,Returning_Visitor,FALSE,FALSE 0,0,0,0,8,781,0.028571429,0.057142857,0,0,Mar,3,2,4,1,Returning_Visitor,FALSE,FALSE 4,61,0,0,14,175.6666667,0,0.010588235,0,0,Mar,2,2,5,2,Returning_Visitor,FALSE,FALSE 3,90,0,0,25,397.8846154,0.005333333,0.033504762,0,0,Mar,2,2,1,1,Returning_Visitor,TRUE,FALSE 0,0,0,0,25,770.0285714,0.0112,0.034933333,0,0,Mar,3,2,8,10,Returning_Visitor,FALSE,FALSE 2,102.5,3,60,22,1034.975,0,0.014,16.14384,0,Mar,1,1,4,2,Returning_Visitor,FALSE,TRUE 0,0,0,0,2,12,0,0.1,0,0,Mar,1,1,5,3,Returning_Visitor,FALSE,FALSE 0,0,0,0,3,8,0.066666667,0.133333333,0,0,Mar,2,4,1,3,Returning_Visitor,FALSE,FALSE 1,17,0,0,22,399.8,0.019047619,0.038095238,0,0,Mar,1,5,7,1,Returning_Visitor,FALSE,FALSE 0,0,0,0,1,0,0.2,0.2,0,0,Mar,2,2,2,3,Returning_Visitor,FALSE,FALSE 9,213.3214286,5,528,61,2258.430586,0.01,0.020655014,2.810812468,0,Mar,1,1,4,2,Returning_Visitor,FALSE,FALSE 0,0,0,0,2,254,0,0.1,0,0,Mar,3,2,4,1,Returning_Visitor,FALSE,FALSE 0,0,0,0,7,67,0,0.028571429,0,0,Mar,2,2,1,2,Returning_Visitor,FALSE,FALSE 0,0,0,0,4,57,0,0.025,0,0,Mar,2,2,6,2,Returning_Visitor,FALSE,FALSE 0,0,0,0,12,240.4166667,0,0.061688312,0,0,Mar,3,2,1,6,Returning_Visitor,FALSE,FALSE 0,0,0,0,8,551.1666667,0.025,0.035,0,0,Mar,3,2,2,3,Returning_Visitor,FALSE,FALSE 6,71.5,0,0,47,1532.2,0.009929078,0.019797366,27.26888014,0,Mar,2,2,1,1,Returning_Visitor,FALSE,TRUE 0,0,0,0,7,379.5,0.057142857,0.085714286,0,0,Mar,3,2,8,1,Returning_Visitor,FALSE,FALSE 1,6,0,0,14,548.1857143,0.028571429,0.02987013,0,0,Mar,3,2,3,1,Returning_Visitor,TRUE,FALSE 3,304,0,0,31,1735.301587,0.015625,0.0515625,0,0,Mar,1,1,2,1,Returning_Visitor,FALSE,FALSE 0,0,0,0,3,103,0,0.022222222,0,0,Mar,1,1,1,3,Returning_Visitor,TRUE,FALSE 7,151,0,0,44,1406.1,0.004081633,0.006501458,0,0,Mar,1,1,4,2,Returning_Visitor,FALSE,FALSE 3,199.75,0,0,8,331.4,0,0.003333333,0,0,Mar,3,2,2,10,New_Visitor,FALSE,FALSE 1,13.5,1,28,88,1759.275703,0.004545455,0.020644542,0,0,Mar,2,2,1,2,Returning_Visitor,FALSE,FALSE 0,0,0,0,1,0,0.2,0.2,0,0,Mar,2,4,1,1,Returning_Visitor,FALSE,FALSE 7,191.2,4,654.3333333,31,1204.600433,0.000854701,0.013342491,11.92009615,0,Mar,2,2,7,2,Returning_Visitor,FALSE,TRUE 2,47,0,0,39,1743.366667,0,0.016410256,78.93483561,0,Mar,3,3,2,1,Returning_Visitor,FALSE,TRUE 0,0,0,0,10,236,0,0.026666667,0,0,Mar,2,2,1,1,Returning_Visitor,FALSE,FALSE 5,197,4,246,17,427.0833333,0,0.008695652,0,0,Mar,1,1,3,2,New_Visitor,FALSE,FALSE 3,115.125,0,0,46,909.522619,0.004255319,0.020449173,0,0,Mar,2,2,6,1,Returning_Visitor,FALSE,FALSE 4,312.75,0,0,14,702.9166667,0,0.014259259,0,0,Mar,3,2,1,2,New_Visitor,FALSE,FALSE 0,0,0,0,40,962.3333333,0,0.01625,0,0,Mar,2,2,9,1,Returning_Visitor,FALSE,FALSE 4,724,0,0,2,159,0,0.008,0,0,Mar,4,2,1,7,New_Visitor,FALSE,FALSE 0,0,0,0,2,24,0,0.1,0,0,Mar,2,6,1,2,Returning_Visitor,FALSE,FALSE 8,237.5,4,142,30,1569.625,0,0.008108108,15.88906362,0,Mar,1,2,1,9,Returning_Visitor,TRUE,TRUE 3,66.33333333,0,0,15,424.8333333,0,0.0125,0,0,Mar,2,2,6,6,Returning_Visitor,FALSE,FALSE 0,0,0,0,16,154.8333333,0,0.00625,0,0,Mar,2,4,7,1,Returning_Visitor,FALSE,FALSE 2,190,1,0,37,758.4428571,0,0.010526316,0,0,Mar,1,1,6,1,Returning_Visitor,TRUE,FALSE 4,174,1,18.5,11,305.875,0,0.001709402,0,0,Mar,1,2,3,8,New_Visitor,TRUE,FALSE 3,227,0,0,2,62,0,0.025,0,0,Mar,3,2,1,10,Returning_Visitor,TRUE,FALSE 1,23,0,0,15,656.25,0.028571429,0.028571429,0,0,Mar,1,1,8,2,Returning_Visitor,TRUE,FALSE 0,0,0,0,29,617.5,0.006896552,0.01954023,0,0,Mar,2,2,3,10,Returning_Visitor,FALSE,FALSE 0,0,2,39,15,245,0,0.0125,11.9955404,0,Mar,2,2,1,3,Returning_Visitor,TRUE,TRUE 0,0,0,0,3,20,0,0.066666667,0,0,Mar,2,2,2,1,Returning_Visitor,TRUE,FALSE 0,0,0,0,1,0,0.2,0.2,0,0,Mar,1,1,1,1,Returning_Visitor,TRUE,FALSE 0,0,0,0,2,22,0,0.1,0,0,Mar,1,2,9,2,Returning_Visitor,FALSE,FALSE 1,26,1,0,55,872.4,0.003508772,0.01754386,0,0,Mar,2,5,1,1,Returning_Visitor,TRUE,FALSE 0,0,0,0,1,0,0.2,0.2,0,0,Mar,3,2,2,1,Returning_Visitor,FALSE,FALSE 0,0,0,0,38,1372,0.005405405,0.018918919,0,0,Mar,2,2,1,2,Returning_Visitor,FALSE,FALSE 2,41,0,0,7,139,0.022222222,0.055555556,0,0,Mar,2,5,6,2,Returning_Visitor,TRUE,FALSE 4,74,2,629,149,5042.458059,0.003870968,0.007285123,18.36804916,0,Mar,2,2,1,2,Returning_Visitor,FALSE,TRUE 0,0,0,0,5,288.5,0,0.0125,0,0,Mar,2,2,1,2,Returning_Visitor,TRUE,FALSE 0,0,0,0,2,0,0.2,0.2,0,0,Mar,2,2,1,3,Returning_Visitor,TRUE,FALSE 0,0,0,0,6,192,0,0.04,0,0,Mar,2,10,4,3,Returning_Visitor,FALSE,FALSE 0,0,0,0,27,954.6666667,0,0.003846154,0,0,Mar,2,2,1,2,Returning_Visitor,FALSE,FALSE 0,0,0,0,1,0,0.2,0.2,0,0,Mar,2,2,4,11,Returning_Visitor,FALSE,FALSE 2,74,0,0,13,429,0.013333333,0.040444444,0,0,Mar,2,4,3,6,Returning_Visitor,FALSE,FALSE 0,0,0,0,41,1546.5,0,0.008333333,0,0,Mar,3,2,1,1,Returning_Visitor,TRUE,FALSE 2,45,0,0,9,96.46666667,0,0.018181818,0,0,Mar,2,2,3,1,Returning_Visitor,FALSE,FALSE 2,112,0,0,9,198.8333333,0,0.006060606,0,0,Mar,3,2,3,2,New_Visitor,TRUE,FALSE 0,0,0,0,8,181,0.075,0.1,0,0,Mar,3,2,1,13,Returning_Visitor,TRUE,FALSE 2,37,0,0,15,208.5,0,0.011764706,0,0,Mar,2,2,3,2,New_Visitor,TRUE,FALSE 0,0,0,0,21,517.4238095,0.019047619,0.025,0,0,Mar,2,5,2,1,Returning_Visitor,FALSE,FALSE 0,0,0,0,12,358.0666667,0,0.025,0,0,Mar,3,3,2,1,Returning_Visitor,FALSE,FALSE 1,5.5,0,0,22,339.25,0,0.026984127,0,0,Mar,2,2,2,3,Returning_Visitor,FALSE,FALSE 3,50,0,0,13,796,0,0.013333333,0,0,Mar,2,2,5,2,Returning_Visitor,FALSE,FALSE 0,0,0,0,31,1759.416667,0,0.002903226,0,0,Mar,2,2,4,2,Returning_Visitor,TRUE,FALSE 2,26,0,0,19,3277,0.015789474,0.037894737,0,0,Mar,2,2,4,6,Returning_Visitor,FALSE,FALSE 0,0,0,0,13,265.1666667,0,0.046153846,0,0,Mar,2,2,4,2,Returning_Visitor,FALSE,FALSE 5,113.5,0,0,13,437.2,0,0.002222222,0,0,Mar,1,8,4,11,New_Visitor,FALSE,FALSE 0,0,0,0,6,92,0,0.066666667,0,0,Mar,1,1,3,10,Returning_Visitor,FALSE,FALSE 3,279,0,0,0,0,0,0.05,0,0,Mar,3,2,1,10,Returning_Visitor,TRUE,FALSE 3,228,1,267.5,23,1266.267677,0.002197802,0.037765568,0,0,Mar,2,2,3,2,Returning_Visitor,FALSE,FALSE 3,55,2,4,2,19,0,0.033333333,0,0,Mar,1,1,8,2,New_Visitor,FALSE,FALSE 6,112.5,0,0,47,1882.113409,0.004,0.019314286,17.527656,0,Mar,1,1,4,3,Returning_Visitor,FALSE,FALSE 1,17,0,0,12,616.4,0,0.018181818,0,0,Mar,1,1,3,1,Returning_Visitor,TRUE,FALSE 0,0,0,0,5,155,0.04,0.053333333,0,0,Mar,2,2,1,2,Returning_Visitor,FALSE,FALSE 0,0,0,0,11,814.5,0,0.01,0,0,Mar,1,1,1,2,New_Visitor,FALSE,FALSE 0,0,0,0,21,460.4166667,0.019047619,0.036507937,0,0,Mar,2,2,6,1,Returning_Visitor,FALSE,FALSE 0,0,0,0,5,819.5,0,0.036,0,0,Mar,2,2,1,2,Returning_Visitor,TRUE,FALSE 1,8.5,0,0,30,659.2,0,0.006451613,0,0,Mar,2,2,3,3,New_Visitor,TRUE,FALSE 0,0,0,0,5,427,0,0.04,0,0,Mar,3,3,3,9,Returning_Visitor,TRUE,FALSE 0,0,0,0,1,0,0.2,0.2,0,0,Mar,2,2,4,1,Returning_Visitor,FALSE,FALSE 0,0,0,0,17,766.5,0,0.0125,61.9395,0,Mar,2,2,1,2,New_Visitor,TRUE,TRUE 0,0,0,0,1,0,0.2,0.2,0,0,Mar,2,5,5,1,Returning_Visitor,FALSE,FALSE 0,0,0,0,14,2425,0,0.007142857,0,0,Mar,2,2,1,3,Returning_Visitor,FALSE,FALSE 7,319.5,0,0,11,416.5,0,0.007142857,0,0,Mar,3,2,1,9,New_Visitor,TRUE,FALSE 0,0,0,0,15,638.6428571,0.003571429,0.053571429,0,0,Mar,3,2,1,1,Returning_Visitor,FALSE,FALSE 0,0,1,132,8,445,0,0.014285714,0,0,Mar,3,2,4,14,Returning_Visitor,TRUE,FALSE 0,0,0,0,4,81,0,0.025,0,0,Mar,2,2,3,2,Returning_Visitor,FALSE,FALSE 0,0,0,0,20,1985,0.03,0.055,0,0,Mar,1,1,1,1,Returning_Visitor,FALSE,FALSE 5,97.5,0,0,24,758.1444444,0.04,0.059846154,0,0,Mar,3,2,2,1,Returning_Visitor,TRUE,FALSE 7,182.0833333,0,0,15,310.9,0,0.01,0,0,Mar,2,2,4,2,Returning_Visitor,FALSE,FALSE 3,97,0,0,15,482,0.0125,0.025,0,0,Mar,2,2,6,1,Returning_Visitor,FALSE,FALSE 0,0,0,0,2,0,0.2,0.2,0,0,Mar,2,5,1,1,Returning_Visitor,FALSE,FALSE 3,34.83333333,0,0,30,709.8333333,0.026666667,0.046666667,0,0,Mar,1,1,2,1,Returning_Visitor,TRUE,FALSE 0,0,3,57,31,4547.166667,0.012903226,0.039247312,8.682741935,0,Mar,2,4,5,2,Returning_Visitor,FALSE,FALSE 0,0,0,0,9,1040,0.077777778,0.116666667,0,0,Mar,2,4,3,1,Returning_Visitor,FALSE,FALSE 0,0,0,0,3,223,0.066666667,0.133333333,0,0,Mar,1,1,8,3,Returning_Visitor,FALSE,FALSE 0,0,2,14,11,687.5,0.020512821,0.035897436,0,0,Mar,3,2,1,3,Returning_Visitor,TRUE,FALSE 0,0,0,0,30,632.5833333,0,0.021666667,0,0,Mar,2,5,1,1,Returning_Visitor,FALSE,FALSE 1,77,0,0,9,312,0,0.044444444,0,0,Mar,1,1,3,3,New_Visitor,TRUE,FALSE 3,107,0,0,45,2502.966667,0,0.008510638,9.702113281,0,Mar,2,4,1,2,New_Visitor,TRUE,FALSE 10,614.1666667,0,0,47,3608.9,0,0.019871795,7.256219133,0,Mar,2,2,3,1,Returning_Visitor,FALSE,FALSE 13,315.9666667,1,0,15,406.575,0.019047619,0.032380952,0,0,Mar,3,2,3,2,Returning_Visitor,FALSE,FALSE 0,0,0,0,1,0,0.2,0.2,0,0,Mar,2,2,3,3,Returning_Visitor,FALSE,FALSE 1,0,0,0,39,2487.5,0.017105263,0.047105263,0,0,Mar,2,2,1,3,Returning_Visitor,FALSE,FALSE 0,0,0,0,15,212.8,0.019047619,0.042857143,0,0,Mar,1,1,3,1,Returning_Visitor,FALSE,FALSE 2,11,0,0,20,510,0.013636364,0.034848485,0,0,Mar,4,1,6,1,Returning_Visitor,FALSE,FALSE 0,0,0,0,1,0,0.2,0.2,0,0,Mar,3,2,3,1,Returning_Visitor,FALSE,FALSE 2,47.66666667,0,0,5,72.33333333,0,0.014285714,0,0,Mar,1,1,8,2,New_Visitor,FALSE,FALSE 0,0,0,0,13,96.5,0.030769231,0.076923077,0,0,Mar,2,6,4,1,Returning_Visitor,FALSE,FALSE 0,0,0,0,8,267,0,0.05,0,0,Mar,2,2,3,3,Returning_Visitor,FALSE,FALSE 0,0,0,0,8,481.1666667,0,0.025,0,0,Mar,2,2,7,7,Returning_Visitor,FALSE,FALSE 0,0,0,0,4,2528,0,0.066666667,0,0,Mar,2,2,9,1,Returning_Visitor,TRUE,FALSE 2,30,1,148.5,24,1530.194444,0.007407407,0.014814815,10.10222222,0,Mar,1,1,1,10,Returning_Visitor,FALSE,FALSE 0,0,0,0,7,289.5,0,0.014285714,0,0,Mar,1,1,1,1,Returning_Visitor,TRUE,FALSE 0,0,0,0,1,10,0,0.1,0,0,Mar,3,2,3,9,Returning_Visitor,TRUE,FALSE 0,0,0,0,16,676.75,0.014285714,0.035714286,0,0,Mar,4,2,1,3,Returning_Visitor,FALSE,FALSE 0,0,0,0,4,36,0.05,0.1,0,0,Mar,2,2,6,1,Returning_Visitor,FALSE,FALSE 2,25,0,0,6,65,0,0.025,0,0,Mar,2,2,1,2,New_Visitor,TRUE,FALSE 0,0,0,0,16,630.75,0,0.04047619,0,0,Mar,3,2,1,1,Returning_Visitor,TRUE,FALSE 8,359,2,954,14,336.5,0,0.005555556,0,0,Mar,3,2,7,9,Returning_Visitor,TRUE,FALSE 0,0,0,0,5,77,0,0.04,0,0,Mar,2,4,1,3,Returning_Visitor,TRUE,FALSE 5,59,0,0,3,44.66666667,0,0.025,0,0,Mar,2,4,6,2,Returning_Visitor,TRUE,FALSE 4,1347.75,0,0,22,3410.875,0.019871795,0.09,0,0,Mar,2,2,1,2,Returning_Visitor,FALSE,FALSE 0,0,0,0,10,322.5,0.04,0.07,0,0,Mar,2,2,4,1,Returning_Visitor,FALSE,FALSE 0,0,0,0,11,919,0,0.027272727,0,0,Mar,2,6,2,3,Returning_Visitor,FALSE,FALSE 18,280.95,6,762.5,79,2096.911364,0.003333333,0.014008551,25.96702005,0,Mar,2,4,2,2,Returning_Visitor,FALSE,FALSE 4,100.3,0,0,66,2868.133333,0.011428571,0.01725974,9.162351243,0,Mar,3,2,1,1,Returning_Visitor,FALSE,TRUE 1,23,0,0,27,1058.893939,0,0.010493827,1.865777778,0,Mar,2,2,3,13,Returning_Visitor,FALSE,FALSE 6,53.38095238,0,0,5,54.38095238,0,0.003571429,0,0,Mar,2,2,6,2,New_Visitor,TRUE,FALSE 2,8,0,0,11,543.5,0.007692308,0.038461538,0,0,Mar,2,2,3,13,Returning_Visitor,FALSE,FALSE 0,0,0,0,29,1089,0.006896552,0.013793103,0,0,Mar,1,6,1,1,Returning_Visitor,FALSE,FALSE 1,0,1,60,43,1131.808333,0.028787879,0.047348485,0,0,Mar,1,1,1,3,Returning_Visitor,FALSE,FALSE 0,0,0,0,7,314.5,0.033333333,0.066666667,0,0,Mar,2,2,3,1,Returning_Visitor,FALSE,FALSE 14,416.3928571,6,449.3333333,258,11301.20416,0,0.00742538,33.6117917,0,Mar,2,4,9,2,Returning_Visitor,FALSE,TRUE 6,573.5,1,0,33,1946.942857,0.005882353,0.020602653,0,0,Mar,3,2,3,2,Returning_Visitor,FALSE,FALSE 0,0,0,0,1,0,0.2,0.2,0,0,Mar,2,2,1,1,Returning_Visitor,FALSE,FALSE 6,465.8333333,2,71,26,1558,0.006666667,0.013333333,7.169617487,0,Mar,2,2,1,1,Returning_Visitor,TRUE,FALSE 0,0,1,212.5,21,1198.75,0,0.003174603,0,0,Mar,2,4,7,2,Returning_Visitor,FALSE,FALSE 0,0,0,0,1,0,0.2,0.2,0,0,Mar,2,2,4,3,Returning_Visitor,FALSE,FALSE 0,0,0,0,17,927.1666667,0,0.014705882,0,0,Mar,1,1,2,3,Returning_Visitor,FALSE,FALSE 9,97.64285714,0,0,37,584.5958874,0.005128205,0.013043478,0,0,Mar,2,4,1,3,Returning_Visitor,TRUE,FALSE 0,0,0,0,4,135.5,0,0.066666667,0,0,Mar,2,2,3,14,Returning_Visitor,FALSE,FALSE 3,148,0,0,8,292.5,0,0.027272727,0,0,Mar,2,2,1,3,Returning_Visitor,TRUE,FALSE 0,0,0,0,2,68.5,0,0.033333333,0,0,Mar,3,2,1,10,Returning_Visitor,FALSE,FALSE 3,58,0,0,23,364.1666667,0.004,0.02,0,0,Mar,2,2,1,3,Returning_Visitor,FALSE,FALSE 0,0,0,0,26,694.3,0.023076923,0.036410256,0,0,Mar,3,2,2,3,Returning_Visitor,TRUE,FALSE 1,3,0,0,3,236,0,0.025,0,0,Mar,3,2,1,3,Returning_Visitor,TRUE,FALSE 4,97.61111111,0,0,24,1128.468254,0.038461538,0.058577534,0,0,Mar,2,4,3,1,Returning_Visitor,FALSE,FALSE 4,57,0,0,5,81,0,0.025,0,0,Mar,1,1,8,10,Returning_Visitor,FALSE,FALSE 2,34,0,0,21,521.5,0.018181818,0.021212121,0,0,Mar,3,2,5,1,Returning_Visitor,TRUE,FALSE 0,0,0,0,7,496.75,0,0.011111111,0,0,Mar,3,2,6,1,Returning_Visitor,FALSE,FALSE 0,0,0,0,9,104.8333333,0.022222222,0.072222222,0,0,Mar,2,2,4,2,Returning_Visitor,FALSE,FALSE 1,7,0,0,4,45,0,0.0125,0,0,Mar,3,2,3,1,Returning_Visitor,FALSE,FALSE 2,37.5,0,0,2,492.5,0,0.05,0,0,Mar,2,2,1,2,Returning_Visitor,FALSE,FALSE 1,3,0,0,40,624.75,0,0.014634146,0,0,Mar,3,7,3,3,Returning_Visitor,FALSE,FALSE 0,0,0,0,1,0,0.2,0.2,0,0,Mar,3,2,7,1,Returning_Visitor,FALSE,FALSE 0,0,0,0,6,116.5,0,0.066666667,0,0,Mar,2,2,2,3,Returning_Visitor,TRUE,FALSE 0,0,0,0,8,144,0.025,0.045833333,0,0,Mar,3,2,7,3,Returning_Visitor,TRUE,FALSE 2,22.5,0,0,28,686.3222222,0,0.001190476,0,0,Mar,2,4,3,1,Returning_Visitor,FALSE,FALSE 0,0,1,1411,1,1411,0,0.1,0,0,Mar,2,2,1,2,Returning_Visitor,FALSE,FALSE 0,0,0,0,7,126,0.028571429,0.057142857,0,0,Mar,2,5,2,10,Returning_Visitor,FALSE,FALSE 0,0,0,0,6,185,0,0.1,0,0,Mar,1,1,1,10,Returning_Visitor,FALSE,FALSE 0,0,0,0,6,218,0,0.066666667,0,0,Mar,2,2,3,1,Returning_Visitor,FALSE,FALSE 0,0,0,0,1,0,0.2,0.2,0,0,Mar,3,2,3,1,Returning_Visitor,FALSE,FALSE 0,0,0,0,30,484.672619,0,0.018965517,0,0,Mar,2,6,7,1,Returning_Visitor,FALSE,FALSE 0,0,0,0,19,439.8333333,0.005555556,0.003703704,0,0,Mar,1,1,7,2,New_Visitor,FALSE,FALSE 2,38,0,0,21,733.7,0,0.014035088,0,0,Mar,2,7,1,3,Returning_Visitor,TRUE,FALSE 6,77,3,447,34,1469.702381,0,0.02398374,22.80152439,0,Mar,2,2,1,2,Returning_Visitor,FALSE,FALSE 2,58.5,2,125,16,485.8333333,0,0.002222222,0,0,Mar,2,2,7,2,New_Visitor,TRUE,FALSE 0,0,0,0,3,19,0,0.066666667,0,0,Mar,2,4,1,1,Returning_Visitor,TRUE,FALSE 6,99.33333333,1,28.5,5,345.5,0,0.011111111,0,0,Mar,2,2,3,8,New_Visitor,FALSE,FALSE 3,117,0,0,20,543.8,0.00952381,0.019047619,0,0,Mar,1,1,1,2,New_Visitor,FALSE,FALSE 0,0,0,0,3,48,0,0.066666667,0,0,Mar,4,1,1,1,Returning_Visitor,FALSE,FALSE 2,16.66666667,0,0,22,343.3690476,0.001298701,0.039924242,0,0,Mar,3,2,1,1,Returning_Visitor,FALSE,FALSE 0,0,0,0,9,171,0.022222222,0.066666667,0,0,Mar,2,10,4,3,Returning_Visitor,FALSE,FALSE 12,81.5,0,0,61,2202.579167,0.005797101,0.017153855,0,0,Mar,2,2,1,1,Returning_Visitor,FALSE,FALSE 0,0,0,0,11,299.5,0.036363636,0.072727273,0,0,Mar,1,1,3,1,Returning_Visitor,FALSE,FALSE 0,0,0,0,1,0,0.2,0.2,0,0,Mar,2,2,1,1,Returning_Visitor,FALSE,FALSE 0,0,1,90,11,1056.5,0.018181818,0.054545455,0,0,Mar,4,2,1,2,Returning_Visitor,FALSE,FALSE 4,34,0,0,46,627.3083333,0,0.004166667,0,0,Mar,2,2,7,8,New_Visitor,FALSE,FALSE 4,38.5,0,0,33,583.8333333,0.002631579,0.007894737,0,0,Mar,2,2,4,3,Returning_Visitor,TRUE,FALSE 0,0,0,0,7,221.5,0,0.028571429,0,0,Mar,4,1,1,3,Returning_Visitor,FALSE,FALSE 0,0,0,0,44,441.7916667,0.004545455,0.018181818,0,0,Mar,4,2,1,3,Returning_Visitor,FALSE,FALSE 7,156.25,0,0,37,1037.355013,0,0.0165,0,0,Mar,3,2,8,2,Returning_Visitor,FALSE,FALSE 3,15,0,0,11,246,0.018181818,0.054545455,0,0,Mar,2,2,1,8,Returning_Visitor,FALSE,FALSE 0,0,0,0,3,10,0,0.066666667,0,0,Mar,2,2,7,1,Returning_Visitor,FALSE,FALSE 0,0,2,18,1,46,0,0.066666667,0,0,Mar,2,2,7,10,Returning_Visitor,FALSE,FALSE 0,0,0,0,80,801.802381,0,0.0035,0,0,Mar,2,2,3,1,Returning_Visitor,FALSE,FALSE 1,19,0,0,40,1361.166667,0.005,0.0225,0,0,Mar,4,1,2,3,Returning_Visitor,TRUE,FALSE 0,0,0,0,5,53,0.04,0.08,0,0,Mar,2,5,2,2,Returning_Visitor,TRUE,FALSE 1,7,0,0,17,2648.5,0.0125,0.03125,0,0,Mar,2,2,1,2,Returning_Visitor,FALSE,FALSE 0,0,0,0,3,27,0.066666667,0.133333333,0,0,Mar,2,2,1,13,Returning_Visitor,FALSE,FALSE 0,0,0,0,13,255,0,0.015384615,0,0,Mar,2,4,1,1,Returning_Visitor,TRUE,FALSE 0,0,0,0,2,221.5,0,0.05,0,0,Mar,3,2,3,10,Returning_Visitor,TRUE,FALSE 0,0,0,0,7,225,0.028571429,0.085714286,0,0,Mar,4,1,4,1,Returning_Visitor,FALSE,FALSE 0,0,0,0,7,125,0,0.028571429,0,0,Mar,2,2,1,1,Returning_Visitor,FALSE,FALSE 0,0,0,0,12,323,0.033333333,0.066666667,0,0,Mar,2,2,1,3,Returning_Visitor,TRUE,FALSE 5,67.5,3,245,32,1929.329762,0,0.00430839,0,0,Mar,2,4,1,1,Returning_Visitor,FALSE,FALSE 12,311.4090909,0,0,96,4233.4807,0.006635802,0.027174712,3.541145833,0,Mar,2,4,1,1,Returning_Visitor,TRUE,FALSE 0,0,0,0,5,116,0,0.04,0,0,Mar,2,5,1,1,Returning_Visitor,FALSE,FALSE 0,0,0,0,2,0,0.2,0.2,0,0,Mar,3,2,6,1,Returning_Visitor,TRUE,FALSE 3,14,0,0,27,1585.666667,0.026666667,0.058333333,0,0,Mar,2,2,6,1,Returning_Visitor,TRUE,FALSE 6,145,0,0,13,644,0,0.013333333,0,0,Mar,2,2,1,2,New_Visitor,FALSE,FALSE 0,0,0,0,5,488.5,0.032727273,0.033846154,0,0,Mar,2,7,7,6,Returning_Visitor,FALSE,FALSE 0,0,1,15,25,1235.65,0.041666667,0.05327381,0,0,Mar,1,1,1,3,Returning_Visitor,TRUE,FALSE 1,43,0,0,37,1505.166667,0,0.005263158,0,0,Mar,2,2,4,8,New_Visitor,FALSE,FALSE 0,0,0,0,7,495,0,0.00952381,0,0,Mar,4,1,3,2,Returning_Visitor,FALSE,FALSE 4,84,0,0,21,270,0,0.002380952,0,0,Mar,1,1,3,2,Returning_Visitor,FALSE,FALSE 0,0,0,0,2,0,0.2,0.2,0,0,Mar,3,2,1,10,Returning_Visitor,FALSE,FALSE 4,120,0,0,13,293,0,0.014285714,0,0,Mar,1,2,1,10,Returning_Visitor,FALSE,FALSE 0,0,1,0,51,2122.656061,0,0.007051282,9.294775641,0,Mar,2,2,1,10,Returning_Visitor,FALSE,TRUE 0,0,0,0,35,1231.160256,0.005714286,0.023619048,0,0,Mar,2,2,1,1,Returning_Visitor,FALSE,FALSE 0,0,0,0,1,0,0.2,0.2,0,0,Mar,2,2,1,1,Returning_Visitor,FALSE,FALSE 0,0,0,0,17,341.6666667,0.011764706,0.041176471,0,0,Mar,2,6,9,6,Returning_Visitor,FALSE,FALSE 0,0,0,0,1,0,0.2,0.2,0,0,Mar,1,1,3,3,Returning_Visitor,FALSE,FALSE 0,0,0,0,21,1686.333333,0,0.030952381,0,0,Mar,2,5,2,1,Returning_Visitor,FALSE,FALSE 5,263,0,0,3,212,0,0.028571429,0,0,Mar,1,1,9,8,New_Visitor,FALSE,FALSE 1,0,0,0,34,2161.979365,0.00625,0.020233586,0,0,Mar,3,2,1,1,Returning_Visitor,FALSE,FALSE 9,72.94444444,2,125.0952381,17,318.0730159,0.008695652,0.035397453,0,0,Mar,3,2,8,2,Returning_Visitor,FALSE,FALSE 0,0,0,0,1,13,0,0.1,0,0,Mar,2,2,3,1,Returning_Visitor,FALSE,FALSE 0,0,0,0,30,390.6666667,0.006666667,0.056666667,0,0,Mar,2,6,6,1,Returning_Visitor,FALSE,FALSE 0,0,0,0,11,253,0,0.027272727,0,0,Mar,2,2,2,1,Returning_Visitor,FALSE,FALSE 1,18.5,0,0,12,208,0.045454545,0.081818182,0,0,Mar,2,2,4,1,Returning_Visitor,FALSE,FALSE 6,87.33333333,0,0,62,1470.853037,0,0.009683372,0,0,Mar,2,4,6,3,Returning_Visitor,TRUE,FALSE 6,99.60588235,0,0,83,2585.61835,0,0.00761877,6.871308983,0,Mar,2,2,2,2,Returning_Visitor,FALSE,TRUE 3,63,0,0,13,400.3333333,0.030769231,0.046153846,0,0,Mar,4,1,3,1,Returning_Visitor,FALSE,FALSE 3,68,1,103,10,808.5,0,0.015384615,35.99261538,0,Mar,2,2,3,7,Returning_Visitor,TRUE,TRUE 0,0,0,0,12,1484,0,0.016666667,0,0,Mar,2,2,7,1,Returning_Visitor,FALSE,FALSE 0,0,0,0,4,505,0,0.05,0,0,Mar,2,2,1,1,Returning_Visitor,FALSE,FALSE 3,68.5,0,0,37,1121.205556,0.005405405,0.00113798,0,0,Mar,1,1,1,2,Returning_Visitor,TRUE,FALSE 1,135,0,0,2,141,0,0.066666667,0,0,Mar,2,2,1,10,Returning_Visitor,FALSE,FALSE 0,0,0,0,3,234,0,0.033333333,0,0,Mar,1,2,1,2,Returning_Visitor,TRUE,FALSE 1,34,0,0,7,179,0,0.028571429,53.13428571,0,Mar,2,4,2,8,New_Visitor,FALSE,TRUE 6,176.25,12,404,41,2720.671429,0.012962963,0.033262108,8.833825926,0,Mar,2,2,2,2,Returning_Visitor,TRUE,TRUE 16,315.1449864,6,147.8666667,173,6255.017866,0.002531328,0.010737154,0,0,Mar,2,2,2,2,Returning_Visitor,FALSE,FALSE 6,523.2142857,2,263,48,2309.732468,0.003846154,0.014903846,10.60089168,0,Mar,2,2,3,1,Returning_Visitor,FALSE,TRUE 4,76,0,0,39,1936.55,0.004761905,0.023809524,5.51397619,0,Mar,4,1,3,2,Returning_Visitor,FALSE,FALSE 3,118.6666667,0,0,17,694,0.003703704,0.008333333,27.45138186,0,Mar,3,2,1,2,Returning_Visitor,TRUE,TRUE 0,0,0,0,11,125,0.018181818,0.081818182,0,0,Mar,2,2,1,13,Returning_Visitor,FALSE,FALSE 8,174.5,0,0,10,195.5,0,0.006060606,0,0,Mar,1,1,3,2,Returning_Visitor,FALSE,FALSE 1,40,0,0,27,1400.5,0,0.007142857,0,0,Mar,1,1,4,2,Returning_Visitor,FALSE,FALSE 8,201.1666667,3,41,18,914.1666667,0,0.0125,0,0,Mar,1,1,1,3,Returning_Visitor,FALSE,FALSE 13,374.875,1,53,88,5958.071931,0.002105263,0.00954386,0,0,Mar,2,2,3,2,Returning_Visitor,FALSE,FALSE 4,56,0,0,31,270.4916667,0,0.00234375,0,0,Mar,2,2,1,3,Returning_Visitor,FALSE,FALSE 3,92.83333333,0,0,24,463.8166667,0.002666667,0.015333333,17.84230699,0,Mar,1,1,1,2,Returning_Visitor,FALSE,TRUE 0,0,0,0,16,371.5,0.0125,0.025,0,0,Mar,2,5,7,1,Returning_Visitor,FALSE,FALSE 0,0,0,0,6,13,0.133333333,0.166666667,0,0,Mar,2,2,1,3,Returning_Visitor,FALSE,FALSE 1,4,3,148.5,5,185.8333333,0,0.01875,0,0,Mar,2,2,7,2,Returning_Visitor,FALSE,FALSE 2,102,0,0,16,502.8333333,0,0.004444444,0,0,Mar,3,2,4,2,Returning_Visitor,TRUE,FALSE 0,0,0,0,3,1016.5,0.066666667,0.066666667,0,0,Mar,3,2,5,1,Returning_Visitor,FALSE,FALSE 1,11,0,0,16,253,0,0.023529412,0,0,Mar,2,2,1,3,New_Visitor,FALSE,FALSE 0,0,1,126,17,887.0666667,0.0125,0.0296875,0,0,Mar,2,2,1,2,Returning_Visitor,FALSE,FALSE 0,0,0,0,58,3200.366667,0.003448276,0.027586207,0,0,Mar,2,2,6,1,Returning_Visitor,FALSE,FALSE 3,187,2,41,48,1769.697383,0.000606061,0.011616162,16.71534488,0,Mar,2,2,3,2,Returning_Visitor,FALSE,FALSE 0,0,0,0,14,1037.5,0.014285714,0.047619048,0,0,Mar,2,2,4,2,Returning_Visitor,FALSE,FALSE 4,185,0,0,40,1694.75,0.020512821,0.031196581,0,0,Mar,2,2,6,1,Returning_Visitor,FALSE,FALSE 0,0,0,0,37,1643.435516,0.005555556,0.032271242,2.188718519,0,Mar,2,4,3,2,Returning_Visitor,TRUE,TRUE 0,0,0,0,7,383.5,0,0.023809524,0,0,Mar,3,2,6,3,Returning_Visitor,FALSE,FALSE 8,166.2,5,371,23,626.2213675,0.004444444,0.008888889,0,0,Mar,1,1,8,3,Returning_Visitor,FALSE,FALSE 0,0,0,0,4,34,0.1,0.125,0,0,Mar,3,2,3,3,Returning_Visitor,FALSE,FALSE 0,0,0,0,27,1070.461905,0,0.007407407,57.7293604,0,Mar,2,2,3,2,Returning_Visitor,FALSE,TRUE 0,0,0,0,1,0,0.2,0.2,0,0,Mar,3,2,3,3,Returning_Visitor,FALSE,FALSE 2,84,0,0,3,195,0,0.02,0,0,Mar,2,2,5,2,Returning_Visitor,FALSE,FALSE 3,86,0,0,22,1187.833333,0.009090909,0.027272727,0,0,Mar,3,2,3,1,Returning_Visitor,TRUE,FALSE 17,754.1095238,2,77,31,1021.619336,0.004545455,0.026479076,2.08053355,0,Mar,1,1,3,2,Returning_Visitor,FALSE,TRUE 3,24,0,0,6,58,0,0.028571429,0,0,Mar,2,2,8,2,Returning_Visitor,FALSE,FALSE 0,0,0,0,18,771.0833333,0.055555556,0.071428571,0,0,Mar,2,2,3,1,Returning_Visitor,FALSE,FALSE 8,17.34848485,0,0,19,291.2589744,0,0.043589744,0,0,Mar,2,2,7,1,Returning_Visitor,FALSE,FALSE 0,0,0,0,15,695.4,0.014285714,0.028571429,0,0,Mar,2,2,2,1,Returning_Visitor,FALSE,FALSE 2,15,0,0,3,21,0,0.05,0,0,Mar,3,2,4,8,Returning_Visitor,FALSE,FALSE 0,0,0,0,45,1594.283333,0,0.015227273,45.4316713,0,Mar,2,2,1,2,Returning_Visitor,TRUE,TRUE 3,36,1,395,12,343.5833333,0,0.014285714,0,0,Mar,2,2,1,2,New_Visitor,FALSE,FALSE 2,34,0,0,81,2241.002381,0.002439024,0.007012195,0,0,Mar,2,2,1,1,Returning_Visitor,FALSE,FALSE 0,0,0,0,2,5,0,0.1,0,0,Mar,2,2,1,13,Returning_Visitor,TRUE,FALSE 0,0,0,0,2,63,0,0.05,0,0,Mar,3,2,1,1,Returning_Visitor,FALSE,FALSE 2,47,0,0,21,825.0833333,0,0.009090909,10.7929507,0,Mar,2,2,2,2,New_Visitor,FALSE,TRUE 0,0,0,0,54,1357.61783,0,0.004812274,0,0,Mar,2,2,2,2,Returning_Visitor,TRUE,FALSE 5,44.5,1,4,37,836.3333333,0.005,0.045416667,0,0,Mar,2,2,4,1,Returning_Visitor,FALSE,FALSE 6,125.5,0,0,19,1409.416667,0.002469136,0.023703704,0,0,Mar,1,8,3,2,Returning_Visitor,FALSE,FALSE 0,0,0,0,10,60.5,0.02,0.04,0,0,Mar,2,2,3,1,Returning_Visitor,FALSE,FALSE 2,42,2,54,10,420,0,0.008333333,0,0,Mar,2,2,8,1,Returning_Visitor,FALSE,FALSE 0,0,0,0,1,0,0.2,0.2,0,0,Mar,2,5,9,1,Returning_Visitor,FALSE,FALSE 0,0,0,0,2,0,0.2,0.2,0,0,Mar,2,5,1,1,Returning_Visitor,FALSE,FALSE 0,0,0,0,9,389,0,0.011111111,0,0,Mar,2,2,3,3,Returning_Visitor,TRUE,FALSE 2,95,0,0,15,449.5,0.0125,0.04375,0,0,Mar,2,2,4,1,Returning_Visitor,FALSE,FALSE 4,56.5,0,0,8,202.1666667,0,0.01,0,0,Mar,3,2,1,8,New_Visitor,TRUE,FALSE 0,0,0,0,2,17,0,0.1,0,0,Mar,1,1,8,3,Returning_Visitor,FALSE,FALSE 0,0,0,0,12,689.2857143,0,0.058333333,0,0,Mar,3,2,3,1,Returning_Visitor,TRUE,FALSE 5,134.9285714,2,41,31,2013.595238,0,0.014141414,8.482727273,0,Mar,2,2,8,2,Returning_Visitor,FALSE,TRUE 0,0,0,0,17,933.4166667,0.031932773,0.043075335,0,0,Mar,3,2,1,3,Returning_Visitor,FALSE,FALSE 2,64,0,0,19,371.2272727,0.001666667,0.029166667,0,0,Mar,2,4,9,10,Returning_Visitor,FALSE,FALSE 3,68,0,0,8,306,0,0.008888889,0,0,Mar,1,1,9,3,New_Visitor,TRUE,FALSE 2,20.5,0,0,21,719.7380952,0.004347826,0.010434783,0,0,Mar,2,2,1,3,Returning_Visitor,FALSE,FALSE 0,0,0,0,1,0,0.2,0.2,0,0,Mar,2,4,6,2,Returning_Visitor,FALSE,FALSE 0,0,0,0,2,56,0,0.1,0,0,Mar,3,2,1,1,Returning_Visitor,FALSE,FALSE 3,11.33333333,0,0,20,302.3888889,0,0.002380952,0,0,Mar,2,2,1,1,Returning_Visitor,FALSE,FALSE 0,0,0,0,19,491.6666667,0.011111111,0.012222222,0,0,Mar,1,1,1,6,Returning_Visitor,TRUE,FALSE 5,504,0,0,13,922.0833333,0.023529412,0.023529412,0,0,Mar,3,2,8,2,Returning_Visitor,FALSE,FALSE 2,25,0,0,20,667.9166667,0,0.00952381,18.5847619,0,Mar,4,1,1,3,Returning_Visitor,FALSE,FALSE 2,20,0,0,13,181.5,0,0.013333333,0,0,Mar,2,2,1,1,Returning_Visitor,FALSE,FALSE 1,19.66666667,0,0,14,512.1111111,0.028571429,0.03,0,0,Mar,3,2,3,9,Returning_Visitor,TRUE,FALSE 1,13,0,0,15,317,0,0.006666667,0,0,Mar,2,2,1,2,New_Visitor,TRUE,FALSE 0,0,0,0,17,85.66666667,0.023529412,0.080392157,0,0,Mar,2,2,6,1,Returning_Visitor,FALSE,FALSE 0,0,0,0,8,128.5,0,0.025,0,0,Mar,2,2,1,1,Returning_Visitor,FALSE,FALSE 6,76.33333333,0,0,7,78.83333333,0.004444444,0.010666667,0,0,Mar,2,2,8,2,Returning_Visitor,FALSE,FALSE 2,45.44444444,1,40.66666667,57,851.6426534,0,0.007923497,3.077836066,0,Mar,2,4,3,1,Returning_Visitor,TRUE,TRUE 3,31,0,0,58,1400.438961,0,0.005951036,0,0,Mar,2,2,1,1,Returning_Visitor,FALSE,FALSE 0,0,0,0,6,84,0,0.033333333,0,0,Mar,1,1,8,3,Returning_Visitor,FALSE,FALSE 0,0,0,0,12,167.2222222,0,0.001666667,0,0,Mar,3,2,1,3,Returning_Visitor,TRUE,FALSE 0,0,0,0,12,396.5,0,0.037222222,0,0,Mar,2,4,3,2,Returning_Visitor,FALSE,FALSE 0,0,0,0,2,22,0,0.05,0,0,Mar,1,1,3,15,Returning_Visitor,TRUE,FALSE 0,0,0,0,5,371,0,0.02,0,0,Mar,2,2,4,10,Returning_Visitor,FALSE,FALSE 0,0,4,399.3333333,46,1814.583333,0.003673469,0.02675737,0,0,Mar,2,2,2,2,Returning_Visitor,FALSE,FALSE 0,0,0,0,4,30.5,0,0.05,0,0,Mar,2,4,3,3,Returning_Visitor,FALSE,FALSE 0,0,0,0,27,644,0.007692308,0.051923077,0,0,Mar,3,2,2,3,Returning_Visitor,FALSE,FALSE 8,125.1666667,0,0,27,1802.25,0.006060606,0.011774152,0,0,Mar,3,2,1,8,Returning_Visitor,TRUE,FALSE 2,13,0,0,23,855.1666667,0.008,0.025333333,0,0,Mar,2,5,1,3,Returning_Visitor,FALSE,FALSE 1,72.66666667,4,345,24,879.9090909,0,0.035897436,6.482798269,0,Mar,2,4,4,1,Returning_Visitor,FALSE,TRUE 5,100.9166667,0,0,56,1432.223333,0,0.002185792,69.43043195,0,Mar,2,2,3,3,Returning_Visitor,TRUE,TRUE 7,60,3,23,111,2524.501905,0.001694915,0.018834175,6.445051499,0,Mar,3,2,1,2,Returning_Visitor,TRUE,FALSE 3,49.5,1,29.5,45,1545.5,0,0.004,20.97144,0,Mar,2,4,3,8,Returning_Visitor,TRUE,TRUE 8,466.5,2,75.25,44,1616.425824,0.006944444,0.010974434,0,0,Mar,3,2,4,1,Returning_Visitor,FALSE,FALSE 0,0,0,0,8,332,0,0.028571429,0,0,Mar,3,2,1,8,New_Visitor,TRUE,FALSE 0,0,0,0,1,40,0,0.1,0,0,Mar,3,2,1,10,Returning_Visitor,FALSE,FALSE 5,175.2307692,0,0,20,2224.85,0.004347826,0.023913043,0,0,Mar,4,2,8,1,Returning_Visitor,FALSE,FALSE 6,96.85714286,0,0,28,672.5452381,0,0.020707071,19.71468924,0,Mar,1,1,2,10,Returning_Visitor,FALSE,TRUE 0,0,0,0,6,551,0.033333333,0.05,0,0,Mar,1,1,3,1,Returning_Visitor,FALSE,FALSE 19,402.1857143,4,116,64,3179.354257,0.002531646,0.016289934,4.642,0,Mar,2,2,2,2,Returning_Visitor,FALSE,TRUE 0,0,0,0,10,203,0,0.026666667,0,0,Mar,3,2,2,3,Returning_Visitor,FALSE,FALSE 4,178.2,0,0,9,368.2,0.02,0.05,0,0,Mar,3,2,8,3,Returning_Visitor,TRUE,FALSE 7,156.5,1,122,26,694.7121212,0,0.00297619,0,0,Mar,2,4,1,2,Returning_Visitor,FALSE,FALSE 5,58,0,0,10,657.2619048,0,0.004166667,0,0,Mar,1,1,2,2,New_Visitor,FALSE,FALSE 0,0,0,0,7,974.6666667,0.085714286,0.128571429,0,0,Mar,2,2,1,10,Returning_Visitor,FALSE,FALSE 1,43,0,0,43,1223.347222,0,0.004545455,0,0,Mar,1,1,3,2,Returning_Visitor,FALSE,FALSE 0,0,0,0,10,276,0.044444444,0.055555556,0,0,Mar,3,2,3,10,Returning_Visitor,FALSE,FALSE 0,0,1,844,6,1108,0.066666667,0.066666667,0,0,Mar,2,2,4,2,Returning_Visitor,FALSE,FALSE 0,0,0,0,7,223,0,0.028571429,0,0,Mar,1,1,1,1,Returning_Visitor,FALSE,FALSE 0,0,0,0,3,261,0,0.066666667,0,0,Mar,2,5,1,10,Returning_Visitor,FALSE,FALSE 0,0,0,0,21,1372.7,0.019047619,0.035714286,0,0,Mar,1,1,3,1,Returning_Visitor,FALSE,FALSE 2,306,0,0,14,964.5,0,0.030952381,0,0,Mar,2,2,4,1,Returning_Visitor,FALSE,FALSE 3,72,1,0,38,461.5,0.01,0.023666667,0,0,Mar,2,2,3,2,Returning_Visitor,FALSE,FALSE 0,0,0,0,4,41,0.05,0.1,0,0,Mar,3,2,2,1,Returning_Visitor,FALSE,FALSE 1,202,0,0,2,8,0,0.066666667,0,0,Mar,2,2,8,6,New_Visitor,FALSE,FALSE 0,0,0,0,1,0,0.2,0.2,0,0,Mar,1,1,3,3,Returning_Visitor,FALSE,FALSE 0,0,0,0,7,39.25,0.028571429,0.034285714,0,0,Mar,2,2,1,2,Returning_Visitor,FALSE,FALSE 4,359.3,4,407,69,5485.105556,0.010526316,0.017493734,4.494741228,0,Mar,1,1,2,1,Returning_Visitor,TRUE,TRUE 0,0,0,0,8,450,0,0.025,0,0,Mar,4,1,9,3,Returning_Visitor,TRUE,FALSE 0,0,0,0,1,0,0.2,0.2,0,0,Mar,1,1,2,1,Returning_Visitor,FALSE,FALSE 0,0,0,0,20,438.3333333,0.02,0.04,0,0,Mar,2,2,7,1,Returning_Visitor,FALSE,FALSE 1,39,0,0,15,787,0,0.021875,0,0,Mar,4,1,1,10,Returning_Visitor,FALSE,FALSE 0,0,0,0,1,0,0.2,0.2,0,0,Mar,2,4,1,3,Returning_Visitor,FALSE,FALSE 0,0,0,0,6,319,0,0.05,0,0,Mar,1,1,6,1,Returning_Visitor,TRUE,FALSE 0,0,0,0,15,1148,0.014285714,0.05,0,0,Mar,3,2,1,3,Returning_Visitor,FALSE,FALSE 2,41.5,2,60.5,21,534.5,0.004347826,0.02173913,0,0,Mar,2,2,2,13,Returning_Visitor,TRUE,FALSE 0,0,0,0,20,644.4,0,0.007777778,0,0,Mar,1,1,7,3,Returning_Visitor,FALSE,FALSE 0,0,0,0,14,875,0,0.014285714,0,0,Mar,1,1,1,3,Returning_Visitor,TRUE,FALSE 2,123,0,0,7,873,0,0.011111111,0,0,Mar,1,2,2,2,Returning_Visitor,FALSE,FALSE 3,42,0,0,31,3121.369048,0.002857143,0.009285714,0,0,Mar,1,1,3,1,Returning_Visitor,TRUE,FALSE 0,0,1,81,82,3214.020563,0,0.007723577,4.667079268,0,Mar,3,3,2,10,Returning_Visitor,FALSE,TRUE 0,0,0,0,3,14,0,0.066666667,0,0,Mar,4,1,3,3,Returning_Visitor,FALSE,FALSE 1,22,0,0,52,1551.666667,0.001818182,0.019393939,7.882377258,0,Mar,1,1,3,2,Returning_Visitor,FALSE,FALSE 2,95.5,0,0,13,334.5,0,0.002666667,0,0,Mar,3,2,1,8,New_Visitor,FALSE,FALSE 2,48.66666667,0,0,48,3726.333333,0,0.004166667,0,0,Mar,3,2,1,9,Returning_Visitor,TRUE,FALSE 5,294.7,6,329.5,59,2816.781781,0.003214696,0.014766169,2.346800426,0,Mar,2,2,4,1,Returning_Visitor,FALSE,FALSE 0,0,0,0,38,667.1666667,0,0.005263158,0,0,Mar,2,2,4,1,Returning_Visitor,TRUE,FALSE 1,0,0,0,41,1202.904762,0.011529933,0.033252033,0,0,Mar,2,2,2,13,Returning_Visitor,FALSE,FALSE 0,0,0,0,3,0,0.2,0.2,0,0,Mar,1,8,2,1,Returning_Visitor,FALSE,FALSE 0,0,0,0,20,1117.333333,0,0.000956938,0,0,Mar,2,4,7,2,Returning_Visitor,FALSE,FALSE 0,0,0,0,9,211,0,0.044444444,0,0,Mar,2,2,3,2,Returning_Visitor,FALSE,FALSE 3,125,1,70,29,705.4,0,0.015625,0,0,Mar,2,2,1,1,Returning_Visitor,FALSE,FALSE 3,12,1,24,3,5.333333333,0.033333333,0.083333333,0,0,Mar,2,2,1,3,Returning_Visitor,FALSE,FALSE 1,10.5,0,0,7,525,0,0.00952381,0,0,Mar,2,4,4,8,Returning_Visitor,FALSE,FALSE 0,0,0,0,13,635.3333333,0,0.009090909,0,0,Mar,2,2,4,1,Returning_Visitor,FALSE,FALSE 0,0,0,0,50,1803.768849,0.001360544,0.017428305,3.609398514,0,Mar,2,4,7,3,Returning_Visitor,TRUE,TRUE 3,99,0,0,37,2419.133333,0.007894737,0.023245614,0,0,Mar,2,2,3,1,Returning_Visitor,FALSE,FALSE 6,171.0833333,0,0,28,952.2857143,0,0.013333333,52.10818182,0,Mar,2,2,1,2,Returning_Visitor,FALSE,TRUE 7,159.5,0,0,14,249,0.011764706,0.041176471,0,0,Mar,1,1,3,2,Returning_Visitor,TRUE,FALSE 1,5,0,0,13,271,0,0.003571429,0,0,Mar,1,1,2,2,New_Visitor,TRUE,FALSE 5,206,0,0,15,650.6961927,0,0.018487395,0,0,Mar,1,1,1,3,New_Visitor,FALSE,FALSE 2,40,2,162,17,481.6666667,0.024561404,0.036842105,32.64521053,0,Mar,2,7,3,2,Returning_Visitor,FALSE,FALSE 0,0,0,0,7,258.5,0.028571429,0.071428571,0,0,Mar,3,2,6,1,Returning_Visitor,FALSE,FALSE 0,0,0,0,14,510.5,0,0.015384615,31.3094364,0,Mar,2,2,1,2,New_Visitor,FALSE,TRUE 0,0,0,0,16,600.75,0,0.025,0,0,Mar,2,2,2,2,Returning_Visitor,FALSE,FALSE 6,262.25,3,298,49,2302.021242,0.000308642,0.026591844,43.3616369,0,Mar,1,1,1,2,Returning_Visitor,FALSE,TRUE 2,44,0,0,26,1636.016667,0.007692308,0.021794872,0,0,Mar,2,6,1,1,Returning_Visitor,FALSE,FALSE 0,0,0,0,16,519.75,0.0125,0.04,0,0,Mar,3,2,3,3,Returning_Visitor,FALSE,FALSE 0,0,0,0,1,0,0.2,0.2,0,0,Mar,1,1,1,3,Returning_Visitor,FALSE,FALSE 0,0,0,0,11,673,0.018181818,0.063636364,0,0,Mar,2,6,1,1,Returning_Visitor,FALSE,FALSE 0,0,0,0,4,173.5,0,0.05,0,0,Mar,3,3,4,3,Returning_Visitor,TRUE,FALSE 0,0,0,0,16,156.5333333,0,0.04375,0,0,Mar,2,4,1,3,Returning_Visitor,FALSE,FALSE 0,0,0,0,21,574.5333333,0,0.011578947,0,0,Mar,2,2,3,1,Returning_Visitor,FALSE,FALSE 2,62.5,0,0,17,548.3333333,0,0.031372549,0,0,Mar,2,2,1,2,Returning_Visitor,FALSE,FALSE 15,937.1746032,3,357,109,6682.677557,0.000833333,0.005345441,2.526616865,0,Mar,1,2,3,2,Returning_Visitor,FALSE,FALSE 2,48,0,0,51,1671.375,0.001098901,0.016433566,0,0,Mar,2,2,1,6,Returning_Visitor,TRUE,FALSE 0,0,0,0,11,1390.166667,0.012121212,0.014545455,0,0,Mar,1,1,1,3,Returning_Visitor,TRUE,FALSE 6,437.3913043,2,235.55,83,2503.881781,0.002197802,0.004916136,2.086218493,0,Mar,2,2,3,2,Returning_Visitor,FALSE,TRUE 7,159.3333333,0,0,24,520.6666667,0,0.006666667,0,0,Mar,2,10,4,3,New_Visitor,TRUE,FALSE 0,0,0,0,1,0,0.2,0.2,0,0,Mar,2,2,4,1,Returning_Visitor,FALSE,FALSE 2,32.5,0,0,19,423.8166667,0.005952381,0.039104938,0,0,Mar,3,2,1,3,Returning_Visitor,FALSE,FALSE 0,0,2,30,11,1114.916667,0,0.030769231,0,0,Mar,3,2,1,3,Returning_Visitor,FALSE,FALSE 11,212.7416667,3,104,71,1678.905125,0,0.016037881,8.600043095,0,Mar,3,2,8,13,Returning_Visitor,TRUE,FALSE 0,0,0,0,29,438.8666667,0,0.020689655,0,0,Mar,4,2,3,1,Returning_Visitor,TRUE,FALSE 3,16.33333333,0,0,23,505.5833333,0.008,0.03,9.15008,0,Mar,4,1,1,2,Returning_Visitor,FALSE,TRUE 0,0,0,0,9,76,0.022222222,0.055555556,0,0,Mar,2,2,6,1,Returning_Visitor,FALSE,FALSE 0,0,0,0,7,203.5,0,0.033333333,0,0,Mar,1,1,1,9,New_Visitor,TRUE,FALSE 4,130,0,0,28,818.7916667,0,0.003448276,0,0,Mar,2,2,1,2,New_Visitor,FALSE,FALSE 4,15,1,17,17,490,0,0.027272727,15.66445171,0,Mar,2,2,4,10,Returning_Visitor,FALSE,TRUE 0,0,1,0,12,645.9166667,0,0.05,0,0,Mar,1,1,1,2,Returning_Visitor,FALSE,FALSE 0,0,0,0,8,142,0,0.05,0,0,Mar,2,7,2,3,Returning_Visitor,FALSE,FALSE 0,0,0,0,6,25,0,0.033333333,0,0,Mar,2,2,1,2,Returning_Visitor,FALSE,FALSE 2,44,0,0,16,672.5,0.005555556,0.025925926,0,0,Mar,2,2,4,2,Returning_Visitor,TRUE,FALSE 9,140.5,0,0,35,1090.490476,0.013675214,0.016892552,0,0,Mar,1,1,3,2,Returning_Visitor,TRUE,FALSE 0,0,0,0,3,30,0,0.066666667,0,0,Mar,2,2,1,1,Returning_Visitor,FALSE,FALSE 0,0,0,0,13,244.1666667,0,0.030769231,0,0,Mar,2,5,3,3,Returning_Visitor,FALSE,FALSE 0,0,0,0,13,129.5,0.061538462,0.092307692,0,0,Mar,1,8,1,1,Returning_Visitor,FALSE,FALSE 4,56,1,88,22,1734.780952,0,0.010142857,0,0,Mar,3,2,3,9,Returning_Visitor,TRUE,FALSE 3,281,0,0,6,145,0,0.025,0,0,Mar,3,2,8,8,New_Visitor,FALSE,FALSE 2,88,0,0,16,1106.333333,0.025,0.053125,0,0,Mar,1,1,3,3,Returning_Visitor,FALSE,FALSE 1,0,0,0,17,684.9166667,0.015686275,0.049411765,0,0,Mar,2,6,4,1,Returning_Visitor,TRUE,FALSE 9,110.4090909,1,51,62,3120.211102,0.00056338,0.017396907,7.891171006,0,Mar,2,2,1,10,Returning_Visitor,FALSE,FALSE 0,0,0,0,12,1178.666667,0.024242424,0.053030303,0,0,Mar,1,1,6,3,Returning_Visitor,FALSE,FALSE 0,0,0,0,3,58,0,0.066666667,0,0,Mar,1,8,4,11,Returning_Visitor,FALSE,FALSE 10,316.1405025,7,321.6666667,287,12983.78771,0.000668896,0.006998399,1.088158231,0,Mar,2,4,3,8,New_Visitor,FALSE,FALSE 0,0,0,0,6,139,0,0.01,0,0,Mar,1,2,1,8,New_Visitor,FALSE,FALSE 2,27,0,0,6,558,0,0.0125,0,0,Mar,2,2,3,8,New_Visitor,FALSE,FALSE 3,74,2,26,53,893.5111111,0.003571429,0.005357143,44.61199852,0,Mar,1,1,4,9,Returning_Visitor,TRUE,TRUE 0,0,0,0,5,26.5,0.04,0.12,0,0,Mar,1,1,3,1,Returning_Visitor,FALSE,FALSE 0,0,0,0,2,0,0.2,0.2,0,0,Mar,2,2,1,3,Returning_Visitor,FALSE,FALSE 0,0,0,0,3,0,0.2,0.2,0,0,Mar,3,2,6,3,Returning_Visitor,FALSE,FALSE 1,0,1,0,15,708.5333333,0,0.04,54.85543,0,Mar,2,4,3,2,Returning_Visitor,FALSE,FALSE 0,0,0,0,8,244,0.025,0.075,0,0,Mar,3,2,1,3,Returning_Visitor,FALSE,FALSE 2,140,0,0,6,130,0,0.025,0,0,Mar,1,1,4,2,New_Visitor,TRUE,FALSE 3,119,0,0,41,1282.5,0,0.007142857,0,0,Mar,2,6,2,2,New_Visitor,FALSE,FALSE 7,301.7,6,336.5,42,1981.016667,0.005660377,0.021383648,13.00183962,0,Mar,1,1,2,2,Returning_Visitor,TRUE,TRUE 0,0,0,0,8,186.5,0,0.025,0,0,Mar,2,2,3,3,Returning_Visitor,TRUE,FALSE 0,0,0,0,6,119,0,0.033333333,0,0,Mar,3,2,7,1,Returning_Visitor,TRUE,FALSE 0,0,0,0,22,1310.5,0.00952381,0.028571429,23.86359286,0,Mar,2,2,1,1,Returning_Visitor,FALSE,TRUE 0,0,0,0,4,1111,0,0.05,0,0,Mar,4,5,1,1,Returning_Visitor,FALSE,FALSE 6,113.5,0,0,18,1066.5,0,0.005072464,0,0,Mar,2,2,7,2,New_Visitor,FALSE,FALSE 3,421,0,0,4,110.5,0,0.011111111,0,0,Mar,3,2,8,2,New_Visitor,TRUE,FALSE 2,115,0,0,5,36,0,0.033333333,0,0,Mar,1,1,3,11,Returning_Visitor,FALSE,FALSE 0,0,2,76.5,22,850.5333333,0.004545455,0.03446281,0,0,Mar,3,2,3,1,Returning_Visitor,FALSE,FALSE 0,0,0,0,3,16,0.033333333,0.1,0,0,Mar,2,6,1,3,Returning_Visitor,FALSE,FALSE 1,9,0,0,5,49,0,0.05,0,0,Mar,2,2,8,1,Returning_Visitor,FALSE,FALSE 0,0,0,0,10,214.25,0,0.025,0,0,Mar,3,2,2,1,Returning_Visitor,FALSE,FALSE 0,0,0,0,3,0,0.2,0.2,0,0,Mar,1,1,3,1,Returning_Visitor,FALSE,FALSE 0,0,0,0,7,141.8333333,0.064285714,0.15,0,0,Mar,2,2,1,1,Returning_Visitor,FALSE,FALSE 0,0,0,0,5,143.5,0,0.04,0,0,Mar,2,5,3,3,Returning_Visitor,FALSE,FALSE 0,0,0,0,51,5818.916667,0.000980392,0.022984749,0,0,Mar,2,2,1,1,Returning_Visitor,FALSE,FALSE 2,208,1,50,30,1461.666667,0,0.02125,0,0,Mar,2,5,1,3,Returning_Visitor,FALSE,FALSE 0,0,0,0,13,123,0,0.020833333,0,0,Mar,2,2,1,3,Returning_Visitor,FALSE,FALSE 5,193,1,13,66,2049.458471,0.004489796,0.012765306,29.45815149,0,Mar,3,2,3,1,Returning_Visitor,TRUE,FALSE 1,3,0,0,11,271.7166667,0,0.016666667,0,0,Mar,4,1,3,2,Returning_Visitor,TRUE,FALSE 4,87.5,1,20,36,1279.166667,0,0.010810811,0,0,Mar,2,2,1,6,Returning_Visitor,FALSE,FALSE 0,0,0,0,4,0,0.2,0.2,0,0,Mar,1,2,1,1,Returning_Visitor,FALSE,FALSE 0,0,0,0,4,70.5,0.05,0.1,0,0,Mar,3,2,4,3,Returning_Visitor,FALSE,FALSE 5,42.18181818,0,0,12,231.1818182,0,0.01,0,0,Mar,2,2,1,2,New_Visitor,TRUE,FALSE 3,132.5,0,0,36,1567.966667,0,0.007207207,0,0,Mar,3,3,2,8,Returning_Visitor,TRUE,FALSE 7,163.6666667,0,0,8,119.3333333,0.015384615,0.023076923,0,0,Mar,3,2,6,11,New_Visitor,TRUE,FALSE 2,83,0,0,15,605.8857143,0,0.005882353,0,0,Mar,1,1,3,2,Returning_Visitor,TRUE,FALSE 5,117.8333333,0,0,35,502.547619,0.01025641,0.036923077,3.148615385,0,Mar,2,2,2,2,Returning_Visitor,FALSE,TRUE 3,95,0,0,27,808.7,0.006896552,0.010344828,0,0,Mar,3,2,7,3,Returning_Visitor,TRUE,FALSE 0,0,0,0,3,58,0,0.066666667,0,0,Mar,1,1,1,10,Returning_Visitor,FALSE,FALSE 3,60,0,0,19,494.5,0,0.01,0,0,Mar,1,1,3,9,Returning_Visitor,TRUE,FALSE 0,0,0,0,6,203.15,0,0.051111111,0,0,Mar,2,2,7,2,Returning_Visitor,FALSE,FALSE 5,55.5,0,0,63,1656.316667,0.004166667,0.015989583,0,0,Mar,2,2,2,2,Returning_Visitor,TRUE,FALSE 0,0,0,0,32,1420.25,0.00625,0.027083333,0,0,Mar,4,2,1,1,Returning_Visitor,FALSE,FALSE 8,160.5,2,1467,26,721,0,0.031029186,0,0,Mar,2,2,1,1,Returning_Visitor,FALSE,FALSE 4,64.33333333,1,13,27,1433.916667,0,0.018226601,3.691344828,0,Mar,1,4,1,8,New_Visitor,FALSE,TRUE 4,73.33333333,0,0,17,478.8333333,0,0.00877193,0,0,Mar,1,1,3,2,Returning_Visitor,FALSE,FALSE 0,0,0,0,7,182,0.028571429,0.071428571,0,0,Mar,3,2,1,13,Returning_Visitor,TRUE,FALSE 9,215.2333333,5,450,35,1974.352381,0.004761905,0.018866213,15.32910159,0,Mar,2,2,3,2,Returning_Visitor,FALSE,FALSE 4,81.5,0,0,12,223.5833333,0,0.002666667,0,0,Mar,1,1,1,3,Returning_Visitor,FALSE,FALSE 0,0,1,26,15,817.4166667,0.026666667,0.038222222,0,0,Mar,2,2,6,1,Returning_Visitor,FALSE,FALSE 7,201.5,0,0,17,750.1666667,0,0.005555556,0,0,Mar,3,2,4,2,Returning_Visitor,FALSE,FALSE 0,0,0,0,2,13,0.1,0.1,0,0,Mar,1,1,2,1,Returning_Visitor,TRUE,FALSE 8,492.4166667,4,384,52,4699.277778,0.007258065,0.018233487,11.1076505,0,Mar,2,2,1,2,Returning_Visitor,FALSE,TRUE 7,146.25,0,0,42,1030.266667,0,0.008695652,0,0,Mar,2,2,1,2,New_Visitor,FALSE,FALSE 5,156.2690476,4,90,35,776.0190476,0,0.005714286,0,0,Mar,3,2,4,2,Returning_Visitor,FALSE,FALSE 0,0,0,0,3,558,0,0.066666667,0,0,Mar,4,1,3,1,Returning_Visitor,FALSE,FALSE 4,13.61904762,0,0,27,520.3333333,0,0.020322581,8.497935484,0,Mar,3,2,4,2,Returning_Visitor,TRUE,FALSE 0,0,0,0,8,349.5,0,0.028571429,25.188,0,Mar,1,2,1,8,New_Visitor,FALSE,TRUE 2,14,0,0,31,754.5,0,0.00625,0,0,Mar,2,2,1,8,Returning_Visitor,TRUE,FALSE 0,0,0,0,13,305,0,0.016666667,0,0,Mar,1,1,1,2,New_Visitor,FALSE,FALSE 0,0,0,0,21,399.8511905,0,0.023933659,0,0,Mar,3,2,1,1,Returning_Visitor,FALSE,FALSE 1,12,0,0,6,74,0,0.028571429,0,0,Mar,1,1,1,10,Returning_Visitor,FALSE,FALSE 6,352.5,0,0,21,696.6666667,0,0.0125,75.06781572,0,Mar,2,2,6,10,New_Visitor,TRUE,TRUE 0,0,0,0,3,59,0,0.033333333,0,0,Mar,3,2,1,1,Returning_Visitor,FALSE,FALSE 0,0,0,0,3,50,0,0.033333333,0,0,Mar,2,2,1,2,Returning_Visitor,FALSE,FALSE 5,92.75,1,74.75,84,2756.215152,0,0.002462121,0,0,Mar,2,2,9,3,New_Visitor,TRUE,FALSE 0,0,0,0,12,425.1666667,0,0.016666667,0,0,Mar,1,1,6,10,Returning_Visitor,FALSE,FALSE 0,0,0,0,78,1781.69697,0.003205128,0.015811966,0,0,Mar,2,2,8,3,Returning_Visitor,FALSE,FALSE 3,168,0,0,7,296.1666667,0,0.011111111,0,0,Mar,2,2,3,2,New_Visitor,FALSE,FALSE 0,0,0,0,1,0,0.2,0.2,0,0,Mar,2,4,1,1,Returning_Visitor,TRUE,FALSE 5,179.5,0,0,36,1792.8,0,0.004390244,0,0,Mar,2,2,3,10,Returning_Visitor,FALSE,FALSE 0,0,0,0,1,0,0.2,0.2,0,0,Mar,3,2,3,1,Returning_Visitor,FALSE,FALSE 8,274.5,0,0,24,515.5757576,0,0.003703704,0,0,Mar,3,2,2,2,Returning_Visitor,TRUE,FALSE 3,64.5,0,0,35,1334.551371,0,0.007309582,0,0,Mar,3,2,4,2,Returning_Visitor,TRUE,FALSE 0,0,0,0,6,64,0.1,0.133333333,0,0,Mar,4,1,1,1,Returning_Visitor,FALSE,FALSE 1,4,2,102,31,624.5833333,0.016216216,0.031531532,8.708324324,0,Mar,2,2,3,13,Returning_Visitor,TRUE,FALSE 9,171.6666667,3,109.75,41,1284.663492,0.016326531,0.032653061,21.22386735,0,Mar,2,2,1,1,Returning_Visitor,FALSE,FALSE 0,0,0,0,10,316.5,0,0.025,17.538,0,Mar,2,4,1,8,New_Visitor,TRUE,TRUE 0,0,0,0,22,925.8333333,0.018181818,0.048484848,0,0,Mar,2,2,3,1,Returning_Visitor,FALSE,FALSE 0,0,0,0,7,97,0.057142857,0.1,0,0,Mar,2,2,4,1,Returning_Visitor,FALSE,FALSE 1,0,0,0,1,28,0,0.1,0,0,Mar,2,2,1,1,Returning_Visitor,FALSE,FALSE 9,311.9166667,0,0,40,633.4,0.002272727,0.017954545,0,0,Mar,2,2,1,3,Returning_Visitor,FALSE,FALSE 1,98.5,0,0,5,436.5,0,0.016666667,0,0,Mar,7,1,1,2,New_Visitor,TRUE,FALSE 9,867.922619,0,0,137,3909.36829,0,0.005055944,9.930187005,0,Mar,2,2,6,2,Returning_Visitor,FALSE,FALSE 3,147,0,0,3,24,0,0.02,0,0,Mar,3,2,7,1,Returning_Visitor,FALSE,FALSE 2,27,0,0,18,915.1666667,0.021052632,0.036842105,0,0,Mar,2,2,4,1,Returning_Visitor,FALSE,FALSE 3,106.75,0,0,5,137.3333333,0,0.005,0,0,Mar,3,2,1,2,New_Visitor,TRUE,FALSE 0,0,0,0,5,95,0.04,0.12,0,0,Mar,2,2,1,1,Returning_Visitor,FALSE,FALSE 0,0,0,0,1,0,0.2,0.2,0,0,Mar,1,1,2,1,Returning_Visitor,FALSE,FALSE 0,0,0,0,24,778.1666667,0,0.0125,0,0,Mar,2,2,1,2,Returning_Visitor,FALSE,FALSE 1,55,1,47,47,1842.95,0,0.01257764,0,0,Mar,2,2,6,1,Returning_Visitor,FALSE,FALSE 0,0,0,0,27,1206,0.016,0.032,0,0,Mar,2,2,1,1,Returning_Visitor,FALSE,FALSE 0,0,0,0,9,324.5,0.014814815,0.022222222,0,0,Mar,1,2,6,1,Returning_Visitor,FALSE,FALSE 5,241.6666667,5,229,38,1054.944444,0.010869565,0.014565217,8.224168543,0,Mar,2,4,3,2,Returning_Visitor,TRUE,FALSE 0,0,0,0,12,402.1666667,0.041666667,0.065,0,0,Mar,3,2,2,1,Returning_Visitor,FALSE,FALSE 1,47,0,0,5,388,0,0.04,31.188,0,Mar,1,1,7,3,Returning_Visitor,TRUE,TRUE 0,0,0,0,1,0,0.2,0.2,0,0,Mar,3,2,3,11,Returning_Visitor,FALSE,FALSE 3,35,0,0,16,352,0,0.011764706,0,0,Mar,2,2,2,1,Returning_Visitor,FALSE,FALSE 0,0,0,0,11,194,0,0.018181818,0,0,Mar,2,2,6,1,Returning_Visitor,FALSE,FALSE 0,0,0,0,15,751.8,0.026666667,0.04,0,0,Mar,1,2,2,1,Returning_Visitor,FALSE,FALSE 0,0,0,0,9,465.5,0.006666667,0.056666667,43.79717364,0,Mar,2,2,1,1,Returning_Visitor,FALSE,FALSE 0,0,0,0,25,585,0,0.016,0,0,Mar,4,1,1,3,Returning_Visitor,FALSE,FALSE 0,0,0,0,10,253.5,0,0.022222222,63.588,0,Mar,1,1,1,2,New_Visitor,TRUE,TRUE 3,33,2,84.5,4,46.5,0,0.010666667,0,0,Mar,2,2,5,8,New_Visitor,FALSE,FALSE 3,152.5,5,243.5,43,3065.945238,0,0.011666667,3.31108,0,Mar,1,2,1,1,Returning_Visitor,TRUE,TRUE 11,828.0526316,6,389.3666667,57,2099.930554,0.003333333,0.009142857,3.29868,0,Mar,2,2,1,2,Returning_Visitor,FALSE,FALSE 0,0,0,0,2,30,0,0.05,0,0,Mar,3,2,1,9,Returning_Visitor,TRUE,FALSE 0,0,0,0,3,52,0,0.066666667,0,0,Mar,2,5,8,3,Returning_Visitor,TRUE,FALSE 4,162,3,75.5,4,158.5,0,0.016666667,0,0,Mar,2,4,1,10,Returning_Visitor,FALSE,FALSE 4,539.5,0,0,1,0,0,0.05,0,0,Mar,3,2,8,8,New_Visitor,FALSE,FALSE 0,0,0,0,9,250.5,0,0.044444444,0,0,Mar,3,2,7,1,Returning_Visitor,TRUE,FALSE 4,120.5,0,0,66,3960.646154,0.002941176,0.013663064,0,0,Mar,2,4,1,1,Returning_Visitor,FALSE,FALSE 0,0,0,0,9,280,0,0.022222222,0,0,Mar,2,2,1,2,Returning_Visitor,FALSE,FALSE 0,0,0,0,13,281.547619,0.003846154,0.017307692,0,0,Mar,1,1,2,2,Returning_Visitor,TRUE,FALSE 1,12,0,0,13,2004.5,0.028571429,0.04047619,0,0,Mar,1,1,6,2,Returning_Visitor,TRUE,FALSE 12,285.0119048,0,0,53,967.210697,0.000892857,0.033208874,0,0,Mar,2,2,3,1,Returning_Visitor,FALSE,FALSE 0,0,0,0,6,100,0,0.033333333,0,0,Mar,2,2,4,6,Returning_Visitor,TRUE,FALSE 10,198.5,2,158,6,73.21428571,0,0.02,0,0,Mar,1,1,1,2,Returning_Visitor,FALSE,FALSE 4,224,1,32,20,789.35,0,0.002721088,0,0,Mar,1,2,4,2,Returning_Visitor,FALSE,FALSE 0,0,0,0,30,1281,0.006666667,0.02,0,0,Mar,2,4,2,1,Returning_Visitor,FALSE,FALSE 4,162.5,2,44.5,20,1115.266667,0,0.020661157,0,0,Mar,1,2,3,10,Returning_Visitor,FALSE,FALSE 0,0,0,0,1,0,0.2,0.2,0,0,Mar,3,2,2,1,Returning_Visitor,FALSE,FALSE 2,77.5,3,92,18,341.2916667,0,0.009090909,0,0,Mar,1,1,1,2,New_Visitor,TRUE,FALSE 6,192.5,0,0,113,3000.666667,0.003478261,0.005217391,0,0,Mar,2,2,3,2,Returning_Visitor,FALSE,FALSE 6,148.8,4,81.33333333,29,1715.687897,0.010909091,0.027138047,0,0,Mar,2,2,9,1,Returning_Visitor,FALSE,FALSE 0,0,0,0,8,607,0.0375,0.0625,0,0,Mar,1,1,1,3,Returning_Visitor,FALSE,FALSE 0,0,0,0,2,31,0,0.05,0,0,Mar,2,2,7,2,Returning_Visitor,FALSE,FALSE 0,0,0,0,16,1027.657143,0.0125,0.01875,0,0,Mar,1,1,1,3,Returning_Visitor,FALSE,FALSE 0,0,0,0,1,0,0.2,0.2,0,0,Mar,2,2,7,10,Returning_Visitor,FALSE,FALSE 0,0,0,0,8,196.5,0,0.025,0,0,Mar,2,2,1,10,Returning_Visitor,FALSE,FALSE 4,1521,2,64,30,2284.687302,0.019354839,0.042741935,2.304322581,0,Mar,2,2,1,1,Returning_Visitor,FALSE,FALSE 13,112.2666667,0,0,49,1503.490909,0,0.003508772,0,0,Mar,2,2,2,2,New_Visitor,FALSE,FALSE 0,0,0,0,1,0,0.2,0.2,0,0,Mar,2,2,1,10,Returning_Visitor,FALSE,FALSE 3,150,0,0,12,271.3333333,0,0.014285714,0,0,Mar,1,1,4,2,New_Visitor,FALSE,FALSE 0,0,0,0,6,97.33333333,0,0.041666667,0,0,Mar,1,2,1,2,Returning_Visitor,TRUE,FALSE 0,0,0,0,5,161.3333333,0,0.05,0,0,Mar,1,1,1,3,Returning_Visitor,FALSE,FALSE 0,0,4,32.5,7,67,0,0.022222222,0,0,Mar,2,2,1,8,New_Visitor,FALSE,FALSE 0,0,0,0,34,1006.257576,0,0.015196078,0,0,Mar,2,2,7,3,Returning_Visitor,FALSE,FALSE 4,167.5,0,0,22,869.9,0,0.026388889,33.53327578,0,Mar,2,6,4,10,Returning_Visitor,FALSE,TRUE 4,57,1,0,19,479.8666667,0,0.018181818,0,0,Mar,4,2,1,2,Returning_Visitor,FALSE,FALSE 5,345,0,0,7,229.5,0.03,0.026666667,0,0,Mar,3,2,4,2,Returning_Visitor,FALSE,FALSE 0,0,0,0,1,0,0.2,0.2,0,0,Mar,1,1,7,2,Returning_Visitor,TRUE,FALSE 0,0,0,0,20,606,0,0.011111111,0,0,Mar,2,2,6,1,Returning_Visitor,FALSE,FALSE 11,267.1666667,0,0,39,881.4416667,0.004166667,0.011458333,20.11751289,0,Mar,2,2,3,1,New_Visitor,FALSE,FALSE 0,0,0,0,1,0,0.2,0.2,0,0,Mar,3,2,3,9,Returning_Visitor,TRUE,FALSE 3,123,0,0,8,371.6666667,0,0.011111111,0,0,Mar,3,2,4,2,New_Visitor,FALSE,FALSE 0,0,0,0,1,0,0.2,0.2,0,0,Mar,2,2,1,1,Returning_Visitor,FALSE,FALSE 3,1373.5,0,0,43,2798.07381,0,0.010971006,2.915511628,0,Mar,2,6,2,3,Returning_Visitor,FALSE,FALSE 5,29,0,0,4,12,0,0.033333333,0,0,Mar,4,1,1,8,New_Visitor,TRUE,FALSE 0,0,0,0,42,1185.666667,0,0.004761905,0,0,Mar,2,2,1,1,Returning_Visitor,FALSE,FALSE 0,0,0,0,2,60,0,0.05,0,0,Mar,3,2,3,1,Returning_Visitor,FALSE,FALSE 8,29.5,0,0,89,6770.274267,0.006315789,0.030125313,0,0,Mar,2,2,3,1,Returning_Visitor,FALSE,FALSE 0,0,0,0,5,337.25,0,0.02,0,0,Mar,2,2,2,10,Returning_Visitor,FALSE,FALSE 0,0,0,0,13,323.9166667,0,0.008333333,35.8370931,0,Mar,1,1,4,10,Returning_Visitor,FALSE,TRUE 5,79.33333333,3,242.0555556,17,1142.816919,0.006818182,0.018519814,62.16886364,0,Mar,3,2,6,2,Returning_Visitor,FALSE,FALSE 0,0,0,0,1,0,0.2,0.2,0,0,Mar,3,2,3,3,Returning_Visitor,FALSE,FALSE 0,0,0,0,4,148.5,0.05,0.083333333,0,0,Mar,1,1,1,1,Returning_Visitor,TRUE,FALSE 3,16,0,0,11,207,0,0.015384615,0,0,Mar,2,2,2,3,Returning_Visitor,FALSE,FALSE 0,0,0,0,2,11,0,0.1,0,0,Mar,4,2,2,10,Returning_Visitor,FALSE,FALSE 0,0,0,0,6,53,0,0.06,0,0,Mar,2,2,7,3,Returning_Visitor,TRUE,FALSE 2,33,0,0,9,354,0,0.02,0,0,Mar,3,2,1,3,New_Visitor,FALSE,FALSE 3,139,0,0,10,277.1428571,0,0.007692308,0,0,Mar,3,2,1,2,Returning_Visitor,TRUE,FALSE 2,47,0,0,17,411.625,0,0.023529412,0,0,Mar,2,2,4,3,Returning_Visitor,FALSE,FALSE 0,0,0,0,16,392.1428571,0.00625,0.027205882,0,0,Mar,3,2,1,2,Returning_Visitor,FALSE,FALSE 1,54.5,0,0,68,3135.517677,0.003361345,0.01146836,4.095288919,0,Mar,1,1,2,1,Returning_Visitor,FALSE,FALSE 14,272.5,2,52.25,55,1127.288305,0.003125,0.014499164,0,0,Mar,3,2,7,1,Returning_Visitor,FALSE,FALSE 0,0,0,0,25,954.4702381,0,0.012742857,0,0,Mar,3,2,1,1,Returning_Visitor,TRUE,FALSE 0,0,0,0,33,3506.166667,0,0.025,0,0,Mar,1,1,1,1,Returning_Visitor,TRUE,FALSE 0,0,0,0,19,370.3809524,0,0.007894737,0,0,Mar,1,1,1,2,Returning_Visitor,FALSE,FALSE 5,169,0,0,3,50,0,0.033333333,0,0,Mar,1,1,1,9,New_Visitor,FALSE,FALSE 4,59.25,0,0,9,139.25,0.015384615,0.031538462,0,0,Mar,3,2,1,2,Returning_Visitor,FALSE,FALSE 4,175,0,0,15,270.5,0,0.006666667,0,0,Mar,1,1,7,2,Returning_Visitor,FALSE,FALSE 0,0,0,0,9,321,0.033333333,0.085185185,0,0,Mar,2,5,5,1,Returning_Visitor,FALSE,FALSE 0,0,0,0,1,0,0.2,0.2,0,0,Mar,2,2,1,1,Returning_Visitor,FALSE,FALSE 4,159,0,0,3,64.75,0,0.014285714,0,0,Mar,1,1,1,2,Returning_Visitor,FALSE,FALSE 0,0,0,0,4,26.5,0,0.05,0,0,Mar,2,2,9,2,Returning_Visitor,TRUE,FALSE 0,0,0,0,14,927.2777778,0,0.014285714,0,0,Mar,2,2,1,2,Returning_Visitor,FALSE,FALSE 0,0,0,0,7,141.1666667,0,0.007142857,0,0,Mar,3,2,3,1,Returning_Visitor,FALSE,FALSE 11,247.3333333,2,369,90,7221,0.001030928,0.003917526,13.46406308,0,Mar,2,2,3,8,Returning_Visitor,FALSE,TRUE 0,0,0,0,12,218.5,0,0.045833333,0,0,Mar,1,1,4,2,Returning_Visitor,FALSE,FALSE 2,78.33333333,0,0,12,262.5,0.028571429,0.033333333,0,0,Mar,1,1,1,1,Returning_Visitor,FALSE,FALSE 0,0,0,0,17,469.4166667,0,0.011764706,0,0,Mar,1,1,2,2,Returning_Visitor,FALSE,FALSE 1,16,0,0,27,1240.916667,0,0.009259259,28.39335492,0,Mar,2,2,3,1,Returning_Visitor,FALSE,TRUE 1,8,0,0,5,143.5,0,0.011111111,0,0,Mar,3,2,2,3,New_Visitor,TRUE,FALSE 0,0,0,0,7,101,0.028571429,0.057142857,0,0,Mar,2,5,1,3,Returning_Visitor,FALSE,FALSE 5,267.5,0,0,5,62.5,0.022222222,0.044444444,0,0,Mar,2,2,2,3,New_Visitor,TRUE,FALSE 1,14,0,0,6,156,0,0.04,0,0,Mar,1,1,3,1,Returning_Visitor,TRUE,FALSE 0,0,0,0,6,104,0,0.033333333,0,0,Mar,3,2,7,6,Returning_Visitor,FALSE,FALSE 2,17,0,0,15,1349,0,0.005882353,0,0,Mar,2,2,1,1,Returning_Visitor,FALSE,FALSE 1,12,0,0,11,331.25,0.016666667,0.033333333,0,0,Mar,2,2,2,2,Returning_Visitor,TRUE,FALSE 5,114.75,0,0,8,301.5,0,0.018181818,0,0,Mar,2,4,4,2,Returning_Visitor,TRUE,FALSE 0,0,0,0,8,157.5714286,0,0.025,0,0,Mar,3,2,2,3,Returning_Visitor,FALSE,FALSE 0,0,0,0,1,0,0.2,0.2,0,0,Mar,1,1,4,9,Returning_Visitor,TRUE,FALSE 0,0,0,0,19,224.9583333,0,0.010526316,0,0,Mar,1,1,1,2,Returning_Visitor,FALSE,FALSE 1,10,0,0,39,1152.5,0.011965812,0.017802198,0,0,Mar,3,2,1,3,Returning_Visitor,FALSE,FALSE 10,164.5,1,3,61,2292.679762,0,0.009552239,0,0,Mar,2,2,1,1,Returning_Visitor,FALSE,FALSE 0,0,0,0,9,329,0.022222222,0.044444444,0,0,Mar,2,2,3,1,Returning_Visitor,FALSE,FALSE 0,0,0,0,8,111.6666667,0,0.025,0,0,Mar,2,2,1,1,Returning_Visitor,FALSE,FALSE 0,0,0,0,15,1074.428571,0.015384615,0.021538462,0,0,Mar,3,2,1,1,Returning_Visitor,FALSE,FALSE 0,0,0,0,32,1080.047619,0,0.01015625,0,0,Mar,2,2,7,2,Returning_Visitor,FALSE,FALSE 4,387.5,0,0,23,666.9464286,0.007692308,0.026089744,0,0,Mar,1,1,6,10,Returning_Visitor,FALSE,FALSE 2,123,0,0,14,239,0,0.0125,0,0,Mar,2,2,1,2,New_Visitor,TRUE,FALSE 4,272.5,0,0,14,279.9,0,0.028571429,0,0,Mar,1,1,3,10,New_Visitor,FALSE,FALSE 0,0,0,0,1,0,0.2,0.2,0,0,Mar,2,2,7,11,Returning_Visitor,FALSE,FALSE 0,0,0,0,15,1674,0,0.04,0,0,Mar,2,2,4,1,Returning_Visitor,FALSE,FALSE 0,0,0,0,3,15,0,0.066666667,0,0,Mar,2,4,3,1,Returning_Visitor,TRUE,FALSE 0,0,0,0,22,480.9666667,0.004848485,0.026652893,0,0,Mar,2,2,4,3,Returning_Visitor,FALSE,FALSE 0,0,0,0,3,62,0,0.066666667,0,0,Mar,2,2,1,1,Returning_Visitor,FALSE,FALSE 4,320.5,0,0,23,1841.171429,0,0.003913043,0,0,Mar,1,1,1,2,New_Visitor,TRUE,FALSE 0,0,0,0,2,37.5,0,0.033333333,0,0,Mar,3,2,4,3,Returning_Visitor,TRUE,FALSE 2,35,0,0,11,131.3,0,0.015384615,0,0,Mar,2,2,1,2,New_Visitor,FALSE,FALSE 0,0,0,0,2,92,0,0.1,0,0,Mar,2,2,8,1,Returning_Visitor,FALSE,FALSE 0,0,0,0,3,78.33333333,0,0.022222222,0,0,Mar,2,2,7,11,Returning_Visitor,FALSE,FALSE 0,0,0,0,7,114.6,0.028571429,0.033333333,0,0,Mar,3,2,1,10,Returning_Visitor,FALSE,FALSE 2,34,0,0,18,671,0,0.010526316,0,0,Mar,3,2,3,11,New_Visitor,TRUE,FALSE 2,47,0,0,11,624,0,0.020512821,0,0,Mar,2,2,1,1,Returning_Visitor,FALSE,FALSE 2,11,3,123.3333333,34,448.75,0.021621622,0.048648649,0,0,Mar,2,10,6,1,Returning_Visitor,FALSE,FALSE 0,0,0,0,4,35,0,0.05,0,0,Mar,2,2,9,3,Returning_Visitor,FALSE,FALSE 3,13,0,0,7,79.5,0,0.02,0,0,Mar,2,2,1,8,New_Visitor,TRUE,FALSE 0,0,0,0,10,189.5,0,0.036666667,0,0,Mar,3,2,3,3,Returning_Visitor,FALSE,FALSE 2,36,0,0,15,2190.666667,0,0.004444444,0,0,Mar,2,2,3,8,Returning_Visitor,FALSE,FALSE 0,0,0,0,2,0,0.2,0.2,0,0,Mar,2,2,3,3,Returning_Visitor,TRUE,FALSE 0,0,0,0,2,0,0.2,0.2,0,0,Mar,1,1,1,1,Returning_Visitor,FALSE,FALSE 0,0,0,0,5,672.5,0,0.04,0,0,Mar,2,2,1,1,Returning_Visitor,FALSE,FALSE 3,33,0,0,67,2502.816667,0.006060606,0.011147186,0,0,Mar,4,1,1,10,Returning_Visitor,FALSE,FALSE 7,97,6,281.5,83,1778.125214,0.007967033,0.019990486,6.626647741,0,Mar,2,2,3,3,Returning_Visitor,FALSE,FALSE 0,0,0,0,4,49.5,0.05,0.075,0,0,Mar,3,2,1,1,Returning_Visitor,FALSE,FALSE 0,0,0,0,12,261,0.033333333,0.083333333,0,0,Mar,2,2,7,1,Returning_Visitor,FALSE,FALSE 1,37,0,0,12,663.5,0,0.016666667,10.83431037,0,Mar,3,2,6,8,New_Visitor,FALSE,TRUE 4,55,0,0,16,284.9,0.003333333,0.033333333,18.8649,0,Mar,1,1,2,10,Returning_Visitor,FALSE,FALSE 0,0,0,0,7,236,0.057142857,0.085714286,0,0,Mar,2,2,3,2,Returning_Visitor,FALSE,FALSE 3,26,0,0,19,576.0833333,0.009090909,0.018181818,30.65045455,0,Mar,2,2,2,1,Returning_Visitor,TRUE,TRUE 0,0,0,0,3,77,0,0.066666667,0,0,Mar,3,6,1,9,Returning_Visitor,TRUE,FALSE 0,0,0,0,1,0,0.2,0.2,0,0,Mar,2,2,1,1,Returning_Visitor,FALSE,FALSE 0,0,0,0,3,79,0.066666667,0.166666667,0,0,Mar,1,1,3,3,Returning_Visitor,FALSE,FALSE 2,24.5,1,710,66,4566.952839,0.006470588,0.018141923,0,0,Mar,3,2,2,2,Returning_Visitor,FALSE,FALSE 1,24,3,125.5,65,8646,0,0.011764706,0,0,Mar,2,2,1,13,Returning_Visitor,TRUE,FALSE 0,0,0,0,17,600.5,0,0.005882353,0,0,Mar,2,2,7,10,Returning_Visitor,FALSE,FALSE 0,0,0,0,21,434.5,0,0.023809524,0,0,Mar,2,2,7,1,Returning_Visitor,FALSE,FALSE 1,0,0,0,35,499.4,0.011764706,0.028151261,0,0,Mar,2,2,3,1,Returning_Visitor,TRUE,FALSE 9,122,1,87,50,1694.797619,0.008928571,0.018154762,0,0,Mar,1,1,1,3,Returning_Visitor,TRUE,FALSE 1,123,0,0,7,181.5,0,0.025,64.7325,0,Mar,2,2,1,2,New_Visitor,TRUE,TRUE 3,308,0,0,54,1306.151974,0.003508772,0.012962963,0,0,Mar,2,2,4,1,Returning_Visitor,FALSE,FALSE 0,0,0,0,6,75,0,0.016666667,0,0,Mar,1,1,1,2,Returning_Visitor,FALSE,FALSE 0,0,0,0,6,61,0,0.066666667,0,0,Mar,2,4,1,3,Returning_Visitor,FALSE,FALSE 0,0,0,0,12,1165.333333,0.004166667,0.043055556,0,0,Mar,2,2,3,2,Returning_Visitor,FALSE,FALSE 0,0,0,0,1,0,0.2,0.2,0,0,Mar,3,2,5,1,Returning_Visitor,FALSE,FALSE 0,0,0,0,6,70.83333333,0,0.016666667,0,0,Mar,2,2,8,2,Returning_Visitor,FALSE,FALSE 5,72.5,0,0,31,649.3071429,0,0.001041667,35.10378279,0,Mar,2,7,2,2,Returning_Visitor,TRUE,TRUE 1,21,1,61,49,1647.152381,0,0.0008,144.3934888,0,Mar,2,2,7,2,Returning_Visitor,TRUE,TRUE 6,233.0833333,2,63,60,3135.442572,0.001666667,0.009358814,0,0,Mar,3,2,1,10,Returning_Visitor,FALSE,FALSE 0,0,0,0,9,181.3333333,0.02962963,0.055026455,0,0,Mar,2,2,1,1,Returning_Visitor,FALSE,FALSE 0,0,0,0,9,159,0,0.022222222,0,0,Mar,2,2,1,2,Returning_Visitor,TRUE,FALSE 4,315,0,0,8,465.8333333,0,0.024242424,0,0,Mar,3,2,3,3,Returning_Visitor,TRUE,FALSE 0,0,0,0,3,112,0.066666667,0.133333333,0,0,Mar,2,2,7,10,Returning_Visitor,FALSE,FALSE 0,0,0,0,40,952.9666667,0.001666667,0.016666667,0,0,Mar,1,1,2,2,Returning_Visitor,FALSE,FALSE 0,0,0,0,8,372.8333333,0,0.0125,0,0,Mar,1,1,1,1,Returning_Visitor,FALSE,FALSE 6,192.5,0,0,14,327,0,0.00625,0,0,Mar,1,1,1,9,Returning_Visitor,TRUE,FALSE 0,0,0,0,1,0,0.2,0.2,0,0,Mar,2,2,2,1,Returning_Visitor,FALSE,FALSE 0,0,0,0,6,1057,0,0.033333333,0,0,Mar,2,4,4,1,Returning_Visitor,FALSE,FALSE 0,0,0,0,3,12,0,0.066666667,0,0,Mar,2,2,1,3,Returning_Visitor,FALSE,FALSE 0,0,0,0,1,36,0,0.066666667,0,0,Mar,3,2,6,2,Returning_Visitor,TRUE,FALSE 3,43.5,0,0,30,2264.333333,0,0.010416667,0,0,Mar,2,2,1,3,Returning_Visitor,TRUE,FALSE 4,76,0,0,28,866,0,0.02183908,0,0,Mar,2,2,4,1,Returning_Visitor,FALSE,FALSE 0,0,0,0,4,97,0,0.05,0,0,Mar,2,2,2,1,Returning_Visitor,FALSE,FALSE 0,0,0,0,17,701.5424242,0.013333333,0.026666667,0,0,Mar,2,2,6,2,Returning_Visitor,FALSE,FALSE 2,35,0,0,20,979.0833333,0,0.00952381,60.04549665,0,Mar,2,2,3,2,Returning_Visitor,FALSE,TRUE 3,13,0,0,28,324.7692308,0,0.001724138,0,0,Mar,2,2,1,6,Returning_Visitor,FALSE,FALSE 6,160.0952381,3,94.5,47,1682.870247,0.001333333,0.007891429,5.698642857,0,Mar,3,2,6,10,Returning_Visitor,FALSE,TRUE 0,0,0,0,9,210,0.044444444,0.055555556,0,0,Mar,1,1,3,3,Returning_Visitor,FALSE,FALSE 0,0,0,0,20,332,0.03,0.06,0,0,Mar,4,1,3,1,Returning_Visitor,FALSE,FALSE 1,45,0,0,13,1610.916667,0,0.007142857,0,0,Mar,1,1,1,3,New_Visitor,TRUE,FALSE 4,177.1666667,0,0,50,1188.566667,0.007843137,0.018300654,0,0,Mar,2,2,3,3,Returning_Visitor,FALSE,FALSE 0,0,0,0,3,98,0,0.066666667,0,0,Mar,2,6,7,1,Returning_Visitor,FALSE,FALSE 0,0,1,36.33333333,38,1663.683333,0.005405405,0.022972973,0,0,Mar,2,2,4,1,Returning_Visitor,FALSE,FALSE 4,43.5,0,0,39,930.3,0.001190476,0.010997732,0,0,Mar,4,5,1,3,Returning_Visitor,TRUE,FALSE 0,0,0,0,21,1003,0,0.008333333,0,0,Mar,2,4,1,1,Returning_Visitor,FALSE,FALSE 0,0,0,0,3,69,0,0.066666667,0,0,Mar,2,2,2,6,Returning_Visitor,FALSE,FALSE 0,0,0,0,27,1199.725758,0.007407407,0.01691358,9.028406684,0,Mar,1,1,2,3,Returning_Visitor,FALSE,FALSE 0,0,0,0,34,1524.058333,0.019393939,0.025282904,11.77367361,0,Mar,3,2,3,1,Returning_Visitor,FALSE,FALSE 0,0,2,45,11,624,0.015384615,0.046153846,0,0,Mar,2,2,4,10,Returning_Visitor,FALSE,FALSE 0,0,0,0,37,1229.766667,0.005405405,0.014414414,0,0,Mar,2,2,1,3,Returning_Visitor,TRUE,FALSE 0,0,0,0,13,972.6,0,0.007692308,0,0,Mar,2,2,3,10,Returning_Visitor,FALSE,FALSE 5,159,0,0,17,438.4333333,0.003030303,0.027272727,0,0,Mar,1,1,3,3,Returning_Visitor,FALSE,FALSE 0,0,0,0,10,270,0,0.004,0,0,Mar,2,2,1,2,Returning_Visitor,FALSE,FALSE 0,0,0,0,3,333,0,0.066666667,0,0,Mar,2,2,2,1,Returning_Visitor,TRUE,FALSE 0,0,0,0,20,504.2333333,0.01,0.021666667,0,0,Mar,3,2,1,1,Returning_Visitor,FALSE,FALSE 0,0,0,0,27,845.5,0,0.007407407,0,0,Mar,4,1,1,11,Returning_Visitor,TRUE,FALSE 1,54,1,237,16,744.5,0.013333333,0.033777778,0,0,Mar,2,2,6,2,Returning_Visitor,FALSE,FALSE 0,0,0,0,21,1129,0.00952381,0.038095238,0,0,Mar,2,5,5,1,Returning_Visitor,FALSE,FALSE 2,34,0,0,8,358,0,0.02,0,0,Mar,2,2,1,2,Returning_Visitor,FALSE,FALSE 0,0,0,0,24,832,0,0.020289855,21.92521348,0,Mar,2,6,6,1,Returning_Visitor,FALSE,FALSE 0,0,0,0,4,36.5,0,0.05,0,0,Mar,2,2,7,1,Returning_Visitor,FALSE,FALSE 0,0,0,0,7,157,0,0.014285714,0,0,Mar,3,2,1,2,Returning_Visitor,FALSE,FALSE 3,36,0,0,9,71.33333333,0,0.02,0,0,Mar,2,2,1,11,New_Visitor,FALSE,FALSE 7,196,3,138.5,11,227.5,0,0.006666667,0,0,Mar,3,2,5,8,New_Visitor,TRUE,FALSE 1,0,2,56,22,1054.309524,0.004761905,0.023809524,0,0,Mar,1,1,1,1,Returning_Visitor,FALSE,FALSE 0,0,0,0,6,308.1666667,0.083333333,0.095,0,0,Mar,1,1,6,1,Returning_Visitor,FALSE,FALSE 0,0,0,0,10,229.25,0,0.06,0,0,Mar,1,1,6,2,Returning_Visitor,FALSE,FALSE 0,0,1,29,9,225.8333333,0,0.02,0,0,Mar,2,2,1,3,Returning_Visitor,TRUE,FALSE 2,21,1,26,22,326.5833333,0.018181818,0.066666667,0,0,Mar,3,2,4,3,Returning_Visitor,FALSE,FALSE 10,231,1,8,104,3486.690476,0.003301887,0.013104972,0,0,Mar,2,10,1,1,Returning_Visitor,TRUE,FALSE 0,0,0,0,8,863.3333333,0,0.014583333,0,0,Mar,2,2,4,3,Returning_Visitor,TRUE,FALSE 0,0,0,0,1,67,0.1,0.133333333,0,0,Mar,3,3,1,3,Returning_Visitor,FALSE,FALSE 15,1011.361111,2,171.5,54,1405.131241,0,0.006127451,37.41814196,0,Mar,3,2,3,2,New_Visitor,TRUE,FALSE 6,60,2,61,23,1031.704762,0,0.007111111,33.14784917,0,Mar,4,2,9,10,Returning_Visitor,TRUE,TRUE 0,0,0,0,24,917.0833333,0.008333333,0.031944444,0,0,Mar,3,2,1,1,Returning_Visitor,TRUE,FALSE 3,61,0,0,19,573.6571429,0.036842105,0.04,0,0,Mar,1,1,2,1,Returning_Visitor,FALSE,FALSE 0,0,0,0,5,94.5,0,0.04,0,0,Mar,2,2,1,1,Returning_Visitor,FALSE,FALSE 0,0,0,0,13,238.1666667,0,0.007692308,0,0,Mar,2,2,6,2,Returning_Visitor,FALSE,FALSE 3,227,0,0,3,164,0,0.013333333,0,0,Mar,1,1,3,2,New_Visitor,FALSE,FALSE 0,0,5,14,31,750.5904762,0.007407407,0.036957672,0,0,Mar,2,2,1,2,Returning_Visitor,FALSE,FALSE 1,464,0,0,19,1834.083333,0.02,0.03125,0,0,Mar,2,2,2,10,Returning_Visitor,FALSE,FALSE 0,0,0,0,19,606.8333333,0.031578947,0.048245614,0,0,Mar,2,2,1,3,Returning_Visitor,FALSE,FALSE 0,0,3,300,13,509.1214286,0.006666667,0.002222222,40.7932,0,Mar,3,2,7,2,Returning_Visitor,TRUE,TRUE 0,0,0,0,2,19,0,0.1,0,0,Mar,1,1,1,1,Returning_Visitor,FALSE,FALSE 3,80.42857143,0,0,61,5900.351378,0,0.007213115,0,0,Mar,3,2,3,10,Returning_Visitor,FALSE,FALSE 0,0,0,0,1,0,0.2,0.2,0,0,Mar,1,2,9,2,Returning_Visitor,FALSE,FALSE 0,0,0,0,1,0,0.2,0.2,0,0,Mar,2,2,1,1,Returning_Visitor,FALSE,FALSE 0,0,0,0,13,449.3333333,0.006153846,0.044615385,0,0,Mar,2,5,1,3,Returning_Visitor,FALSE,FALSE 0,0,0,0,12,200,0,0.047222222,0,0,Mar,2,2,1,2,Returning_Visitor,TRUE,FALSE 3,61.5,0,0,16,946.1666667,0,0.010526316,0,0,Mar,2,2,1,10,New_Visitor,FALSE,FALSE 0,0,0,0,2,0,0.2,0.2,0,0,Mar,2,2,1,1,Returning_Visitor,FALSE,FALSE 0,0,0,0,3,514,0,0.066666667,0,0,Mar,2,2,3,1,Returning_Visitor,FALSE,FALSE 1,58,0,0,5,64,0,0.016666667,0,0,Mar,1,1,4,2,New_Visitor,FALSE,FALSE 0,0,0,0,2,210,0,0.1,0,0,Mar,3,2,1,2,Returning_Visitor,FALSE,FALSE 2,243,0,0,12,1180.5,0,0.007692308,0,0,Mar,1,1,1,9,Returning_Visitor,FALSE,FALSE 0,0,0,0,9,771.5,0.022222222,0.044444444,0,0,Mar,1,1,6,1,Returning_Visitor,FALSE,FALSE 1,4,0,0,37,1096.75,0,0.020175439,0,0,Mar,2,2,6,10,Returning_Visitor,FALSE,FALSE 0,0,0,0,5,381.6666667,0,0.02,0,0,Mar,1,1,3,2,Returning_Visitor,FALSE,FALSE 9,210.6666667,3,127,55,1509.594444,0.006451613,0.02016129,14.68324522,0,Mar,2,5,1,2,Returning_Visitor,FALSE,FALSE 0,0,0,0,4,69,0,0.05,0,0,Mar,1,1,1,2,Returning_Visitor,FALSE,FALSE 0,0,0,0,1,0,0.2,0.2,0,0,Mar,2,4,5,1,Returning_Visitor,FALSE,FALSE 0,0,0,0,5,103,0,0.04,0,0,Mar,2,2,1,2,Returning_Visitor,FALSE,FALSE 0,0,0,0,2,36.5,0,0.033333333,0,0,Mar,3,2,1,1,Returning_Visitor,FALSE,FALSE 0,0,0,0,6,226,0,0.04,0,0,Mar,2,2,1,2,Returning_Visitor,FALSE,FALSE 1,156.1428571,0,0,18,1181.198413,0,0.048235294,0,0,Mar,3,2,4,1,Returning_Visitor,FALSE,FALSE 2,0,0,0,17,1394.666667,0,0.029761905,0,0,Mar,2,4,2,6,Returning_Visitor,FALSE,FALSE 5,282,0,0,8,326,0,0.022222222,0,0,Mar,1,1,1,3,Returning_Visitor,FALSE,FALSE 0,0,0,0,9,459.8307692,0,0.001587302,0,0,Mar,3,2,8,2,Returning_Visitor,TRUE,FALSE 3,512.8333333,2,278,3,162,0,0.014285714,0,0,Mar,3,2,1,8,New_Visitor,TRUE,FALSE 0,0,0,0,1,0,0.2,0.2,0,0,Mar,2,6,1,1,Returning_Visitor,FALSE,FALSE 0,0,0,0,1,0,0.2,0.2,0,0,Mar,1,1,3,1,Returning_Visitor,FALSE,FALSE 1,112,0,0,37,2043.5,0.005555556,0.016666667,12.3578472,0,Mar,2,5,3,6,Returning_Visitor,FALSE,TRUE 4,73.5,0,0,24,663.4666667,0.002564103,0.004395604,0,0,Mar,3,2,1,1,Returning_Visitor,FALSE,FALSE 3,184.6888889,0,0,129,7669.250794,0,0.009135204,0,0,Mar,2,2,8,13,Returning_Visitor,FALSE,FALSE 5,75.16666667,0,0,12,115.1666667,0.028571429,0.042857143,0,0,Mar,2,2,8,1,Returning_Visitor,FALSE,FALSE 0,0,0,0,11,296.45,0,0.007792208,0,0,Mar,3,2,6,2,Returning_Visitor,FALSE,FALSE 0,0,0,0,56,2633.869048,0.003703704,0.016666667,0,0,Mar,2,5,1,1,Returning_Visitor,FALSE,FALSE 0,0,0,0,19,392,0.021052632,0.052631579,0,0,Mar,2,2,5,1,Returning_Visitor,TRUE,FALSE 1,19,0,0,13,748.6666667,0,0.020512821,0,0,Mar,1,2,6,3,Returning_Visitor,FALSE,FALSE 6,302,0,0,18,834.6666667,0.004761905,0.021269841,0,0,Mar,1,1,1,9,Returning_Visitor,TRUE,FALSE 0,0,0,0,2,5,0,0.1,0,0,Mar,2,2,1,1,Returning_Visitor,TRUE,FALSE 0,0,0,0,1,0,0.2,0.2,0,0,Mar,1,1,1,9,Returning_Visitor,TRUE,FALSE 0,0,0,0,3,11,0.05,0.15,0,0,Mar,3,3,1,1,Returning_Visitor,FALSE,FALSE 0,0,1,37,15,497.3333333,0.0125,0.0375,0,0,Mar,1,1,2,1,Returning_Visitor,FALSE,FALSE 10,117.3333333,0,0,28,631.9896825,0.017647059,0.022794118,10.16517157,0,Mar,3,7,1,2,Returning_Visitor,FALSE,TRUE 0,0,0,0,11,315.6,0,0.018181818,0,0,Mar,1,1,6,3,Returning_Visitor,TRUE,FALSE 1,7,0,0,14,183.3333333,0,0.006666667,0,0,Mar,2,2,3,10,Returning_Visitor,FALSE,FALSE 1,0,0,0,8,192.1666667,0,0.025,0,0,Mar,1,1,9,9,Returning_Visitor,TRUE,FALSE 1,0,0,0,16,1296.6,0.022222222,0.033333333,13.28355556,0,Mar,3,2,4,2,Returning_Visitor,FALSE,TRUE 0,0,0,0,3,17,0.1,0.111111111,0,0,Mar,1,1,1,1,Returning_Visitor,FALSE,FALSE 0,0,1,41,13,698,0,0.032142857,0,0,Mar,2,5,2,1,Returning_Visitor,FALSE,FALSE 1,14,0,0,17,1093.142857,0,0.02125,0,0,Mar,2,2,2,2,Returning_Visitor,FALSE,FALSE 1,5,0,0,9,2397.5,0,0.02,0,0,Mar,4,1,3,3,Returning_Visitor,FALSE,FALSE 2,107,0,0,3,56,0,0.04,0,0,Mar,1,1,3,2,New_Visitor,FALSE,FALSE 1,31,3,62,11,196.2,0,0.013333333,0,0,Mar,2,2,1,10,Returning_Visitor,FALSE,FALSE 0,0,0,0,3,39.66666667,0,0.033333333,0,0,Mar,2,4,1,10,Returning_Visitor,FALSE,FALSE 0,0,0,0,1,0,0.2,0.2,0,0,Mar,2,2,1,3,Returning_Visitor,FALSE,FALSE 0,0,0,0,1,0,0.2,0.2,0,0,Mar,3,2,1,1,Returning_Visitor,FALSE,FALSE 0,0,1,42,20,408.3333333,0,0.003174603,33.47777322,0,Mar,2,4,7,2,Returning_Visitor,FALSE,TRUE 0,0,0,0,2,0,0.2,0.2,0,0,Mar,1,1,1,10,Returning_Visitor,FALSE,FALSE 1,7,0,0,29,906.5666667,0,0.006896552,0,0,Mar,4,1,1,2,New_Visitor,TRUE,FALSE 0,0,0,0,4,149,0,0.05,0,0,Mar,2,2,1,2,Returning_Visitor,FALSE,FALSE 0,0,0,0,30,1224.75,0,0.007142857,21.04864167,0,Mar,2,4,2,2,New_Visitor,FALSE,TRUE 0,0,0,0,1,0,0.2,0.2,0,0,Mar,2,4,1,1,Returning_Visitor,FALSE,FALSE 2,52,1,17.66666667,18,1262,0,0.00952381,0,0,Mar,2,2,1,2,New_Visitor,FALSE,FALSE 0,0,0,0,12,207,0,0.036666667,0,0,Mar,2,2,3,2,Returning_Visitor,FALSE,FALSE 1,19,0,0,7,115.6666667,0,0.028571429,0,0,Mar,3,2,2,10,Returning_Visitor,FALSE,FALSE 0,0,0,0,1,0,0.2,0.2,0,0,Mar,2,2,1,2,Returning_Visitor,FALSE,FALSE 1,0,0,0,3,321.5,0.05,0.1,0,0,Mar,3,2,2,1,Returning_Visitor,FALSE,FALSE 9,204.0833333,0,0,63,3552.702381,0,0.007,19.95572297,0,Mar,1,2,8,8,New_Visitor,FALSE,TRUE 2,151,0,0,19,716.3571429,0.004761905,0.015873016,10.8692605,0,Mar,1,1,7,1,Returning_Visitor,TRUE,TRUE 6,98,0,0,12,343,0.019444444,0.032222222,0,0,Mar,1,2,1,2,Returning_Visitor,TRUE,FALSE 6,52,1,24,56,3763.166667,0.003389831,0.01480226,10.37042373,0,Mar,2,2,1,1,Returning_Visitor,FALSE,TRUE 0,0,0,0,7,272.5,0,0.033333333,0,0,Mar,1,1,6,2,New_Visitor,TRUE,FALSE 0,0,0,0,3,81,0,0.066666667,0,0,Mar,1,1,1,1,Returning_Visitor,FALSE,FALSE 4,600.4230769,0,0,17,1883.827222,0.00075188,0.013061325,0.120699914,0,Mar,2,2,1,8,Returning_Visitor,FALSE,TRUE 0,0,0,0,7,251.6666667,0.028571429,0.011428571,0,0,Mar,3,2,1,6,Returning_Visitor,TRUE,FALSE 4,132,0,0,7,1413.333333,0.036,0.029285714,0,0,Mar,3,2,7,8,Returning_Visitor,FALSE,FALSE 0,0,0,0,3,79.5,0,0.033333333,0,0,Mar,3,2,2,2,Returning_Visitor,FALSE,FALSE 3,23,0,0,6,83,0,0.014285714,0,0,Mar,2,2,6,2,New_Visitor,FALSE,FALSE 2,20,0,0,17,584.5,0,0.011111111,0,0,Mar,2,2,9,2,Returning_Visitor,FALSE,FALSE 3,39,0,0,30,940.8333333,0.012121212,0.024242424,0,0,Mar,1,8,3,3,Returning_Visitor,FALSE,FALSE 2,152.8333333,0,0,20,791.6666667,0.011111111,0.016666667,41.35007203,0,Mar,1,1,1,2,New_Visitor,FALSE,TRUE 0,0,0,0,77,4346.541667,0.003070175,0.023099415,0,0,Mar,4,1,1,2,Returning_Visitor,FALSE,FALSE 0,0,0,0,5,50.5,0,0.04,0,0,Mar,2,2,1,1,Returning_Visitor,TRUE,FALSE 0,0,0,0,6,74,0,0.02,0,0,Mar,2,4,6,8,Returning_Visitor,FALSE,FALSE 0,0,0,0,8,83,0.057142857,0.095238095,0,0,Mar,1,2,2,1,Returning_Visitor,FALSE,FALSE 4,16.66666667,0,0,68,2593.741667,0.002941176,0.012843137,0,0,Mar,2,2,4,8,Returning_Visitor,FALSE,FALSE 0,0,0,0,6,59,0,0.033333333,0,0,Mar,2,2,1,2,Returning_Visitor,FALSE,FALSE 2,197,4,219,53,1518.761111,0.007272727,0.005454545,0,0,Mar,1,1,6,2,Returning_Visitor,TRUE,FALSE 0,0,0,0,6,62,0,0.04,0,0,Mar,2,2,1,1,Returning_Visitor,FALSE,FALSE 0,0,0,0,30,365.4,0.027586207,0.035862069,0,0,Mar,3,2,6,1,Returning_Visitor,FALSE,FALSE 2,15,0,0,7,50.5,0,0.028571429,0,0,Mar,2,2,1,3,Returning_Visitor,FALSE,FALSE 0,0,0,0,12,468,0.016666667,0.033333333,0,0,Mar,4,1,4,3,Returning_Visitor,FALSE,FALSE 5,710.3333333,0,0,23,7533.272727,0,0.007407407,0,0,Mar,2,2,1,2,Returning_Visitor,FALSE,FALSE 4,84,0,0,35,1059,0,0.005882353,0,0,Mar,2,2,3,2,New_Visitor,FALSE,FALSE 0,0,0,0,5,52,0,0.02,0,0,Mar,2,4,7,2,Returning_Visitor,FALSE,FALSE 3,151,0,0,6,892,0,0.018518519,0,0,Mar,3,2,3,3,Returning_Visitor,TRUE,FALSE 4,83.5,0,0,9,430.5,0,0.015384615,0,0,Mar,2,2,1,2,New_Visitor,FALSE,FALSE 0,0,0,0,17,132.6333333,0.011764706,0.007843137,0,0,Mar,1,1,1,2,Returning_Visitor,FALSE,FALSE 0,0,0,0,1,0,0.2,0.2,0,0,Mar,3,2,3,1,Returning_Visitor,FALSE,FALSE 0,0,0,0,1,0,0.2,0.2,0,0,Mar,1,1,1,3,Returning_Visitor,FALSE,FALSE 0,0,0,0,14,363.3181818,0.014285714,0.023809524,0,0,Mar,1,1,1,3,Returning_Visitor,FALSE,FALSE 0,0,0,0,3,73,0,0.066666667,0,0,Mar,2,4,1,7,Returning_Visitor,FALSE,FALSE 0,0,0,0,8,612,0,0.014285714,0,0,Mar,3,3,1,2,New_Visitor,TRUE,FALSE 9,848.1428571,4,388.6666667,33,595.0095238,0,0.004651163,0,0,Mar,2,2,1,10,New_Visitor,FALSE,FALSE 0,0,0,0,6,495,0,0.02,77.96,0,Mar,1,2,9,2,Returning_Visitor,TRUE,TRUE 0,0,0,0,4,49.5,0,0.016666667,0,0,Mar,2,2,9,2,Returning_Visitor,FALSE,FALSE 3,130.8333333,1,109,47,1813.208333,0,0.002083333,0,0,Mar,2,2,3,2,Returning_Visitor,TRUE,FALSE 0,0,0,0,9,218,0,0.033333333,0,0,Mar,2,2,6,3,Returning_Visitor,TRUE,FALSE 0,0,0,0,4,102,0,0.05,0,0,Mar,2,2,3,2,Returning_Visitor,FALSE,FALSE 2,27,0,0,23,1278.090909,0,0.008333333,0,0,Mar,2,2,2,2,New_Visitor,TRUE,FALSE 0,0,0,0,3,25,0,0.066666667,0,0,Mar,1,1,4,1,Returning_Visitor,FALSE,FALSE 0,0,2,445,38,996.5166667,0.000854701,0.016544567,0,0,Mar,2,2,3,2,Returning_Visitor,FALSE,FALSE 0,0,0,0,6,69.5,0,0.033333333,0,0,Mar,2,2,7,1,Returning_Visitor,FALSE,FALSE 0,0,0,0,40,1530.248485,0.005128205,0.029795821,0,0,Mar,3,2,1,2,Returning_Visitor,FALSE,FALSE 2,12,0,0,43,2595.280952,0.009090909,0.025378788,0,0,Mar,2,4,7,1,Returning_Visitor,FALSE,FALSE 0,0,0,0,12,471.5,0.016666667,0.066666667,0,0,Mar,1,1,6,3,Returning_Visitor,FALSE,FALSE 11,281.6309524,0,0,54,923.6757353,0,0.012261867,5.150663602,0,Mar,1,1,7,2,Returning_Visitor,FALSE,FALSE 0,0,0,0,10,153,0.02,0.04,0,0,Mar,1,1,4,1,Returning_Visitor,TRUE,FALSE 9,160,0,0,48,3946.008333,0.007272727,0.011515152,20.32388105,0,Mar,2,6,1,8,Returning_Visitor,TRUE,TRUE 0,0,0,0,26,648.4,0,0.018,0,0,Mar,1,1,1,3,Returning_Visitor,TRUE,FALSE 0,0,0,0,5,56,0.04,0.08,0,0,Mar,2,4,1,1,Returning_Visitor,TRUE,FALSE 0,0,0,0,1,0,0.2,0.2,0,0,Mar,3,2,3,2,Returning_Visitor,FALSE,FALSE 0,0,0,0,21,263,0.00952381,0.030952381,0,0,Mar,2,6,1,3,Returning_Visitor,FALSE,FALSE 0,0,0,0,6,193,0,0.033333333,0,0,Mar,4,1,3,3,Returning_Visitor,TRUE,FALSE 0,0,0,0,3,38,0.1,0.122222222,0,0,Mar,3,2,1,1,Returning_Visitor,FALSE,FALSE 0,0,0,0,8,116,0.025,0.1,0,0,Mar,2,4,5,1,Returning_Visitor,FALSE,FALSE 3,31.33333333,0,0,33,1077.869048,0.0015625,0.01953125,0,0,Mar,3,2,6,2,Returning_Visitor,FALSE,FALSE 0,0,0,0,5,266.3333333,0.04,0.08,0,0,Mar,3,2,1,3,Returning_Visitor,FALSE,FALSE 0,0,0,0,3,17,0,0.033333333,0,0,Mar,1,1,1,3,Returning_Visitor,FALSE,FALSE 0,0,0,0,3,67.5,0,0.066666667,0,0,Mar,1,1,3,3,Returning_Visitor,TRUE,FALSE 3,81,1,0,28,499.6666667,0,0.022222222,0,0,Mar,2,6,1,1,Returning_Visitor,FALSE,FALSE 1,5.5,0,0,7,286,0.025,0.05,0,0,Mar,2,2,6,1,Returning_Visitor,FALSE,FALSE 4,81,0,0,8,112.7285714,0,0.02,0,0,Mar,2,2,2,1,Returning_Visitor,FALSE,FALSE 1,18,0,0,24,1571,0,0.016666667,0,0,Mar,2,2,2,3,Returning_Visitor,FALSE,FALSE 0,0,0,0,11,241,0,0.018181818,0,0,Mar,2,2,5,1,Returning_Visitor,FALSE,FALSE 0,0,0,0,11,187,0.031818182,0.068181818,0,0,Mar,2,2,1,1,Returning_Visitor,FALSE,FALSE 3,91,2,338,11,451,0,0.006666667,45.7504,0,Mar,2,2,1,10,New_Visitor,FALSE,TRUE 2,85.5,0,0,42,1741.483333,0,0.002051282,27.12804201,0,Mar,2,10,1,1,Returning_Visitor,FALSE,FALSE 1,42,2,80.5,46,838.6833333,0,0.015416667,0,0,Mar,2,2,1,10,Returning_Visitor,FALSE,FALSE 5,36.33333333,3,53,22,406,0.002564103,0.013461538,0,0,Mar,4,1,1,1,Returning_Visitor,FALSE,FALSE 0,0,0,0,18,437.1875,0.011111111,0.006878307,0,0,Mar,1,1,7,9,Returning_Visitor,TRUE,FALSE 2,33,0,0,28,898.5,0,0.013333333,0,0,Mar,2,10,6,10,Returning_Visitor,FALSE,FALSE 0,0,0,0,19,215.75,0,0.021052632,0,0,Mar,2,2,3,10,Returning_Visitor,FALSE,FALSE 7,110.25,0,0,20,266.5833333,0.011111111,0.039753086,0,0,Mar,2,2,1,2,Returning_Visitor,FALSE,FALSE 0,0,0,0,6,546,0,0.04,35.49,0,Mar,2,2,3,1,Returning_Visitor,FALSE,TRUE 0,0,0,0,16,2156,0.01875,0.035416667,0,0,Mar,2,2,3,1,Returning_Visitor,FALSE,FALSE 0,0,0,0,17,557.1666667,0.023529412,0.049509804,0,0,Mar,3,2,1,1,Returning_Visitor,FALSE,FALSE 2,27,1,35,19,461.05,0.016666667,0.026190476,16.33942857,0,Mar,3,2,1,1,Returning_Visitor,FALSE,FALSE 0,0,0,0,18,1473.333333,0.011111111,0.044444444,0,0,Mar,1,1,1,2,Returning_Visitor,FALSE,FALSE 4,155.5,0,0,13,474.8333333,0,0.00625,0,0,Mar,3,2,1,1,Returning_Visitor,FALSE,FALSE 0,0,0,0,1,0,0.2,0.2,0,0,Mar,1,1,4,1,Returning_Visitor,FALSE,FALSE 0,0,0,0,6,212,0.026666667,0.03968254,0,0,Mar,3,2,3,1,Returning_Visitor,FALSE,FALSE 5,487.5,0,0,11,500,0,0.015384615,0,0,Mar,1,8,1,11,New_Visitor,FALSE,FALSE 0,0,3,56,10,369,0,0.016666667,0,0,Mar,1,1,4,2,New_Visitor,TRUE,FALSE 0,0,0,0,5,136.6666667,0.02,0.12,0,0,Mar,3,2,1,10,Returning_Visitor,TRUE,FALSE 0,0,0,0,10,243.2,0.02,0.04,0,0,Mar,2,2,4,1,Returning_Visitor,FALSE,FALSE 0,0,0,0,12,459,0,0.02,0,0,Mar,2,4,3,1,Returning_Visitor,FALSE,FALSE 5,376.8,3,105.6666667,6,441.4666667,0,0.003333333,0,0,Mar,2,2,1,1,Returning_Visitor,FALSE,FALSE 4,985.5,4,308,6,328,0,0.015384615,0,0,Mar,2,2,3,2,New_Visitor,FALSE,FALSE 0,0,0,0,3,36,0,0.033333333,0,0,Mar,2,2,1,2,Returning_Visitor,FALSE,FALSE 0,0,0,0,20,518.6666667,0,0.011111111,0,0,Mar,2,2,2,1,Returning_Visitor,FALSE,FALSE 7,606,1,33,26,631.4583333,0,0.007142857,0,0,Mar,1,1,7,2,Returning_Visitor,FALSE,FALSE 11,1640.590909,0,0,16,1849.833333,0,0.013636364,10.78136364,0,Mar,1,1,2,3,Returning_Visitor,FALSE,FALSE 1,0,0,0,8,436,0,0.022222222,0,0,Mar,2,4,6,1,Returning_Visitor,TRUE,FALSE 0,0,0,0,5,132,0,0.04,0,0,Mar,1,1,1,1,Returning_Visitor,TRUE,FALSE 2,63,0,0,35,850.3333333,0,0.005405405,0,0,Mar,2,2,3,2,New_Visitor,TRUE,FALSE 11,228.0277778,1,32,17,457.7957265,0.007258065,0.044351348,0,0,Mar,1,2,9,8,Returning_Visitor,FALSE,FALSE 0,0,0,0,5,143,0,0.04,0,0,Mar,2,4,1,1,Returning_Visitor,FALSE,FALSE 6,136.5555556,0,0,59,3224.819084,0.002985075,0.011923715,8.567426866,0,Mar,2,4,2,2,Returning_Visitor,TRUE,TRUE 3,76,0,0,24,1038.845238,0,0.008333333,0,0,Mar,2,2,4,3,New_Visitor,FALSE,FALSE 0,0,0,0,1,0,0.2,0.2,0,0,Mar,1,1,1,1,Returning_Visitor,FALSE,FALSE 0,0,0,0,6,216,0,0.033333333,0,0,Mar,2,4,7,10,Returning_Visitor,FALSE,FALSE 2,11,0,0,20,363.6666667,0.01,0.016666667,0,0,Mar,2,2,1,3,Returning_Visitor,TRUE,FALSE 1,0,0,0,17,1314.25,0.045833333,0.1,0,0,Mar,2,2,7,1,Returning_Visitor,FALSE,FALSE 0,0,0,0,5,146,0.146666667,0.186666667,0,0,Mar,3,2,7,3,Returning_Visitor,FALSE,FALSE 0,0,0,0,8,228,0,0.028571429,0,0,Mar,4,1,3,2,Returning_Visitor,FALSE,FALSE 0,0,0,0,2,0,0.2,0.2,0,0,Mar,2,2,1,1,Returning_Visitor,FALSE,FALSE 0,0,0,0,11,419.75,0,0.006060606,0,0,Mar,2,2,6,1,Returning_Visitor,FALSE,FALSE 0,0,0,0,15,270,0.028571429,0.035714286,0,0,Mar,2,2,7,1,Returning_Visitor,FALSE,FALSE 1,1.333333333,0,0,32,395.2222222,0.006060606,0.011111111,0,0,Mar,2,4,1,2,Returning_Visitor,TRUE,FALSE 0,0,0,0,4,9,0.1,0.15,0,0,Mar,3,2,3,1,Returning_Visitor,FALSE,FALSE 0,0,0,0,17,945.0108696,0.006334842,0.023889556,0,0,Mar,3,2,3,10,Returning_Visitor,FALSE,FALSE 0,0,0,0,3,90,0,0.066666667,0,0,Mar,1,2,5,10,Returning_Visitor,FALSE,FALSE 0,0,1,55,9,936.8333333,0,0.022222222,53.25533333,0,Mar,1,1,1,2,Returning_Visitor,TRUE,TRUE 0,0,0,0,3,8,0,0.066666667,0,0,Mar,2,2,6,1,Returning_Visitor,FALSE,FALSE 0,0,0,0,20,447.5,0.02,0.04,0,0,Mar,2,2,1,1,Returning_Visitor,FALSE,FALSE 5,310,1,73,16,416.4,0,0.011111111,0,0,Mar,1,1,1,2,Returning_Visitor,TRUE,FALSE 5,1417.5,0,0,36,1051.248276,0,0.002293202,0,0,Mar,2,2,5,2,Returning_Visitor,TRUE,FALSE 3,960,0,0,10,1218,0,0.018181818,0,0,Mar,2,5,1,2,New_Visitor,FALSE,FALSE 2,18,0,0,15,756.3333333,0.0125,0.0625,29.24325,0,Mar,2,10,3,1,Returning_Visitor,FALSE,TRUE 0,0,0,0,5,210,0.12,0.16,0,0,Mar,1,10,1,1,Returning_Visitor,FALSE,FALSE 2,52,0,0,24,427.0833333,0.026388889,0.059722222,0,0,Mar,2,2,1,1,Returning_Visitor,FALSE,FALSE 0,0,0,0,2,31,0,0.025,0,0,Mar,3,2,3,3,Returning_Visitor,FALSE,FALSE 0,0,0,0,4,388,0.05,0.075,0,0,Mar,2,2,6,1,Returning_Visitor,FALSE,FALSE 0,0,0,0,4,151.5,0,0.05,0,0,Mar,3,2,1,1,Returning_Visitor,FALSE,FALSE 2,18.33333333,0,0,36,1387.606061,0.005263158,0.025438596,0,0,Mar,2,2,8,1,Returning_Visitor,TRUE,FALSE 5,106,0,0,30,1418,0,0.010344828,0,0,Mar,2,2,3,2,New_Visitor,FALSE,FALSE 6,91.88888889,0,0,25,437.2222222,0.028571429,0.035,0,0,Mar,3,2,1,1,Returning_Visitor,TRUE,FALSE 3,18,0,0,14,411.5,0,0.023529412,0,0,Mar,2,2,8,2,Returning_Visitor,FALSE,FALSE 2,36,0,0,13,142,0,0.013333333,0,0,Mar,4,2,4,8,Returning_Visitor,FALSE,FALSE 0,0,1,28,13,474.6666667,0,0.014285714,33.41,0,Mar,2,2,1,6,Returning_Visitor,FALSE,TRUE 2,17,0,0,20,1415.625,0.012698413,0.044761905,32.578313,0,Mar,3,3,1,10,Returning_Visitor,FALSE,FALSE 0,0,0,0,1,0,0,0.2,0,0,Mar,2,2,5,1,Returning_Visitor,FALSE,FALSE 7,139.5,0,0,30,1448.752381,0.006060606,0.012878788,0,0,Mar,1,2,3,2,New_Visitor,FALSE,FALSE 0,0,0,0,3,96.5,0,0.022222222,0,0,Mar,1,1,1,3,Returning_Visitor,TRUE,FALSE 4,178,2,209,17,2777.666667,0,0.009090909,0,0,Mar,3,2,7,1,Returning_Visitor,FALSE,FALSE 3,180,0,0,12,1436.5,0,0.013333333,27.75904925,0,Mar,2,2,2,2,Returning_Visitor,TRUE,TRUE 2,107.3333333,0,0,8,224,0,0.01,0,0,Mar,1,2,7,2,Returning_Visitor,TRUE,FALSE 4,52,1,7,46,3087,0.003773585,0.021383648,16.94643793,0,Mar,2,2,3,8,Returning_Visitor,FALSE,TRUE 2,211,1,58,71,3424.583333,0.001315789,0.015,0,0,Mar,2,2,5,3,Returning_Visitor,TRUE,FALSE 0,0,0,0,1,0,0.2,0.2,0,0,Mar,1,1,3,3,Returning_Visitor,FALSE,FALSE 0,0,2,46,9,546,0,0.02,0,0,Mar,2,6,1,2,Returning_Visitor,FALSE,FALSE 1,4,0,0,43,1418.5,0,0.018181818,0,0,Mar,4,1,2,3,Returning_Visitor,TRUE,FALSE 1,69,0,0,8,423,0,0.025,28.05094335,0,Mar,2,4,1,8,New_Visitor,FALSE,TRUE 4,183.25,1,21,20,651.4,0,0.004166667,0,0,Mar,1,1,1,2,Returning_Visitor,FALSE,FALSE 2,55,0,0,6,249.5,0,0.033333333,0,0,Mar,2,2,7,2,New_Visitor,FALSE,FALSE 0,0,0,0,11,337,0.045454545,0.061818182,0,0,Mar,1,1,9,3,Returning_Visitor,FALSE,FALSE 0,0,0,0,1,0,0.2,0.2,0,0,Mar,1,1,1,3,Returning_Visitor,TRUE,FALSE 0,0,0,0,26,1377.769697,0,0.008,92.4822063,0,Mar,2,2,7,2,Returning_Visitor,FALSE,TRUE 0,0,1,83,16,223,0,0.023529412,0,0,Mar,2,2,1,2,Returning_Visitor,FALSE,FALSE 1,3,2,373,6,66.83333333,0,0.0125,0,0,Mar,2,4,1,8,New_Visitor,FALSE,FALSE 1,613.6666667,0,0,4,957,0.161904762,0.096298701,0,0,Mar,3,2,8,10,Returning_Visitor,FALSE,FALSE 0,0,0,0,2,135,0,0.1,0,0,Mar,1,1,1,2,Returning_Visitor,FALSE,FALSE 0,0,0,0,36,1166.72619,0,0.025,4.599,0,Mar,1,1,1,3,Returning_Visitor,TRUE,TRUE 2,11.92307692,2,139,80,4099.737392,0.004117063,0.005279668,16.88069613,0,Mar,3,2,1,2,Returning_Visitor,TRUE,TRUE 1,0,0,0,4,31,0,0.05,0,0,Mar,2,2,2,10,Returning_Visitor,TRUE,FALSE 1,8,1,101,12,205.9761905,0.015384615,0.024358974,0,0,Mar,2,4,3,1,Returning_Visitor,FALSE,FALSE 1,7,7,141,25,1644,0.002150538,0.018817204,0,0,Mar,2,4,1,10,Returning_Visitor,FALSE,FALSE 2,102,0,0,4,268,0,0.013333333,0,0,Mar,2,2,1,3,Returning_Visitor,TRUE,FALSE 0,0,0,0,8,158.5,0,0.05,0,0,Mar,1,1,1,3,Returning_Visitor,TRUE,FALSE 1,7,0,0,11,298,0,0.006666667,0,0,Mar,3,2,3,3,Returning_Visitor,TRUE,FALSE 3,1499.571429,1,186,31,2905.943651,0,0.010215054,0,0,Mar,2,6,7,1,Returning_Visitor,FALSE,FALSE 0,0,0,0,41,419,0.01300813,0.03495935,0,0,Mar,2,2,1,3,Returning_Visitor,FALSE,FALSE 0,0,0,0,5,279,0,0.04,0,0,Mar,2,2,2,1,Returning_Visitor,FALSE,FALSE 1,0,0,0,43,1286.688889,0.004545455,0.030266123,4.289292857,0,Mar,3,2,1,6,Returning_Visitor,FALSE,FALSE 1,74,0,0,49,1616.083333,0.004,0.016,0,0,Mar,2,2,6,1,Returning_Visitor,FALSE,FALSE 2,1223,4,416,21,2755.67094,0.004545455,0.027575758,0,0,Mar,3,2,1,2,Returning_Visitor,TRUE,FALSE 0,0,0,0,26,655.7833333,0.003846154,0.013782051,0,0,Mar,1,1,8,3,Returning_Visitor,FALSE,FALSE 4,206.3333333,0,0,20,917.35,0,0.008333333,23.83323089,0,Mar,4,2,1,2,Returning_Visitor,TRUE,TRUE 3,67,0,0,12,353,0,0.015384615,0,0,Mar,2,2,3,2,New_Visitor,FALSE,FALSE 0,0,0,0,2,27,0,0.1,0,0,Mar,4,1,4,2,Returning_Visitor,FALSE,FALSE 0,0,0,0,2,0,0.2,0.2,0,0,Mar,1,1,1,1,Returning_Visitor,FALSE,FALSE 3,242,0,0,12,286,0,0.014285714,0,0,Mar,1,1,1,2,New_Visitor,TRUE,FALSE 0,0,0,0,6,835.8333333,0.033333333,0.066666667,0,0,Mar,1,1,1,1,Returning_Visitor,FALSE,FALSE 7,217.8333333,1,23.5,18,338.1272727,0,0.014393939,0,0,Mar,3,2,1,10,Returning_Visitor,FALSE,FALSE 0,0,0,0,2,22,0,0.1,0,0,Mar,2,2,1,1,Returning_Visitor,FALSE,FALSE 2,136.6666667,1,9.5,20,1572.571429,0,0.015873016,0,0,Mar,3,2,3,1,Returning_Visitor,FALSE,FALSE 12,739.2091954,1,16,21,1321.059195,0.009032258,0.025133361,78.34560177,0,Mar,2,2,1,8,Returning_Visitor,FALSE,TRUE 0,0,0,0,2,0,0.2,0.2,0,0,Mar,1,2,1,1,Returning_Visitor,FALSE,FALSE 0,0,0,0,2,187,0.1,0.15,0,0,Mar,3,2,1,3,Returning_Visitor,FALSE,FALSE 4,33,0,0,19,502.4583333,0,0.011904762,0,0,Mar,2,2,1,1,Returning_Visitor,FALSE,FALSE 0,0,0,0,1,0,0.2,0.2,0,0,Mar,1,1,8,1,Returning_Visitor,FALSE,FALSE 3,14,0,0,20,1424.928571,0.01,0.022222222,0,0,Mar,2,2,1,1,Returning_Visitor,FALSE,FALSE 4,68.5,2,845,82,2448.784448,0.000374532,0.008907697,6.506831398,0,Mar,1,1,2,2,Returning_Visitor,FALSE,TRUE 5,91.83333333,0,0,32,1480.666667,0,0.006857143,0,0,Mar,1,1,1,3,Returning_Visitor,TRUE,FALSE 1,31,0,0,6,25.5,0.066666667,0.083333333,0,0,Mar,3,2,3,1,Returning_Visitor,FALSE,FALSE 0,0,0,0,5,236,0,0.04,0,0,Mar,1,1,6,1,Returning_Visitor,FALSE,FALSE 2,58,0,0,50,952.4345238,0,0.011538462,2.179730769,0,Mar,4,2,2,11,Returning_Visitor,TRUE,TRUE 0,0,0,0,5,182,0,0.04,0,0,Mar,3,2,6,1,Returning_Visitor,FALSE,FALSE 0,0,0,0,24,854.3333333,0,0.008333333,0,0,Mar,4,1,1,10,Returning_Visitor,FALSE,FALSE 0,0,0,0,3,39,0,0.022222222,0,0,Mar,2,2,1,3,Returning_Visitor,FALSE,FALSE 0,0,0,0,15,1332.833333,0.014285714,0.005714286,0,0,Mar,2,5,4,2,Returning_Visitor,TRUE,FALSE 5,89.75,0,0,16,467.9166667,0.011111111,0.016666667,0,0,Mar,3,2,4,2,Returning_Visitor,TRUE,FALSE 1,9.5,0,0,25,674.25,0,0.017307692,0,0,Mar,2,2,7,2,Returning_Visitor,FALSE,FALSE 9,165.5,0,0,50,2207.391667,0.001724138,0.009972955,15.30003763,0,Mar,1,1,4,3,Returning_Visitor,TRUE,FALSE 0,0,0,0,10,300,0,0.05,0,0,Mar,2,2,1,1,Returning_Visitor,FALSE,FALSE 0,0,0,0,1,0,0.2,0.2,0,0,Mar,1,1,4,1,Returning_Visitor,FALSE,FALSE 0,0,0,0,6,177.5,0.033333333,0.044444444,0,0,Mar,2,2,8,1,Returning_Visitor,FALSE,FALSE 0,0,0,0,34,311.5,0,0.011764706,0,0,Mar,2,2,4,1,Returning_Visitor,FALSE,FALSE 3,57,0,0,15,486.5166667,0,0.011111111,0,0,Mar,2,2,5,6,Returning_Visitor,FALSE,FALSE 0,0,1,255,15,1418.25,0,0.00625,0,0,Mar,2,2,3,1,Returning_Visitor,FALSE,FALSE 1,13,0,0,10,391.85,0.018055556,0.046984127,0,0,Mar,3,2,4,3,Returning_Visitor,TRUE,FALSE 3,63,0,0,10,142.6666667,0,0.015384615,0,0,Mar,2,2,7,2,New_Visitor,TRUE,FALSE 3,134,0,0,53,1659.319697,0,0.00380117,62.34813701,0,Mar,4,1,1,2,Returning_Visitor,FALSE,TRUE 0,0,0,0,1,0,0.2,0.2,0,0,Mar,3,2,3,1,Returning_Visitor,TRUE,FALSE 0,0,0,0,1,0,0.2,0.2,0,0,Mar,2,2,1,1,Returning_Visitor,FALSE,FALSE 0,0,0,0,59,1560.601163,0.005172414,0.010673235,0,0,Mar,1,1,1,2,Returning_Visitor,FALSE,FALSE 2,34,0,0,65,1451.227362,0.003030303,0.008492896,0,0,Mar,2,2,1,1,Returning_Visitor,FALSE,FALSE 4,67.07142857,0,0,21,2588.958333,0,0.011041667,0,0,Mar,3,2,2,8,New_Visitor,TRUE,FALSE 0,0,0,0,1,0,0.2,0.2,0,0,Mar,2,2,1,1,Returning_Visitor,FALSE,FALSE 0,0,0,0,3,80,0,0.033333333,0,0,Mar,3,2,1,10,Returning_Visitor,TRUE,FALSE 0,0,0,0,1,0,0.2,0.2,0,0,Mar,2,2,9,2,New_Visitor,TRUE,FALSE 8,87.5,0,0,31,496.5833333,0.005405405,0.012612613,3.889459459,0,Mar,2,2,7,2,Returning_Visitor,FALSE,TRUE 1,22,0,0,21,678.2738095,0,0.012878788,0,0,Mar,3,2,1,2,New_Visitor,FALSE,FALSE 0,0,0,0,4,55,0,0.05,0,0,Mar,2,2,1,1,Returning_Visitor,FALSE,FALSE 0,0,0,0,4,34,0.1,0.15,0,0,Mar,3,2,6,1,Returning_Visitor,FALSE,FALSE 1,17.5,0,0,8,163,0,0.011111111,0,0,Mar,3,2,1,2,New_Visitor,TRUE,FALSE 0,0,0,0,7,286.5,0,0.042857143,0,0,Mar,3,3,3,1,Returning_Visitor,FALSE,FALSE 0,0,0,0,20,1395.258333,0,0.011111111,0,0,Mar,1,1,1,9,New_Visitor,TRUE,FALSE 1,14,0,0,10,90,0,0.018181818,0,0,Mar,2,2,2,10,New_Visitor,FALSE,FALSE 5,69.33333333,1,0,5,142,0,0.022222222,0,0,Mar,2,2,3,2,New_Visitor,FALSE,FALSE 2,148,0,0,9,416.6666667,0,0.004545455,0,0,Mar,3,2,6,2,New_Visitor,TRUE,FALSE 1,85,4,208.5,12,413.8,0,0.003921569,0,0,Mar,1,1,1,2,Returning_Visitor,FALSE,FALSE 0,0,0,0,2,403,0.1,0.15,0,0,Mar,2,10,3,3,Returning_Visitor,FALSE,FALSE 0,0,0,0,6,106,0.1,0.116666667,0,0,Mar,3,2,1,1,Returning_Visitor,TRUE,FALSE 4,103,1,41,16,515,0,0.010526316,0,0,Mar,3,2,3,6,Returning_Visitor,TRUE,FALSE 0,0,0,0,26,392,0.007692308,0.030128205,0,0,Mar,2,2,4,1,Returning_Visitor,FALSE,FALSE 0,0,0,0,1,0,0.2,0.2,0,0,Mar,2,4,3,1,Returning_Visitor,FALSE,FALSE 2,33,0,0,25,1720,0,0.007692308,60.29018448,0,Mar,2,5,3,1,Returning_Visitor,FALSE,TRUE 1,69.5,0,0,8,226.5,0,0.0125,0,0,Mar,3,2,1,13,Returning_Visitor,FALSE,FALSE 0,0,0,0,10,130.1666667,0.055555556,0.083333333,26.66133333,0,Mar,3,2,1,8,Returning_Visitor,FALSE,FALSE 7,77.5,0,0,14,192.9333333,0,0.013333333,0,0,Mar,2,2,1,1,Returning_Visitor,FALSE,FALSE 4,171,0,0,20,745.3333333,0.00952381,0.028571429,0,0,Mar,2,2,1,10,Returning_Visitor,FALSE,FALSE 2,94,0,0,5,97,0,0.033333333,0,0,Mar,3,3,1,10,Returning_Visitor,FALSE,FALSE 0,0,0,0,4,233,0.05,0.075,0,0,Mar,2,4,2,1,Returning_Visitor,FALSE,FALSE 7,64.14285714,1,0,20,1409.809524,0.008695652,0.035507246,0,0,Mar,3,2,3,2,Returning_Visitor,FALSE,FALSE 0,0,0,0,12,448.5,0,0.033333333,0,0,Mar,2,2,1,2,Returning_Visitor,FALSE,FALSE 0,0,0,0,6,73.75,0.033333333,0.044444444,0,0,Mar,3,2,3,2,Returning_Visitor,FALSE,FALSE 4,143,1,20.5,10,386.15,0,0.015384615,64.6,0,Mar,1,1,7,8,Returning_Visitor,FALSE,TRUE 1,26,0,0,16,638.8333333,0,0.0125,20.26155684,0,Mar,2,4,2,2,New_Visitor,FALSE,FALSE 3,221,0,0,17,489.5833333,0,0.011764706,0,0,Mar,1,1,1,3,New_Visitor,TRUE,FALSE 1,10,0,0,23,660.8166667,0,0.025,0,0,Mar,2,4,1,3,Returning_Visitor,TRUE,FALSE 4,118,3,67,44,1489.144444,0.004081633,0.014693878,0,0,Mar,3,2,6,1,Returning_Visitor,TRUE,FALSE 1,0,0,0,20,2270.833333,0,0.023809524,10.23187302,0,Mar,2,2,3,6,Returning_Visitor,FALSE,TRUE 1,38,0,0,12,121,0.028571429,0.025,0,0,Mar,3,2,4,2,Returning_Visitor,FALSE,FALSE 0,0,0,0,15,296,0,0.04,0,0,Mar,2,5,2,3,Returning_Visitor,FALSE,FALSE 3,124,0,0,0,0,0,0.033333333,0,0,Mar,3,2,6,3,Returning_Visitor,FALSE,FALSE 0,0,0,0,12,481.8333333,0,0.018181818,74.088,0,Mar,1,1,1,2,New_Visitor,FALSE,TRUE 2,117,0,0,13,309.9333333,0,0.013333333,0,0,Mar,2,2,2,2,New_Visitor,FALSE,FALSE 0,0,0,0,7,55,0.057142857,0.114285714,0,0,Mar,2,2,1,1,Returning_Visitor,FALSE,FALSE 2,75,0,0,14,851,0,0.014285714,22.88908649,0,Mar,1,1,1,2,New_Visitor,TRUE,TRUE 0,0,0,0,1,0,0.2,0.2,0,0,Mar,3,2,3,1,Returning_Visitor,FALSE,FALSE 0,0,0,0,3,55,0.066666667,0.1,0,0,Mar,3,2,3,3,Returning_Visitor,FALSE,FALSE 0,0,0,0,7,88.33333333,0,0.042857143,0,0,Mar,2,2,1,1,Returning_Visitor,FALSE,FALSE 2,94,0,0,25,893.1,0,0.009615385,0,0,Mar,2,4,4,1,Returning_Visitor,FALSE,FALSE 3,219,0,0,12,526.3333333,0.022222222,0.058571429,0,0,Mar,1,1,3,1,Returning_Visitor,FALSE,FALSE 2,45,0,0,26,535.3459596,0.007407407,0.01051332,0,0,Mar,2,4,3,10,Returning_Visitor,FALSE,FALSE 3,60.83333333,0,0,18,884.6333333,0.021052632,0.041140351,0,0,Mar,2,2,3,10,Returning_Visitor,FALSE,FALSE 0,0,0,0,2,24,0,0.05,0,0,Mar,3,2,3,3,Returning_Visitor,FALSE,FALSE 0,0,3,100,22,2175.993651,0.01,0.049312469,0,0,Mar,2,2,3,1,Returning_Visitor,FALSE,FALSE 2,14,0,0,8,57,0,0.057142857,0,0,Mar,1,2,4,8,New_Visitor,FALSE,FALSE 0,0,0,0,28,1028.116667,0,0.008928571,0,0,Mar,2,4,1,2,Returning_Visitor,FALSE,FALSE 0,0,0,0,4,93,0,0.1,0,0,Mar,2,2,9,6,Returning_Visitor,FALSE,FALSE 8,182.6480519,6,291.5,35,1373.464719,0.001550388,0.018148931,14.322,0,Mar,2,2,6,2,Returning_Visitor,FALSE,FALSE 8,154.2916667,0,0,34,991.3678571,0.005263158,0.01443609,10.93482456,0,Mar,2,2,2,2,Returning_Visitor,FALSE,FALSE 0,0,0,0,1,1,0,0.1,0,0,Mar,2,2,2,1,Returning_Visitor,FALSE,FALSE 0,0,0,0,7,147.25,0.028571429,0.045714286,0,0,Mar,1,1,6,3,Returning_Visitor,FALSE,FALSE 0,0,0,0,2,0,0.2,0.2,0,0,Mar,3,2,4,1,Returning_Visitor,FALSE,FALSE 0,0,0,0,1,0,0.2,0.2,0,0,Mar,2,2,1,1,Returning_Visitor,FALSE,FALSE 0,0,0,0,9,1225.5,0.022222222,0.044444444,0,0,Mar,1,2,9,1,Returning_Visitor,FALSE,FALSE 0,0,0,0,3,183,0,0.066666667,0,0,Mar,1,1,1,1,Returning_Visitor,TRUE,FALSE 0,0,0,0,10,250.8,0,0.01,0,0,Mar,2,2,4,3,Returning_Visitor,FALSE,FALSE 2,17,1,105,9,148.25,0.02,0.022,0,0,Mar,3,2,1,1,Returning_Visitor,FALSE,FALSE 2,32,1,70,16,504.0833333,0.005714286,0.019047619,9.543060317,0,Mar,2,2,3,8,Returning_Visitor,FALSE,FALSE 0,0,0,0,43,1650.416667,0,0.003875969,0,0,Mar,2,2,7,3,Returning_Visitor,TRUE,FALSE 0,0,1,0,28,2743.216667,0.020689655,0.042528736,0,0,Mar,2,5,2,1,Returning_Visitor,FALSE,FALSE 5,108.2,0,0,37,1288.533333,0.005263158,0.028070175,0,0,Mar,2,2,1,1,Returning_Visitor,FALSE,FALSE 0,0,0,0,15,447.5,0.03,0.065333333,0,0,Mar,3,2,4,1,Returning_Visitor,TRUE,FALSE 10,1559.75,1,7,41,1663.761905,0.004347826,0.015942029,13.63463318,0,Mar,1,1,1,2,Returning_Visitor,FALSE,TRUE 5,249.1666667,0,0,22,1165.583333,0,0.001242236,0,0,Mar,2,2,8,2,New_Visitor,FALSE,FALSE 2,37,0,0,3,17,0,0.05,0,0,Mar,3,2,1,2,New_Visitor,TRUE,FALSE 1,0,1,60.5,46,1038.916667,0.021276596,0.039007092,13.10326241,0,Mar,2,2,6,1,Returning_Visitor,FALSE,TRUE 0,0,0,0,12,249.5,0.005555556,0.025,0,0,Mar,2,7,7,1,Returning_Visitor,FALSE,FALSE 5,65,2,34,17,1302.5,0,0.016666667,0,0,Mar,1,1,1,10,Returning_Visitor,FALSE,FALSE 0,0,0,0,10,172.5,0.01,0.043333333,0,0,Mar,2,2,3,6,Returning_Visitor,FALSE,FALSE 6,143.1666667,0,0,50,925.947619,0,0.010160428,13.60512012,0,Mar,2,2,1,2,Returning_Visitor,FALSE,TRUE 4,66.33333333,2,58.16666667,38,1524.702381,0,0.006628788,0,0,Mar,2,2,3,2,New_Visitor,TRUE,FALSE 4,14,0,0,13,288,0.0125,0.0375,0,0,Mar,1,1,4,2,Returning_Visitor,TRUE,FALSE 0,0,0,0,1,0,0.2,0.2,0,0,Mar,2,2,1,1,Returning_Visitor,TRUE,FALSE 0,0,2,31,3,47,0.04,0.08,0,0,Mar,1,1,6,1,Returning_Visitor,TRUE,FALSE 0,0,0,0,1,0,0.2,0.2,0,0,Mar,2,2,3,1,Returning_Visitor,FALSE,FALSE 0,0,0,0,4,63,0,0.05,0,0,Mar,1,1,2,3,Returning_Visitor,FALSE,FALSE 0,0,0,0,13,1476.3,0.007692308,0.012820513,0,0,Mar,2,2,3,8,Returning_Visitor,FALSE,FALSE 0,0,0,0,9,685,0,0.022222222,0,0,Mar,2,2,7,1,Returning_Visitor,FALSE,FALSE 5,102.5333333,0,0,74,2396.690316,0.002699055,0.020349845,15.03328131,0,Mar,2,5,6,1,Returning_Visitor,FALSE,FALSE 0,0,0,0,7,451.8333333,0.028571429,0.076190476,0,0,Mar,3,2,1,3,Returning_Visitor,TRUE,FALSE 0,0,0,0,25,1512.52381,0,0.024,0,0,Mar,2,2,3,11,Returning_Visitor,FALSE,FALSE 1,85,0,0,14,306.5,0,0.004444444,0,0,Mar,2,2,3,2,Returning_Visitor,FALSE,FALSE 0,0,0,0,5,62,0.12,0.14,0,0,Mar,3,2,3,1,Returning_Visitor,FALSE,FALSE 6,197.8,0,0,48,3332.293511,0,0.007847222,11.45469444,0,Mar,1,1,1,3,Returning_Visitor,FALSE,FALSE 1,63,1,0,22,2815.416667,0,0.0125,0,0,Mar,2,2,3,2,Returning_Visitor,FALSE,FALSE 3,31.5,0,0,13,956.5,0,0.04,0,0,Mar,2,2,1,13,Returning_Visitor,FALSE,FALSE 7,81.25,0,0,37,1500.422543,0.014634146,0.015338753,39.03580426,0,Mar,2,4,1,2,Returning_Visitor,FALSE,FALSE 0,0,0,0,20,2271,0,0.025,0,0,Mar,2,2,1,1,Returning_Visitor,FALSE,FALSE 2,120,0,0,8,588.1666667,0,0.007407407,0,0,Mar,1,1,1,2,Returning_Visitor,FALSE,FALSE 4,149.25,0,0,27,1212.809524,0,0.020967742,14.89210837,0,Mar,2,2,7,2,Returning_Visitor,FALSE,TRUE 5,172.2,0,0,23,792.7222222,0,0.010714286,261.4912857,0,Mar,2,2,1,8,New_Visitor,FALSE,TRUE 5,523.9166667,2,21,25,439.5238095,0,0.023333333,0,0,Mar,2,2,7,2,Returning_Visitor,FALSE,FALSE 0,0,0,0,2,5,0,0.1,0,0,Mar,2,2,1,2,Returning_Visitor,FALSE,FALSE 1,19,0,0,22,525.2666667,0,0.007709751,0,0,Mar,3,2,7,1,Returning_Visitor,FALSE,FALSE 11,377.8333333,0,0,40,1337.842794,0.008695652,0.022304507,0,0,Mar,3,2,3,3,Returning_Visitor,TRUE,FALSE 4,129.4166667,3,62,43,1770.625,0.004255319,0.015153664,0,0,Mar,2,5,6,2,Returning_Visitor,FALSE,FALSE 9,298.6666667,0,0,27,1084.5,0,0.010784314,22.96411765,0,Mar,2,2,1,8,New_Visitor,FALSE,FALSE 0,0,0,0,1,0,0.2,0.2,0,0,Mar,2,2,7,1,Returning_Visitor,TRUE,FALSE 0,0,0,0,93,4261.842857,0.002930403,0.021080839,0,0,Mar,3,3,8,1,Returning_Visitor,FALSE,FALSE 3,59,0,0,29,527.2083333,0,0.011330049,0,0,Mar,2,4,1,6,Returning_Visitor,FALSE,FALSE 0,0,0,0,4,0,0.2,0.2,0,0,Mar,2,2,3,1,Returning_Visitor,FALSE,FALSE 0,0,0,0,7,110,0.057142857,0.142857143,0,0,Mar,3,2,8,1,Returning_Visitor,FALSE,FALSE 0,0,0,0,15,1733.666667,0,0.026666667,0,0,Mar,2,2,7,2,Returning_Visitor,FALSE,FALSE 1,5,0,0,12,306,0,0.02,0,0,Mar,2,2,6,8,Returning_Visitor,FALSE,FALSE 0,0,0,0,2,62,0,0.05,0,0,Mar,1,1,2,10,Returning_Visitor,FALSE,FALSE 0,0,0,0,2,5,0,0.1,0,0,Mar,2,2,3,1,Returning_Visitor,FALSE,FALSE 0,0,0,0,14,198.5221501,0.001230769,0.06797235,0,0,Mar,1,1,9,1,Returning_Visitor,FALSE,FALSE 0,0,0,0,17,504,0,0.02125,33.32796813,0,Mar,2,2,3,1,Returning_Visitor,FALSE,FALSE 24,876.2333333,5,625.5,34,1588.666667,0,0.013800705,0,0,Mar,2,2,5,2,Returning_Visitor,FALSE,FALSE 1,95.5,0,0,7,381.5,0,0.025,0,0,Mar,1,1,3,2,Returning_Visitor,TRUE,FALSE 6,129.75,0,0,42,1173.07,0,0.005319149,18.95323404,0,Mar,2,2,3,8,New_Visitor,TRUE,TRUE 6,408.2,0,0,33,878.6238095,0,0.02045045,19.3654955,0,Mar,2,2,1,2,Returning_Visitor,FALSE,TRUE 0,0,1,0,35,1928,0.008333333,0.023611111,0,0,Mar,2,5,8,1,Returning_Visitor,TRUE,FALSE 0,0,0,0,5,83,0.04,0.06,0,0,Mar,2,2,1,13,Returning_Visitor,FALSE,FALSE 0,0,0,0,1,0,0.2,0.2,0,0,Mar,1,1,1,3,Returning_Visitor,TRUE,FALSE 2,24.42857143,7,278.1666667,76,2735.728571,0.002439024,0.024843206,8.631719512,0,Mar,2,5,2,7,Returning_Visitor,FALSE,TRUE 4,52.33333333,1,239,14,337.5,0,0.011764706,0,0,Mar,2,5,1,2,Returning_Visitor,FALSE,FALSE 0,0,0,0,1,0,0.2,0.2,0,0,Mar,3,2,1,3,Returning_Visitor,FALSE,FALSE 10,382.8333333,4,319.3,72,4163.797619,0.000297619,0.010912698,12.0921619,0,Mar,2,2,6,2,Returning_Visitor,FALSE,TRUE 0,0,1,65,15,1728.321429,0.014285714,0.016428571,0,0,Mar,1,1,6,1,Returning_Visitor,FALSE,FALSE 0,0,0,0,6,255,0,0.008333333,0,0,Mar,1,2,8,2,Returning_Visitor,TRUE,FALSE 0,0,0,0,13,449.6,0,0.015384615,0,0,Mar,3,2,1,10,Returning_Visitor,FALSE,FALSE 1,27.5,0,0,11,243.6666667,0,0.018181818,0,0,Mar,3,2,1,3,Returning_Visitor,FALSE,FALSE 6,475,0,0,33,2760.716667,0.038596491,0.056282051,0,0,Mar,4,2,1,13,Returning_Visitor,FALSE,FALSE 2,6,0,0,42,1503.25,0.001550388,0.031007752,0,0,Mar,2,2,6,1,Returning_Visitor,FALSE,FALSE 2,210,0,0,10,591.1666667,0.02,0.016,0,0,Mar,3,2,1,2,Returning_Visitor,FALSE,FALSE 2,31.5,0,0,12,402.5,0,0.0025,0,0,Mar,2,2,7,1,Returning_Visitor,FALSE,FALSE 5,46,1,17,33,1540.583333,0,0.01547619,10.84440741,0,Mar,2,2,2,2,Returning_Visitor,TRUE,FALSE 9,546,0,0,28,1561.083333,0.01,0.03,3.810575284,0,Mar,2,2,7,10,Returning_Visitor,FALSE,TRUE 1,47.5,0,0,15,1330.3,0,0.005128205,0,0,Mar,2,4,2,10,Returning_Visitor,FALSE,FALSE 0,0,0,0,2,42,0,0.05,0,0,Mar,2,2,6,3,Returning_Visitor,FALSE,FALSE 0,0,0,0,5,360.75,0,0.04,0,0,Mar,2,4,3,1,Returning_Visitor,FALSE,FALSE 3,48.33333333,0,0,15,245.5,0.014285714,0.028571429,0,0,Mar,3,2,3,1,Returning_Visitor,FALSE,FALSE 0,0,0,0,4,7,0,0.05,0,0,Mar,2,2,1,1,Returning_Visitor,FALSE,FALSE 3,28.5,0,0,15,608.4166667,0,0.035294118,0,0,Mar,2,5,1,3,Returning_Visitor,FALSE,FALSE 0,0,0,0,18,708.25,0,0.045098039,0,0,Mar,1,1,1,3,Returning_Visitor,FALSE,FALSE 1,30,0,0,7,157.5,0,0.025,0,0,Mar,2,2,3,3,New_Visitor,FALSE,FALSE 2,16,0,0,33,1123.242857,0.006060606,0.021969697,11.12342124,0,Mar,2,2,3,13,Returning_Visitor,FALSE,FALSE 0,0,0,0,19,926.6333333,0.011111111,0.02962963,0,0,Mar,2,2,2,1,Returning_Visitor,TRUE,FALSE 0,0,0,0,39,2808.666667,0,0.01025641,0,0,Mar,2,2,1,10,Returning_Visitor,FALSE,FALSE 0,0,0,0,26,1685.266667,0.007692308,0.034615385,0,0,Mar,2,2,6,3,Returning_Visitor,TRUE,FALSE 1,10,1,0,69,2124.726768,0.002898551,0.009090177,0,0,Mar,2,2,2,1,Returning_Visitor,FALSE,FALSE 3,79,0,0,38,1272.916667,0.004878049,0.020139373,9.401136585,0,Mar,2,4,2,2,Returning_Visitor,FALSE,FALSE 2,18,0,0,30,474.5,0,0.003225806,0,0,Mar,1,1,3,2,Returning_Visitor,TRUE,FALSE 7,250.3333333,0,0,7,180.3333333,0,0.02,0,0,Mar,2,2,1,2,New_Visitor,TRUE,FALSE 2,77,0,0,43,1607.297619,0.002272727,0.015909091,0,0,Mar,2,2,1,2,Returning_Visitor,FALSE,FALSE 2,12,0,0,8,60,0,0.02,0,0,Mar,2,2,3,2,New_Visitor,TRUE,FALSE 6,77.33333333,1,52.33333333,6,126.6666667,0,0.016666667,0,0,Mar,1,1,1,2,Returning_Visitor,FALSE,FALSE 0,0,0,0,7,865.2777778,0,0.029004329,0,0,Mar,1,1,1,10,Returning_Visitor,FALSE,FALSE 0,0,0,0,49,2612.357143,0,0.00952381,0,0,Mar,2,2,1,1,Returning_Visitor,FALSE,FALSE 0,0,0,0,22,850.2083333,0,0.016666667,0,0,Mar,1,1,1,2,Returning_Visitor,FALSE,FALSE 4,63.33333333,0,0,13,556.6666667,0,0.001666667,27.3063666,0,Mar,1,1,1,2,Returning_Visitor,FALSE,TRUE 0,0,0,0,9,186.7,0,0.022222222,0,0,Mar,2,2,1,2,Returning_Visitor,TRUE,FALSE 7,108.6666667,6,853.75,194,6737.271825,0.000123762,0.006138614,0,0,Mar,2,5,1,3,Returning_Visitor,TRUE,FALSE 0,0,0,0,24,1762.25,0.008333333,0.016666667,0,0,Mar,2,2,2,1,Returning_Visitor,FALSE,FALSE 7,230.1666667,3,222,140,8961.407671,0.00137931,0.012980564,0,0,Mar,2,5,2,1,Returning_Visitor,TRUE,FALSE 1,3,0,0,14,128,0,0.028571429,0,0,Mar,2,2,1,2,Returning_Visitor,FALSE,FALSE 0,0,0,0,8,213.5,0,0.05,0,0,Mar,2,5,1,1,Returning_Visitor,TRUE,FALSE 0,0,0,0,21,849.8833333,0.012698413,0.032063492,0,0,Mar,1,1,1,1,Returning_Visitor,FALSE,FALSE 0,0,0,0,1,0,0.2,0.2,0,0,Mar,2,2,2,3,Returning_Visitor,FALSE,FALSE 2,15,0,0,13,77,0.014285714,0.028571429,0,0,Mar,2,2,9,1,Returning_Visitor,FALSE,FALSE 0,0,0,0,2,73,0.05,0.15,0,0,Mar,2,2,1,1,Returning_Visitor,FALSE,FALSE 0,0,0,0,8,316,0,0.019047619,0,0,Mar,1,1,1,2,New_Visitor,TRUE,FALSE 1,18,4,123.5,24,4114.083333,0,0.003571429,20.91831644,0,Mar,1,1,1,2,New_Visitor,FALSE,TRUE 1,5.5,1,12,54,1470.966667,0.009090909,0.024545455,0,0,Mar,3,2,4,2,Returning_Visitor,FALSE,FALSE 0,0,0,0,7,451,0,0.033333333,49.82333333,0,Mar,2,2,4,1,New_Visitor,FALSE,TRUE 0,0,0,0,3,48,0,0.033333333,0,0,Mar,2,2,5,1,Returning_Visitor,TRUE,FALSE 0,0,0,0,3,189,0,0.066666667,0,0,Mar,3,2,8,1,Returning_Visitor,FALSE,FALSE 0,0,0,0,27,780.1666667,0,0.022222222,0,0,Mar,2,4,3,2,Returning_Visitor,FALSE,FALSE 3,79.75,0,0,3,73.5,0.04,0.077777778,0,0,Mar,3,2,1,2,Returning_Visitor,FALSE,FALSE 2,60,0,0,20,717.6166667,0,0.004761905,60.19477937,0,Mar,2,5,9,2,Returning_Visitor,FALSE,TRUE 11,2047.234848,9,1146.666667,45,3641.213151,0.002636535,0.014311471,7.753880158,0,Mar,2,2,1,2,Returning_Visitor,FALSE,TRUE 0,0,0,0,1,0,0.2,0.2,0,0,Mar,2,2,1,1,Returning_Visitor,FALSE,FALSE 3,14.5,0,0,25,1040.75,0,0.024266667,0,0,Mar,4,1,1,2,Returning_Visitor,TRUE,FALSE 1,11.42857143,0,0,13,833.2333333,0,0.005555556,0,0,Mar,2,2,3,2,Returning_Visitor,TRUE,FALSE 0,0,0,0,1,0,0.2,0.2,0,0,Mar,2,2,7,1,Returning_Visitor,FALSE,FALSE 3,32,0,0,20,453.625,0,0.001058201,0,0,Mar,2,2,1,2,New_Visitor,TRUE,FALSE 0,0,0,0,5,51.25,0,0.02,0,0,Mar,3,2,1,1,Returning_Visitor,FALSE,FALSE 11,348.6547619,1,11,64,1229.289286,0,0.002493853,54.95126905,0,Mar,2,2,1,2,New_Visitor,FALSE,FALSE 0,0,0,0,28,1202,0,0.014285714,0,0,Mar,2,2,1,10,Returning_Visitor,FALSE,FALSE 0,0,0,0,4,538,0,0.075,0,0,Mar,1,1,4,3,Returning_Visitor,TRUE,FALSE 7,187.8333333,0,0,36,1775.516667,0,0.0089599,4.745736842,0,Mar,1,1,1,10,Returning_Visitor,FALSE,TRUE 1,9,0,0,7,638,0.0375,0.045833333,0,0,Mar,1,1,1,3,Returning_Visitor,FALSE,FALSE 5,184.3333333,1,108,10,558.8,0,0.014285714,0,0,Mar,3,2,7,8,New_Visitor,FALSE,FALSE 0,0,0,0,3,190,0.033333333,0.1,0,0,Mar,2,2,4,1,Returning_Visitor,FALSE,FALSE 2,66,1,0,10,302,0.042857143,0.05952381,19.53495497,0,Mar,2,6,3,2,Returning_Visitor,FALSE,TRUE 0,0,0,0,5,164,0.04,0.04,0,0,Mar,3,2,1,1,Returning_Visitor,FALSE,FALSE 0,0,0,0,5,218,0,0.04,0,0,Mar,2,2,1,2,Returning_Visitor,FALSE,FALSE 2,124,0,0,13,495,0,0.028571429,0,0,Mar,2,4,5,1,Returning_Visitor,FALSE,FALSE 0,0,0,0,2,30,0,0.1,0,0,Mar,3,2,4,1,Returning_Visitor,FALSE,FALSE 2,69,2,92,37,796.9571429,0,0.0242741,3.978276423,0,Mar,2,2,7,2,Returning_Visitor,FALSE,TRUE 1,41,0,0,9,314.9,0,0.04,0,0,Mar,1,1,6,9,New_Visitor,TRUE,FALSE 2,14,0,0,49,789.9583333,0.004166667,0.015097222,0,0,Mar,3,2,7,1,Returning_Visitor,FALSE,FALSE 0,0,0,0,19,422.8333333,0.010526316,0.045614035,0,0,Mar,2,2,1,3,Returning_Visitor,FALSE,FALSE 0,0,0,0,7,362,0,0.028571429,0,0,Mar,2,2,1,1,Returning_Visitor,FALSE,FALSE 0,0,0,0,18,628.8333333,0,0.005555556,0,0,Mar,1,1,2,2,Returning_Visitor,TRUE,FALSE 1,24,1,239,11,839.5,0.016666667,0.05,0,0,Mar,1,1,4,2,Returning_Visitor,FALSE,FALSE 0,0,0,0,31,5764.366667,0.032258065,0.055591398,0,0,Mar,2,2,7,10,Returning_Visitor,FALSE,FALSE 0,0,0,0,3,161,0,0.066666667,0,0,Mar,2,2,1,1,Returning_Visitor,FALSE,FALSE 1,6,0,0,20,739.8333333,0.019047619,0.030952381,0,0,Mar,2,2,3,3,Returning_Visitor,TRUE,FALSE 0,0,0,0,5,66,0,0.053333333,0,0,Mar,1,2,3,3,Returning_Visitor,FALSE,FALSE 1,412,0,0,54,2377,0,0.005090909,0,0,Mar,2,2,9,10,Returning_Visitor,FALSE,FALSE 0,0,0,0,2,22,0,0.1,0,0,Mar,2,2,3,1,Returning_Visitor,FALSE,FALSE 0,0,0,0,9,381,0,0.025,0,0,Mar,2,2,6,2,New_Visitor,FALSE,FALSE 14,1130.502941,6,261.6333333,26,2559.898333,0.015789474,0.017422643,20.30438949,0,Mar,1,2,1,2,Returning_Visitor,FALSE,FALSE 0,0,0,0,23,553.15,0.034782609,0.069565217,0,0,Mar,2,2,3,1,Returning_Visitor,FALSE,FALSE 0,0,0,0,37,651,0.005405405,0.018918919,0,0,Mar,2,2,3,1,Returning_Visitor,FALSE,FALSE 0,0,0,0,1,0,0.2,0.2,0,0,Mar,2,2,7,1,Returning_Visitor,FALSE,FALSE 0,0,3,65,15,1957.616667,0,0.00625,0,0,Mar,1,1,1,1,Returning_Visitor,TRUE,FALSE 0,0,0,0,2,0,0.2,0.2,0,0,Mar,2,2,6,1,Returning_Visitor,FALSE,FALSE 5,132.4666667,0,0,59,1660.222222,0,0.011753515,6.475910157,0,Mar,3,2,2,1,Returning_Visitor,FALSE,FALSE 0,0,0,0,8,170,0.075,0.08,0,0,Mar,1,1,7,3,Returning_Visitor,TRUE,FALSE 0,0,0,0,4,47,0.1,0.125,0,0,Mar,2,2,1,3,Returning_Visitor,TRUE,FALSE 0,0,0,0,7,165,0.042857143,0.1,0,0,Mar,2,2,2,3,Returning_Visitor,FALSE,FALSE 1,4,0,0,0,0,0,0.066666667,0,0,Mar,4,1,8,6,Returning_Visitor,FALSE,FALSE 0,0,0,0,14,406.8666667,0,0.007142857,0,0,Mar,2,4,1,1,Returning_Visitor,FALSE,FALSE 4,82,0,0,23,570.5166667,0.007407407,0.024074074,0,0,Mar,1,2,7,8,Returning_Visitor,FALSE,FALSE 0,0,0,0,37,1138.5,0,0.013513514,0,0,Mar,2,2,1,8,Returning_Visitor,FALSE,FALSE 0,0,0,0,63,2777.616667,0,0.014086022,0,0,Mar,2,2,3,1,Returning_Visitor,FALSE,FALSE 5,73.5,0,0,26,463.7583333,0,0.001346801,0,0,Mar,2,2,3,2,New_Visitor,FALSE,FALSE 0,0,0,0,1,0,0.2,0.2,0,0,Mar,3,3,1,3,Returning_Visitor,TRUE,FALSE 4,25.5,0,0,28,775.5,0.006451613,0.027915633,24.13073118,0,Mar,2,2,4,2,Returning_Visitor,FALSE,FALSE 2,236,0,0,18,490.0666667,0,0.013333333,0,0,Mar,1,1,4,1,Returning_Visitor,FALSE,FALSE 0,0,0,0,14,266.0166667,0,0.042857143,0,0,Mar,3,2,1,1,Returning_Visitor,TRUE,FALSE 0,0,0,0,5,1306,0.06,0.12,0,0,Mar,2,2,5,13,Returning_Visitor,FALSE,FALSE 4,158.3333333,1,9,9,634.2,0.005555556,0.008095238,0,0,Mar,3,2,1,1,Returning_Visitor,TRUE,FALSE 0,0,0,0,1,0,0,0.2,0,0,Mar,2,2,1,1,Returning_Visitor,FALSE,FALSE 6,187,0,0,22,1220.433333,0.007692308,0.01025641,0,0,Mar,2,2,3,1,Returning_Visitor,FALSE,FALSE 8,206,3,132,39,1750.333333,0,0.008695652,0,0,Mar,2,2,1,1,Returning_Visitor,FALSE,FALSE 1,41,0,0,43,1087.25,0,0.013636364,0,0,Mar,4,2,1,1,Returning_Visitor,FALSE,FALSE 0,0,0,0,22,762.547619,0,0.017391304,0,0,Mar,2,2,1,3,Returning_Visitor,TRUE,FALSE 0,0,0,0,9,244.5,0,0.011111111,0,0,Mar,3,2,1,3,Returning_Visitor,FALSE,FALSE 0,0,0,0,10,1010,0.022222222,0.044444444,0,0,Mar,2,2,1,1,Returning_Visitor,FALSE,FALSE 0,0,0,0,2,32,0,0.1,0,0,Mar,2,4,3,3,Returning_Visitor,FALSE,FALSE 7,170,0,0,27,1342.666667,0.003333333,0.023333333,29.35629782,0,Mar,2,2,1,3,Returning_Visitor,FALSE,TRUE 2,9,1,201,33,1593.388889,0.011428571,0.031515152,0,0,Mar,2,2,1,1,Returning_Visitor,TRUE,FALSE 0,0,0,0,9,639,0,0.025,81.2475,0,Mar,1,1,2,9,Returning_Visitor,TRUE,TRUE 0,0,0,0,4,521,0.1,0.15,0,0,Mar,3,3,7,10,Returning_Visitor,FALSE,FALSE 1,87,3,40,17,546,0,0.004761905,0,0,Mar,3,6,1,1,Returning_Visitor,TRUE,FALSE 0,0,0,0,8,377.5,0.0125,0.025,0,0,Mar,3,2,4,11,Returning_Visitor,TRUE,FALSE 0,0,0,0,14,183.6666667,0,0.004761905,0,0,Mar,3,2,3,3,Returning_Visitor,FALSE,FALSE 0,0,0,0,5,84,0.04,0.06,0,0,Mar,2,2,2,1,Returning_Visitor,FALSE,FALSE 5,217.6666667,0,0,18,494.6666667,0,0.019931973,33.14514286,0,Mar,3,2,1,2,Returning_Visitor,FALSE,TRUE 8,699.1666667,0,0,62,1003.965152,0.009850746,0.039110006,0,0,Mar,2,4,3,1,Returning_Visitor,FALSE,FALSE 1,9,0,0,32,536.6833333,0,0.00058651,0,0,Mar,2,2,1,3,Returning_Visitor,FALSE,FALSE 8,227.5326923,3,292.6842105,66,3025.521713,0.015995116,0.025541275,7.371682608,0,Mar,3,2,3,6,Returning_Visitor,FALSE,TRUE 2,78,0,0,24,424,0,0.001190476,0,0,Mar,1,1,3,10,New_Visitor,FALSE,FALSE 0,0,0,0,5,171.5,0,0.02,0,0,Mar,1,1,1,2,Returning_Visitor,FALSE,FALSE 1,4.75,2,78,67,1231.136508,0.002985075,0.0110199,0,0,Mar,2,6,1,1,Returning_Visitor,FALSE,FALSE 6,101.3333333,0,0,10,266.5,0.028571429,0.028571429,0,0,Mar,2,2,4,1,Returning_Visitor,FALSE,FALSE 0,0,0,0,16,1533,0,0.00625,0,0,Mar,2,2,7,1,Returning_Visitor,FALSE,FALSE 1,7,2,562,36,1300.190476,0.010810811,0.038738739,0,0,Mar,2,2,1,1,Returning_Visitor,TRUE,FALSE 0,0,0,0,2,74,0.1,0.15,0,0,Mar,3,2,1,3,Returning_Visitor,FALSE,FALSE 3,14.5,2,907.8333333,28,804.2833333,0.014141414,0.044155844,7.945805543,0,Mar,2,5,9,2,Returning_Visitor,FALSE,FALSE 1,9,0,0,19,734.5833333,0.016666667,0.044444444,0,0,Mar,2,10,1,1,Returning_Visitor,FALSE,FALSE 2,60,0,0,44,3113.516667,0.001481481,0.016,0,0,Mar,2,2,3,2,Returning_Visitor,FALSE,FALSE 0,0,0,0,3,64,0,0.066666667,0,0,Mar,3,2,1,1,Returning_Visitor,TRUE,FALSE 1,3,0,0,7,114.8333333,0.014285714,0.007142857,0,0,Mar,3,2,1,10,Returning_Visitor,FALSE,FALSE 0,0,0,0,6,95.5,0,0.033333333,0,0,Mar,2,2,7,13,Returning_Visitor,TRUE,FALSE 0,0,0,0,7,93.66666667,0.028571429,0.04,0,0,Mar,1,1,1,3,Returning_Visitor,FALSE,FALSE 1,54,0,0,3,59,0,0.05,0,0,Mar,2,2,9,8,Returning_Visitor,FALSE,FALSE 0,0,0,0,3,26,0,0.05,0,0,Mar,3,2,1,1,Returning_Visitor,FALSE,FALSE 0,0,0,0,62,1136.60303,0,0.003517665,0,0,Mar,1,1,1,3,Returning_Visitor,TRUE,FALSE 6,190.2857143,6,609,47,2989.494048,0,0.004813218,30.897985,0,Mar,2,2,1,8,New_Visitor,TRUE,TRUE 2,15.66666667,0,0,30,1724.25,0.013793103,0.032413793,0,0,Mar,2,2,3,1,Returning_Visitor,TRUE,FALSE 8,183,4,571.5,14,333.6,0,0.01,15.43344,0,Mar,1,1,1,2,Returning_Visitor,TRUE,TRUE 1,14,0,0,21,334.7857143,0,0.023333333,0,0,Mar,2,2,1,1,Returning_Visitor,FALSE,FALSE 0,0,0,0,9,355.1666667,0,0.004166667,18.02533,0,Mar,2,4,1,2,Returning_Visitor,FALSE,TRUE 5,248.5,0,0,11,456.6666667,0,0.003571429,0,0,Mar,3,2,4,2,New_Visitor,FALSE,FALSE 0,0,0,0,15,1298.5,0.026666667,0.04,0,0,Mar,3,2,8,1,Returning_Visitor,FALSE,FALSE 1,25,0,0,28,2258.833333,0.007142857,0.046428571,0,0,Mar,2,2,3,3,Returning_Visitor,TRUE,FALSE 2,21,0,0,65,2881.133333,0,0.002985075,80.68062856,0,Mar,2,2,4,2,New_Visitor,FALSE,TRUE 0,0,0,0,10,206.5,0,0.02,0,0,Mar,2,2,1,1,Returning_Visitor,FALSE,FALSE 0,0,0,0,8,143,0,0.0125,0,0,Mar,2,2,1,1,Returning_Visitor,FALSE,FALSE 1,146,0,0,11,267.8333333,0.018181818,0.036363636,0,0,Mar,1,1,4,2,Returning_Visitor,TRUE,FALSE 0,0,1,57,5,454,0,0.05,0,0,Mar,2,2,3,6,Returning_Visitor,FALSE,FALSE 5,274.3333333,0,0,13,717.6,0,0.001960784,0,0,Mar,1,1,3,10,New_Visitor,FALSE,FALSE 14,1155.666667,0,0,20,1069.16829,0,0.013,0,0,Mar,2,5,1,3,Returning_Visitor,TRUE,FALSE 0,0,0,0,27,807.8333333,0.022222222,0.041975309,0,0,Mar,2,2,3,1,Returning_Visitor,FALSE,FALSE 0,0,0,0,15,477,0,0.02,0,0,Mar,2,2,2,1,Returning_Visitor,FALSE,FALSE 6,355.5,0,0,11,134.9,0.013333333,0.005333333,0,0,Mar,3,2,1,8,New_Visitor,TRUE,FALSE 3,35,0,0,16,314,0,0.052631579,0,0,Mar,2,5,7,2,Returning_Visitor,FALSE,FALSE 0,0,0,0,5,59,0.08,0.12,0,0,Mar,2,2,7,1,Returning_Visitor,FALSE,FALSE 6,99,1,1,23,637,0,0.002469136,0,0,Mar,2,5,9,8,New_Visitor,FALSE,FALSE 0,0,6,270.3333333,11,160.5,0,0.004761905,0,0,Mar,2,4,1,8,New_Visitor,FALSE,FALSE 0,0,0,0,8,1577,0,0.025,0,0,Mar,1,1,1,2,Returning_Visitor,FALSE,FALSE 2,21,0,0,11,660.1666667,0,0.016666667,0,0,Mar,2,2,1,3,Returning_Visitor,FALSE,FALSE 0,0,0,0,7,169,0,0.057142857,0,0,Mar,2,2,3,3,Returning_Visitor,TRUE,FALSE 0,0,0,0,6,89.33333333,0,0.066666667,0,0,Mar,2,4,3,3,Returning_Visitor,FALSE,FALSE 4,74,0,0,37,2517.048459,0.005128205,0.019615385,0,0,Mar,3,2,2,1,Returning_Visitor,TRUE,FALSE 0,0,1,0,1,44,0,0.1,0,0,Mar,2,2,6,2,Returning_Visitor,TRUE,FALSE 0,0,0,0,5,29,0,0.04,0,0,Mar,2,2,7,2,Returning_Visitor,FALSE,FALSE 0,0,0,0,2,86,0,0.1,0,0,Mar,2,4,3,1,Returning_Visitor,TRUE,FALSE 2,66,0,0,18,229.75,0.021052632,0.031578947,0,0,Mar,4,2,1,3,Returning_Visitor,FALSE,FALSE 0,0,0,0,4,17,0,0.075,0,0,Mar,2,2,1,1,Returning_Visitor,FALSE,FALSE 1,24,0,0,17,498.3333333,0,0.021052632,76.78667659,0,Mar,2,2,6,10,Returning_Visitor,FALSE,TRUE 0,0,0,0,1,0,0.2,0.2,0,0,Mar,2,2,2,1,Returning_Visitor,FALSE,FALSE 5,32,2,3,33,1039.766667,0,0.02027027,0,0,Mar,2,4,3,2,Returning_Visitor,TRUE,FALSE 3,78,0,0,11,369.1666667,0,0.023076923,0,0,Mar,3,2,3,1,Returning_Visitor,TRUE,FALSE 0,0,0,0,23,1669,0,0.017391304,0,0,Mar,2,4,7,2,Returning_Visitor,FALSE,FALSE 0,0,0,0,3,44.5,0,0.066666667,0,0,Mar,1,1,3,1,Returning_Visitor,FALSE,FALSE 0,0,0,0,6,140,0,0.033333333,0,0,Mar,3,2,1,1,Returning_Visitor,FALSE,FALSE 1,102.5,0,0,5,97,0,0.011111111,0,0,Mar,3,2,1,9,New_Visitor,TRUE,FALSE 4,103,0,0,13,250.1666667,0,0.030612245,0,0,Mar,3,2,1,11,Returning_Visitor,FALSE,FALSE 2,52,0,0,17,221.2,0,0.001754386,0,0,Mar,2,2,4,2,New_Visitor,FALSE,FALSE 0,0,0,0,12,127,0.016666667,0.041666667,0,0,Mar,2,2,9,1,Returning_Visitor,FALSE,FALSE 4,352,0,0,17,229.3333333,0.011111111,0.031481481,0,0,Mar,1,1,1,1,Returning_Visitor,FALSE,FALSE 0,0,0,0,7,370.8333333,0.028571429,0.057142857,0,0,Mar,2,2,2,3,Returning_Visitor,FALSE,FALSE 5,120,1,0,27,1583.596154,0,0.028282828,39.63270309,0,Mar,2,2,4,2,Returning_Visitor,FALSE,FALSE 9,162.05,0,0,27,399.6939394,0,0.009195402,0,0,Mar,1,1,7,2,New_Visitor,FALSE,FALSE 1,26,1,55,12,457,0.014285714,0.04047619,0,0,Mar,2,2,9,6,Returning_Visitor,TRUE,FALSE 0,0,0,0,3,24,0.066666667,0.133333333,0,0,Mar,3,2,3,1,Returning_Visitor,TRUE,FALSE 0,0,0,0,5,180.3333333,0.02,0.02,0,0,Mar,1,1,1,3,Returning_Visitor,TRUE,FALSE 0,0,0,0,15,581.4166667,0,0.007142857,0,0,Mar,2,5,1,2,Returning_Visitor,FALSE,FALSE 0,0,0,0,13,574.6666667,0,0.061538462,0,0,Mar,2,2,8,1,Returning_Visitor,FALSE,FALSE 0,0,0,0,5,56,0,0.08,0,0,Mar,2,2,1,1,Returning_Visitor,FALSE,FALSE 0,0,0,0,16,1011,0,0.025,0,0,Mar,2,2,1,1,Returning_Visitor,FALSE,FALSE 0,0,0,0,15,139.8333333,0.026666667,0.053333333,0,0,Mar,2,2,6,1,Returning_Visitor,FALSE,FALSE 1,6,0,0,16,608.8333333,0.011764706,0.026666667,52.70052142,0,Mar,3,2,1,11,Returning_Visitor,FALSE,TRUE 0,0,0,0,6,1251,0.033333333,0.066666667,0,0,Mar,3,2,1,1,Returning_Visitor,FALSE,FALSE 0,0,0,0,14,264.5,0,0.007142857,0,0,Mar,2,2,1,10,Returning_Visitor,FALSE,FALSE 6,45.83333333,2,284,54,1509.8,0,0.006779661,7.272067797,0,Mar,2,2,7,2,New_Visitor,FALSE,FALSE 0,0,0,0,22,356.8,0,0.016666667,0,0,Mar,2,2,2,8,Returning_Visitor,FALSE,FALSE 2,77.5,0,0,19,598.7833333,0,0.002631579,0,0,Mar,3,2,2,9,New_Visitor,TRUE,FALSE 0,0,0,0,4,298,0.1,0.15,0,0,Mar,2,2,1,13,Returning_Visitor,FALSE,FALSE 4,136,0,0,31,1141.833333,0,0.006060606,10.63449802,0,Mar,1,1,1,1,Returning_Visitor,FALSE,TRUE 0,0,0,0,6,93,0,0.033333333,0,0,Mar,1,2,8,2,Returning_Visitor,FALSE,FALSE 2,76,0,0,40,1209.802992,0.004878049,0.01624838,0,0,Mar,1,1,2,2,Returning_Visitor,FALSE,FALSE 0,0,0,0,1,0,0.2,0.2,0,0,Mar,3,2,2,10,Returning_Visitor,FALSE,FALSE 0,0,0,0,2,67,0,0.1,0,0,Mar,1,1,4,1,Returning_Visitor,FALSE,FALSE 3,275,1,29.5,30,1107.047619,0,0.017760943,35.98106601,0,Mar,3,2,3,1,Returning_Visitor,TRUE,TRUE 7,161,3,775.5,19,4996.666667,0.008,0.026,14.10053333,0,Mar,2,4,8,2,Returning_Visitor,FALSE,TRUE 3,206.6666667,0,0,15,936.1666667,0.01875,0.09375,0,0,Mar,2,4,3,1,Returning_Visitor,FALSE,FALSE 0,0,0,0,16,666.1,0,0.014285714,0,0,Mar,2,2,1,2,New_Visitor,FALSE,FALSE 2,28,0,0,6,170.8333333,0,0.007142857,0,0,Mar,3,2,7,1,Returning_Visitor,FALSE,FALSE 0,0,0,0,37,1118.647186,0,0.008148148,0,0,Mar,1,1,1,2,Returning_Visitor,TRUE,FALSE 0,0,0,0,2,86,0,0.05,0,0,Mar,1,1,1,9,Returning_Visitor,TRUE,FALSE 2,24,0,0,28,1206.614719,0,0.023222222,5.417644444,0,Mar,2,2,1,2,Returning_Visitor,FALSE,TRUE 5,238.375,0,0,24,2174.375,0.008928571,0.017460317,27.07791667,0,Mar,3,2,3,1,Returning_Visitor,FALSE,FALSE 0,0,0,0,2,30.5,0,0.05,0,0,Mar,3,2,4,10,Returning_Visitor,FALSE,FALSE 0,0,0,0,6,330,0.066666667,0.1,0,0,Mar,3,2,8,10,Returning_Visitor,FALSE,FALSE 0,0,0,0,13,437.25,0.069230769,0.102564103,0,0,Mar,1,1,3,3,Returning_Visitor,FALSE,FALSE 1,4,2,11,110,3211.827706,0.003571429,0.012916667,0,0,Mar,2,2,1,2,Returning_Visitor,FALSE,FALSE 0,0,0,0,59,3036.4,0,0.021186441,0,0,Mar,2,2,6,13,Returning_Visitor,TRUE,FALSE 0,0,0,0,9,640.6666667,0,0.011111111,0,0,Mar,3,2,7,1,Returning_Visitor,TRUE,FALSE 0,0,0,0,30,480.25,0.007777778,0.025,0,0,Mar,2,2,7,1,Returning_Visitor,FALSE,FALSE 7,213.3916667,0,0,32,1350.391667,0,0.017877551,0,0,Mar,2,2,1,8,Returning_Visitor,TRUE,FALSE 0,0,0,0,7,38,0,0.042857143,0,0,Mar,2,2,3,1,Returning_Visitor,FALSE,FALSE 0,0,0,0,12,976.2,0.016666667,0.041666667,0,0,Mar,2,2,7,1,Returning_Visitor,FALSE,FALSE 0,0,0,0,8,70.2,0.025,0.061111111,0,0,Mar,1,2,8,1,Returning_Visitor,FALSE,FALSE 7,156,0,0,60,2126.461941,0.00808768,0.017066636,0,0,Mar,3,2,4,2,Returning_Visitor,FALSE,FALSE 0,0,0,0,44,1112,0,0.006818182,0,0,Mar,4,1,4,3,Returning_Visitor,TRUE,FALSE 5,84.25,0,0,19,409.75,0,0.01038961,0,0,Mar,3,2,2,3,New_Visitor,FALSE,FALSE 0,0,0,0,2,171,0,0.1,0,0,Mar,3,2,2,2,Returning_Visitor,TRUE,FALSE 4,135,0,0,12,401.2857143,0,0.016,0,0,Mar,2,2,1,8,New_Visitor,FALSE,FALSE 0,0,0,0,10,423.8333333,0,0.01,0,0,Mar,2,2,5,2,Returning_Visitor,FALSE,FALSE 0,0,0,0,2,115,0,0.05,0,0,Mar,3,3,1,9,Returning_Visitor,TRUE,FALSE 3,43,0,0,10,427.1666667,0,0.015384615,0,0,Mar,3,2,9,3,Returning_Visitor,FALSE,FALSE 0,0,0,0,39,609.5,0,0.005128205,0,0,Mar,2,2,2,2,Returning_Visitor,TRUE,FALSE 0,0,0,0,12,547.3333333,0,0.033333333,0,0,Mar,2,2,1,3,Returning_Visitor,FALSE,FALSE 8,377.5128205,3,265,60,2062.890972,0.014009662,0.026580162,1.70506715,0,Mar,1,1,4,2,Returning_Visitor,FALSE,TRUE 6,137,0,0,49,1077.007576,0,0.015030864,0,0,Mar,3,2,1,2,Returning_Visitor,FALSE,FALSE 0,0,0,0,2,19,0,0.1,0,0,Mar,2,2,1,3,Returning_Visitor,FALSE,FALSE 0,0,0,0,20,1606.333333,0.01,0.034761905,0,0,Mar,2,2,2,1,Returning_Visitor,FALSE,FALSE 2,37,0,0,21,775.3333333,0,0.009090909,0,0,Mar,4,1,1,2,Returning_Visitor,TRUE,FALSE 0,0,0,0,13,973.5,0,0.046153846,0,0,Mar,2,2,1,1,Returning_Visitor,TRUE,FALSE 0,0,0,0,4,32,0,0.075,0,0,Mar,3,2,6,1,Returning_Visitor,FALSE,FALSE 0,0,0,0,24,727.5,0,0.016666667,0,0,Mar,2,2,3,1,Returning_Visitor,FALSE,FALSE 1,14,1,0,14,710.25,0,0.017777778,0,0,Mar,2,2,1,1,Returning_Visitor,FALSE,FALSE 0,0,0,0,48,3644.752381,0,0.016666667,18.20995652,0,Mar,2,2,7,2,Returning_Visitor,FALSE,FALSE 0,0,1,44,41,1089.75,0,0.005853659,0,0,Mar,2,2,3,2,New_Visitor,FALSE,FALSE 0,0,0,0,14,494,0,0.042857143,0,0,Mar,4,1,1,3,Returning_Visitor,FALSE,FALSE 6,155,0,0,84,2566.220887,0,0.007575758,16.56314545,0,Mar,2,2,7,2,Returning_Visitor,FALSE,FALSE 3,42.6,2,41.5,18,1049.516667,0.023809524,0.03968254,14.25648352,0,Mar,3,2,2,3,Returning_Visitor,TRUE,TRUE 0,0,0,0,1,0,0.2,0.2,0,0,Mar,3,2,1,1,Returning_Visitor,FALSE,FALSE 3,62,2,193,4,139,0,0.0125,0,0,Mar,3,2,1,10,Returning_Visitor,FALSE,FALSE 4,55.75,0,0,19,685.95,0,0.017724868,7.141428571,0,Mar,3,2,7,2,Returning_Visitor,TRUE,TRUE 0,0,0,0,8,758.1666667,0.025,0.05,0,0,Mar,1,1,1,1,Returning_Visitor,FALSE,FALSE 2,26,0,0,46,1852.75,0,0.005797101,0,0,Mar,2,2,2,3,Returning_Visitor,FALSE,FALSE 3,79,0,0,36,2517,0,0.005405405,27.23866195,0,Mar,2,10,2,2,Returning_Visitor,TRUE,TRUE 0,0,0,0,19,751.3333333,0,0.002777778,13.62630613,0,Mar,2,2,1,2,Returning_Visitor,TRUE,TRUE 8,429.3333333,1,56,35,1549.857143,0.005263158,0.019210526,0,0,Mar,2,2,1,2,Returning_Visitor,TRUE,FALSE 6,111,0,0,12,202.25,0.025,0.029166667,0,0,Mar,3,2,1,1,Returning_Visitor,TRUE,FALSE 0,0,0,0,29,669.9679487,0.013793103,0.017241379,0,0,Mar,1,2,6,2,Returning_Visitor,FALSE,FALSE 0,0,0,0,7,95,0.014285714,0.061904762,0,0,Mar,2,6,1,1,Returning_Visitor,FALSE,FALSE 1,40,5,279.5,62,1088.830952,0.000980392,0.007230392,11.48461765,0,Mar,3,2,1,9,Returning_Visitor,TRUE,TRUE 3,73,0,0,9,2151,0,0.018181818,10.91300022,0,Mar,2,5,1,1,Returning_Visitor,FALSE,TRUE 0,0,0,0,12,130.5,0,0.008333333,0,0,Mar,2,2,1,10,Returning_Visitor,FALSE,FALSE 4,40,2,146,56,2866.90202,0,0.010451977,85.7528587,0,Mar,2,2,2,2,Returning_Visitor,TRUE,FALSE 2,11,0,0,19,608.9428571,0.009090909,0.041915584,0,0,Mar,2,2,1,1,Returning_Visitor,FALSE,FALSE 0,0,0,0,7,360.75,0,0.033333333,0,0,Mar,1,1,1,2,New_Visitor,TRUE,FALSE 0,0,0,0,2,7,0,0.1,0,0,Mar,2,5,6,1,Returning_Visitor,FALSE,FALSE 6,55,1,7,132,4056.175369,0.005882353,0.019282953,0,0,Mar,2,2,8,2,Returning_Visitor,FALSE,FALSE 0,0,0,0,4,46,0,0.05,0,0,Mar,2,2,1,3,Returning_Visitor,FALSE,FALSE 0,0,0,0,1,0,0.2,0.2,0,0,Mar,2,2,2,10,Returning_Visitor,FALSE,FALSE 8,167,3,869,34,771.5916667,0.009090909,0.030876623,0,0,Mar,2,4,1,1,Returning_Visitor,FALSE,FALSE 0,0,0,0,5,112,0,0.02,0,0,Mar,3,2,1,2,Returning_Visitor,TRUE,FALSE 6,139,0,0,29,491.1,0,0.003333333,0,0,Mar,2,2,1,2,New_Visitor,TRUE,FALSE 0,0,0,0,2,14,0,0.1,0,0,Mar,2,2,6,3,Returning_Visitor,FALSE,FALSE 2,22,1,49,22,602.1666667,0,0.008695652,0,0,Mar,2,2,1,2,New_Visitor,TRUE,FALSE 2,134,0,0,7,77.33333333,0,0.022222222,0,0,Mar,1,1,2,2,Returning_Visitor,FALSE,FALSE 3,39,0,0,2,4,0,0.04,0,0,Mar,2,5,1,1,Returning_Visitor,FALSE,FALSE 0,0,2,90,9,103.6666667,0.018181818,0.061818182,0,0,Mar,3,2,1,1,Returning_Visitor,FALSE,FALSE 0,0,0,0,1,0,0.2,0.2,0,0,Mar,1,1,8,1,Returning_Visitor,FALSE,FALSE 2,110,0,0,4,125,0,0.016666667,0,0,Mar,3,2,2,1,Returning_Visitor,FALSE,FALSE 0,0,0,0,24,387.8846154,0.025,0.038095238,0,0,Mar,2,2,3,3,Returning_Visitor,FALSE,FALSE 2,43,0,0,3,23,0,0.04,0,0,Mar,2,4,9,2,Returning_Visitor,FALSE,FALSE 0,0,0,0,3,249,0,0.066666667,0,0,Mar,2,4,9,3,Returning_Visitor,FALSE,FALSE 4,485.3333333,2,18,19,345.3833333,0,0.005797101,0,0,Mar,3,2,3,8,New_Visitor,TRUE,FALSE 1,100,2,111,13,1001,0,0.013333333,0,0,Mar,2,2,1,1,Returning_Visitor,FALSE,FALSE 0,0,0,0,8,70.66666667,0.025,0.05,0,0,Mar,2,2,9,10,Returning_Visitor,FALSE,FALSE 4,70.5,0,0,20,1298,0,0.018666667,40.76128,0,Mar,2,2,3,2,New_Visitor,FALSE,FALSE 0,0,0,0,4,75.5,0,0.05,0,0,Mar,2,2,1,1,Returning_Visitor,TRUE,FALSE 0,0,0,0,2,57,0,0.1,0,0,Mar,3,2,1,1,Returning_Visitor,TRUE,FALSE 2,10,2,451.5,27,1686.966667,0.006451613,0.023502304,7.964649501,0,Mar,2,6,7,2,Returning_Visitor,FALSE,FALSE 3,160.5,0,0,10,370.5,0,0.016666667,0,0,Mar,1,1,7,10,Returning_Visitor,FALSE,FALSE 0,0,0,0,4,738,0.1,0.15,0,0,Mar,2,5,4,2,Returning_Visitor,TRUE,FALSE 0,0,0,0,13,89,0.046153846,0.1,0,0,Mar,2,2,2,1,Returning_Visitor,FALSE,FALSE 0,0,0,0,5,164.3333333,0.06,0.06,0,0,Mar,3,2,2,1,Returning_Visitor,FALSE,FALSE 0,0,1,0,24,2244.666667,0.008,0.028,0,0,Mar,2,2,1,1,Returning_Visitor,FALSE,FALSE 0,0,0,0,2,5,0,0.1,0,0,Mar,2,2,9,1,Returning_Visitor,FALSE,FALSE 5,1283,3,148.5,50,3246.977381,0,0.003846154,0,0,Mar,2,2,3,2,New_Visitor,FALSE,FALSE 7,63.5,5,100.25,62,1573.233333,0,0.004594595,0,0,Mar,2,2,6,3,Returning_Visitor,TRUE,FALSE 2,24.6,0,0,26,779.5051587,0.011111111,0.034403292,12.55220741,0,Mar,1,1,3,1,Returning_Visitor,TRUE,TRUE 3,22.83333333,0,0,115,2625.009524,0,0.004013317,0,0,Mar,2,5,6,1,Returning_Visitor,FALSE,FALSE 12,187.2777778,3,88.58333333,43,3266.127778,0.004242424,0.043712341,3.332333333,0,Mar,1,1,1,10,Returning_Visitor,FALSE,FALSE 2,98.25,0,0,10,303.25,0,0.011111111,0,0,Mar,3,2,2,2,New_Visitor,TRUE,FALSE 0,0,0,0,17,806.6666667,0,0.027058824,0,0,Mar,2,2,2,3,Returning_Visitor,FALSE,FALSE 3,23,0,0,30,465.4166667,0,0.009375,50.66498303,0,Mar,4,1,4,3,Returning_Visitor,FALSE,FALSE 0,0,0,0,7,1018,0,0.028571429,0,0,Mar,2,2,1,2,Returning_Visitor,FALSE,FALSE 0,0,0,0,7,261.5,0,0.00952381,0,0,Mar,2,2,4,1,Returning_Visitor,FALSE,FALSE 0,0,0,0,4,910,0,0.025,0,0,Mar,2,5,1,10,Returning_Visitor,FALSE,FALSE 2,89,3,119,41,4270.141667,0.004444444,0.027160494,0,0,Mar,2,2,4,1,Returning_Visitor,FALSE,FALSE 1,45,0,0,10,413,0,0.009090909,0,0,Mar,2,4,1,1,Returning_Visitor,TRUE,FALSE 3,31.6,0,0,17,372.8,0.013333333,0.026428571,2.3976,0,Mar,3,2,7,1,Returning_Visitor,FALSE,FALSE 0,0,0,0,18,550.5,0,0.031481481,0,0,Mar,1,1,1,2,Returning_Visitor,TRUE,FALSE 3,648,4,168.8333333,31,1300.728987,0.000555556,0.021511104,6.938235354,0,Mar,2,2,1,2,Returning_Visitor,FALSE,FALSE 0,0,0,0,4,44,0.05,0.1,0,0,Mar,2,2,9,10,Returning_Visitor,FALSE,FALSE 0,0,0,0,1,0,0.2,0.2,0,0,Mar,2,2,1,3,Returning_Visitor,FALSE,FALSE 3,18.75,0,0,7,68.75,0,0.025,0,0,Mar,2,2,5,2,New_Visitor,FALSE,FALSE 0,0,0,0,37,684.7272727,0.005405405,0.023423423,0,0,Mar,2,4,3,1,Returning_Visitor,FALSE,FALSE 2,28,2,175.5,27,1253.333333,0.006666667,0.014666667,39.7173,0,Mar,2,2,1,2,Returning_Visitor,FALSE,TRUE 0,0,0,0,4,331,0.05,0.1,0,0,Mar,2,2,1,1,Returning_Visitor,FALSE,FALSE 0,0,0,0,30,1791.7,0.013333333,0.031111111,0,0,Mar,2,4,1,3,Returning_Visitor,FALSE,FALSE 4,197,1,25,46,1596.672282,0,0.008,19.92584938,0,Mar,4,2,2,2,New_Visitor,TRUE,FALSE 7,211.5,0,0,54,9128.283883,0.011461988,0.039251601,0,0,Mar,2,2,6,1,Returning_Visitor,FALSE,FALSE 0,0,0,0,7,128,0,0.016666667,0,0,Mar,2,2,1,1,Returning_Visitor,FALSE,FALSE 0,0,0,0,13,313,0,0.015384615,0,0,Mar,1,1,1,3,Returning_Visitor,FALSE,FALSE 5,494.1666667,0,0,8,204,0,0.030769231,0,0,Mar,2,4,4,1,Returning_Visitor,FALSE,FALSE 0,0,0,0,2,49,0,0.1,0,0,Mar,2,2,1,3,Returning_Visitor,TRUE,FALSE 10,511.5,4,164,73,2490.138095,0,0.010365854,30.09281343,0,Mar,2,2,7,2,Returning_Visitor,FALSE,FALSE 3,58.06666667,0,0,5,86,0,0.0125,0,0,Mar,3,2,2,3,New_Visitor,FALSE,FALSE 0,0,0,0,9,140,0.022222222,0.055555556,0,0,Mar,2,2,6,1,Returning_Visitor,FALSE,FALSE 2,125.5,0,0,6,162.75,0,0.025,0,0,Mar,1,1,1,2,Returning_Visitor,FALSE,FALSE 8,387.6,3,32,328,9694.260714,0.001780415,0.010905045,6.467831696,0,Mar,2,2,3,1,Returning_Visitor,FALSE,FALSE 5,143.8333333,1,55,19,1762.5,0,0.013461538,31.54642738,0,Mar,3,2,1,2,Returning_Visitor,FALSE,TRUE 1,6,0,0,61,2183.5,0,0.003225806,0,0,Mar,2,2,7,9,Returning_Visitor,TRUE,FALSE 1,11.5,0,0,10,234.475,0,0.011111111,0,0,Mar,1,1,1,2,Returning_Visitor,TRUE,FALSE 0,0,0,0,10,139.7083333,0,0.02,0,0,Mar,2,2,1,2,Returning_Visitor,FALSE,FALSE 5,141,0,0,33,1371.633333,0,0.005555556,44.71103438,0,Mar,2,4,3,8,New_Visitor,TRUE,TRUE 2,135,1,2,11,711.5666667,0,0.00515873,0,0,Mar,3,2,1,2,Returning_Visitor,FALSE,FALSE 0,0,0,0,7,232.6666667,0,0.014285714,0,0,Mar,2,2,6,2,Returning_Visitor,FALSE,FALSE 0,0,0,0,3,51.5,0,0.066666667,0,0,Mar,3,2,1,10,Returning_Visitor,TRUE,FALSE 4,48,0,0,2,11,0,0.008,0,0,Mar,2,6,6,7,New_Visitor,FALSE,FALSE 8,57.0625,3,172,23,176.8193627,0.019444444,0.065189145,0,0,Mar,2,2,1,1,Returning_Visitor,FALSE,FALSE 0,0,0,0,1,0,0.2,0.2,0,0,Mar,1,1,1,9,Returning_Visitor,TRUE,FALSE 2,6,1,35,12,240,0.014285714,0.042857143,0,0,Mar,2,2,7,10,Returning_Visitor,FALSE,FALSE 0,0,0,0,9,415.8333333,0,0.0125,0,0,Mar,3,2,9,2,New_Visitor,FALSE,FALSE 2,7,0,0,3,66,0,0.066666667,0,0,Mar,2,2,1,2,Returning_Visitor,FALSE,FALSE 4,63,0,0,5,95.5,0,0.025,0,0,Mar,3,2,1,9,New_Visitor,TRUE,FALSE 0,0,0,0,11,200.85,0.036363636,0.047107438,0,0,Mar,1,1,2,1,Returning_Visitor,FALSE,FALSE 1,40,0,0,15,839.5,0.01875,0.054166667,0,0,Mar,2,2,4,1,Returning_Visitor,FALSE,FALSE 5,28,0,0,6,80,0,0.028571429,0,0,Mar,2,4,3,1,Returning_Visitor,FALSE,FALSE 1,12.83333333,3,77,31,2372.333333,0,0.012941176,10.32166068,0,Mar,2,2,1,2,Returning_Visitor,FALSE,FALSE 0,0,1,65.5,49,574.8333333,0,0.012,0,0,Mar,2,2,2,10,Returning_Visitor,FALSE,FALSE 4,179.6071429,2,5.5,34,1045.102381,0,0.007603276,0,0,Mar,1,1,1,3,Returning_Visitor,FALSE,FALSE 0,0,0,0,7,971,0.028571429,0.071428571,0,0,Mar,2,2,2,1,Returning_Visitor,FALSE,FALSE 6,135.65,0,0,40,1589.27381,0,0.020767196,0,0,Mar,4,1,3,1,Returning_Visitor,TRUE,FALSE 0,0,0,0,10,1656.666667,0.06,0.1,0,0,Mar,4,2,1,3,Returning_Visitor,FALSE,FALSE 4,62.5,1,64,56,1246.919048,0.003333333,0.005367521,0,0,Mar,3,2,1,3,Returning_Visitor,FALSE,FALSE 1,30,0,0,16,597.6,0,0.0125,0,0,Mar,1,1,3,2,Returning_Visitor,FALSE,FALSE 0,0,0,0,31,3888.166667,0.02,0.031111111,0,0,Mar,2,4,3,1,Returning_Visitor,FALSE,FALSE 2,21,0,0,34,2600.258333,0.011111111,0.037464387,9.938399233,0,Mar,1,1,1,3,Returning_Visitor,FALSE,FALSE 0,0,0,0,9,223,0,0.022222222,0,0,Mar,3,2,5,2,Returning_Visitor,FALSE,FALSE 4,56,0,0,10,709,0,0.016666667,0,0,Mar,2,2,1,2,New_Visitor,FALSE,FALSE 0,0,0,0,5,82,0.1,0.146666667,0,0,Mar,1,1,3,1,Returning_Visitor,FALSE,FALSE 0,0,0,0,23,760.25,0,0.009090909,0,0,Mar,2,2,7,2,New_Visitor,FALSE,FALSE 3,40,0,0,9,1362,0,0.008333333,0,0,Mar,2,2,7,6,Returning_Visitor,FALSE,FALSE 0,0,0,0,1,0,0.2,0.2,0,0,Mar,2,2,5,3,Returning_Visitor,TRUE,FALSE 3,36,1,62,19,924.3333333,0,0.004761905,0,0,Mar,2,2,1,2,New_Visitor,FALSE,FALSE 4,45.33333333,0,0,53,1440.298918,0.012727273,0.021974359,1.660118182,0,Mar,3,2,7,3,Returning_Visitor,TRUE,TRUE 3,39.6,0,0,53,856.7522398,0,0.002272727,0,0,Mar,2,2,1,2,Returning_Visitor,TRUE,FALSE 0,0,0,0,2,8,0,0.1,0,0,Mar,2,4,2,1,Returning_Visitor,FALSE,FALSE 2,35,0,0,2,6,0,0.05,0,0,Mar,2,2,9,2,New_Visitor,FALSE,FALSE 6,228,5,89.33333333,18,513.4666667,0,0.003461538,0,0,Mar,1,1,3,3,New_Visitor,FALSE,FALSE 0,0,0,0,2,0,0.2,0.2,0,0,Mar,3,2,6,1,Returning_Visitor,FALSE,FALSE 0,0,0,0,15,748.5666667,0.016,0.041333333,0,0,Mar,2,2,1,2,Returning_Visitor,FALSE,FALSE 1,34,2,9,3,149,0,0.05,0,0,Mar,2,2,1,1,Returning_Visitor,FALSE,FALSE 0,0,0,0,2,288,0,0.1,0,0,Mar,1,1,1,1,Returning_Visitor,TRUE,FALSE 9,309,0,0,25,383.0833333,0.018181818,0.037575758,0,0,Mar,2,2,3,1,Returning_Visitor,FALSE,FALSE 13,456.9880952,3,72.83333333,39,976.7103175,0.004166667,0.009722222,39.65538156,0,Mar,1,1,1,1,Returning_Visitor,TRUE,TRUE 0,0,0,0,32,1916.633333,0.015625,0.0265625,0,0,Mar,2,2,1,10,Returning_Visitor,FALSE,FALSE 5,78.5,1,26,48,1266.119048,0,0.005788982,29.01038502,0,Mar,2,2,1,8,New_Visitor,FALSE,FALSE 1,75,4,250.6666667,44,1679.833333,0,0.001086957,45.8293144,0,Mar,2,2,1,2,New_Visitor,TRUE,TRUE 4,99.5,0,0,16,315.1666667,0,0.016666667,12.53314286,0,Mar,1,1,6,3,Returning_Visitor,FALSE,TRUE 1,28,0,0,8,290,0,0.01,0,0,Mar,2,2,1,2,Returning_Visitor,FALSE,FALSE 0,0,0,0,6,463.5,0,0.033333333,0,0,Mar,2,5,1,3,Returning_Visitor,FALSE,FALSE 0,0,0,0,4,1833,0,0.05,0,0,Mar,2,5,2,10,Returning_Visitor,TRUE,FALSE 0,0,0,0,9,196,0,0.022222222,0,0,Mar,2,2,3,10,Returning_Visitor,FALSE,FALSE 0,0,0,0,2,0,0.2,0.2,0,0,Mar,2,2,1,13,Returning_Visitor,FALSE,FALSE 4,64,0,0,53,1200.892857,0.001818182,0.018030303,0,0,Mar,2,2,4,1,Returning_Visitor,TRUE,FALSE 2,37,0,0,7,139.3333333,0,0.011111111,0,0,Mar,3,2,7,8,New_Visitor,TRUE,FALSE 6,163.6666667,10,403,15,665.5666667,0.007142857,0.004761905,0,0,Mar,3,2,8,8,New_Visitor,FALSE,FALSE 5,333,2,166,12,151.9,0.031111111,0.064444444,0,0,Mar,2,2,1,1,Returning_Visitor,FALSE,FALSE 0,0,0,0,5,1620,0.14,0.166666667,0,0,Mar,2,2,1,1,Returning_Visitor,FALSE,FALSE 0,0,0,0,24,1268,0.0125,0.015277778,0,0,Mar,4,1,3,2,Returning_Visitor,TRUE,FALSE 1,51,2,117.2857143,15,969.102381,0.005882353,0.008888889,0,0,Mar,3,3,1,2,Returning_Visitor,FALSE,FALSE 1,44,0,0,25,773.4666667,0,0.029142857,10.3572,0,Mar,2,2,1,2,Returning_Visitor,TRUE,TRUE 5,825,0,0,33,808.1666667,0,0.005405405,20.85956757,0,Mar,2,4,9,10,New_Visitor,FALSE,TRUE 3,44,0,0,14,469.8761905,0,0.005357143,0,0,Mar,2,4,1,10,Returning_Visitor,FALSE,FALSE 4,203.8333333,0,0,30,823.5,0,0.001666667,0,0,Mar,3,2,1,11,New_Visitor,FALSE,FALSE 0,0,0,0,8,201,0,0.0625,0,0,Mar,2,4,2,1,Returning_Visitor,FALSE,FALSE 8,195.3166667,0,0,54,1063.95,0,0.019047619,54.24886418,0,Mar,2,2,1,2,Returning_Visitor,FALSE,FALSE 0,0,0,0,13,412,0,0.028205128,0,0,Mar,2,2,4,3,Returning_Visitor,FALSE,FALSE 0,0,0,0,1,0,0.2,0.2,0,0,Mar,3,2,1,1,Returning_Visitor,FALSE,FALSE 0,0,0,0,14,1789.39881,0,0.029047619,0,0,Mar,1,1,2,2,Returning_Visitor,FALSE,FALSE 0,0,0,0,18,644.125,0.00625,0.021924603,0,0,Mar,3,2,3,10,Returning_Visitor,FALSE,FALSE 2,9.5,0,0,18,529.702381,0,0.005263158,0,0,Mar,2,2,4,2,Returning_Visitor,TRUE,FALSE 0,0,0,0,10,254,0.02,0.04,14.0292,0,Mar,2,2,9,2,New_Visitor,FALSE,TRUE 0,0,0,0,7,221.7142857,0.028571429,0.060714286,0,0,Mar,3,3,1,3,Returning_Visitor,FALSE,FALSE 0,0,0,0,1,0,0.2,0.2,0,0,Mar,2,5,1,3,Returning_Visitor,FALSE,FALSE 0,0,0,0,30,312.3333333,0,0.008888889,0,0,Mar,2,6,1,2,Returning_Visitor,FALSE,FALSE 0,0,0,0,1,0,0.2,0.2,0,0,Mar,2,2,1,1,Returning_Visitor,FALSE,FALSE 0,0,0,0,22,325.75,0,0.031818182,0,0,Mar,2,10,7,2,Returning_Visitor,FALSE,FALSE 9,74.33333333,1,419,63,2717.327381,0.002857143,0.012857143,0,0,Mar,2,2,3,2,Returning_Visitor,TRUE,FALSE 1,7,1,0,79,3380.691558,0.002666667,0.023772222,0,0,Mar,2,2,3,2,Returning_Visitor,TRUE,FALSE 1,13,0,0,19,614.9444444,0,0.022105263,0,0,Mar,3,2,3,3,Returning_Visitor,FALSE,FALSE 1,0,0,0,1,19,0,0.1,0,0,Mar,1,1,3,1,Returning_Visitor,FALSE,FALSE 4,55.5,0,0,33,1274.190476,0.005714286,0.009285714,0,0,Mar,2,2,6,2,Returning_Visitor,TRUE,FALSE 2,80.66666667,0,0,12,1071.611111,0.030769231,0.046666667,0,0,Mar,2,2,2,1,Returning_Visitor,FALSE,FALSE 0,0,0,0,10,408,0,0.022222222,88.78460596,0,Mar,2,2,1,2,Returning_Visitor,FALSE,TRUE 7,156.7166667,0,0,52,2335.064286,0.007142857,0.010563841,0,0,Mar,3,2,1,2,Returning_Visitor,FALSE,FALSE 0,0,0,0,2,18,0.1,0.15,0,0,Mar,2,5,3,10,Returning_Visitor,FALSE,FALSE 3,172,0,0,53,1299.383333,0,0.003703704,0,0,Mar,2,6,7,2,Returning_Visitor,TRUE,FALSE 0,0,0,0,6,83,0.016666667,0.033333333,0,0,Mar,3,2,8,8,Returning_Visitor,FALSE,FALSE 5,146,0,0,8,489,0,0.015384615,0,0,Mar,2,2,8,3,Returning_Visitor,FALSE,FALSE 3,13,0,0,16,226.5,0,0.013333333,0,0,Mar,2,5,1,2,Returning_Visitor,FALSE,FALSE 0,0,0,0,8,233.6666667,0.025,0.0875,0,0,Mar,2,2,3,13,Returning_Visitor,FALSE,FALSE 0,0,0,0,1,0,0.2,0.2,0,0,Mar,2,2,1,1,Returning_Visitor,FALSE,FALSE 0,0,0,0,1,0,0.2,0.2,0,0,Mar,3,3,3,3,Returning_Visitor,FALSE,FALSE 1,11,0,0,5,71,0,0.033333333,0,0,Mar,2,2,6,3,Returning_Visitor,FALSE,FALSE 3,42,0,0,16,1266.5,0,0.003508772,0,0,Mar,2,2,6,1,Returning_Visitor,FALSE,FALSE 3,33,0,0,8,182.5833333,0,0.03125,0,0,Mar,2,4,6,3,Returning_Visitor,FALSE,FALSE 0,0,0,0,12,169.3,0,0.016666667,0,0,Mar,2,2,3,2,Returning_Visitor,TRUE,FALSE 0,0,1,0,37,1358.75,0.001754386,0.03245614,0,0,Mar,2,5,3,1,Returning_Visitor,FALSE,FALSE 1,38,0,0,5,131,0.033333333,0.013333333,0,0,Mar,1,2,1,9,New_Visitor,TRUE,FALSE 9,324.3,4,205,71,2816.43881,0,0.002666667,0,0,Mar,2,2,1,8,New_Visitor,TRUE,FALSE 5,413.5,0,0,25,3484.233333,0,0.02,0,0,Mar,2,4,1,3,Returning_Visitor,TRUE,FALSE 0,0,2,156,6,131,0,0.025,0,0,Mar,2,2,3,2,Returning_Visitor,FALSE,FALSE 5,55.5,0,0,12,1076.5,0,0.011764706,0,0,Mar,2,2,1,2,New_Visitor,FALSE,FALSE 0,0,0,0,8,357.25,0,0.0625,0,0,Mar,2,10,4,3,Returning_Visitor,TRUE,FALSE 0,0,0,0,6,197,0,0.011111111,0,0,Mar,2,2,1,2,Returning_Visitor,FALSE,FALSE 7,75.05,0,0,31,489.1452381,0.011111111,0.022222222,27.38224222,0,Mar,2,2,5,2,Returning_Visitor,TRUE,TRUE 3,43,0,0,11,610,0,0.015384615,0,0,Mar,2,4,1,1,Returning_Visitor,FALSE,FALSE 0,0,0,0,17,445,0.047058824,0.062745098,0,0,Mar,2,2,6,1,Returning_Visitor,FALSE,FALSE 0,0,0,0,17,588.6666667,0.011764706,0.035294118,0,0,Mar,2,4,4,1,Returning_Visitor,FALSE,FALSE 0,0,0,0,24,580.2,0,0.012121212,17.12545455,0,Mar,2,2,1,1,Returning_Visitor,FALSE,TRUE 0,0,0,0,32,999,0,0.01344086,9.053081561,0,Mar,1,1,1,1,Returning_Visitor,FALSE,TRUE 0,0,0,0,16,622,0,0.0125,0,0,Mar,2,10,2,6,Returning_Visitor,FALSE,FALSE 2,77,0,0,6,112,0,0.028571429,0,0,Mar,1,1,2,2,Returning_Visitor,TRUE,FALSE 1,29,1,13.5,24,1005.849206,0,0.007986111,0,0,Mar,1,1,3,3,Returning_Visitor,FALSE,FALSE 3,78,0,0,3,29,0.033333333,0.066666667,0,0,Mar,2,2,7,2,New_Visitor,TRUE,FALSE 3,52,0,0,60,3451.751515,0.006666667,0.011565934,0,0,Mar,3,2,3,1,Returning_Visitor,TRUE,FALSE 0,0,0,0,5,96.5,0,0.04,0,0,Mar,2,2,1,3,Returning_Visitor,FALSE,FALSE 8,354.8333333,1,57,19,2217.333333,0.008333333,0.017361111,0,0,Mar,3,2,1,1,Returning_Visitor,FALSE,FALSE 0,0,0,0,4,50,0,0.05,0,0,Mar,2,2,3,1,Returning_Visitor,FALSE,FALSE 0,0,1,0,16,485.6666667,0,0.0375,0,0,Mar,2,2,6,3,Returning_Visitor,TRUE,FALSE 0,0,0,0,9,388,0.066666667,0.088888889,0,0,Mar,3,2,2,1,Returning_Visitor,FALSE,FALSE 7,169.5,0,0,21,484.95,0.008695652,0.037681159,0,0,Mar,1,2,2,2,Returning_Visitor,FALSE,FALSE 0,0,0,0,3,55,0,0.066666667,0,0,Mar,2,2,1,10,Returning_Visitor,FALSE,FALSE 0,0,0,0,7,111.6666667,0,0.014285714,0,0,Mar,2,4,1,2,Returning_Visitor,TRUE,FALSE 6,104,1,0,14,269.5,0,0.021568627,0,0,Mar,1,1,1,3,Returning_Visitor,FALSE,FALSE 2,4,0,0,10,284,0,0.016666667,33.32834503,0,Mar,2,5,1,8,New_Visitor,FALSE,TRUE 9,81,0,0,34,1219.113725,0.007017544,0.022556391,9.517421053,0,Mar,3,2,3,1,Returning_Visitor,FALSE,TRUE 1,0,0,0,21,574.722619,0,0.018888889,6.284044913,0,Mar,2,2,1,2,Returning_Visitor,FALSE,TRUE 0,0,1,0,46,4053.092208,0.014184397,0.029684228,0,0,Mar,1,1,1,2,Returning_Visitor,FALSE,FALSE 0,0,0,0,18,1485.416667,0,0.027777778,0,0,Mar,2,4,1,1,Returning_Visitor,FALSE,FALSE 12,765.6666667,0,0,7,634.1666667,0.015384615,0.015384615,0,0,Mar,1,2,3,2,Returning_Visitor,FALSE,FALSE 13,214.8823529,0,0,41,1182.106162,0,0.018485839,4.465398354,0,Mar,2,4,3,2,Returning_Visitor,FALSE,FALSE 1,143,4,48,16,333.75,0,0.003333333,0,0,Mar,1,1,1,2,New_Visitor,FALSE,FALSE 0,0,0,0,2,4,0,0.1,0,0,Mar,2,2,4,1,Returning_Visitor,FALSE,FALSE 2,15.5,0,0,28,550.0666667,0,0.006666667,0,0,Mar,2,2,1,2,New_Visitor,FALSE,FALSE 0,0,0,0,1,0,0.2,0.2,0,0,Mar,1,1,3,9,Returning_Visitor,TRUE,FALSE 2,42,0,0,29,218,0,0.003225806,0,0,Mar,2,2,1,2,Returning_Visitor,FALSE,FALSE 0,0,0,0,93,2243.666667,0.010989011,0.031501832,0,0,Mar,2,4,8,1,Returning_Visitor,FALSE,FALSE 1,702,0,0,38,2193.150794,0.012280702,0.015614035,9.09277193,0,Mar,1,1,2,2,Returning_Visitor,FALSE,TRUE 4,22.5,0,0,15,289.85,0,0.022222222,57.96417654,0,Mar,2,2,2,3,New_Visitor,TRUE,TRUE 1,14,0,0,13,909.5,0,0.014285714,0,0,Mar,1,1,1,10,Returning_Visitor,FALSE,FALSE 3,188,0,0,22,1436.45,0,0.009090909,0,0,Mar,2,2,3,1,Returning_Visitor,FALSE,FALSE 8,352.2428571,6,241.5,45,1998.444081,0.009090909,0.01644733,2.842307011,0,Mar,3,2,1,2,Returning_Visitor,TRUE,FALSE 4,73.75,1,58,12,777.5,0,0.015555556,10.782,0,Mar,2,2,1,2,Returning_Visitor,TRUE,TRUE 0,0,0,0,4,126.5,0.016666667,0.091666667,0,0,Mar,2,5,3,2,Returning_Visitor,TRUE,FALSE 0,0,0,0,8,100,0,0.025,0,0,Mar,1,1,7,1,Returning_Visitor,FALSE,FALSE 1,95,0,0,27,598.45,0,0.014814815,0,0,Mar,2,2,2,2,Returning_Visitor,FALSE,FALSE 0,0,0,0,7,87.71428571,0.042857143,0.089285714,0,0,Mar,3,2,1,3,Returning_Visitor,FALSE,FALSE 6,145,6,199,40,1834.166667,0,0.006734694,49.47040982,0,Mar,2,5,3,2,Returning_Visitor,FALSE,TRUE 0,0,0,0,2,15,0,0.1,0,0,Mar,1,1,1,2,Returning_Visitor,FALSE,FALSE 3,221.5,4,172,28,1365.216667,0,0.012301587,0,0,Mar,2,2,3,2,Returning_Visitor,FALSE,FALSE 0,0,0,0,6,36,0.066666667,0.133333333,0,0,Mar,4,1,1,3,Returning_Visitor,TRUE,FALSE 4,82.5,3,419,22,1005.806061,0,0.007407407,0,0,Mar,2,2,2,2,New_Visitor,TRUE,FALSE 6,92,3,101,76,5186.116667,0.004878049,0.024837398,6.548011382,0,Mar,2,2,3,2,Returning_Visitor,FALSE,FALSE 9,183.5,2,29.66666667,52,2211.608333,0.008888889,0.010251724,1.816474074,0,Mar,4,2,1,2,Returning_Visitor,FALSE,TRUE 0,0,0,0,8,167,0,0.05,0,0,Mar,2,5,1,3,Returning_Visitor,FALSE,FALSE 2,36,0,0,47,2102.581502,0.004166667,0.010416667,14.63957834,0,Mar,1,1,2,1,Returning_Visitor,FALSE,FALSE 8,139.6818182,8,353.3333333,45,2003.415152,0.007142857,0.00755102,5.576720936,0,Mar,2,2,4,2,Returning_Visitor,FALSE,TRUE 4,50.5,3,54,41,1490.552778,0,0.006589147,0,0,Mar,2,2,1,2,Returning_Visitor,FALSE,FALSE 9,124.125,0,0,35,1988.658333,0.008943089,0.030662021,0,0,Mar,2,2,3,2,Returning_Visitor,FALSE,FALSE 0,0,0,0,8,148.75,0,0.025,0,0,Mar,2,4,3,2,Returning_Visitor,TRUE,FALSE 0,0,0,0,2,134,0,0.1,0,0,Mar,3,2,7,9,Returning_Visitor,TRUE,FALSE 1,5,0,0,16,191.0714286,0,0.0125,0,0,Mar,2,4,1,10,Returning_Visitor,FALSE,FALSE 0,0,1,0,16,222,0.025,0.03125,0,0,Mar,1,2,1,2,Returning_Visitor,FALSE,FALSE 2,47.5,0,0,5,116.5,0,0.005714286,0,0,Mar,3,2,5,2,Returning_Visitor,TRUE,FALSE 0,0,0,0,3,190,0,0.066666667,0,0,Mar,2,7,4,3,Returning_Visitor,TRUE,FALSE 0,0,0,0,68,809.2456349,0.002985075,0.005970149,109.9124051,0,Mar,2,5,4,2,Returning_Visitor,FALSE,TRUE 0,0,0,0,8,262.1666667,0,0.020833333,0,0,Mar,2,2,2,2,Returning_Visitor,FALSE,FALSE 0,0,0,0,4,22,0.05,0.1,0,0,Mar,2,4,6,3,Returning_Visitor,TRUE,FALSE 4,55.32575758,0,0,11,234.9924242,0,0.018181818,0,0,Mar,2,2,3,1,Returning_Visitor,FALSE,FALSE 0,0,0,0,8,258,0.025,0.075,0,0,Mar,2,2,3,1,Returning_Visitor,FALSE,FALSE 0,0,0,0,2,0,0.2,0.2,0,0,Mar,2,4,1,1,Returning_Visitor,FALSE,FALSE 1,185,6,263,6,133.3333333,0,0.019230769,0,0,Mar,3,2,3,3,Returning_Visitor,FALSE,FALSE 13,241.7666667,0,0,12,255.3,0.002,0.036666667,0,0,Mar,2,2,8,2,Returning_Visitor,FALSE,FALSE 5,145.5,0,0,96,1524.748377,0,0.007239057,0,0,Mar,2,2,9,2,Returning_Visitor,FALSE,FALSE 2,160,0,0,17,1306.666667,0.010526316,0.014035088,0,0,Mar,3,2,2,3,Returning_Visitor,FALSE,FALSE 2,28.66666667,0,0,12,224.1666667,0,0.023333333,44.73466667,0,Mar,3,2,6,2,Returning_Visitor,FALSE,TRUE 0,0,0,0,24,473.6833333,0.008333333,0.00952381,0,0,Mar,3,2,3,1,Returning_Visitor,FALSE,FALSE 6,253,0,0,21,686.4,0.003846154,0.012179487,0,0,Mar,1,1,3,3,Returning_Visitor,FALSE,FALSE 0,0,0,0,4,80,0.05,0.075,0,0,Mar,3,2,4,1,Returning_Visitor,FALSE,FALSE 0,0,0,0,1,0,0,0.2,0,0,Mar,2,2,1,2,Returning_Visitor,FALSE,FALSE 0,0,0,0,1,61,0,0.1,0,0,Mar,3,2,4,15,Returning_Visitor,TRUE,FALSE 2,220.3333333,1,70.5,75,3537.991667,0.011714286,0.027362361,5.770959977,0,Mar,2,2,6,1,Returning_Visitor,FALSE,FALSE 7,445.3333333,1,135.5,52,1896.272222,0.005310734,0.013192815,3.795661017,0,Mar,3,2,6,1,Returning_Visitor,FALSE,FALSE 2,50,0,0,29,598.297619,0,0.016666667,0,0,Mar,2,6,3,1,Returning_Visitor,FALSE,FALSE 0,0,0,0,10,127.5,0,0.02,0,0,Mar,3,2,1,10,Returning_Visitor,FALSE,FALSE 5,76,0,0,7,154,0,0.005555556,0,0,Mar,1,2,3,2,New_Visitor,TRUE,FALSE 13,654.775,8,604.8,160,5349.563713,0.001142857,0.001799477,17.34893623,0,Mar,2,2,1,2,Returning_Visitor,TRUE,TRUE 0,0,0,0,1,0,0,0.1,0,0,Mar,2,2,2,6,Returning_Visitor,FALSE,FALSE 0,0,0,0,3,77,0,0.066666667,0,0,Mar,4,2,6,1,Returning_Visitor,FALSE,FALSE 0,0,0,0,7,98.5,0.071428571,0.1,0,0,Mar,1,1,6,1,Returning_Visitor,FALSE,FALSE 0,0,0,0,12,810,0,0.016666667,0,0,Mar,2,2,6,10,Returning_Visitor,FALSE,FALSE 0,0,0,0,3,903,0,0.066666667,0,0,Mar,2,2,1,1,Returning_Visitor,FALSE,FALSE 5,87,0,0,86,2973.008333,0.002222222,0.015740741,0,0,Mar,2,2,8,1,Returning_Visitor,FALSE,FALSE 0,0,0,0,1,0,0.2,0.2,0,0,Mar,2,5,2,3,Returning_Visitor,FALSE,FALSE 2,17,0,0,7,1093,0.0375,0.1125,0,0,Mar,2,2,3,1,Returning_Visitor,FALSE,FALSE 0,0,0,0,3,137,0,0.1,0,0,Mar,2,2,3,2,Returning_Visitor,FALSE,FALSE 0,0,0,0,8,136.75,0.005,0.105,0,0,Mar,2,2,2,10,Returning_Visitor,FALSE,FALSE 0,0,0,0,1,0,0.2,0.2,0,0,Mar,3,2,3,1,Returning_Visitor,FALSE,FALSE 0,0,0,0,1,0,0.2,0.2,0,0,Mar,2,4,1,1,Returning_Visitor,FALSE,FALSE 3,96,0,0,6,384,0,0.025,0,0,Mar,3,2,1,2,New_Visitor,FALSE,FALSE 0,0,0,0,9,125,0,0.022222222,0,0,Mar,2,2,3,1,Returning_Visitor,FALSE,FALSE 0,0,0,0,2,44,0,0.05,0,0,Mar,2,4,1,3,Returning_Visitor,FALSE,FALSE 0,0,0,0,1,0,0.2,0.2,0,0,Mar,2,5,1,3,Returning_Visitor,FALSE,FALSE 4,35.125,0,0,31,1975.458333,0,0.01,0,0,Mar,2,2,3,3,New_Visitor,TRUE,FALSE 4,71.5,0,0,42,758.75,0.004444444,0.015555556,0,0,Mar,2,5,2,2,Returning_Visitor,FALSE,FALSE 9,222.5,2,46.66666667,20,1267.155556,0.001481481,0.017977208,16.1728,0,Mar,2,2,3,2,Returning_Visitor,FALSE,TRUE 4,200.8333333,0,0,18,381.3333333,0,0.009090909,0,0,Mar,2,4,7,2,New_Visitor,TRUE,FALSE 2,33.5,1,91,31,1271.821212,0,0.011505376,1.592774194,0,Mar,1,8,4,1,Returning_Visitor,FALSE,TRUE 0,0,0,0,32,827.6462121,0,0.00058651,54.67634844,0,Mar,2,2,7,2,Returning_Visitor,FALSE,TRUE 0,0,0,0,5,36,0.15,0.1625,0,0,Mar,3,2,3,1,Returning_Visitor,FALSE,FALSE 7,120.5,0,0,19,1011.5,0,0.00952381,0,0,Mar,2,2,1,1,Returning_Visitor,FALSE,FALSE 5,243.6666667,1,415,20,1267.233333,0,0.01,91.98,0,Mar,1,1,3,1,Returning_Visitor,FALSE,TRUE 5,53,1,97,66,4891.972222,0,0.002159624,0,0,Mar,2,2,8,1,Returning_Visitor,FALSE,FALSE 0,0,0,0,27,694.3035714,0,0.007692308,11.99264025,0,Mar,1,1,1,2,Returning_Visitor,TRUE,TRUE 4,148.5,1,118,150,4764.429997,0,0.005636724,33.33074497,0,Mar,2,2,5,2,Returning_Visitor,FALSE,FALSE 0,0,0,0,31,1350.5,0.006451613,0.016129032,0,0,Mar,2,5,2,2,Returning_Visitor,FALSE,FALSE 0,0,1,56,17,1385.833333,0.005882353,0.017647059,39.85866667,0,Mar,2,2,1,2,Returning_Visitor,FALSE,TRUE 1,6,0,0,15,500,0.026666667,0.073333333,19.996,0,Mar,2,2,3,1,Returning_Visitor,FALSE,TRUE 2,26,0,0,22,285.9880952,0,0.0125,0,0,Mar,1,1,6,8,Returning_Visitor,FALSE,FALSE 1,6,0,0,6,45.5,0.057142857,0.114285714,0,0,Mar,1,1,3,3,Returning_Visitor,FALSE,FALSE 5,129.5,0,0,13,1507.333333,0.016666667,0.028125,0,0,Mar,2,2,3,2,Returning_Visitor,FALSE,FALSE 3,223,1,24,27,1460.566667,0,0.006666667,0,0,Mar,2,2,8,2,New_Visitor,TRUE,FALSE 0,0,0,0,4,214,0,0.05,0,0,Mar,2,2,3,3,Returning_Visitor,FALSE,FALSE 0,0,0,0,6,136.5,0,0.033333333,0,0,Mar,3,3,1,2,Returning_Visitor,FALSE,FALSE 0,0,0,0,6,254,0.025,0.0875,0,0,Mar,1,1,6,10,Returning_Visitor,FALSE,FALSE 1,5,0,0,13,545.5714286,0.013333333,0.020784314,26.12015415,0,Mar,2,2,3,2,Returning_Visitor,TRUE,TRUE 2,91,3,865,27,1244.833333,0,0.006896552,0,0,Mar,2,2,1,2,New_Visitor,TRUE,FALSE 1,78,0,0,21,555.8944444,0,0.002380952,24.75047619,0,Mar,1,1,1,2,New_Visitor,FALSE,TRUE 0,0,0,0,19,429.6333333,0,0.005263158,0,0,Mar,2,4,1,3,Returning_Visitor,TRUE,FALSE 0,0,0,0,10,111,0,0.02,0,0,Mar,2,4,7,3,Returning_Visitor,FALSE,FALSE 0,0,0,0,1,0,0.2,0.2,0,0,Mar,3,2,1,3,Returning_Visitor,TRUE,FALSE 3,46.5,0,0,16,402.6904762,0,0.0125,0,0,Mar,2,4,1,1,Returning_Visitor,TRUE,FALSE 0,0,0,0,15,292.6944444,0,0.005128205,54.98,0,May,3,2,4,4,Returning_Visitor,FALSE,TRUE 0,0,0,0,24,654.0666667,0,0.001449275,0,0.8,May,3,2,1,2,Returning_Visitor,FALSE,FALSE 2,254,1,39,95,4990.959524,0.004123711,0.015307716,5.638103093,0,May,2,2,1,6,Returning_Visitor,FALSE,TRUE 0,0,0,0,4,158,0.05,0.1,0,0,May,2,2,4,13,Returning_Visitor,FALSE,FALSE 0,0,0,0,77,3332.208333,0.002040816,0.024719927,0,0.6,May,2,2,3,6,Returning_Visitor,FALSE,FALSE 0,0,0,0,130,4164.936905,0.006153846,0.012319347,0,0,May,3,2,6,13,Returning_Visitor,FALSE,FALSE 0,0,0,0,1,0,0.2,0.2,0,0,May,1,1,1,3,Returning_Visitor,FALSE,FALSE 5,85.66666667,0,0,25,1194.5,0,0.007692308,11.51923077,0,May,2,2,1,2,New_Visitor,FALSE,TRUE 3,154.5,0,0,21,638.3333333,0,0.047222222,0,0,May,2,5,4,4,Returning_Visitor,FALSE,FALSE 0,0,0,0,13,152.25,0.030769231,0.025641026,0,0,May,1,2,5,2,Returning_Visitor,FALSE,FALSE 0,0,0,0,17,631.5,0.011764706,0.064705882,0,0.8,May,2,2,1,13,Returning_Visitor,FALSE,FALSE 0,0,0,0,9,58,0.155555556,0.177777778,0,0,May,1,1,1,3,Returning_Visitor,TRUE,FALSE 7,598,3,144.5,83,6860.632738,0.011512297,0.039305682,0,0.4,May,2,2,1,2,Returning_Visitor,FALSE,FALSE 0,0,0,0,2,0,0.2,0.2,0,0.6,May,1,1,1,3,Returning_Visitor,FALSE,FALSE 1,3,0,0,28,271.125,0,0.002564103,0,0,May,4,2,1,3,New_Visitor,FALSE,FALSE 0,0,0,0,1,0,0.2,0.2,0,0,May,1,1,1,6,Returning_Visitor,FALSE,FALSE 1,7,0,0,151,2564.278157,0.00917226,0.0210895,0,0.6,May,1,1,1,15,Returning_Visitor,FALSE,FALSE 0,0,0,0,12,896.5,0.008333333,0.025,14.6895,0.6,May,2,5,1,5,Returning_Visitor,FALSE,TRUE 3,226.9545455,0,0,57,4895.022989,0.003333333,0.023321256,0,0,May,2,2,1,13,Returning_Visitor,FALSE,FALSE 6,97.88235294,2,9,58,2293.59902,0.008357771,0.033973374,6.7485,0,May,2,2,7,4,Returning_Visitor,FALSE,TRUE 4,515.6,0,0,77,1449.683333,0,0.002631579,0,0,May,2,2,4,2,Returning_Visitor,FALSE,FALSE 1,397.5,3,89.5,8,249,0.066666667,0.091666667,0,0,May,3,2,1,6,Returning_Visitor,TRUE,FALSE 2,16.5,0,0,5,37.5,0,0.033333333,0,0,May,2,2,1,3,Returning_Visitor,FALSE,FALSE 2,30,0,0,10,121,0.016666667,0.025,0,0,May,3,6,3,3,New_Visitor,FALSE,FALSE 6,60,1,30,51,1403.841919,0.003636364,0.009090909,20.2453697,0,May,2,4,1,2,Returning_Visitor,FALSE,FALSE 0,0,0,0,8,227,0.025,0.05,0,0,May,3,2,6,13,Returning_Visitor,FALSE,FALSE 0,0,0,0,8,228,0.025,0.075,0,0,May,2,2,3,3,Returning_Visitor,FALSE,FALSE 1,22.75,0,0,16,461.0833333,0,0.011458333,17.991,0,May,2,2,1,4,Returning_Visitor,FALSE,FALSE 0,0,0,0,2,12,0,0.1,0,0,May,1,1,1,3,Returning_Visitor,TRUE,FALSE 4,46.5,0,0,34,762.5,0,0.013513514,0,0.6,May,2,2,1,4,Returning_Visitor,FALSE,FALSE 0,0,0,0,5,74.5,0,0.08,0,0.6,May,1,1,1,3,Returning_Visitor,FALSE,FALSE 3,27,0,0,21,898,0.015384615,0.042307692,10.94723077,0.6,May,2,2,1,3,Returning_Visitor,FALSE,TRUE 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0,0,0,0,3,125,0.05,0.075,0,0.8,May,2,2,1,4,Returning_Visitor,FALSE,FALSE 4,31,1,46,80,1793.777778,0.017857143,0.031725417,0,0.6,May,2,2,3,3,Returning_Visitor,FALSE,FALSE 1,3,0,0,26,885,0,0.036111111,17.5455,0,May,2,2,1,2,Returning_Visitor,FALSE,TRUE 1,34,0,0,36,646.2857143,0,0.005882353,0,0,May,2,4,6,3,Returning_Visitor,FALSE,FALSE 6,200.2222222,2,126,146,7253.676493,0.006570513,0.020305773,5.942025641,0.4,May,2,2,3,1,Returning_Visitor,FALSE,FALSE 2,40,0,0,4,55,0,0.033333333,0,0.2,May,4,2,1,13,Returning_Visitor,FALSE,FALSE 0,0,1,2,25,344.3214286,0.00617284,0.033333333,0,0.4,May,2,2,1,1,Returning_Visitor,FALSE,FALSE 5,33.66666667,0,0,4,21.66666667,0,0.02,0,0,May,2,2,1,5,New_Visitor,FALSE,FALSE 1,0,0,0,4,334,0.08,0.116,0,0,May,1,1,3,1,Returning_Visitor,TRUE,FALSE 0,0,0,0,1,0,0.2,0.2,0,0,May,3,3,1,3,Returning_Visitor,FALSE,FALSE 0,0,0,0,28,608.5227273,0.015384615,0.021952663,0,0,May,2,2,1,6,Returning_Visitor,TRUE,FALSE 0,0,0,0,4,4,0.125,0.175,0,0.8,May,3,2,4,13,Returning_Visitor,FALSE,FALSE 4,24.16666667,0,0,56,1977.385088,0,0.007471264,0,0.4,May,2,2,1,4,Returning_Visitor,FALSE,FALSE 9,194.4166667,2,20,11,1569.166667,0.013333333,0.028666667,18.80505,0,May,3,2,7,6,Returning_Visitor,FALSE,FALSE 4,58,0,0,31,806.5,0,0.009375,9.7455,0,May,2,4,1,4,Returning_Visitor,FALSE,TRUE 0,0,0,0,36,631.3068627,0.021142857,0.047901224,0,0.8,May,2,2,3,6,Returning_Visitor,FALSE,FALSE 0,0,1,35,33,782.0833333,0,0.005882353,23.03329412,0,May,2,4,9,2,New_Visitor,FALSE,TRUE 2,11,0,0,12,347,0,0.023076923,9.960923077,0,May,1,1,3,3,Returning_Visitor,TRUE,TRUE 1,149.5,0,0,32,1562.333333,0.038709677,0.062580645,7.636645161,0.2,May,2,2,3,2,Returning_Visitor,FALSE,FALSE 3,453,3,493.5,36,1199.522619,0.004761905,0.011534392,9.323531746,0,May,6,2,2,4,Returning_Visitor,FALSE,FALSE 1,32,0,0,18,299.5,0.015789474,0.022807018,19.448,0,May,2,2,5,1,Returning_Visitor,TRUE,TRUE 0,0,0,0,18,103.8333333,0.033333333,0.055555556,0,0,May,2,2,3,3,Returning_Visitor,FALSE,FALSE 3,101.6666667,0,0,22,2250.083333,0,0.019565217,32.70730435,0,May,2,4,4,1,Returning_Visitor,FALSE,TRUE 2,59,0,0,3,82,0.04,0.06,0,0,May,1,1,6,3,New_Visitor,FALSE,FALSE 0,0,0,0,6,747,0.033333333,0.066666667,0,0,May,2,2,3,3,Returning_Visitor,FALSE,FALSE 4,316.5,0,0,8,298.6904762,0,0.002083333,0,0,May,3,2,2,2,Returning_Visitor,TRUE,FALSE 9,309.25,0,0,58,1572.27381,0.003870968,0.017803379,0,0,May,2,2,6,2,Returning_Visitor,FALSE,FALSE 3,35.5,0,0,14,241.7692308,0,0.020416667,0,0.6,May,3,2,3,2,Returning_Visitor,FALSE,FALSE 5,69.5,4,116,84,2839.75,0,0.006593407,6.281494505,0,May,2,4,9,2,New_Visitor,FALSE,FALSE 2,42,0,0,17,275.702381,0.04,0.06,0,0.6,May,3,2,8,2,Returning_Visitor,FALSE,FALSE 4,85.92857143,0,0,39,681.7619048,0.015384615,0.032988166,0,0,May,1,1,7,3,Returning_Visitor,FALSE,FALSE 0,0,0,0,5,95.5,0,0.013333333,0,0,May,2,4,1,3,Returning_Visitor,FALSE,FALSE 2,76,1,79,35,2072.666667,0.007207207,0.031081081,8.1,0.8,May,2,4,7,2,Returning_Visitor,FALSE,FALSE 0,0,0,0,13,214.25,0.091666667,0.097777778,0,0.8,May,3,2,3,13,Returning_Visitor,FALSE,FALSE 0,0,0,0,1,207.5702479,0,0.007936508,0,0,Oct,2,2,1,5,Returning_Visitor,FALSE,FALSE 3,40.2,0,0,11,201.1333333,0,0.02,0,0,June,2,2,2,1,New_Visitor,FALSE,FALSE 1,63.4,0,0,3,120.4,0,0,42.29306752,0,Oct,1,1,3,3,New_Visitor,FALSE,TRUE 6,618.0666667,0,0,50,1342.083333,0,0.004385965,28.06804986,0,June,2,2,6,11,New_Visitor,FALSE,TRUE 0,0,0,0,2,7,0,0.1,0,0,June,2,2,7,1,Returning_Visitor,FALSE,FALSE 3,152.6,0,0,28,608.8833333,0.009375,0.03125,19.82770888,0,Jul,2,2,7,13,Returning_Visitor,FALSE,TRUE 7,56.3,0,0,15,315.4,0,0.011111111,0,0,Oct,2,2,1,2,New_Visitor,FALSE,FALSE 0,0,0,0,20,419.6,0.038095238,0.068253968,0,0,Aug,3,2,3,3,Returning_Visitor,TRUE,FALSE 1,39.2,2,120.8,7,80.5,0,0.01,0,0,Nov,3,2,4,2,New_Visitor,TRUE,FALSE 3,89.6,0,0,57,1721.906667,0,0.005932203,204.0079491,0,Nov,2,2,1,4,Returning_Visitor,TRUE,TRUE 2,104.3,0,0,48,2307.661212,0.025510204,0.035488428,4.348145623,0,Oct,1,1,4,1,Returning_Visitor,TRUE,FALSE 0,0,0,0,4,41.4,0.075,0.125,0,0,Jul,3,2,6,1,Returning_Visitor,FALSE,FALSE 4,204.2,0,0,31,652.3766667,0.012121212,0.016161616,0,0,Nov,1,1,6,1,Returning_Visitor,FALSE,FALSE 17,180.1333333,1,80.6,180,6512.010952,0.002645503,0.012248677,0,0,Sep,2,2,1,3,Returning_Visitor,FALSE,FALSE 1,12,0,0,36,493.3033901,0,0.01660575,0,0,Oct,2,2,5,2,Returning_Visitor,FALSE,FALSE 2,13,0,0,17,209.7,0.023529412,0.038235294,0,0,June,1,1,3,4,Returning_Visitor,FALSE,FALSE 0,0,0,0,5,68.4,0,0.08,0,0,June,2,4,2,4,Returning_Visitor,FALSE,FALSE 3,41.2,0,0,7,942.8,0,0.03,0,0,Aug,1,1,4,5,New_Visitor,FALSE,FALSE 4,101.8,0,0,91,5692.211713,0.006451613,0.033623455,0,0,Sep,4,1,9,1,Returning_Visitor,FALSE,FALSE 1,26.2,0,0,4,106.8,0,0.04,36.66178288,0,Oct,2,2,2,5,New_Visitor,FALSE,TRUE 2,79.6,3,332.08,34,1011.016667,0.018918919,0.043243243,5.128358233,0,Oct,2,2,7,1,Returning_Visitor,FALSE,TRUE 4,159,0,0,16,307.6166667,0,0.010526316,0,0,Oct,1,1,3,2,Returning_Visitor,TRUE,FALSE 7,227.9,0,0,22,701.0166667,0,0.001666667,0,0,Oct,2,4,4,2,Returning_Visitor,FALSE,FALSE 2,47.4,0,0,14,478.6,0.04375,0.079166667,0,0,Jul,4,1,1,4,Returning_Visitor,TRUE,FALSE 0,0,0,0,13,710.0666667,0,0.007692308,72.52283848,0,Nov,1,1,2,2,Returning_Visitor,FALSE,TRUE 1,0,0,0,3,259,0,0.04,48.04726202,0,Oct,2,2,1,4,Returning_Visitor,FALSE,TRUE 0,0,0,0,34,1228.5,0.039705882,0.059313725,0,0,Aug,3,2,1,4,Returning_Visitor,FALSE,FALSE 2,196.6,0,0,31,1514.3,0.036363636,0.066666667,0,0,Aug,1,8,7,1,Returning_Visitor,TRUE,FALSE 3,33.2,0,0,10,278.4333333,0.007142857,0.026190476,32.70034468,0,Jul,2,2,6,1,Returning_Visitor,FALSE,TRUE 0,0,0,0,6,86.53333333,0,0.066666667,0,0,Oct,1,1,7,2,Returning_Visitor,TRUE,FALSE 3,9.5,0,0,25,528.9666667,0.007692308,0.037179487,3.296827304,0,June,2,2,2,3,Returning_Visitor,FALSE,FALSE 2,55.4,0,0,25,402.75,0.015384615,0.034065934,0,0,Jul,3,2,5,1,Returning_Visitor,FALSE,FALSE 0,0,0,0,1,0,0.1,0.2,0,0,Jul,2,2,2,5,New_Visitor,TRUE,FALSE 10,1222.4,0,0,26,2988.883333,0,0.002903226,0,0,Sep,2,2,1,2,Returning_Visitor,FALSE,FALSE 0,0,0,0,42,844.58,0.05952381,0.085396825,0,0,Oct,3,2,1,3,Returning_Visitor,FALSE,FALSE 3,13.73333333,1,2,27,407.6833333,0.006666667,0.011555556,0,0,Jul,2,2,1,4,Returning_Visitor,FALSE,TRUE 1,23.2,0,0,30,964.4666667,0,0.003571429,51.03362522,0,Oct,2,2,5,2,New_Visitor,FALSE,TRUE 5,150.7,0,0,12,201.6,0.026666667,0.04,0,0,Oct,1,1,4,2,New_Visitor,FALSE,FALSE 5,66.6,0,0,13,523.6,0,0.013333333,0,0,Oct,2,2,6,3,Returning_Visitor,FALSE,FALSE 0,0,0,0,24,968.6924242,0,0,106.2525169,0,Nov,1,1,9,2,Returning_Visitor,FALSE,TRUE 1,92.6,0,0,4,207.9333333,0,0.04,0,0,Jul,1,1,9,20,New_Visitor,FALSE,FALSE 0,0,0,0,16,1062.666667,0,0.0125,54.56704868,0,Sep,4,1,1,4,New_Visitor,FALSE,TRUE 13,1013.056909,2,102.8,77,2697.022104,0,0.009471471,1.798669862,0,Nov,1,1,9,2,Returning_Visitor,FALSE,FALSE 1,48.4,0,0,22,2707.386667,0,0.009722222,0,0,Sep,3,2,1,1,Returning_Visitor,TRUE,FALSE 1,57.4,0,0,6,303.1,0.028571429,0.014285714,0,0,June,1,2,3,2,Returning_Visitor,FALSE,FALSE 7,297.6,0,0,66,3572.241757,0.005797101,0.012314449,7.766280897,0,Oct,2,2,8,2,Returning_Visitor,FALSE,FALSE 0,0,0,0,2,12,0.1,0.1,0,0,Sep,4,1,1,2,Returning_Visitor,FALSE,FALSE 0,0,0,0,38,3287,0.036842105,0.075814536,0,0,June,2,5,1,1,Returning_Visitor,FALSE,FALSE 0,0,0,0,47,902.77,0.022222222,0.04037037,0,0,Jul,3,2,8,1,Returning_Visitor,FALSE,FALSE 0,0,0,0,1,0,0.2,0.2,0,0,Oct,1,1,8,1,Returning_Visitor,TRUE,FALSE 0,0,0,0,16,519.8666667,0,0,0,0,Jul,1,1,1,4,Returning_Visitor,FALSE,FALSE 2,56.73333333,0,0,33,1091.394074,0,0.003594771,0,0,Oct,4,2,3,2,New_Visitor,TRUE,FALSE 1,86.6,0,0,14,349.5761905,0,0.004761905,60.43737784,0,Nov,1,1,1,3,New_Visitor,FALSE,TRUE 7,38.6,0,0,70,1087.7,0,0.004861111,0,0,Oct,2,2,4,5,Returning_Visitor,FALSE,FALSE 6,545.65,3,204.3466667,69,1182.432333,0.000684932,0.002998087,19.71529542,0,Nov,2,2,1,13,Returning_Visitor,TRUE,TRUE 3,94.6,0,0,16,1254,0,0.019327731,0,0,Nov,3,2,7,2,New_Visitor,TRUE,FALSE 6,53,0,0,40,778.8333333,0,0.009469697,8.480846261,0,Aug,1,1,1,4,New_Visitor,TRUE,FALSE 3,28.8,5,2195.3,21,378.4,0.02962963,0.030864198,0,0,Jul,1,1,3,1,Returning_Visitor,FALSE,FALSE 5,46.52,0,0,133,2248.914286,0.012077922,0.029349567,6.373770623,0,Jul,1,1,1,4,Returning_Visitor,FALSE,TRUE 1,41.4,0,0,30,558.6111111,0.009090909,0.046464646,0,0,Sep,2,10,2,1,Returning_Visitor,FALSE,TRUE 6,82.14,0,0,12,191.1,0,0.0125,0,0,Nov,2,4,9,4,Returning_Visitor,FALSE,FALSE 0,0,0,0,11,267.5666667,0,0.009090909,7.68745056,0,Oct,2,2,1,3,New_Visitor,FALSE,TRUE 3,397.7,4,1093.3,66,4152.7631,0.014979592,0.017561939,6.282904602,0,Sep,3,2,8,4,Returning_Visitor,TRUE,FALSE 0,0,0,0,16,645.2,0.0125,0.05,0,0,Jul,4,1,3,1,Returning_Visitor,TRUE,FALSE 16,932.7971429,0,0,40,991.7155466,0.008888889,0.011659808,0,0,Sep,1,2,1,1,Returning_Visitor,FALSE,FALSE 3,56.8,1,3,73,1677.666667,0.010666667,0.040444444,0,0,June,2,2,7,1,Returning_Visitor,FALSE,FALSE 1,10,0,0,5,354.9,0,0.06,0,0,Sep,2,2,5,1,Returning_Visitor,FALSE,FALSE 0,0,0,0,3,74.6,0,0.066666667,0,0,Jul,3,2,3,4,Returning_Visitor,TRUE,FALSE 10,89.98,0,0,96,1304.973333,0.003921569,0.008309991,6.175932014,0,Oct,2,2,1,3,Returning_Visitor,FALSE,FALSE 0,0,0,0,6,95,0.028571429,0.028571429,86.33623272,0,Oct,1,1,4,2,New_Visitor,FALSE,TRUE 1,67.5,0,0,28,873.64,0,0.006785714,0,0,Sep,3,2,1,6,Returning_Visitor,FALSE,FALSE 13,234.5666667,0,0,80,1807.227778,0.016091954,0.0348659,4.192980384,0,Jul,4,2,3,13,Returning_Visitor,FALSE,FALSE 2,63.5,0,0,104,1667.158421,0,0.002547045,0,0,Jul,2,4,2,1,Returning_Visitor,TRUE,FALSE 1,4,0,0,8,227.1,0.022222222,0.066666667,0,0,Aug,2,2,3,13,Returning_Visitor,FALSE,FALSE 0,0,0,0,14,331.3,0,0.015384615,0,0,Jul,1,8,1,1,Returning_Visitor,FALSE,FALSE 5,196.88,0,0,7,118.9133333,0,0.01473138,16.56478478,0,Oct,3,2,7,2,Returning_Visitor,FALSE,FALSE 5,60.2,0,0,12,165.4,0.0125,0.041666667,0,0,Aug,2,5,3,6,Returning_Visitor,FALSE,FALSE 5,271,2,42.2,6,147.9,0,0.036363636,0,0,Aug,3,2,7,2,New_Visitor,TRUE,FALSE 6,85.2,0,0,11,203,0,0,0,0,Nov,2,2,5,2,Returning_Visitor,FALSE,FALSE 1,62.46666667,0,0,5,165.1,0,0.016666667,0,0,Sep,3,2,3,3,New_Visitor,FALSE,FALSE 10,164.0666667,0,0,37,1553.52381,0.005128205,0.01025641,0,0,Sep,2,2,1,2,Returning_Visitor,FALSE,FALSE 18,189.2646154,0,0,38,777.4146154,0.004255319,0.016960486,0,0,Sep,2,2,1,3,Returning_Visitor,TRUE,FALSE 2,196.6,0,0,0,0,0,0.033333333,0,0,Nov,3,2,9,5,New_Visitor,TRUE,FALSE 1,5.066666667,0,0,32,441.6045455,0,0.00625,0,0,Sep,2,2,6,3,New_Visitor,FALSE,FALSE 2,94.7,0,0,23,897.19,0,0.002898551,0,0,Aug,3,2,3,2,New_Visitor,TRUE,FALSE 1,45.4,0,0,7,352.7,0,0.014285714,0,0,Oct,1,1,3,4,Returning_Visitor,FALSE,FALSE 1,38.8,0,0,11,214.0533333,0.016666667,0.03015873,0,0,Oct,3,2,1,2,Returning_Visitor,FALSE,FALSE 2,21,0,0,25,278.1366667,0,0.007692308,77.4579855,0,Nov,2,2,7,2,New_Visitor,FALSE,FALSE 5,82.6,1,14.2,8,66.53333333,0,0.02,0,0,Aug,4,1,3,1,New_Visitor,TRUE,FALSE 0,0,0,0,22,1321.7,0,0.010833333,0,0,June,2,2,2,13,Returning_Visitor,FALSE,FALSE 6,89.8,0,0,26,1570.133333,0,0.005357143,0,0,June,2,2,8,4,New_Visitor,TRUE,FALSE 5,850,0,0,20,1893.466667,0,0.02,0,0,Oct,2,4,9,2,Returning_Visitor,FALSE,FALSE 0,0,0,0,34,1070.098072,0.018627451,0.027103099,0,0,Jul,3,2,7,1,Returning_Visitor,FALSE,FALSE 0,0,0,0,26,1114.733333,0.015384615,0.026098901,26.13034871,0,Nov,3,2,9,20,Returning_Visitor,TRUE,FALSE 0,0,0,0,22,469.54,0,0.008695652,14.00979215,0,Oct,2,2,9,2,New_Visitor,FALSE,TRUE 4,585.8,0,0,29,1582.533333,0.006666667,0.022222222,0,0,Oct,2,2,4,1,Returning_Visitor,FALSE,FALSE 0,0,0,0,3,33.2,0,0.066666667,0,0,June,1,1,3,1,Returning_Visitor,TRUE,FALSE 0,0,2,88.6,35,682.1833333,0,0.001388889,0,0,Jul,1,1,3,1,Returning_Visitor,FALSE,FALSE 0,0,0,0,11,287.5,0,0.027272727,0,0,Oct,2,4,2,1,Returning_Visitor,FALSE,FALSE 10,491.6666667,0,0,48,1939.571111,0.003773585,0.010539084,0,0,Nov,2,2,7,20,Returning_Visitor,FALSE,TRUE 10,340.9,3,269.8,99,3611.978569,0.000641026,0.00210649,0.847337986,0,June,2,2,1,7,Returning_Visitor,FALSE,TRUE 2,112.8,0,0,27,1555.967143,0,0.001724138,44.85638692,0,Nov,1,1,4,2,New_Visitor,FALSE,TRUE 0,0,0,0,0,0,0.2,0.2,0,0,Nov,1,1,9,3,Returning_Visitor,FALSE,FALSE 0,0,0,0,8,413,0,0.0125,0,0,Sep,1,1,4,3,New_Visitor,FALSE,FALSE 0,0,0,0,3,62.4,0,0.066666667,0,0,June,2,2,3,3,Returning_Visitor,FALSE,FALSE 0,0,0,0,33,889.3666667,0,0.003030303,8.424787355,0,Oct,2,2,3,2,Returning_Visitor,FALSE,TRUE 17,698.1333333,1,9,83,4306.612355,0.001075269,0.019375648,0,0,Oct,7,1,2,1,Returning_Visitor,TRUE,TRUE 0,0,0,0,15,1069.7,0,0.046666667,0,0,Jul,3,2,6,1,Returning_Visitor,FALSE,FALSE 3,32.2,0,0,81,1199.616667,0,0.00617284,0,0,Sep,2,2,2,1,Returning_Visitor,TRUE,TRUE 1,16.6,0,0,84,1893.619048,0.016825397,0.030138889,0,0,Oct,1,1,6,3,Returning_Visitor,FALSE,FALSE 4,54.2,1,33.2,64,1324.68,0,0.003864734,0,0,June,2,2,1,4,New_Visitor,FALSE,FALSE 0,0,0,0,16,230.5666667,0,0.023958333,0,0,Aug,2,2,3,4,Returning_Visitor,FALSE,FALSE 1,11,0,0,8,98.56666667,0.066666667,0.101666667,0,0,Sep,1,8,3,1,Returning_Visitor,TRUE,FALSE 0,0,0,0,3,33.2,0.133333333,0.166666667,0,0,Aug,3,2,1,2,Returning_Visitor,TRUE,FALSE 11,455.0666667,3,306.1,41,5194.857143,0.001754386,0.013972431,22.65941331,0,Oct,2,4,1,20,Returning_Visitor,FALSE,FALSE 2,32.2,1,0,20,342.6866667,0,0.009090909,0,0,Aug,2,2,1,4,New_Visitor,FALSE,FALSE 4,601.9,0,0,41,2517.583333,0.007751938,0.013881932,20.55494533,0,Sep,3,2,1,1,Returning_Visitor,FALSE,TRUE 1,136.5,0,0,25,806.5,0,0.009615385,39.96971545,0,Aug,1,1,1,4,Returning_Visitor,TRUE,FALSE 0,0,0,0,24,348.0333333,0.077777778,0.111111111,0,0,Oct,1,1,1,3,Returning_Visitor,FALSE,FALSE 4,49.3,0,0,9,283.7,0.018181818,0.036363636,0,0,June,1,1,3,3,Returning_Visitor,FALSE,FALSE 10,278.6666667,0,0,50,907.2363636,0.020411523,0.023212197,1.981038388,0,Oct,3,2,3,4,Returning_Visitor,FALSE,FALSE 0,0,0,0,10,122.1666667,0,0.02,0,0,Nov,1,1,1,5,New_Visitor,TRUE,FALSE 1,118.8,0,0,8,259.2,0,0.007407407,0,0,Sep,2,2,4,2,New_Visitor,FALSE,FALSE 0,0,0,0,3,58.2,0.066666667,0.044444444,0,0,Aug,3,2,1,3,Returning_Visitor,FALSE,FALSE 2,13,0,0,6,47.13333333,0.04,0.06,20.94544525,0,Nov,1,2,2,4,Returning_Visitor,FALSE,FALSE 1,44,0,0,1,0,0,0.066666667,0,0,Sep,1,1,1,5,New_Visitor,FALSE,TRUE 1,4,0,0,73,2000.618333,0,0.003703704,0,0,Sep,2,10,7,3,Returning_Visitor,FALSE,FALSE 0,0,0,0,4,140,0.05,0.1,0,0,Sep,3,2,1,13,Returning_Visitor,FALSE,FALSE 2,75.05,0,0,106,4143.175238,0.019711538,0.03900641,4.484328486,0,Aug,3,2,1,1,Returning_Visitor,FALSE,FALSE 1,17.1,1,31.2,45,812.2873894,0,0.022732152,4.395471544,0,Aug,2,4,3,2,Returning_Visitor,FALSE,TRUE 1,2,6,657.8333333,140,3252.200476,0.005479452,0.024292237,0,0,Jul,2,2,4,1,Returning_Visitor,FALSE,FALSE 1,4,0,0,20,471.4777778,0,0.004545455,32.95851549,0,Jul,1,1,1,4,Returning_Visitor,FALSE,TRUE 1,5,0,0,41,764.1333333,0,0.014444444,0,0,June,2,2,1,4,Returning_Visitor,FALSE,FALSE 0,0,0,0,21,1275.9,0.052380952,0.101587302,0,0,Sep,2,4,5,1,Returning_Visitor,FALSE,FALSE 0,0,0,0,8,458.5,0.025,0.03,0,0,Sep,3,2,1,11,Returning_Visitor,FALSE,FALSE 0,0,0,0,4,86.36,0.0625,0.11875,0,0,Aug,3,2,4,1,Returning_Visitor,TRUE,FALSE 0,0,0,0,9,152.6,0.044444444,0.096296296,0,0,June,2,2,1,3,Returning_Visitor,FALSE,FALSE 2,268.08,0,0,13,420.2466667,0.042105263,0.064912281,0,0,Sep,3,2,1,13,Returning_Visitor,TRUE,TRUE 1,22.2,0,0,5,136.1,0.066666667,0.066666667,0,0,Oct,1,1,1,20,Returning_Visitor,FALSE,TRUE 0,0,0,0,1,0,0.2,0.2,0,0,June,1,1,3,2,Returning_Visitor,FALSE,FALSE 0,0,0,0,63,1779.163333,0.003278689,0.020243418,0,0,Aug,2,2,1,1,Returning_Visitor,FALSE,FALSE 7,68.4,0,0,145,5175.377371,0.002666667,0.008978355,2.042762326,0,Aug,2,2,1,1,Returning_Visitor,FALSE,TRUE 24,468.100663,6,191.8666667,189,3428.527381,0.018642295,0.043830926,0.870148477,0,June,2,2,2,1,Returning_Visitor,FALSE,FALSE 3,43.2,0,0,7,137,0,0.02,0,0,June,2,4,6,2,New_Visitor,TRUE,FALSE 2,132,1,28.2,35,1356.866667,0,0.010526316,0,0,Oct,2,2,4,13,Returning_Visitor,FALSE,FALSE 6,45.03333333,1,69.6,120,5771.466862,0.002755906,0.014537746,4.586910248,0,Aug,4,2,1,2,Returning_Visitor,FALSE,FALSE 0,0,0,0,2,0,0.2,0.2,0,0,June,3,2,7,13,Returning_Visitor,FALSE,FALSE 1,80.6,0,0,21,479.5857143,0.005,0.010555556,10.89761784,0,Oct,1,1,3,2,Returning_Visitor,TRUE,TRUE 4,26.2,0,0,13,131.2,0,0.026666667,0,0,June,1,2,1,5,Returning_Visitor,FALSE,FALSE 12,137.9,0,0,13,161.8666667,0,0.010526316,0,0,Aug,2,2,3,5,New_Visitor,TRUE,FALSE 0,0,0,0,8,261.8,0.01,0.04,10.99901844,0,Sep,2,2,4,1,Returning_Visitor,TRUE,FALSE 2,9.6,3,118.1,10,184.1,0,0.035714286,0,0,June,2,2,8,4,Returning_Visitor,FALSE,FALSE 2,161.2,0,0,7,555.4,0.044444444,0.055555556,21.09392209,0,Oct,3,2,2,1,Returning_Visitor,FALSE,FALSE 9,301,0,0,38,2621.621429,0.021212121,0.044507576,10.72117234,0,Nov,2,2,1,13,Returning_Visitor,FALSE,FALSE 2,45.2,0,0,19,230.2,0,0.019047619,0,0,Jul,2,2,2,2,New_Visitor,FALSE,FALSE 2,167.4,0,0,8,191.15,0,0.02,0,0,Oct,1,1,6,2,New_Visitor,FALSE,FALSE 0,0,0,0,2,4,0,0.1,0,0,Aug,2,2,1,1,Returning_Visitor,FALSE,FALSE 0,0,0,0,10,45.2,0.08,0.1,0,0,Aug,1,1,1,3,Returning_Visitor,FALSE,FALSE 2,19.4,3,45.26666667,10,136.0666667,0,0.052857143,0,0,Sep,2,2,1,5,Returning_Visitor,FALSE,FALSE 3,157.4,0,0,9,128.5,0.036363636,0.081818182,0,0,Jul,3,2,1,3,Returning_Visitor,TRUE,FALSE 0,0,0,0,15,2653.5,0,0.04,0,0,Sep,4,1,7,1,Returning_Visitor,FALSE,FALSE 5,56.8,0,0,3,27.2,0,0.033333333,0,0,Nov,2,2,6,2,New_Visitor,FALSE,FALSE 4,459.5,4,199.1,94,6186.17226,0.022541254,0.03820917,3.085103642,0,Jul,3,2,8,13,Returning_Visitor,FALSE,FALSE 0,0,0,0,3,58.4,0,0.066666667,0,0,Jul,2,4,1,2,Returning_Visitor,FALSE,FALSE 0,0,0,0,24,1163.266667,0.061111111,0.083333333,27.287869,0,Oct,1,1,4,1,Returning_Visitor,TRUE,TRUE 2,42.2,0,0,17,440.2261905,0.026315789,0.023684211,0,0,Jul,3,2,3,1,Returning_Visitor,FALSE,FALSE 0,0,0,0,37,1489.590909,0.009459459,0.03816999,0,0,Sep,3,2,2,4,Returning_Visitor,FALSE,FALSE 1,12,0,0,0,0,0,0.022222222,0,0,Aug,3,2,6,2,Returning_Visitor,TRUE,TRUE 0,0,0,0,58,1590.42,0.010344828,0.021551724,1.023391581,0,Jul,3,2,8,4,Returning_Visitor,FALSE,TRUE 0,0,0,0,20,545.1,0.01,0.031666667,0,0,Jul,3,2,8,4,Returning_Visitor,FALSE,FALSE 0,0,0,0,13,517.4,0,0.034285714,4.856377111,0,Nov,2,2,1,20,Returning_Visitor,TRUE,TRUE 0,0,0,0,3,275,0,0.05,32.89701477,0,Oct,3,2,2,2,New_Visitor,FALSE,TRUE 3,69.6,0,0,9,199.1333333,0,0.006060606,0,0,Jul,1,1,1,2,New_Visitor,FALSE,FALSE 0,0,0,0,24,583,0,0.025,0,0,Jul,2,2,1,4,Returning_Visitor,FALSE,FALSE 6,66.55,0,0,41,1556.523333,0.005681818,0.012878788,0,0,Aug,3,2,1,4,Returning_Visitor,TRUE,FALSE 8,103.7333333,0,0,17,328.3,0,0.010526316,0,0,June,3,2,8,2,New_Visitor,TRUE,FALSE 4,60.4,0,0,11,572.6,0,0.013333333,0,0,Oct,2,2,7,4,Returning_Visitor,TRUE,FALSE 2,423.6,0,0,46,2031.506667,0.013043478,0.023913043,0,0,Aug,2,2,4,1,Returning_Visitor,FALSE,FALSE 6,63.68333333,0,0,8,129.7611111,0.017857143,0.068923933,0,0,June,3,2,3,3,Returning_Visitor,FALSE,FALSE 5,148.5,0,0,25,307.7,0.007692308,0.034615385,0,0,Oct,4,1,9,3,Returning_Visitor,TRUE,FALSE 4,96.6,0,0,15,276.7333333,0.010526316,0.021052632,69.46239458,0,Oct,2,2,1,4,Returning_Visitor,TRUE,TRUE 0,0,0,0,13,187,0,0,0,0,Sep,2,5,4,1,Returning_Visitor,FALSE,FALSE 8,628.3666667,1,0,131,4044.465674,0.008889185,0.030612404,0,0,Aug,1,1,3,2,Returning_Visitor,FALSE,FALSE 1,13.1,0,0,111,4208.752099,0.003477744,0.015856149,0,0,Jul,2,2,1,1,Returning_Visitor,TRUE,FALSE 4,25,0,0,193,7548.633333,0.013471503,0.029620035,0,0,Jul,2,2,1,3,Returning_Visitor,FALSE,FALSE 3,58.4,0,0,11,267.3,0,0.016666667,0,0,Nov,2,2,4,2,New_Visitor,TRUE,FALSE 0,0,0,0,57,820.3636364,0.035087719,0.061650605,0,0,June,3,2,3,13,Returning_Visitor,FALSE,FALSE 3,118,0,0,32,791.8,0.021621622,0.046846847,1.297677921,0,Sep,2,2,1,3,Returning_Visitor,FALSE,FALSE 8,77.5,0,0,35,2518.266667,0.01025641,0.012576313,0,0,Jul,2,2,2,2,New_Visitor,TRUE,FALSE 4,61.63333333,0,0,33,1804.011111,0,0.005022447,41.64590199,0,Nov,2,2,2,2,Returning_Visitor,FALSE,TRUE 5,68.8,0,0,33,781.5166667,0.005555556,0.008333333,22.53693712,0,Jul,2,2,6,2,New_Visitor,TRUE,FALSE 3,11.6,0,0,25,510.38,0.007692308,0.023076923,0,0,Oct,2,2,2,1,Returning_Visitor,FALSE,FALSE 0,0,0,0,5,87.6,0,0.04,0,0,Aug,2,4,1,1,Returning_Visitor,FALSE,FALSE 2,34.18095238,0,0,70,2012.660952,0,0.001408451,0,0,Nov,2,4,3,5,New_Visitor,TRUE,FALSE 3,36.3,0,0,13,309.55,0,0.029166667,0,0,June,2,2,3,1,Returning_Visitor,FALSE,FALSE 3,498.6,1,152.2,25,1556.491429,0.036206897,0.067624521,5.689147469,0,Jul,2,5,4,1,Returning_Visitor,TRUE,TRUE 1,23.2,0,0,27,1061.55,0.003703704,0.018518519,43.29821166,0,Aug,2,2,6,5,Returning_Visitor,FALSE,TRUE 4,194.4,0,0,12,182.5142857,0,0.001666667,0,0,Oct,1,1,6,2,Returning_Visitor,FALSE,FALSE 0,0,0,0,9,125.2666667,0.044444444,0.103703704,0,0,Oct,2,2,3,4,Returning_Visitor,TRUE,FALSE 3,28.65,0,0,9,75.05,0.066666667,0.111111111,0,0,Sep,3,2,4,3,Returning_Visitor,FALSE,FALSE 2,66.5,0,0,5,118.5,0,0.042857143,0,0,June,2,2,7,2,Returning_Visitor,FALSE,FALSE 2,194.9,0,0,14,227.3333333,0,0.009183673,0,0,Nov,1,1,4,3,Returning_Visitor,FALSE,TRUE 0,0,0,0,5,0,0.2,0.2,0,0,Jul,3,2,2,13,Returning_Visitor,TRUE,FALSE 5,44.66666667,1,144,101,1808.162266,0.045307443,0.06290128,2.153839297,0,Jul,3,2,1,3,Returning_Visitor,FALSE,FALSE 2,83.6,1,0,18,1219.7,0,0.00952381,16.36245374,0,Aug,2,2,1,4,New_Visitor,FALSE,TRUE 0,0,0,0,32,893.45,0,0.011458333,0,0,Oct,4,1,1,5,Returning_Visitor,FALSE,FALSE 0,0,0,0,6,268.4,0,0.04,0,0,Jul,2,4,9,6,Returning_Visitor,FALSE,FALSE 1,11,0,0,1,0,0,0.1,0,0,Aug,2,6,1,3,Returning_Visitor,FALSE,FALSE 0,0,0,0,45,2347.333333,0.004545455,0.015909091,0,0,Nov,2,4,1,1,Returning_Visitor,FALSE,FALSE 0,0,0,0,41,817.7958109,0.034492302,0.05715957,0,0,June,3,2,1,13,Returning_Visitor,FALSE,FALSE 7,79.2,0,0,30,1011.916667,0,0.018285714,12.9058765,0,Sep,2,2,4,4,Returning_Visitor,FALSE,FALSE 0,0,1,0,7,1646.2,0.025,0.075,0,0,June,2,2,1,2,Returning_Visitor,FALSE,FALSE 5,35.7,5,95.65,95,2383.139223,0.019666667,0.036723457,0,0,Aug,3,2,1,3,Returning_Visitor,FALSE,FALSE 3,59.79,0,0,39,752.56,0,0.000696864,0,0,Nov,2,4,1,2,New_Visitor,FALSE,FALSE 0,0,0,0,1,0,0.2,0.2,0,0,Jul,1,1,1,1,Returning_Visitor,FALSE,FALSE 1,15.2,0,0,6,199.6,0,0.014285714,0,0,Oct,1,8,2,6,New_Visitor,FALSE,FALSE 0,0,0,0,23,386.18,0.004347826,0.016521739,0,0,Jul,1,1,1,4,Returning_Visitor,FALSE,FALSE 0,0,0,0,31,464.9333333,0.006451613,0.033870968,0,0,June,2,2,3,3,Returning_Visitor,FALSE,FALSE 4,96.4,0,0,10,217.6,0,0,0,0,Nov,3,2,2,2,New_Visitor,TRUE,FALSE 5,77,0,0,94,3210.848218,0.00212766,0.020921986,0,0,Oct,2,2,3,2,Returning_Visitor,TRUE,FALSE 0,0,2,38.2,39,873,0.01,0.02,0,0,Sep,1,1,1,4,Returning_Visitor,FALSE,FALSE 1,0,0,0,12,277.45,0.06,0.09,0,0,June,3,2,3,13,Returning_Visitor,FALSE,FALSE 0,0,0,0,28,512.7166667,0.007142857,0.013571429,1.401949127,0,Jul,2,2,7,1,Returning_Visitor,TRUE,TRUE 2,8,2,90.2,13,1286.050476,0.0125,0.019886364,0,0,Aug,2,2,3,2,Returning_Visitor,FALSE,FALSE 0,0,4,225.7666667,222,9630.209524,0.053355457,0.066158871,0,0,June,8,13,9,5,Other,FALSE,FALSE 0,0,0,0,9,140.2,0,0.044444444,0,0,Sep,2,2,9,2,New_Visitor,TRUE,FALSE 1,11,0,0,6,43.7,0,0.057142857,0,0,Oct,2,2,1,2,Returning_Visitor,FALSE,FALSE 1,66.4,0,0,39,606.3666667,0.015789474,0.026754386,0,0,Jul,2,2,1,4,Returning_Visitor,FALSE,FALSE 4,28.73333333,0,0,12,243.75,0,0.038571429,8.150811238,0,June,3,2,4,2,Returning_Visitor,TRUE,TRUE 6,661.7666667,6,204,8,254.25,0.025,0.042222222,0,0,Aug,3,2,4,2,Returning_Visitor,TRUE,FALSE 1,53.4,0,0,18,539.5,0.011111111,0.018888889,0,0,Oct,2,2,3,1,Returning_Visitor,FALSE,FALSE 2,23.8,0,0,6,135.2,0,0.025,0,0,Sep,2,5,3,1,New_Visitor,FALSE,FALSE 2,13,0,0,16,346.3866667,0,0.029411765,0,0,Jul,3,2,3,4,Returning_Visitor,FALSE,FALSE 1,23.96,0,0,94,2103.938772,0.013731061,0.038809156,1.47623044,0,Jul,2,2,1,1,Returning_Visitor,FALSE,FALSE 9,64.5,3,99.6,33,768.4133333,0.004878049,0.013617886,41.34613116,0,Sep,2,2,4,2,Returning_Visitor,FALSE,FALSE 9,358.2583333,3,377.4333333,64,5738.902193,0,0.020187793,12.08836478,0,Oct,2,2,1,13,Returning_Visitor,FALSE,TRUE 0,0,0,0,8,64.6,0.05,0.075,0,0,Jul,2,2,2,13,Returning_Visitor,FALSE,FALSE 0,0,0,0,26,559.56,0.008,0.008,0,0,Jul,1,1,4,4,Returning_Visitor,FALSE,FALSE 2,82.6,0,0,10,352,0,0.007692308,27.19298393,0,Sep,2,2,1,2,New_Visitor,TRUE,TRUE 0,0,0,0,5,53.4,0.12,0.14,0,0,June,3,2,3,13,Returning_Visitor,TRUE,FALSE 2,31.2,0,0,3,55.4,0,0.05,0,0,Oct,2,2,1,5,New_Visitor,FALSE,FALSE 0,0,0,0,9,206.6,0,0.005555556,0,0,Jul,2,4,3,4,New_Visitor,FALSE,FALSE 7,199.85,0,0,39,1267.453333,0.004761905,0.020634921,17.92758699,0,Nov,2,4,1,4,Returning_Visitor,FALSE,FALSE 1,5.5,6,387.5,25,621.2,0,0.009333333,0,0,Jul,3,2,1,4,Returning_Visitor,TRUE,FALSE 0,0,0,0,15,148.9333333,0,0.04,0,0,June,4,2,1,13,Returning_Visitor,FALSE,FALSE 4,64.6,2,28.7,16,597.6,0.019047619,0.018594104,0,0,Oct,3,2,1,5,Returning_Visitor,TRUE,FALSE 0,0,0,0,43,654.0666667,0,0.029844961,0,0,Jul,2,2,7,3,Returning_Visitor,FALSE,FALSE 2,53.4,0,0,2,0,0.05,0.1,0,0,Sep,1,1,6,3,Returning_Visitor,FALSE,FALSE 5,59.9,2,8,84,2359.522438,0.016666667,0.028709742,3.478135617,0,June,1,1,4,3,Returning_Visitor,FALSE,FALSE 1,23.2,0,0,4,44.8,0,0.05,0,0,June,2,10,7,4,Returning_Visitor,TRUE,FALSE 1,0,0,0,23,579.3,0,0.009090909,0,0,Sep,2,2,2,2,New_Visitor,FALSE,FALSE 1,46.4,0,0,13,118.2666667,0,0.014285714,0,0,Jul,1,2,1,4,Returning_Visitor,TRUE,FALSE 8,176.25,1,141,20,651.7658974,0,0.009230769,19.13285864,0,Nov,2,2,4,2,Returning_Visitor,FALSE,TRUE 9,68.4,1,6,30,728.8666667,0.037373737,0.063636364,0,0,Oct,2,2,3,20,Returning_Visitor,TRUE,FALSE 5,265.0433333,1,0,38,1712.776667,0.013333333,0.040833333,0,0,Aug,2,2,8,1,Returning_Visitor,FALSE,FALSE 4,68.73333333,0,0,44,2498.866667,0,0.014444444,11.33062869,0,Oct,2,2,4,1,Returning_Visitor,FALSE,TRUE 9,219.9333333,0,0,56,1371.415714,0.002539683,0.011752445,0,0,Oct,3,2,1,5,Returning_Visitor,TRUE,FALSE 0,0,2,38.2,121,3362.749679,0.025170765,0.042322417,4.212415755,0,Jul,1,1,1,3,Returning_Visitor,FALSE,FALSE 2,70.6,0,0,30,515.2333333,0,0.02,0,0,Aug,1,1,3,1,Returning_Visitor,FALSE,FALSE 0,0,0,0,62,2636.513492,0.04781874,0.061364567,0,0,Jul,3,2,3,13,Returning_Visitor,TRUE,FALSE 1,70,0,0,12,1644.233333,0,0.015384615,0,0,Aug,1,1,4,4,New_Visitor,FALSE,FALSE 8,166.7,0,0,9,171.7,0.016666667,0.05,0,0,Nov,2,2,1,3,Returning_Visitor,FALSE,FALSE 0,0,1,322.4,27,1747.7,0.007692308,0.025641026,0,0,Oct,2,4,5,1,Returning_Visitor,FALSE,FALSE 1,54.4,0,0,6,270.9,0,0.014285714,0,0,Oct,1,1,7,3,New_Visitor,FALSE,FALSE 5,152.1,0,0,27,1474.533333,0.014814815,0.030123457,27.89717093,0,Oct,3,3,6,1,Returning_Visitor,TRUE,FALSE 0,0,0,0,3,255,0,0.066666667,0,0,June,2,2,3,4,Returning_Visitor,FALSE,FALSE 0,0,0,0,25,1131.8,0.024,0.033142857,0,0,June,1,1,4,1,Returning_Visitor,TRUE,FALSE 2,58.4,0,0,5,303.1,0,0.033333333,0,0,Oct,3,2,3,6,Returning_Visitor,FALSE,TRUE 1,106.8,0,0,17,327.5380952,0,0.00625,0,0,Oct,3,2,3,2,New_Visitor,FALSE,FALSE 0,0,0,0,39,1867.033333,0,0.020512821,0,0,Nov,2,4,4,2,Returning_Visitor,FALSE,FALSE 0,0,0,0,1,0,0.2,0.2,0,0,June,2,4,3,4,New_Visitor,FALSE,FALSE 0,0,0,0,3,50.2,0,0.066666667,0,0,Sep,2,2,1,13,Returning_Visitor,FALSE,FALSE 3,82.6,0,0,1,16.2,0,0.05,0,0,Nov,2,2,9,2,New_Visitor,FALSE,FALSE 7,275.0844444,0,0,70,806.0266667,0,0.002666667,0,0,Oct,2,5,1,2,New_Visitor,FALSE,FALSE 1,17.2,0,0,6,243.3,0,0.05,22.09581063,0,Oct,1,8,8,1,Returning_Visitor,FALSE,FALSE 11,189.4833333,1,303.2,91,4864.147143,0,0.002380952,0,0,Oct,2,2,1,2,New_Visitor,TRUE,FALSE 0,0,0,0,13,374.9,0,0.047619048,16.72561819,0,Aug,4,1,1,3,Returning_Visitor,FALSE,TRUE 4,74.5,0,0,22,672.95,0,0.027179487,0,0,Sep,2,2,9,2,Returning_Visitor,FALSE,TRUE 3,33.6,0,0,4,109.5,0,0.013333333,0,0,Oct,3,2,1,2,New_Visitor,FALSE,FALSE 0,0,0,0,19,623.2,0.022222222,0.044444444,0,0,Jul,2,5,4,1,Returning_Visitor,TRUE,FALSE 4,304.6,0,0,7,137.6,0,0.0375,0,0,Aug,3,2,1,5,New_Visitor,FALSE,FALSE 0,0,0,0,36,1083.75163,0.006666667,0.011684359,0,0,June,1,1,3,2,Returning_Visitor,FALSE,FALSE 1,5.5,0,0,103,1955.168685,0.015359477,0.037057104,0,0,June,2,2,9,13,Returning_Visitor,TRUE,FALSE 0,0,0,0,103,3357.053333,0.015533981,0.043527508,0,0,Sep,2,2,4,1,Returning_Visitor,FALSE,FALSE 0,0,0,0,62,1200.354444,0.003225806,0.023655914,0,0,Nov,2,2,4,1,Returning_Visitor,FALSE,FALSE 0,0,0,0,74,1268.266667,0.002702703,0.00954955,0,0,June,2,2,2,4,Returning_Visitor,FALSE,FALSE 0,0,0,0,27,4622,0.027160494,0.090123457,0,0,Oct,2,5,6,1,Returning_Visitor,FALSE,FALSE 2,22.2,0,0,18,470.8966667,0,0.005,0,0,Nov,2,2,2,4,New_Visitor,FALSE,FALSE 0,0,0,0,50,1085.891081,0.025952381,0.041398682,0,0,Oct,1,1,1,4,Returning_Visitor,FALSE,FALSE 3,102.8,0,0,25,1109.233333,0,0.001666667,61.30486592,0,Aug,2,2,6,5,New_Visitor,FALSE,TRUE 4,353.4,2,0,11,587.2,0.013333333,0.04,0,0,Jul,2,5,2,3,Returning_Visitor,TRUE,FALSE 5,151.3333333,0,0,11,283.3333333,0.033333333,0.058333333,13.91046998,0,Nov,2,2,6,20,Returning_Visitor,FALSE,TRUE 2,36.2,3,52,4,48,0.057142857,0.057142857,0,0,Jul,1,1,1,4,Returning_Visitor,TRUE,FALSE 10,208.5133333,0,0,17,369.1,0,0.004,0,0,Sep,3,2,1,6,New_Visitor,FALSE,FALSE 0,0,0,0,26,436.8,0.074074074,0.088888889,0,0,Jul,2,2,1,3,Returning_Visitor,FALSE,FALSE 4,175.8666667,0,0,2,5,0,0.04,0,0,Oct,1,1,3,2,New_Visitor,FALSE,FALSE 5,30,1,4,107,4376.902655,0.007272727,0.021707552,0,0,Oct,2,2,2,4,Returning_Visitor,FALSE,FALSE 1,26.2,0,0,32,1427.676667,0,0.021875,19.9656361,0,Sep,2,4,3,4,Returning_Visitor,FALSE,TRUE 0,0,0,0,16,511.2,0.0125,0.01875,0,0,Jul,2,2,3,3,Returning_Visitor,FALSE,FALSE 6,1672.033333,0,0,28,518.3966667,0.006060606,0.013333333,0,0,Oct,2,2,3,2,Returning_Visitor,TRUE,FALSE 5,187.1,0,0,101,7220.776667,0.001960784,0.013366013,3.998184141,0,Nov,2,2,7,3,Returning_Visitor,FALSE,TRUE 8,133.35,2,383.8,14,552.48,0.005263158,0.016140351,20.33155364,0,Aug,1,1,1,6,Returning_Visitor,FALSE,FALSE 9,570.2666667,0,0,11,259.5533333,0.011111111,0.05,6.80128988,0,Oct,3,2,3,2,Returning_Visitor,FALSE,FALSE 15,238.6838095,0,0,33,799.1049417,0.004347826,0.017574237,19.03293603,0,Sep,3,2,3,2,Returning_Visitor,TRUE,FALSE 6,144.8333333,0,0,23,1007.233333,0,0.027586207,19.57059225,0,Oct,2,2,1,2,Returning_Visitor,FALSE,FALSE 2,40.8,0,0,45,1668.125238,0.004444444,0.024107744,47.57360345,0,Sep,1,1,4,2,Returning_Visitor,FALSE,FALSE 0,0,0,0,7,189.2,0,0.014285714,78.56959864,0,Sep,3,2,3,2,Returning_Visitor,FALSE,TRUE 9,122.2,0,0,51,2303.96,0.019496855,0.036855346,0,0,Aug,2,4,4,2,Returning_Visitor,FALSE,FALSE 0,0,0,0,8,158.42,0,0.05,0,0,June,2,2,1,2,Returning_Visitor,FALSE,FALSE 2,25.2,4,86.6,21,1702.5,0.016666667,0.037179487,0,0,Oct,2,2,7,3,Returning_Visitor,TRUE,FALSE 0,0,0,0,2,11,0,0.1,0,0,Jul,2,2,1,1,Returning_Visitor,TRUE,FALSE 2,73.75,0,0,20,546.669697,0,0.004393939,0,0,Oct,2,2,1,2,Returning_Visitor,FALSE,FALSE 2,68.6,0,0,10,279.7666667,0,0.033333333,0,0,Oct,1,2,7,4,New_Visitor,FALSE,FALSE 1,19.4,0,0,18,1077.373333,0.034126984,0.035714286,18.22752549,0,Nov,3,2,9,2,Returning_Visitor,FALSE,FALSE 10,242.6333333,4,23,47,1534.815256,0.022807018,0.036908939,5.194081873,0,Sep,1,1,1,3,Returning_Visitor,FALSE,FALSE 3,17.94545455,1,6,18,268.4525974,0,0.014267677,0,0,Oct,2,2,1,2,Returning_Visitor,FALSE,TRUE 0,0,0,0,7,116.8,0.028571429,0.114285714,0,0,Jul,2,2,6,13,Returning_Visitor,FALSE,FALSE 6,80.5,1,10.06666667,23,477.7,0,0.007142857,0,0,Jul,4,2,7,2,New_Visitor,TRUE,FALSE 4,158.2,0,0,62,4402.25,0,0.001041667,246.7585902,0,Oct,4,1,8,2,New_Visitor,FALSE,FALSE 0,0,0,0,5,334.4,0,0.025,0,0,Sep,3,2,6,1,Returning_Visitor,FALSE,FALSE 3,180.8,0,0,10,408.4666667,0,0,0,0,Aug,4,1,3,1,Returning_Visitor,TRUE,FALSE 7,2720.5,3,353.4,68,5943.547619,0.032236842,0.038623482,0,0,Jul,3,2,1,13,Returning_Visitor,FALSE,FALSE 2,154.6,2,69.6,20,479.1,0,0.009375,0,0,Sep,3,2,2,1,New_Visitor,FALSE,FALSE 1,125,0,0,24,677.3333333,0,0.032,0,0,Nov,3,2,8,1,Returning_Visitor,FALSE,FALSE 2,75.6,1,11,28,2074.929524,0,0.016935484,0,0,Oct,2,4,3,2,Returning_Visitor,FALSE,FALSE 2,10.3,0,0,142,4204.433333,0.009722222,0.021345899,0.770991864,0,Jul,2,2,4,13,Returning_Visitor,FALSE,FALSE 4,124.6,0,0,4,71,0,0.028571429,0,0,Jul,3,3,4,1,New_Visitor,FALSE,FALSE 12,139.3666667,0,0,13,139.4333333,0,0.007017544,47.30370179,0,Oct,3,2,1,5,New_Visitor,TRUE,FALSE 1,12.06666667,0,0,45,591.2640602,0,0.013028631,0,0,Sep,2,2,1,2,Returning_Visitor,FALSE,TRUE 0,0,0,0,43,3747.833333,0.020930233,0.051162791,0,0,Jul,2,2,3,13,Returning_Visitor,FALSE,FALSE 0,0,0,0,10,274.3,0,0.06,0,0,June,2,2,4,3,Returning_Visitor,FALSE,FALSE 0,0,0,0,21,1280.02,0,0.019047619,7.232201859,0,Nov,2,2,1,20,Returning_Visitor,FALSE,TRUE 3,45.8,0,0,63,2435.697531,0.00952381,0.017460317,0,0,Jul,3,2,4,3,Returning_Visitor,FALSE,FALSE 1,15.2,0,0,81,4536.308139,0.032304527,0.054759418,0,0,Jul,3,2,1,1,Returning_Visitor,FALSE,FALSE 0,0,0,0,15,203.35,0.025,0.066666667,0,0,Sep,2,2,3,3,Returning_Visitor,FALSE,FALSE 4,133.8,2,54.4,13,421.7857143,0,0.023529412,0,0,Nov,1,1,3,2,New_Visitor,FALSE,FALSE 0,0,0,0,31,485.6333333,0.019354839,0.038709677,0,0,Jul,2,2,1,4,Returning_Visitor,FALSE,FALSE 1,21.2,0,0,21,836.1,0,0.004545455,0,0,Oct,2,4,9,4,New_Visitor,FALSE,FALSE 0,0,0,0,30,1500.213333,0,0.003333333,0,0,Oct,2,6,1,1,Returning_Visitor,FALSE,FALSE 0,0,0,0,3,845.3,0,0.033333333,0,0,Sep,1,2,1,2,New_Visitor,TRUE,FALSE 0,0,0,0,8,194.4,0,0.025,0,0,Aug,2,10,6,4,Returning_Visitor,FALSE,FALSE 0,0,0,0,2,58.6,0,0.05,0,0,Jul,1,2,2,1,Returning_Visitor,FALSE,FALSE 0,0,2,842.3,26,1911.711111,0.015384615,0.036666667,8.018832458,0,Jul,3,2,1,2,Returning_Visitor,TRUE,TRUE 0,0,0,0,14,253.1,0.057142857,0.078571429,0,0,June,1,1,2,3,Returning_Visitor,FALSE,FALSE 0,0,0,0,3,36.6,0,0.033333333,0,0,Aug,2,2,6,2,Returning_Visitor,FALSE,FALSE 0,0,0,0,0,0,0.18,0.181818182,0,0,June,2,2,7,6,Returning_Visitor,FALSE,FALSE 0,0,0,0,22,821.8933333,0.001515152,0.042424242,0,0,Jul,2,4,6,1,Returning_Visitor,FALSE,FALSE 2,23,1,80.6,53,1693.73,0.034545455,0.054839161,0,0,Aug,2,2,1,1,Returning_Visitor,FALSE,FALSE 0,0,1,426.2,10,157.0666667,0,0.016666667,32.73061637,0,Jul,2,2,3,2,Returning_Visitor,FALSE,TRUE 0,0,0,0,31,804.0430818,0,0.002090109,0,0,June,3,2,1,3,Returning_Visitor,FALSE,FALSE 9,115.4,0,0,29,1514.703333,0.005,0.011342593,4.702065533,0,Jul,3,2,8,1,Returning_Visitor,TRUE,FALSE 9,211,1,52.4,22,501.0714286,0,0.034666667,0,0,Aug,1,2,1,4,Returning_Visitor,TRUE,TRUE 1,28.2,1,36.2,130,9447.578514,0.012661499,0.025758194,0,0,Sep,1,2,5,1,Returning_Visitor,FALSE,FALSE 2,40.9,0,0,9,437.4,0.018181818,0.027272727,0,0,Jul,3,2,1,5,Returning_Visitor,TRUE,FALSE 10,275.3333333,1,6,19,719.8833333,0,0.018518519,0,0,Sep,2,2,5,5,Returning_Visitor,TRUE,TRUE 2,35.2,0,0,15,521.8666667,0,0.00625,0,0,June,2,2,9,2,New_Visitor,FALSE,FALSE 0,0,0,0,8,55.3,0,0.025,0,0,Jul,1,1,3,3,Returning_Visitor,FALSE,FALSE 8,246.5333333,0,0,79,2150.433333,0.007228916,0.020883534,17.35448787,0,Nov,1,1,1,3,Returning_Visitor,FALSE,FALSE 4,245.8,1,0,28,707.9116667,0,0.017241379,0,0,Sep,1,1,3,2,Returning_Visitor,FALSE,FALSE 0,0,0,0,3,96.7,0,0,0,0,Jul,1,1,1,2,Returning_Visitor,TRUE,FALSE 16,391.6833333,0,0,24,443.75,0,0.006451613,0,0,Aug,2,10,7,3,Returning_Visitor,FALSE,FALSE 4,43.46666667,0,0,4,27.26666667,0,0.028571429,0,0,Sep,1,1,1,5,New_Visitor,FALSE,TRUE 8,452.5,0,0,30,643.4333333,0,0.019047619,0,0,Oct,1,1,9,2,Returning_Visitor,FALSE,FALSE 7,123.2,0,0,16,283.7214286,0.014473684,0.022222222,0,0,Jul,3,2,3,13,Returning_Visitor,FALSE,FALSE 0,0,0,0,25,883.9761905,0.02,0.029333333,0,0,Sep,3,2,3,4,Returning_Visitor,TRUE,FALSE 2,35.3,0,0,3,18.1,0.033333333,0.061111111,0,0,Jul,3,2,3,3,Returning_Visitor,TRUE,TRUE 0,0,0,0,29,1970,0,0,100.7256048,0,Oct,4,1,5,2,Returning_Visitor,FALSE,TRUE 0,0,0,0,4,52.3,0,0.1,0,0,Aug,2,2,6,5,Returning_Visitor,FALSE,FALSE 4,96.8,1,535,9,1888.2,0,0.027272727,0,0,Nov,2,2,9,2,Returning_Visitor,FALSE,FALSE 4,103,0,0,32,1092.183333,0,0.00625,0,0,Jul,2,2,6,1,Returning_Visitor,FALSE,FALSE 2,15.2,0,0,22,244.2,0.027272727,0.063636364,0,0,Aug,2,2,7,1,Returning_Visitor,TRUE,FALSE 4,71.7,0,0,8,74.6,0.109090909,0.127272727,0,0,Jul,3,2,6,13,Returning_Visitor,FALSE,FALSE 6,631.1073171,3,7.6,28,2104.573984,0.007189542,0.034273974,35.51518951,0,Nov,2,2,5,6,Returning_Visitor,FALSE,FALSE 7,94.84,0,0,43,1487.229317,0.004545455,0.019638695,0,0,Oct,2,4,1,2,Returning_Visitor,FALSE,FALSE 11,196.15,0,0,141,2623.580864,0,0.004244028,12.50786523,0,Nov,2,2,6,2,Returning_Visitor,FALSE,FALSE 0,0,0,0,32,875.9333333,0.031770833,0.072395833,0,0,Oct,1,1,3,3,Returning_Visitor,FALSE,FALSE 1,215.6,2,8,27,2968.9,0.021428571,0.036904762,0,0,Nov,3,2,6,1,Returning_Visitor,FALSE,FALSE 6,88.28,0,0,17,310.9714286,0.031578947,0.035247209,0,0,Oct,3,2,1,13,Returning_Visitor,FALSE,FALSE 5,271.75,1,0,40,675.2020988,0.015,0.019412202,0,0,Oct,2,2,3,2,Returning_Visitor,FALSE,FALSE 0,0,2,32.2,41,1321.322222,0,0.021428571,3.979543638,0,Oct,4,1,1,2,Returning_Visitor,FALSE,FALSE 1,8.1,0,0,16,2227.8,0,0.021428571,0,0,Sep,2,2,1,5,New_Visitor,FALSE,TRUE 3,195.4,2,294.6,93,6412.233333,0,0.026687598,0,0,Sep,2,2,9,2,Returning_Visitor,FALSE,TRUE 3,36.2,0,0,2,4,0,0.04,27.79373861,0,Oct,2,2,6,2,New_Visitor,TRUE,FALSE 4,71.33333333,0,0,21,295.3333333,0.016666667,0.058333333,0,0,Jul,2,10,3,2,Returning_Visitor,TRUE,TRUE 0,0,3,44,52,1216.364286,0.050909091,0.073225108,0,0,Sep,3,2,1,13,Returning_Visitor,TRUE,FALSE 0,0,0,0,35,1608.7,0,0.002857143,0,0,Sep,2,4,3,2,New_Visitor,FALSE,FALSE 0,0,0,0,24,1476.021429,0.013043478,0.024378882,0,0,Sep,2,2,8,2,Returning_Visitor,FALSE,FALSE 0,0,0,0,6,1156.5,0,0.033333333,0,0,Nov,2,2,1,20,Returning_Visitor,FALSE,FALSE 3,25.6,0,0,10,327.7733333,0,0.013095238,11.78466261,0,Jul,1,1,1,5,Returning_Visitor,FALSE,TRUE 6,103.9,3,50.2,9,1759.066667,0.011764706,0.024705882,0,0,Sep,1,1,7,5,Returning_Visitor,FALSE,FALSE 11,603.8,2,102.75,305,5018.328289,0.001607717,0.005625881,0,0,Oct,3,2,6,3,Returning_Visitor,FALSE,FALSE 0,0,0,0,3,11,0.066666667,0.133333333,0,0,June,2,2,1,4,Returning_Visitor,FALSE,FALSE 0,0,0,0,31,631.0857143,0.012903226,0.02078853,0,0,June,2,2,8,1,Returning_Visitor,FALSE,FALSE 1,17.2,0,0,4,192.4,0,0.01,0,0,Sep,2,2,2,2,New_Visitor,FALSE,FALSE 0,0,0,0,1,0,0.2,0.2,0,0,Jul,2,4,7,1,Returning_Visitor,FALSE,FALSE 3,61.4,1,0,19,381.6,0.040909091,0.056818182,8.330574815,0,Aug,1,1,4,1,Returning_Visitor,FALSE,TRUE 0,0,0,0,20,1002.366667,0,0.007894737,47.57898514,0,Oct,2,2,4,2,Returning_Visitor,TRUE,TRUE 0,0,0,0,12,213.2833333,0.016666667,0.041666667,0,0,Aug,3,2,3,4,Returning_Visitor,FALSE,FALSE 2,61.5,0,0,15,1950.386667,0,0.007291667,0,0,Nov,2,10,2,4,Returning_Visitor,TRUE,FALSE 4,134.1,0,0,7,110.3,0,0.015151515,0,0,Sep,3,2,1,5,New_Visitor,FALSE,FALSE 1,65.4,0,0,37,2097.42,0.003603604,0.010990991,0,0,Jul,1,1,5,2,Returning_Visitor,FALSE,FALSE 0,0,0,0,60,1288.563333,0,0.017777778,0,0,June,2,4,3,4,Returning_Visitor,FALSE,FALSE 2,55.4,0,0,20,654.2,0,0.00952381,0,0,Sep,4,1,1,2,New_Visitor,TRUE,FALSE 0,0,0,0,2,28.2,0,0.1,0,0,Jul,2,2,1,1,Returning_Visitor,FALSE,FALSE 0,0,0,0,1,0,0.2,0.2,0,0,Nov,2,2,3,13,Returning_Visitor,FALSE,FALSE 0,0,0,0,45,507.1111111,0.008888889,0.024444444,0,0,June,4,2,3,1,Returning_Visitor,FALSE,FALSE 11,456.2333333,1,13,89,1904.653507,0.01827957,0.027002924,0,0,Oct,1,1,4,3,Returning_Visitor,FALSE,FALSE 2,95.3,0,0,14,262.587619,0.0125,0.004910714,0,0,Nov,1,1,4,2,New_Visitor,FALSE,FALSE 0,0,1,37.2,28,761.5,0.006896552,0.032183908,0,0,Jul,2,5,1,1,Returning_Visitor,FALSE,FALSE 7,70.5,0,0,24,463.6940476,0.013620072,0.045071685,0,0,June,2,2,1,1,Returning_Visitor,FALSE,FALSE 0,0,0,0,16,196.3333333,0.0125,0.023958333,0,0,Oct,1,1,3,2,Returning_Visitor,TRUE,FALSE 0,0,0,0,28,403.8333333,0.017857143,0.029829932,0,0,Jul,1,1,6,4,Returning_Visitor,FALSE,FALSE 0,0,0,0,20,799.0619048,0.0125,0.04275,0,0,June,1,1,6,4,Returning_Visitor,FALSE,FALSE 0,0,0,0,17,472.9,0.0125,0.025,0,0,Oct,3,2,3,1,Returning_Visitor,FALSE,FALSE 1,10,1,36.6,78,1983.225629,0.0025,0.007916667,15.50362136,0,June,3,2,1,2,Returning_Visitor,FALSE,FALSE 0,0,0,0,9,311.7,0,0.055555556,0,0,June,2,2,4,13,Returning_Visitor,FALSE,FALSE 2,11,0,0,59,779.2628571,0,0.007471264,0,0,Aug,4,1,1,4,Returning_Visitor,FALSE,FALSE 0,0,0,0,25,334.3833333,0,0.008333333,0,0,Sep,2,2,3,4,Returning_Visitor,FALSE,FALSE 1,15.8,3,97.8,32,1213.65,0.005405405,0.024324324,1.273317415,0,Aug,2,2,3,2,Returning_Visitor,FALSE,TRUE 4,68.66666667,0,0,14,617.15,0,0.004444444,9.253161679,0,Sep,1,1,6,5,New_Visitor,FALSE,TRUE 1,30.86666667,3,7,15,398.2566667,0,0.025,0,0,Nov,1,1,3,2,Returning_Visitor,FALSE,FALSE 0,0,0,0,4,271.4666667,0,0.021428571,0,0,Jul,3,2,1,6,Returning_Visitor,FALSE,FALSE 8,498.9809654,6,433.1,99,2129.989707,0.001869159,0.006805318,129.1013738,0,Nov,2,2,2,1,Returning_Visitor,TRUE,FALSE 0,0,0,0,18,476.1777778,0.05,0.066666667,1.718218835,0,Aug,3,2,5,13,Returning_Visitor,FALSE,FALSE 5,84.34545455,3,106.8,80,4240.036205,0.000169693,0.011635537,1.257695521,0,Jul,3,2,1,2,Returning_Visitor,FALSE,FALSE 0,0,0,0,4,35.33333333,0.05,0.1,0,0,June,1,1,1,2,Returning_Visitor,TRUE,FALSE 1,0,0,0,53,1170.733333,0.007272727,0.025454545,31.90320066,0,Aug,2,4,2,3,Returning_Visitor,FALSE,TRUE 1,32.7,0,0,27,687.1966667,0,0.019393939,0,0,Nov,3,2,1,1,Returning_Visitor,FALSE,FALSE 4,438.8,0,0,4,599,0,0.011111111,0,0,Aug,3,2,3,5,New_Visitor,TRUE,FALSE 0,0,0,0,3,0,0.2,0.2,0,0,Aug,1,1,3,4,Returning_Visitor,FALSE,FALSE 9,181.8166667,0,0,9,126.9166667,0,0.015384615,0,0,Sep,1,1,7,4,Returning_Visitor,FALSE,FALSE 0,0,0,0,7,70.4,0.114285714,0.128571429,0,0,Jul,3,2,4,1,Returning_Visitor,FALSE,FALSE 0,0,0,0,5,27.2,0,0.04,0,0,Nov,2,2,1,4,Returning_Visitor,FALSE,FALSE 1,49.4,0,0,18,213.1714286,0,0.003921569,0,0,Oct,2,2,1,2,New_Visitor,FALSE,FALSE 0,0,0,0,22,681.58,0.00952381,0.025396825,14.63523974,0,Nov,1,1,1,3,Returning_Visitor,TRUE,TRUE 6,141.0583333,0,0,46,1728.866268,0,0.019059741,0,0,June,2,2,7,4,Returning_Visitor,FALSE,FALSE 5,103.9,1,15.1,199,7959.909524,0.028594771,0.058447712,0,0,Aug,2,2,3,13,Returning_Visitor,FALSE,FALSE 0,0,2,10,18,222.3,0.01,0.025,0,0,June,2,2,4,1,Returning_Visitor,TRUE,FALSE 8,112.27,0,0,149,4304.366905,0.01025641,0.027991453,0,0,Sep,2,5,1,1,Returning_Visitor,FALSE,FALSE 0,0,0,0,4,145.2,0,0.025,0,0,Aug,2,4,7,2,Returning_Visitor,FALSE,FALSE 4,227.48,3,432.2,56,4895.26,0.016949153,0.043785311,5.763678818,0,Nov,2,4,3,1,Returning_Visitor,FALSE,FALSE 0,0,0,0,5,186.0666667,0.146666667,0.15952381,0,0,June,3,2,1,13,Returning_Visitor,FALSE,FALSE 2,37.2,0,0,22,295.0333333,0,0.008333333,0,0,Nov,2,4,3,2,New_Visitor,FALSE,FALSE 2,267.8,0,0,6,616.5,0.028571429,0.038095238,0,0,Sep,3,2,3,4,Returning_Visitor,FALSE,FALSE 3,107.8,0,0,11,362.6,0,0.004761905,0,0,Sep,2,2,2,4,Returning_Visitor,FALSE,FALSE 12,232.3404762,1,19.2,22,504.0285714,0,0.003763441,0,0,Sep,1,1,1,3,New_Visitor,FALSE,FALSE 3,50.94615385,1,42.4,38,1323.093695,0.007738095,0.012865853,5.62486136,0,Sep,3,2,8,2,Returning_Visitor,TRUE,FALSE 14,603.9,2,52.4,46,1760.657143,0.01010929,0.0254213,2.006032937,0,Sep,1,1,6,4,Returning_Visitor,TRUE,FALSE 5,168.2,0,0,15,2026.633333,0.008333333,0.039,0,0,Nov,3,2,7,13,Returning_Visitor,FALSE,TRUE 1,18.2,0,0,6,32.2,0.085714286,0.10952381,0,0,June,1,1,1,3,Returning_Visitor,FALSE,FALSE 0,0,0,0,4,157.2,0.04,0.1,0,0,Oct,1,8,3,1,Returning_Visitor,TRUE,FALSE 1,5.5,3,169.2,14,517.66,0.011111111,0.022222222,50.18313917,0,Jul,3,2,1,2,Returning_Visitor,TRUE,FALSE 0,0,0,0,4,1623.666667,0.133333333,0.140833333,0,0,Aug,1,1,1,3,Returning_Visitor,FALSE,FALSE 4,239.6,2,70.5,50,981.5083333,0,0.003636364,0,0,Oct,2,2,2,2,New_Visitor,TRUE,FALSE 10,172.3277778,0,0,20,902.9738095,0,0.009876543,0,0,Sep,4,2,4,4,Returning_Visitor,FALSE,FALSE 3,39.3,0,0,29,316.2,0.006666667,0.02,0,0,Sep,2,2,3,6,Returning_Visitor,FALSE,FALSE 1,28.2,0,0,17,608.2166667,0.047777778,0.051851852,0,0,Jul,2,2,2,1,Returning_Visitor,FALSE,FALSE 2,214.6,3,34.4,1,165.2,0,0.033333333,0,0,Jul,2,2,7,2,Returning_Visitor,FALSE,FALSE 5,154.2,0,0,12,269.5357143,0.004444444,0.006111111,0,0,Aug,3,2,1,4,Returning_Visitor,FALSE,FALSE 2,165.1333333,0,0,87,3220.336667,0.004597701,0.01954023,0,0,June,2,5,8,1,Returning_Visitor,FALSE,TRUE 1,22.2,0,0,43,1257.9,0.00952381,0.020634921,0,0,Sep,2,4,1,3,Returning_Visitor,FALSE,FALSE 12,245.7333333,4,1511.7,439,21857.04648,0.003588626,0.012497612,11.43923328,0,Sep,2,2,1,13,Returning_Visitor,FALSE,FALSE 15,169.0333333,0,0,223,2477.53655,0,0.003694084,0.17982681,0,Oct,2,2,7,3,Returning_Visitor,TRUE,FALSE 6,176.6,2,204.4,45,789.3666667,0,0.004255319,0,0,June,2,5,7,2,New_Visitor,FALSE,FALSE 2,18,2,6,10,363.6,0,0.015384615,0,0,Sep,2,2,1,5,New_Visitor,TRUE,TRUE 0,0,0,0,19,281.4333333,0.038596491,0.082807018,0,0,June,2,2,3,4,Returning_Visitor,FALSE,FALSE 3,81.6,0,0,9,1346.2,0,0.016666667,0,0,Nov,2,2,2,3,New_Visitor,FALSE,FALSE 4,140.0666667,0,0,74,3146.709481,0.0192,0.042311111,0.93770521,0,Jul,1,1,1,1,Returning_Visitor,FALSE,FALSE 0,0,0,0,3,57.4,0,0.133333333,0,0,Aug,3,2,1,3,Returning_Visitor,FALSE,FALSE 0,0,0,0,2,78.1,0.18,0.171428571,0,0,Sep,2,5,3,2,Returning_Visitor,FALSE,FALSE 0,0,0,0,15,431.2,0,0.007142857,0,0,Sep,2,5,9,5,New_Visitor,FALSE,FALSE 9,252.667619,1,0,67,1060.693791,0.022685185,0.029967788,0.131837013,0,Oct,3,2,1,13,Returning_Visitor,FALSE,TRUE 0,0,0,0,24,431.2,0.0125,0.055555556,0,0,Sep,1,8,2,1,Returning_Visitor,FALSE,FALSE 5,43.325,1,295.4,20,675.6846244,0.026634769,0.049669967,0,0,Jul,1,1,4,2,Returning_Visitor,FALSE,FALSE 4,135.4,0,0,2,46.8,0,0.0375,0,0,Sep,1,1,5,2,Returning_Visitor,FALSE,TRUE 0,0,0,0,6,127.4,0,0,0,0,Aug,1,1,8,2,Returning_Visitor,FALSE,FALSE 0,0,0,0,81,2092.67381,0.015,0.027155449,0,0,Oct,2,2,1,1,Returning_Visitor,FALSE,FALSE 0,0,0,0,16,149.9,0.05625,0.1,0,0,June,2,2,7,1,Returning_Visitor,FALSE,FALSE 0,0,0,0,1,0,0.2,0.2,0,0,Sep,2,2,1,1,Returning_Visitor,TRUE,FALSE 5,68.01428571,1,21.2,63,1033.206667,0,0.009068627,0,0,Aug,2,2,2,2,Returning_Visitor,FALSE,FALSE 1,129,1,26.7,28,1392.1,0.031111111,0.056333333,0,0,Sep,3,2,7,13,Returning_Visitor,TRUE,FALSE 0,0,0,0,49,1571.12381,0.004255319,0.010638298,0,0,Jul,2,2,8,4,New_Visitor,TRUE,FALSE 11,362.3,0,0,41,1081.713333,0.01,0.028830409,27.5128393,0,Sep,2,2,1,1,Returning_Visitor,TRUE,FALSE 3,90.7,3,65.4,13,1036.53178,0.035294118,0.048235852,19.74930078,0,Nov,3,2,1,4,Returning_Visitor,TRUE,FALSE 8,760.9,3,206.1,36,2400.667778,0.008527132,0.02372093,0,0,Nov,1,2,7,2,Returning_Visitor,FALSE,FALSE 6,67.4,0,0,13,217.7611111,0.0125,0.025,0,0,Oct,2,2,5,7,Returning_Visitor,TRUE,FALSE 1,17.2,0,0,11,394.8,0,0.027272727,0,0,Nov,2,2,4,13,Returning_Visitor,FALSE,FALSE 1,1566.6,0,0,34,2256.74954,0,0.000250438,0,0,Oct,2,2,3,2,Returning_Visitor,FALSE,FALSE 0,0,0,0,29,1373.933333,0,0.020689655,0,0,Oct,2,4,7,1,Returning_Visitor,FALSE,FALSE 0,0,0,0,4,141,0,0.05,0,0,Aug,4,1,3,3,New_Visitor,FALSE,FALSE 0,0,0,0,34,2091.480392,0.046212121,0.071855922,0,0,Oct,1,1,8,1,Returning_Visitor,FALSE,FALSE 3,59.2,0,0,9,1261.7,0.016666667,0.05,0,0,Aug,2,5,1,5,Returning_Visitor,FALSE,FALSE 3,50.15,0,0,26,1107.116667,0,0.009490509,0,0,Aug,2,2,1,1,Returning_Visitor,FALSE,FALSE 8,166.46,1,25.2,47,981.6014286,0.007272727,0.006818182,0,0,Aug,1,1,1,3,Returning_Visitor,FALSE,FALSE 0,0,0,0,19,187.2,0.068421053,0.131578947,0,0,Nov,3,2,3,13,Returning_Visitor,FALSE,FALSE 1,6,0,0,21,332.8,0,0.008695652,0,0,Sep,2,5,4,5,Returning_Visitor,TRUE,FALSE 5,241.35,0,0,5,244.7,0.011111111,0.033333333,0,0,Nov,2,2,4,1,Returning_Visitor,TRUE,FALSE 4,48.83243423,4,165.6666667,22,365.774339,0.004365079,0.015583028,0,0,Aug,3,2,1,4,Returning_Visitor,FALSE,FALSE 3,60.16666667,0,0,14,2322.866667,0.011764706,0.028431373,0,0,June,2,2,2,1,Returning_Visitor,FALSE,FALSE 4,131.4,0,0,4,199.4,0,0.025,0,0,Aug,2,6,3,5,New_Visitor,FALSE,FALSE 0,0,0,0,5,20.2,0.18,0.186666667,0,0,Aug,3,2,4,20,Returning_Visitor,FALSE,FALSE 4,42.2,0,0,98,2260.838889,0.004040404,0.007070707,32.9346011,0,Nov,2,4,1,3,New_Visitor,FALSE,TRUE 3,62.9,1,0,30,647.518523,0,0.010437463,13.73292849,0,Jul,2,2,3,1,Returning_Visitor,TRUE,TRUE 0,0,0,0,4,288.4333333,0,0.0125,0,0,Aug,1,1,4,2,Returning_Visitor,FALSE,FALSE 0,0,0,0,16,394.5580952,0,0.015,0,0,Oct,2,2,1,20,New_Visitor,FALSE,FALSE 0,0,0,0,9,532,0,0.066666667,0,0,Oct,2,2,3,4,Returning_Visitor,FALSE,FALSE 3,80.6,2,16.2,33,770.1071795,0.005405405,0.016216216,0,0,Oct,3,2,1,5,Returning_Visitor,FALSE,FALSE 3,13,1,0,51,4651.612222,0.004155844,0.020606061,0,0,Jul,2,4,3,4,Returning_Visitor,FALSE,FALSE 3,89.8,0,0,12,355.85,0.013333333,0.016,0,0,Oct,1,1,1,3,New_Visitor,TRUE,FALSE 0,0,0,0,18,432.7666667,0,0.027777778,0,0,Oct,3,2,1,2,Returning_Visitor,FALSE,FALSE 6,177.6,0,0,17,284.7833333,0.009090909,0.022727273,31.99181419,0,Oct,4,1,1,2,Returning_Visitor,FALSE,TRUE 4,103.8,0,0,14,314.2933333,0,0.013333333,79.44353446,0,Jul,2,2,5,2,New_Visitor,FALSE,TRUE 0,0,0,0,13,328.9,0.015384615,0.046153846,0,0,June,2,2,3,3,Returning_Visitor,FALSE,FALSE 0,0,0,0,2,179.8,0,0.033333333,0,0,Nov,3,3,1,3,New_Visitor,TRUE,FALSE 3,230.1,0,0,35,905.1226263,0.017647059,0.019206774,0,0,Jul,3,2,6,1,Returning_Visitor,FALSE,FALSE 10,228.35,0,0,3,28.6,0,0.001851852,0,0,Oct,2,2,7,5,New_Visitor,FALSE,FALSE 12,381.5,1,22.2,154,7835.874629,0.012549679,0.022587818,0,0,Aug,3,2,1,2,Returning_Visitor,TRUE,FALSE 6,188.5,0,0,25,590.6,0,0.003703704,0,0,Aug,2,2,1,2,New_Visitor,TRUE,FALSE 1,37.2,0,0,14,387.7666667,0.013333333,0.031111111,0,0,Oct,1,8,3,1,Returning_Visitor,FALSE,FALSE 1,5,0,0,40,2731.5,0,0.012357724,0,0,Jul,2,10,2,2,Returning_Visitor,TRUE,FALSE 2,34.2,0,0,18,1567.846667,0,0.017894737,0,0,Nov,2,2,1,2,Returning_Visitor,FALSE,FALSE 4,53.4,0,0,4,53.4,0,0.025,0,0,Oct,1,1,7,5,New_Visitor,FALSE,FALSE 0,0,0,0,3,145,0.066666667,0.133333333,0,0,June,2,2,1,3,Returning_Visitor,FALSE,FALSE 8,168.1666667,0,0,70,2146.757984,0,0.009316323,67.30929078,0,Nov,2,4,5,2,Returning_Visitor,FALSE,FALSE 0,0,0,0,1,0,0.2,0.2,0,0,Aug,3,2,1,1,Returning_Visitor,TRUE,FALSE 2,41.2,0,0,36,595.8,0,0.005405405,0,0,Nov,4,2,2,4,Returning_Visitor,FALSE,FALSE 4,160.2,2,21.2,43,1568.320833,0.009183673,0.02292517,28.05716135,0,Oct,1,1,3,2,Returning_Visitor,FALSE,FALSE 2,9,0,0,38,1147.696667,0,0.027642276,3.673829068,0,June,2,2,4,1,Returning_Visitor,FALSE,TRUE 3,214.6,0,0,59,1992.733333,0.003225806,0.034331797,13.18078221,0,Aug,2,2,1,2,Returning_Visitor,TRUE,FALSE 0,0,0,0,18,587.7333333,0,0.022222222,32.52380772,0,Nov,2,2,7,4,Returning_Visitor,TRUE,TRUE 0,0,0,0,21,1294.566667,0.035,0.067,0,0,Oct,1,1,1,1,Returning_Visitor,FALSE,FALSE 3,29.2,0,0,17,652.5666667,0.022222222,0.033333333,24.12558571,0,June,1,1,6,3,Returning_Visitor,FALSE,TRUE 0,0,0,0,77,1750.371795,0.021367521,0.031794872,0,0,Aug,1,1,1,6,Returning_Visitor,FALSE,FALSE 4,26,0,0,12,704.95,0,0.011764706,0,0,Oct,2,4,3,2,Returning_Visitor,FALSE,TRUE 4,59.86,1,38.3,36,725.21,0.005,0.034166667,0,0,Oct,2,2,9,2,Returning_Visitor,TRUE,FALSE 9,399.8,5,923.9,115,7243.649316,0.003225806,0.018928571,3.585439826,0,Aug,2,2,8,4,Returning_Visitor,FALSE,TRUE 0,0,0,0,21,192.9,0.059090909,0.104545455,0,0,Jul,2,2,1,3,Returning_Visitor,TRUE,FALSE 0,0,0,0,2,21.2,0,0.1,0,0,June,2,2,5,1,Returning_Visitor,FALSE,FALSE 6,344.2,3,201.4,51,1693.633333,0.003703704,0.014814815,11.27445733,0,Nov,2,6,1,2,Returning_Visitor,FALSE,FALSE 4,108.5333333,0,0,23,411.4666667,0.051851852,0.072469136,31.91414045,0,Sep,1,1,3,2,Returning_Visitor,FALSE,TRUE 0,0,0,0,9,257.9,0.011111111,0.044444444,0,0,June,1,1,1,20,Returning_Visitor,TRUE,FALSE 0,0,3,44,179,1738.472529,2.73E-05,0.025997611,0,0,Aug,2,4,9,11,Returning_Visitor,FALSE,FALSE 0,0,0,0,8,20.2,0.1,0.175,0,0,June,3,2,1,1,Returning_Visitor,FALSE,FALSE 13,408.6,0,0,21,589.4214286,0.004444444,0.014814815,18.09857173,0,Aug,2,2,8,2,Returning_Visitor,FALSE,TRUE 0,0,0,0,32,1028.566667,0.0125,0.015625,0,0,Aug,1,1,6,3,Returning_Visitor,TRUE,FALSE 0,0,1,0,56,1510.006667,0.074137931,0.094482759,0,0,Aug,3,2,1,3,Returning_Visitor,FALSE,FALSE 1,21.2,0,0,12,249.4095238,0.015384615,0.019230769,0,0,Oct,2,2,8,2,Returning_Visitor,TRUE,FALSE 2,91.6,2,678,66,2101.305092,0,0.010049751,48.04390582,0,Oct,2,2,3,2,Returning_Visitor,FALSE,TRUE 1,77.1,0,0,35,365.5833333,0.017142857,0.026857143,0,0,Nov,2,2,1,4,Returning_Visitor,FALSE,FALSE 3,67.4,2,16.2,8,281.1,0,0.041666667,15.82044157,0,Nov,3,2,1,20,Returning_Visitor,FALSE,TRUE 6,1013.886667,1,378.8,8,230.28,0,0.02254902,13.86451214,0,Sep,2,2,3,2,Returning_Visitor,TRUE,FALSE 7,117.6666667,0,0,74,1540.290115,0.00617284,0.016237507,0,0,Aug,2,2,7,1,Returning_Visitor,FALSE,FALSE 5,401.15,0,0,38,1570.681813,0.005980066,0.015724311,24.51269374,0,Sep,2,4,7,4,Returning_Visitor,FALSE,FALSE 0,0,0,0,40,1178.496667,0.026666667,0.031153846,0,0,Aug,2,2,4,1,Returning_Visitor,FALSE,FALSE 4,206.25,0,0,16,572.65,0,0.011111111,10.17711218,0,Sep,3,2,1,4,Returning_Visitor,TRUE,TRUE 3,552.2,1,0,40,829.8,0.074418605,0.090697674,0,0,Nov,3,2,1,13,Returning_Visitor,FALSE,FALSE 6,130.5,2,439.2,78,5526.080123,0,0.010699588,0.838988251,0,Nov,2,2,9,1,Returning_Visitor,FALSE,FALSE 4,65.6,0,0,25,575.8166667,0,0.007142857,0,0,Nov,2,4,2,20,Returning_Visitor,TRUE,FALSE 0,0,1,0,48,1062.812542,0,0.027253521,0,0,Sep,1,1,4,2,Returning_Visitor,FALSE,FALSE 6,47.3,0,0,14,858.2,0,0.010526316,0,0,Oct,4,1,3,5,New_Visitor,FALSE,TRUE 0,0,0,0,48,3025.333333,0.004166667,0.009375,0,0,Aug,1,1,1,20,Returning_Visitor,FALSE,FALSE 3,57.6,0,0,19,384.4030303,0.009090909,0.020295983,0,0,Sep,3,7,8,2,Returning_Visitor,FALSE,FALSE 0,0,0,0,11,192.7333333,0,0,0,0,Oct,2,2,2,2,New_Visitor,FALSE,FALSE 1,55.4,0,0,29,451.7,0.003448276,0.022988506,0,0,Aug,2,2,3,4,Returning_Visitor,FALSE,FALSE 0,0,0,0,39,1294.599603,0.002702703,0.011568387,0,0,Nov,3,2,1,20,Returning_Visitor,TRUE,FALSE 8,116.9722222,9,2252.033333,19,1135.880556,0.009677419,0.011841507,0,0,Jul,3,2,4,2,Returning_Visitor,TRUE,FALSE 0,0,0,0,5,75.6,0,0,0,0,Oct,1,1,3,4,Returning_Visitor,TRUE,FALSE 8,167.45,1,168.2,33,755.8769841,0.026315789,0.041315789,0,0,Oct,2,2,3,3,Returning_Visitor,FALSE,FALSE 4,32.2,2,28.2,23,934.3166667,0,0.01862069,11.28379079,0,Aug,2,2,5,4,Returning_Visitor,FALSE,FALSE 0,0,0,0,11,164,0.036363636,0.054545455,0,0,Jul,2,5,6,1,Returning_Visitor,FALSE,FALSE 6,100.1,0,0,4,60.4,0,0.022222222,0,0,Sep,2,4,7,2,New_Visitor,FALSE,TRUE 0,0,0,0,6,75.2,0.033333333,0.05,0,0,Jul,3,2,2,3,Returning_Visitor,FALSE,FALSE 0,0,0,0,17,639.7566667,0,0.011764706,63.45892077,0,Oct,2,2,3,4,New_Visitor,FALSE,TRUE 6,69.4,1,25.2,18,1009.55,0,0.002380952,0,0,Jul,2,2,2,2,New_Visitor,FALSE,FALSE 6,288,0,0,16,475.3,0.010526316,0.036842105,0,0,Sep,2,2,2,13,Returning_Visitor,FALSE,FALSE 12,187.775,0,0,98,2138.481263,0.001886792,0.017985395,0.129676893,0,Jul,2,5,3,2,Returning_Visitor,TRUE,FALSE 3,39,0,0,10,176.8,0,0.0375,0,0,June,3,2,5,4,Returning_Visitor,FALSE,FALSE 0,0,0,0,44,1341.716667,0.008888889,0.046666667,0,0,June,2,2,2,2,Returning_Visitor,FALSE,FALSE 2,21,0,0,9,84.1,0,0.006060606,0,0,Oct,2,2,3,2,New_Visitor,FALSE,FALSE 0,0,2,110.8666667,67,3264.128466,0.01352657,0.021901262,0,0,Aug,1,1,1,2,Returning_Visitor,FALSE,FALSE 4,57.3,0,0,25,488.7166667,0.021428571,0.067517007,0,0,June,2,5,1,1,Returning_Visitor,FALSE,FALSE 3,40.6,0,0,18,340.9166667,0,0.018253968,0,0,Aug,2,4,3,5,Returning_Visitor,FALSE,FALSE 0,0,0,0,9,250.2,0.022222222,0.033333333,0,0,June,2,2,4,1,Returning_Visitor,FALSE,FALSE 6,91.2,1,69.6,29,1618.233333,0,0.01620753,9.231934863,0,Nov,2,2,1,6,Returning_Visitor,FALSE,FALSE 2,17.2,0,0,18,168.5285714,0.010526316,0.024561404,0,0,Oct,1,1,3,2,Returning_Visitor,FALSE,FALSE 3,26,0,0,20,384.8,0,0.027272727,0,0,Jul,2,4,1,4,Returning_Visitor,FALSE,TRUE 5,102.8666667,0,0,12,219.6,0,0.035294118,0,0,Oct,2,2,6,20,Returning_Visitor,FALSE,FALSE 0,0,0,0,21,1041.733333,0,0.053333333,0,0,Nov,3,2,8,1,Returning_Visitor,FALSE,FALSE 0,0,0,0,43,1606.45,0,0.004761905,18.51505467,0,June,4,1,1,2,New_Visitor,FALSE,TRUE 0,0,0,0,30,599.9190476,0,0.004285714,0,0,Jul,1,1,1,4,Returning_Visitor,FALSE,FALSE 0,0,0,0,19,1138.793636,0,0.007017544,0,0,Aug,2,4,3,2,Returning_Visitor,FALSE,FALSE 11,110.7866667,5,89.1,230,9050.459482,0.006722689,0.01755845,2.664994971,0,June,2,2,6,1,Returning_Visitor,FALSE,FALSE 1,14.2,2,133,13,825.7990476,0.04,0.041666667,14.8397824,0,Nov,3,2,4,2,Returning_Visitor,FALSE,FALSE 2,39.2,0,0,19,1553.8,0.045,0.0825,0,0,Oct,3,2,9,13,Returning_Visitor,FALSE,FALSE 0,0,0,0,5,43.2,0.08,0.12,0,0,Jul,2,2,3,13,Returning_Visitor,TRUE,FALSE 5,106.8,0,0,84,1242.776046,0,0.006589147,4.17431402,0,Nov,1,1,2,4,Returning_Visitor,TRUE,FALSE 5,211.05,0,0,75,3275.113333,0,0.009824561,29.61151281,0,Aug,4,1,1,3,Returning_Visitor,FALSE,TRUE 1,6,0,0,23,2767.2,0,0.008333333,0,0,Sep,2,2,7,1,New_Visitor,FALSE,FALSE 10,700.6333333,0,0,29,4897.845238,0,0.015740741,0.700155006,0,Sep,3,2,1,5,Returning_Visitor,TRUE,FALSE 0,0,0,0,6,222.4,0,0.033333333,63.55337685,0,Jul,1,1,4,4,Returning_Visitor,TRUE,FALSE 2,12.6,0,0,7,62.8,0,0.022222222,0,0,June,1,1,4,4,Returning_Visitor,FALSE,FALSE 0,0,0,0,27,263.2666667,0,0.038518519,0,0,June,2,2,1,3,Returning_Visitor,FALSE,FALSE 0,0,0,0,27,1871.9,0.014814815,0.020740741,0,0,Sep,1,1,1,3,Returning_Visitor,FALSE,FALSE 3,102.6,0,0,35,871.8333333,0,0.002631579,0,0,Oct,1,1,1,2,New_Visitor,TRUE,FALSE 8,139.2,0,0,49,3346.969048,0,0.008471594,12.5740183,0,Nov,2,6,1,2,Returning_Visitor,FALSE,FALSE 6,134.36,0,0,22,568.9683333,0.017391304,0.021614907,12.39204312,0,Sep,1,2,6,2,Returning_Visitor,TRUE,TRUE 4,65.9,0,0,73,3799.2,0.079279279,0.08481982,0,0,Aug,3,2,3,13,Returning_Visitor,FALSE,FALSE 0,0,1,0,5,85.6,0.011111111,0.046666667,0,0,June,3,2,6,2,Returning_Visitor,FALSE,FALSE 4,69.23333333,3,1657.3,26,2306.047619,0.046666667,0.076388889,5.761347436,0,Oct,1,1,2,3,Returning_Visitor,FALSE,FALSE 0,0,0,0,3,0,0.2,0.2,0,0,Jul,1,2,8,1,Returning_Visitor,FALSE,FALSE 1,72.6,0,0,44,1256.825,0,0,0,0,Nov,1,1,4,5,New_Visitor,FALSE,FALSE 14,1220.914286,4,1005.4,280,18171.79454,0.017486109,0.038980418,5.697658998,0,Jul,2,2,1,3,Returning_Visitor,FALSE,FALSE 6,754.5333333,0,0,8,749.2333333,0,0.008974359,0,0,Nov,3,2,3,1,Returning_Visitor,FALSE,FALSE 0,0,0,0,2,93.6,0,0.05,0,0,Oct,2,4,1,2,Returning_Visitor,TRUE,FALSE 1,46,0,0,98,3098.649444,0.018814433,0.037472815,6.153201384,0,Aug,2,2,6,1,Returning_Visitor,FALSE,FALSE 1,28.2,0,0,2,0,0.066666667,0.133333333,0,0,June,1,8,2,3,New_Visitor,TRUE,FALSE 6,113,0,0,18,359.4,0.008333333,0.05,0,0,Sep,2,2,3,1,Returning_Visitor,FALSE,FALSE 8,1013.171429,4,353.9666667,90,4984.371429,0.042666667,0.066033333,1.464010432,0,Aug,2,2,3,13,Returning_Visitor,TRUE,FALSE 0,0,0,0,6,102.2,0,0,40.27815244,0,Oct,2,2,2,20,Returning_Visitor,FALSE,TRUE 8,131,0,0,32,5330.633333,0.008108108,0.051351351,0,0,Aug,2,2,2,1,Returning_Visitor,TRUE,FALSE 0,0,0,0,1,0,0.2,0.2,0,0,June,2,2,1,3,Returning_Visitor,FALSE,FALSE 4,102.3,0,0,59,1684.496667,0,0.00952381,25.85442016,0,Nov,2,2,2,4,Returning_Visitor,FALSE,FALSE 0,0,0,0,66,1216.352121,0,0,62.853261,0,Sep,2,2,4,2,New_Visitor,FALSE,TRUE 4,99.33333333,1,103.8,40,2011.966667,0.018181818,0.023484848,9.869649371,0,Oct,2,2,1,20,Returning_Visitor,FALSE,TRUE 2,17.2,0,0,0,0,0,0.05,0,0,Sep,2,2,2,6,New_Visitor,TRUE,TRUE 3,79.22,2,297.8333333,62,1685.747985,0,0.013128205,0,0,Jul,2,2,3,2,Returning_Visitor,FALSE,FALSE 1,51.4,0,0,25,1417.022222,0.022666667,0.055466667,0,0,Jul,3,2,1,2,Returning_Visitor,FALSE,FALSE 6,92.57142857,1,793.8,38,2436.605534,0,0.035895135,27.31414742,0,Jul,2,10,1,2,Returning_Visitor,FALSE,FALSE 2,112.9,0,0,32,505.55,0,0.003225806,0,0,Oct,2,2,6,2,New_Visitor,FALSE,FALSE 0,0,0,0,4,43.2,0,0.05,0,0,Jul,3,2,4,3,Returning_Visitor,FALSE,FALSE 3,67.2,0,0,46,987.1190476,0.004081633,0.012244898,22.25592956,0,Sep,2,2,8,4,Returning_Visitor,FALSE,TRUE 0,0,2,76.6,377,11729.40804,0.032877299,0.04524582,0,0,Jul,3,2,1,3,Returning_Visitor,FALSE,FALSE 0,0,0,0,3,103.6,0,0.033333333,0,0,Jul,3,2,1,6,Returning_Visitor,FALSE,FALSE 0,0,0,0,4,14.2,0.1,0.15,0,0,Oct,1,1,1,1,Returning_Visitor,TRUE,FALSE 4,69.96666667,0,0,28,2728.755,0,0.009313725,0,0,Oct,1,1,1,5,Returning_Visitor,FALSE,FALSE 5,78.63333333,0,0,7,176.8333333,0,0.023076923,0,0,Jul,2,4,1,5,New_Visitor,FALSE,FALSE 0,0,0,0,6,111.6,0,0.016666667,24.9184548,0,Oct,1,2,3,2,Returning_Visitor,TRUE,TRUE 6,186.6666667,2,100.8,11,347.4,0,0.015,0,0,Aug,3,2,4,5,Returning_Visitor,FALSE,TRUE 0,0,0,0,8,286.7,0.05,0.075,0,0,Jul,3,2,1,1,Returning_Visitor,FALSE,FALSE 2,145.2,0,0,14,266.66,0.0125,0.029166667,0,0,Sep,3,2,3,1,Returning_Visitor,FALSE,FALSE 0,0,0,0,12,222.2557143,0.009090909,0.012121212,0,0,Sep,3,2,5,2,Returning_Visitor,FALSE,FALSE 6,106.18,2,115.8,32,518.4433333,0.001960784,0.014215686,0,0,Aug,3,2,6,4,Returning_Visitor,FALSE,FALSE 0,0,0,0,10,490.5666667,0.02,0.035,0,0,June,3,2,2,1,Returning_Visitor,FALSE,FALSE 0,0,0,0,94,9836.068333,0.007446809,0.028191489,0,0,Sep,2,2,1,13,Returning_Visitor,FALSE,FALSE 5,140.6,0,0,16,423.5,0.0125,0.0125,0,0,Aug,3,2,1,3,Returning_Visitor,TRUE,FALSE 2,38.04,0,0,8,886.57,0,0.030769231,0,0,Nov,1,1,1,2,Returning_Visitor,FALSE,FALSE 2,9,0,0,18,558.1933333,0,0.011111111,0,0,Aug,2,2,3,1,Returning_Visitor,FALSE,FALSE 6,440.2813953,1,17.72,18,864.1,0,0.002777778,0,0,Sep,2,2,5,2,New_Visitor,FALSE,FALSE 9,216.23,1,140.5,310,8468.339278,0.002821317,0.012414294,0.875047932,0,Sep,2,10,3,2,Returning_Visitor,TRUE,FALSE 7,154.4,0,0,33,1267.06,0.021621622,0.040540541,0,0,June,2,2,1,3,Returning_Visitor,FALSE,FALSE 5,153,0,0,25,1074.683333,0.0125,0.01875,19.23268976,0,Nov,1,1,6,4,Returning_Visitor,FALSE,FALSE 0,0,0,0,12,139.8,0.066666667,0.091666667,0,0,Aug,3,2,1,13,Returning_Visitor,FALSE,FALSE 1,39.2,0,0,23,526,0,0.002173913,0,0,Oct,4,2,3,2,Returning_Visitor,FALSE,FALSE 3,37.6,0,0,12,299.2666667,0,0.030769231,0,0,Sep,3,2,1,6,Returning_Visitor,FALSE,FALSE 1,45.4,0,0,60,2580.634935,0,0.009442743,0,0,Oct,1,1,1,2,Returning_Visitor,FALSE,FALSE 0,0,2,101.7,42,1892.198319,0,0.004545455,153.5776975,0,Aug,2,2,1,1,Returning_Visitor,FALSE,TRUE 3,82.53333333,1,0,19,668.7,0.022727273,0.032424242,11.5952257,0,Nov,1,1,3,2,Returning_Visitor,FALSE,FALSE 1,227.6,0,0,27,1301.3,0.020689655,0.022413793,5.904407563,0,Aug,2,2,1,3,Returning_Visitor,FALSE,FALSE 2,129,0,0,16,1835.5,0.003703704,0.031481481,0,0,Aug,2,4,9,6,Returning_Visitor,FALSE,FALSE 0,0,0,0,10,155.9,0.02,0.04,0,0,Jul,2,2,6,20,Returning_Visitor,FALSE,FALSE 0,0,0,0,3,36.4,0.066666667,0.1,0,0,June,3,2,1,13,Returning_Visitor,FALSE,FALSE 3,273.66,0,0,24,751.6266667,0.004571429,0.024838095,14.85337253,0,Jul,3,2,2,1,Returning_Visitor,TRUE,FALSE 5,32.6,0,0,70,939.7833333,0,0.002702703,0,0,Aug,2,4,1,5,New_Visitor,TRUE,TRUE 2,22.2,0,0,18,584.74,0.005882353,0.023529412,0,0,Aug,1,1,3,2,Returning_Visitor,FALSE,FALSE 4,381.8,0,0,1,31.2,0,0.04,0,0,Sep,2,4,4,2,Returning_Visitor,TRUE,FALSE 0,0,0,0,42,570.1832287,0.01,0.016547619,0,0,Aug,2,2,1,2,Returning_Visitor,FALSE,FALSE 0,0,1,0,14,331.55,0.013333333,0.017777778,0,0,June,1,1,1,2,Returning_Visitor,TRUE,FALSE 0,0,0,0,15,469.8681818,0,0.014285714,0,0,Nov,2,2,8,2,New_Visitor,FALSE,FALSE 0,0,0,0,31,881.4416667,0.017204301,0.02609319,0,0,Oct,3,2,2,13,Returning_Visitor,TRUE,FALSE 0,0,3,541,32,776.9232515,0.001176471,0.013779093,0,0,Nov,1,1,3,20,Returning_Visitor,FALSE,FALSE 4,423.1,0,0,16,982.6,0,0.005263158,0,0,Nov,3,2,2,20,Returning_Visitor,TRUE,FALSE 3,103.8,0,0,4,683.4,0.057142857,0.04,0,0,Aug,3,2,6,1,Returning_Visitor,FALSE,FALSE 15,104.6545455,1,148,48,1029.170303,0.001724138,0.022131145,16.96712066,0,Jul,2,2,6,1,Returning_Visitor,FALSE,FALSE 0,0,0,0,12,797.84,0,0.018181818,0,0,Aug,2,2,3,4,Returning_Visitor,FALSE,FALSE 4,211.8,0,0,15,512.9,0,0.00625,0,0,Nov,1,1,3,2,New_Visitor,FALSE,FALSE 0,0,0,0,1,0,0.2,0.2,0,0,June,2,2,1,4,Returning_Visitor,FALSE,FALSE 0,0,0,0,49,1023.96,0.034693878,0.057823129,0,0,Aug,2,2,3,3,Returning_Visitor,FALSE,FALSE 0,0,0,0,12,146.1333333,0.05,0.066666667,0,0,Jul,2,2,1,1,Returning_Visitor,FALSE,FALSE 7,54.84285714,7,475.2,20,393.4095238,0.027380952,0.033928571,0,0,Jul,2,2,1,4,Returning_Visitor,FALSE,TRUE 0,0,0,0,16,129.5833333,0.075,0.0875,0,0,June,3,2,1,13,Returning_Visitor,TRUE,FALSE 1,7.1,0,0,16,222.2333333,0,0.029411765,0,0,June,2,5,3,2,Returning_Visitor,FALSE,FALSE 11,265.4333333,2,35.7,158,2110.255093,0.003614458,0.009762498,0,0,Aug,3,2,8,1,Returning_Visitor,FALSE,FALSE 1,38.2,0,0,24,1282.953333,0.034782609,0.049565217,0,0,Nov,2,2,4,1,Returning_Visitor,TRUE,FALSE 0,0,1,11,54,1299.196667,0.003636364,0.022545455,0,0,Sep,1,1,3,2,Returning_Visitor,TRUE,FALSE 1,197.4,0,0,83,1631.4,0.027380952,0.068650794,0,0,Oct,2,2,3,13,Returning_Visitor,FALSE,FALSE 0,0,0,0,16,340.65,0.060416667,0.063690476,0,0,Sep,3,2,2,3,Returning_Visitor,TRUE,FALSE 0,0,0,0,8,846.2,0,0.025,33.22292729,0,Jul,2,2,1,2,Returning_Visitor,FALSE,TRUE 0,0,0,0,4,39.95,0,0.05,0,0,Jul,3,2,1,4,Returning_Visitor,TRUE,FALSE 1,58.4,0,0,18,1045.3,0,0.026315789,0,0,Sep,2,2,2,1,Returning_Visitor,FALSE,FALSE 6,107.04,3,53.2,114,5066.6611,0.006666667,0.016180556,0,0,Nov,3,2,3,3,Returning_Visitor,TRUE,FALSE 2,28.2,1,0,114,3161.074762,0.017699115,0.042576205,0,0,Aug,2,2,2,1,Returning_Visitor,FALSE,FALSE 4,209.5,0,0,23,1780.7,0,0.011538462,0,0,Nov,2,2,3,2,New_Visitor,FALSE,FALSE 0,0,0,0,1,0,0.2,0.2,0,0,Nov,2,4,1,2,Returning_Visitor,FALSE,FALSE 3,23,0,0,16,431.9,0.010526316,0.033684211,0,0,Jul,2,2,7,13,Returning_Visitor,TRUE,FALSE 4,115.7,0,0,16,655.3833333,0,0.012037037,0,0,Nov,1,1,2,3,Returning_Visitor,FALSE,FALSE 3,41.2,1,99.8,2,10,0,0.033333333,0,0,Nov,1,2,4,2,Returning_Visitor,FALSE,FALSE 12,255.5166667,0,0,38,436.8583333,0,0.010852713,0,0,Oct,2,2,7,5,Returning_Visitor,TRUE,FALSE 8,108.7,0,0,85,2651.943186,0.000888889,0.01426455,0.898495409,0,Jul,2,2,9,4,Returning_Visitor,FALSE,FALSE 4,34.2,0,0,27,809.6933333,0.023214286,0.033826531,27.6509675,0,Sep,3,2,4,13,Returning_Visitor,TRUE,FALSE 0,0,0,0,37,456.15,0.121621622,0.131891892,0,0,Jul,3,2,1,3,Returning_Visitor,TRUE,FALSE 3,39.2,0,0,12,209,0,0.02,0,0,Sep,2,5,6,2,Returning_Visitor,FALSE,FALSE 4,234.3,0,0,2,11.1,0,0.04,0,0,Oct,1,1,8,5,New_Visitor,TRUE,FALSE 5,44.01666667,0,0,4,162.5333333,0,0,39.27673964,0,Aug,4,2,1,2,Returning_Visitor,FALSE,TRUE 4,20,0,0,34,461.2333333,0,0.022685185,0,0,Jul,2,4,1,4,Returning_Visitor,TRUE,FALSE 8,116.9607143,4,225.3666667,42,539.227381,0.015510204,0.011281179,0,0,Jul,1,1,1,4,Returning_Visitor,FALSE,TRUE 3,142.2,0,0,24,755,0,0.007407407,0,0,Oct,2,5,7,2,New_Visitor,TRUE,FALSE 9,501.4833333,0,0,97,2293.490129,0.011455734,0.02697054,2.209972688,0,Aug,1,1,3,4,Returning_Visitor,FALSE,FALSE 6,207.0928571,1,52.4,35,2239.502857,0.010833333,0.024333333,0,0,Nov,1,1,8,3,Returning_Visitor,FALSE,FALSE 1,14.2,0,0,22,839.568189,0,0.000952381,60.58437066,0,Nov,3,2,3,2,New_Visitor,TRUE,TRUE 0,0,0,0,27,1838.15,0.053205128,0.056282051,0,0,June,3,2,2,3,Returning_Visitor,FALSE,FALSE 10,2407.42381,3,434.3,486,23050.10414,0.000323719,0.011248517,0,0,Jul,2,2,1,3,Returning_Visitor,FALSE,FALSE 6,60.36666667,3,189.3,12,304.7,0,0.038596491,2.848312236,0,June,2,2,1,4,Returning_Visitor,FALSE,TRUE 6,78.51333333,1,63.4,110,1691.236147,0.003448276,0.019359924,19.18310758,0,Aug,2,2,7,4,Returning_Visitor,TRUE,FALSE 1,6,0,0,25,1177.506667,0.008333333,0.0375,0,0,Oct,2,2,3,1,Returning_Visitor,FALSE,FALSE 0,0,0,0,65,6053.466667,0.005208333,0.026197917,0,0,Sep,2,4,9,3,Returning_Visitor,FALSE,FALSE 4,117.32,2,15,230,6978.613711,0.015327635,0.036530714,0,0,Jul,2,2,6,1,Returning_Visitor,TRUE,FALSE 0,0,0,0,1,0,0.2,0.2,0,0,Sep,4,1,2,1,Returning_Visitor,FALSE,FALSE 2,67.6,0,0,8,183.8,0,0.02,0,0,Oct,1,1,1,5,New_Visitor,FALSE,FALSE 2,75.6,2,652.8,10,1143.666667,0,0.023333333,0,0,Aug,2,2,4,2,Returning_Visitor,FALSE,FALSE 0,0,0,0,19,753.3,0,0.002222222,0,0,Aug,2,4,1,4,Returning_Visitor,FALSE,FALSE 1,137.5,1,59.4,53,1844.65,0.022641509,0.049595687,0,0,June,4,2,1,3,Returning_Visitor,FALSE,FALSE 3,89.25,0,0,8,67.85,0.018181818,0.033333333,0,0,June,2,2,8,1,Returning_Visitor,FALSE,TRUE 0,0,0,0,9,281.5,0,0.008333333,0,0,Oct,4,2,9,7,Returning_Visitor,FALSE,FALSE 9,1104.2,0,0,36,1826.441667,0.007317073,0.012601626,0,0,Oct,1,1,3,2,Returning_Visitor,TRUE,FALSE 1,56.4,1,14.2,24,1183.826667,0,0.005128205,0,0,Jul,2,2,8,2,New_Visitor,TRUE,FALSE 0,0,0,0,153,4565.930621,0.00130719,0.006432173,6.189215434,0,Aug,2,2,4,1,Returning_Visitor,TRUE,TRUE 3,73.6,0,0,10,104.95,0,0.001851852,0,0,Oct,3,2,3,2,Returning_Visitor,TRUE,FALSE 3,152,1,64.4,9,358.4,0,0.015384615,0,0,Oct,2,2,1,2,New_Visitor,TRUE,FALSE 0,0,0,0,3,50.4,0,0.066666667,0,0,Jul,2,2,3,1,Returning_Visitor,FALSE,FALSE 1,65.4,0,0,55,1033.65,0.017857143,0.024404762,0,0,Oct,3,2,1,1,Returning_Visitor,FALSE,FALSE 0,0,0,0,5,64,0,0.1,0,0,Sep,2,2,6,2,Returning_Visitor,FALSE,FALSE 8,279.2666667,0,0,16,453.4,0,0.011403509,0,0,Jul,3,2,8,4,Returning_Visitor,FALSE,FALSE 5,318.2666667,0,0,23,1037.833333,0.017857143,0.039285714,0,0,Aug,2,2,5,1,Returning_Visitor,FALSE,FALSE 0,0,0,0,42,2385.666667,0.003333333,0.035833333,0,0,Jul,2,5,7,1,Returning_Visitor,FALSE,FALSE 0,0,0,0,3,0,0.2,0.2,0,0,Nov,1,1,6,6,Returning_Visitor,FALSE,FALSE 12,217.6491228,4,1009.733333,38,1639.069361,0.02173913,0.031621722,5.287005762,0,Sep,3,2,6,1,Returning_Visitor,TRUE,FALSE 7,317.9142857,4,216.5666667,35,910.0809524,0,0.01,40.78047078,0,Sep,2,4,3,5,New_Visitor,FALSE,FALSE 4,30.3,0,0,7,493.4,0,0.016666667,0,0,Oct,2,5,1,2,New_Visitor,FALSE,TRUE 0,0,0,0,34,538.48,0,0.005882353,5.860439769,0,Sep,2,2,7,2,Returning_Visitor,FALSE,TRUE 8,289,0,0,139,5107.034928,0.01544437,0.044005026,8.304903935,0,Aug,2,2,2,1,Returning_Visitor,FALSE,FALSE 0,0,0,0,4,34.2,0.05,0.1,0,0,Oct,2,2,1,5,Returning_Visitor,FALSE,FALSE 0,0,0,0,36,788.5,0,0.025,0,0,Nov,2,6,3,3,Returning_Visitor,FALSE,FALSE 3,33.46666667,0,0,113,1576.252222,0.003508772,0.009314954,40.08238165,0,June,2,2,7,3,Returning_Visitor,FALSE,FALSE 0,0,0,0,12,223.8,0.027777778,0.017777778,0,0,Jul,1,1,2,4,Returning_Visitor,TRUE,FALSE 3,50.37333333,4,63.1,91,3827.498846,0.057801418,0.072399527,0,0,Sep,3,2,6,13,Returning_Visitor,FALSE,FALSE 4,50.93333333,0,0,67,860.021746,0,0.002083333,0,0,Sep,2,4,1,5,New_Visitor,FALSE,FALSE 4,45.8,2,43.2,30,1021.838095,0,0.005882353,40.43578994,0,Nov,2,2,4,2,New_Visitor,FALSE,FALSE 0,0,0,0,5,150.8,0,0.02,0,0,Aug,1,1,1,2,Returning_Visitor,FALSE,FALSE 1,0,1,66.4,14,668.3166667,0.014285714,0.028571429,44.89345937,0,Oct,4,2,1,20,Returning_Visitor,TRUE,TRUE 7,397.2644444,1,246.8,23,672.2644444,0.032051282,0.046153846,0,0,Oct,3,2,4,1,Returning_Visitor,FALSE,FALSE 7,105.4666667,0,0,44,778.9666667,0,0.004166667,37.92414395,0,Aug,2,2,6,2,New_Visitor,FALSE,TRUE 9,306.46,0,0,40,550.7095238,0,0.00952381,0,0,Sep,2,2,1,2,Returning_Visitor,FALSE,FALSE 3,67.86666667,2,12,16,393,0,0.015,0,0,Sep,2,10,1,5,New_Visitor,FALSE,FALSE 5,140.6,0,0,81,2408.312828,0.002554278,0.007459475,23.07581495,0,Oct,2,4,1,2,Returning_Visitor,FALSE,TRUE 5,60.8,0,0,3,20.6,0,0.033333333,0,0,Aug,2,5,5,2,New_Visitor,TRUE,FALSE 0,0,0,0,101,2611.747653,0.004356436,0.029287373,0,0,Oct,2,2,1,1,Returning_Visitor,FALSE,FALSE 0,0,0,0,27,458.2560317,0.007407407,0.022222222,38.12368584,0,June,1,1,2,2,Returning_Visitor,FALSE,TRUE 3,96.8,0,0,29,2041.626667,0.026075269,0.047324629,0,0,Sep,2,2,3,1,Returning_Visitor,FALSE,FALSE 9,779,0,0,15,1386.35,0.016666667,0.02747619,6.344631767,0,Aug,1,2,1,4,Returning_Visitor,FALSE,FALSE 7,778.4,0,0,17,827.5333333,0,0.018181818,0,0,Sep,1,2,6,2,Returning_Visitor,FALSE,FALSE 2,28.05714286,0,0,73,2260.857143,0.002666667,0.018933333,0,0,June,2,2,7,4,Returning_Visitor,TRUE,FALSE 0,0,0,0,10,158.3666667,0,0,54.9950922,0,Jul,2,2,1,1,Returning_Visitor,FALSE,TRUE 2,76.6,0,0,33,1932.716667,0,0.025196078,0,0,Aug,2,2,4,13,Returning_Visitor,FALSE,FALSE 2,134,0,0,15,284.1,0,0.011764706,0,0,Nov,2,4,8,2,New_Visitor,FALSE,FALSE 1,18.2,0,0,58,2007.206667,0.003448276,0.027298851,0,0,Jul,4,2,8,1,Returning_Visitor,FALSE,FALSE 5,368.4,0,0,14,481.9666667,0.010526316,0.031578947,0,0,Sep,1,1,1,2,Returning_Visitor,FALSE,FALSE 7,54.33333333,0,0,9,619.5333333,0,0.025,0,0,Oct,4,5,3,11,Returning_Visitor,FALSE,FALSE 5,87.8,0,0,38,842.4235931,0,0.011382114,52.28594057,0,Oct,1,1,1,2,Returning_Visitor,TRUE,TRUE 7,58.6,0,0,20,585.8833333,0.017391304,0.044637681,0,0,Jul,1,1,1,4,Returning_Visitor,TRUE,FALSE 0,0,0,0,12,599.6133333,0,0.020833333,0,0,Nov,2,2,4,20,Returning_Visitor,TRUE,FALSE 2,190.4,1,274,39,1321.853333,0.00952381,0.018253968,0,0,Aug,2,2,8,4,Returning_Visitor,FALSE,FALSE 0,0,0,0,5,60.9,0.04,0.12,0,0,Aug,1,1,1,3,Returning_Visitor,FALSE,FALSE 0,0,0,0,8,321.2333333,0,0.0125,0,0,Jul,3,5,1,1,Returning_Visitor,FALSE,FALSE 2,213.6,4,108,19,989.3333333,0.029166667,0.0375,0,0,June,1,1,1,2,Returning_Visitor,TRUE,FALSE 14,293.6333333,3,11.5,182,3539.944615,0.010209424,0.033524993,0.702086761,0,Aug,2,5,1,1,Returning_Visitor,FALSE,FALSE 2,7,0,0,15,533.11,0.011111111,0.05,4.307627247,0,Nov,2,2,1,3,Returning_Visitor,FALSE,FALSE 1,22.2,0,0,16,694.4549784,0,0.001470588,54.91911804,0,Sep,2,2,7,2,New_Visitor,FALSE,TRUE 0,0,0,0,4,6,0.1,0.15,0,0,June,2,2,2,4,Returning_Visitor,FALSE,FALSE 0,0,0,0,16,709.4,0.1125,0.145833333,0,0,Aug,2,2,9,1,Returning_Visitor,FALSE,FALSE 8,126,1,13.4,13,217.7166667,0.033333333,0.062,0,0,Oct,2,2,1,13,Returning_Visitor,FALSE,FALSE 4,54.93333333,0,0,35,1126.666667,0,0.005952381,28.32886648,0,Jul,2,2,7,2,Returning_Visitor,FALSE,FALSE 0,0,0,0,6,1054.3,0.066666667,0.1,0,0,Jul,2,12,6,3,Returning_Visitor,FALSE,FALSE 1,27.2,0,0,21,1003.658333,0.010526316,0.019064327,0,0,Oct,3,2,4,20,Returning_Visitor,FALSE,FALSE 6,322.35,0,0,33,1845.394615,0,0.009714286,39.42556512,0,Jul,2,2,1,1,Returning_Visitor,FALSE,TRUE 1,52.4,0,0,13,447.2,0,0.007692308,42.30391708,0,Aug,1,1,1,20,Returning_Visitor,TRUE,TRUE 9,96.18666667,1,35.2,63,1345.136667,0.029166667,0.053287037,2.10185027,0,Jul,2,2,9,13,Returning_Visitor,FALSE,FALSE 16,254.253245,3,223.15,221,6606.726779,0.005533063,0.016561375,4.083948025,0,Oct,4,1,4,1,Returning_Visitor,FALSE,TRUE 4,169.2,0,0,14,315.4666667,0,0.011111111,34.75023292,0,Jul,2,2,1,2,Returning_Visitor,TRUE,TRUE 3,86.95,6,173.2,17,481.31,0,0,27.69500776,0,Nov,2,2,9,20,New_Visitor,FALSE,TRUE 5,49.8,0,0,32,955.3333333,0.01875,0.0390625,0,0,Aug,2,2,1,13,Returning_Visitor,FALSE,FALSE 2,15,0,0,11,223.4666667,0,0.028571429,0,0,Sep,2,2,3,6,New_Visitor,TRUE,TRUE 0,0,1,0,17,982.757619,0.0125,0.035416667,0,0,Sep,2,5,2,1,Returning_Visitor,FALSE,FALSE 2,298.2,0,0,48,1850.116667,0.004081633,0.004421769,7.56292207,0,Nov,1,1,2,2,Returning_Visitor,TRUE,TRUE 4,275.1,1,16.6,229,6984.611525,0.017126437,0.021755245,0.780905815,0,Aug,3,2,8,13,Returning_Visitor,FALSE,FALSE 1,29.2,1,322.4,10,126.9333333,0.014285714,0.036904762,13.59420027,0,Sep,2,2,1,2,Returning_Visitor,FALSE,TRUE 2,53.4,0,0,3,66.4,0,0.04,0,0,Oct,1,1,4,3,Returning_Visitor,TRUE,FALSE 2,46.4,0,0,30,2559.433333,0.027586207,0.064367816,0,0,Sep,3,2,6,1,Returning_Visitor,TRUE,FALSE 5,118.9,4,380.7,4,188.4,0,0.006666667,0,0,Oct,4,2,1,4,Returning_Visitor,FALSE,FALSE 11,169.8833333,2,160.4,25,489.15,0.0125,0.0171875,0,0,Aug,3,2,3,5,Returning_Visitor,TRUE,FALSE 1,0,0,0,10,645.2,0.03,0.05,0,0,Jul,3,2,4,1,Returning_Visitor,FALSE,FALSE 1,12,0,0,4,63.4,0,0.04,0,0,Oct,1,8,6,20,New_Visitor,FALSE,FALSE 1,21.2,0,0,0,0,0.05,0.1,0,0,Oct,1,1,1,5,Returning_Visitor,FALSE,FALSE 1,26.2,0,0,13,245.1666667,0,0,58.9241766,0,Aug,2,5,3,4,New_Visitor,FALSE,TRUE 4,206.0666667,0,0,9,113.4333333,0,0.018181818,0,0,Oct,2,2,1,6,Returning_Visitor,FALSE,FALSE 1,3,0,0,24,342,0.024,0.048,0,0,Oct,2,2,1,1,Returning_Visitor,FALSE,FALSE 0,0,0,0,1,0,0.2,0.2,0,0,Nov,2,2,1,13,Returning_Visitor,FALSE,FALSE 10,264.9,1,14.8,24,718.9566667,0,0.003448276,0,0,Oct,3,2,1,4,Returning_Visitor,FALSE,FALSE 0,0,0,0,113,1341.632619,0.004804805,0.011846847,0,0,Jul,2,2,1,13,Returning_Visitor,FALSE,FALSE 0,0,0,0,22,641.5166667,0.00952381,0.00952381,34.5222117,0,Oct,1,1,1,2,Returning_Visitor,FALSE,FALSE 0,0,0,0,17,477.7166667,0,0,0,0,Sep,2,2,6,2,New_Visitor,TRUE,FALSE 1,29.2,0,0,13,199.2844444,0,0,0,0,Jul,1,1,1,4,Returning_Visitor,FALSE,FALSE 7,101.0833333,0,0,26,349.1166667,0.006060606,0.023484848,39.41924911,0,Aug,2,4,3,4,Returning_Visitor,FALSE,FALSE 0,0,0,0,4,166.9,0,0,0,0,Aug,3,2,4,4,New_Visitor,FALSE,FALSE 0,0,0,0,1,0,0.2,0.2,0,0,Oct,3,2,2,3,New_Visitor,TRUE,FALSE 1,39.2,0,0,26,1210.917778,0.014814815,0.014814815,2.480024644,0,Nov,4,2,1,1,Returning_Visitor,FALSE,TRUE 6,333.54,0,0,12,568.1,0,0.0125,19.63031016,0,Nov,2,4,5,2,New_Visitor,FALSE,TRUE 12,569.825,0,0,127,6065.016218,0.006716418,0.009265038,0.152167439,0,Aug,1,1,2,4,Returning_Visitor,FALSE,TRUE 4,48.8,0,0,11,344.8,0.015384615,0.054395604,0,0,Oct,3,2,1,4,Returning_Visitor,TRUE,FALSE 15,304.5833333,0,0,33,761.2233333,0.007619048,0.020793651,0,0,Oct,3,2,1,3,Returning_Visitor,TRUE,FALSE 1,18.2,0,0,17,225.68,0,0.003703704,0,0,Nov,2,10,1,2,Returning_Visitor,FALSE,FALSE 0,0,0,0,2,34.2,0,0.1,0,0,Sep,2,2,1,2,Returning_Visitor,FALSE,FALSE 2,28.2,0,0,7,212.5,0,0.007407407,94.01683315,0,Nov,2,2,3,2,New_Visitor,TRUE,TRUE 2,82.6,6,316,34,1446.733333,0,0.002439024,0,0,Oct,4,1,4,2,New_Visitor,TRUE,FALSE 5,39.8,2,239.8,216,14577.08498,0.002739726,0.013368848,0.888485964,0,Aug,2,2,3,13,Returning_Visitor,FALSE,FALSE 0,0,0,0,3,35.4,0,0.066666667,0,0,Nov,1,1,1,1,Returning_Visitor,FALSE,FALSE 1,0,0,0,3,178.5,0,0.066666667,8.04958574,0,Sep,2,6,2,1,Returning_Visitor,FALSE,FALSE 3,81.4,0,0,18,240.3,0,0.030434783,0,0,Aug,2,2,1,6,Returning_Visitor,FALSE,FALSE 0,0,1,0,45,1165.84,0,0.034042553,7.198820003,0,Oct,2,2,5,1,Returning_Visitor,FALSE,TRUE 0,0,0,0,10,421.6,0,0.011111111,0,0,Nov,1,1,1,4,Returning_Visitor,FALSE,FALSE 3,48.4,0,0,137,3004.628109,0.002380952,0.011750149,0,0,Oct,2,4,9,2,Returning_Visitor,FALSE,FALSE 1,11.1,0,0,48,830.3142529,0.004347826,0.017619048,0,0,Nov,1,1,1,3,Returning_Visitor,FALSE,FALSE 2,53.85,0,0,2,161.2,0,0,0,0,Aug,2,4,3,1,Returning_Visitor,FALSE,FALSE 0,0,0,0,14,598.4333333,0.014285714,0.035714286,0,0,Jul,2,4,9,3,Returning_Visitor,FALSE,FALSE 8,89.9,3,20,84,1854.915556,0.004210526,0.012385965,3.170426459,0,Jul,2,2,7,1,Returning_Visitor,FALSE,FALSE 2,23.2,0,0,5,59.4,0,0.00952381,32.73373471,0,Nov,2,4,4,5,New_Visitor,FALSE,FALSE 1,29.2,0,0,0,0,0,0.066666667,0,0,Nov,1,1,4,2,Returning_Visitor,TRUE,TRUE 0,0,0,0,3,27.2,0.133333333,0.133333333,0,0,Oct,3,2,4,1,Returning_Visitor,FALSE,FALSE 2,27.46666667,3,245.8,28,524.06,0.02,0.054666667,20.42600013,0,Oct,2,2,7,1,Returning_Visitor,FALSE,TRUE 5,419,0,0,14,362.1029825,0.022916667,0.021979167,3.164088313,0,Jul,3,2,3,2,Returning_Visitor,TRUE,TRUE 0,0,0,0,14,422.1333333,0.071428571,0.103333333,0,0,Jul,3,2,4,3,Returning_Visitor,TRUE,FALSE 2,61.48,0,0,40,797.0033333,0,0.012539087,10.20777685,0,Oct,2,2,3,2,Returning_Visitor,FALSE,FALSE 6,268.95,2,153,139,5286.587577,0.002959159,0.018209889,0,0,Nov,1,1,1,3,Returning_Visitor,FALSE,FALSE 9,178.52,3,183.2666667,7,205.8566667,0,0.035555556,0,0,Jul,2,2,1,2,New_Visitor,FALSE,FALSE 2,175.2,0,0,60,1635.466667,0.006557377,0.014754098,0,0,Nov,2,5,7,3,Returning_Visitor,FALSE,FALSE 4,228.72,0,0,4,154.9,0,0.008333333,0,0,Oct,1,8,1,11,New_Visitor,FALSE,FALSE 3,80.6,0,0,37,936.4166667,0,0.005128205,0,0,Jul,1,1,4,2,Returning_Visitor,TRUE,FALSE 8,146.5333333,0,0,107,3739.635556,0,0.009316239,2.551140814,0,Oct,2,2,3,2,Returning_Visitor,FALSE,FALSE 0,0,1,297.2,8,320.2666667,0,0.044444444,17.79528223,0,Oct,2,2,1,20,Returning_Visitor,FALSE,TRUE 8,101.1428571,0,0,109,1370.127838,0.004512398,0.019698317,0,0,Jul,2,2,1,3,Returning_Visitor,FALSE,FALSE 11,201.84,0,0,48,2074.401905,0,0.016071429,7.248757745,0,Aug,2,7,3,1,Returning_Visitor,FALSE,FALSE 1,129.36,0,0,6,341.36,0,0.0625,11.27546478,0,Nov,2,5,3,1,Returning_Visitor,FALSE,FALSE 7,142.35,0,0,73,3566.992857,0,0.003947368,23.15073799,0,Oct,2,5,3,5,New_Visitor,FALSE,TRUE 0,0,0,0,10,349.1111111,0.02,0.02,88.39170518,0,Oct,3,2,1,20,Returning_Visitor,TRUE,TRUE 1,32.2,0,0,32,396.4714286,0.021212121,0.050688705,0,0,Jul,1,1,2,3,Returning_Visitor,TRUE,FALSE 3,55.33333333,0,0,4,420,0.0125,0.0625,0,0,Aug,1,1,4,3,Returning_Visitor,FALSE,TRUE 9,103.9333333,0,0,16,172.1333333,0.012,0.049,0,0,Oct,1,1,1,2,Returning_Visitor,FALSE,TRUE 21,157.8883333,3,84.8,170,3888.278636,0,0.004514952,0,0,Nov,2,2,7,2,Returning_Visitor,FALSE,FALSE 0,0,0,0,31,641.6558915,0,0.005376344,60.04472938,0,Sep,1,1,4,2,New_Visitor,FALSE,TRUE 4,82.46666667,0,0,10,153.9,0.016666667,0.016666667,0,0,Jul,3,2,6,4,Returning_Visitor,FALSE,TRUE 0,0,0,0,7,296.7666667,0,0,0,0,Aug,1,8,7,3,Returning_Visitor,FALSE,FALSE 0,0,0,0,2,19.2,0.1,0.15,0,0,Sep,3,2,3,1,Returning_Visitor,FALSE,FALSE 1,94.8,0,0,6,223.1,0,0.00952381,0,0,Sep,5,11,8,14,Returning_Visitor,FALSE,FALSE 1,21.2,0,0,24,1123.246667,0,0.017391304,30.04921703,0,Sep,2,2,3,2,Returning_Visitor,FALSE,TRUE 2,69,0,0,51,2103.628837,0,0.015384615,40.659727,0,Sep,2,2,4,13,Returning_Visitor,FALSE,TRUE 8,115.2,1,11,18,436.6,0.008695652,0.008695652,0,0,Nov,2,2,7,2,Returning_Visitor,FALSE,FALSE 4,118.8,0,0,11,805.5,0,0.033333333,0,0,Oct,1,1,9,3,Returning_Visitor,FALSE,FALSE 0,0,0,0,11,199.5,0.127272727,0.142424242,0,0,Jul,3,2,1,1,Returning_Visitor,FALSE,FALSE 8,296.86,8,179.5,157,16093.30941,0.007903295,0.027247449,2.051882078,0,June,2,2,1,5,Returning_Visitor,TRUE,FALSE 0,0,0,0,3,135,0,0.066666667,0,0,June,1,1,6,1,Returning_Visitor,TRUE,FALSE 11,361.6,4,112.6,12,147.7333333,0.057142857,0.055555556,0,0,Aug,3,2,1,3,Returning_Visitor,FALSE,FALSE 7,61.08,0,0,39,1324.85,0,0.01627907,0,0,Oct,2,2,1,4,Returning_Visitor,FALSE,FALSE 6,71.5,0,0,79,1854.26,0.039,0.06880303,2.852036737,0,Jul,2,2,9,1,Returning_Visitor,FALSE,FALSE 0,0,0,0,14,1049.05,0,0.016666667,0,0,Nov,3,2,1,20,Returning_Visitor,FALSE,FALSE 10,168.9066667,2,18.4,36,853.8502721,0,0.007751938,5.6479944,0,Oct,3,2,3,2,Returning_Visitor,TRUE,FALSE 2,42.4,0,0,5,163.2,0,0.028571429,20.71678117,0,Aug,2,10,3,3,Returning_Visitor,FALSE,TRUE 5,34.44242424,0,0,103,1843.759347,0,0.019613005,0,0,June,2,4,1,2,Returning_Visitor,TRUE,FALSE 1,7,2,15,17,2269.9,0,0.019047619,0,0,Jul,2,2,3,4,Returning_Visitor,FALSE,TRUE 7,99.8,5,466.4,28,839.7333333,0.001754386,0.009231412,0,0,Nov,2,2,3,1,Returning_Visitor,FALSE,TRUE 0,0,0,0,1,1195.8,0,0,0,0,Aug,2,2,7,13,Returning_Visitor,FALSE,FALSE 0,0,0,0,37,856.2,0.052252252,0.108558559,0,0,Sep,2,2,2,1,Returning_Visitor,FALSE,FALSE 2,43.2,1,32.2,202,13842.45833,0.01804878,0.050155633,0,0,Sep,2,4,7,1,Returning_Visitor,FALSE,FALSE 2,56.4,1,0,72,2101.467925,0.006140351,0.029986961,0,0,Aug,1,1,2,2,Returning_Visitor,FALSE,FALSE 3,84.5,0,0,346,12541.77628,0.001709402,0.006473478,5.177106841,0,Aug,2,2,3,1,Returning_Visitor,TRUE,TRUE 0,0,0,0,80,1566.333333,0.005,0.027916667,0.720181023,0,Sep,2,2,6,1,Returning_Visitor,FALSE,TRUE 1,28.2,0,0,35,942.6879221,0.017142857,0.035442177,8.435206908,0,Sep,1,1,1,2,Returning_Visitor,TRUE,FALSE 1,20.2,6,226,76,1308.573395,0.032611502,0.054407935,0,0,June,3,2,6,2,Returning_Visitor,FALSE,FALSE 2,58.4,0,0,24,589.8,0.032098765,0.05617284,1.696968944,0,Sep,2,2,1,1,Returning_Visitor,TRUE,FALSE 0,0,0,0,7,159.2,0.085714286,0.128571429,0,0,Aug,3,3,3,1,Returning_Visitor,TRUE,FALSE 3,40.55,0,0,10,151.0818182,0.008333333,0.038888889,0,0,Jul,2,2,1,5,New_Visitor,FALSE,FALSE 1,23.2,0,0,3,397.8,0,0,0,0,Aug,2,2,1,4,Returning_Visitor,FALSE,FALSE 0,0,0,0,2,31.2,0,0.1,0,0,Aug,3,2,1,3,Returning_Visitor,FALSE,FALSE 0,0,0,0,18,481.4,0.068518519,0.091604938,0,0,Aug,3,2,3,3,Returning_Visitor,FALSE,FALSE 11,471.6266667,4,235.6,22,883.12,0.006451613,0.018196857,19.43795945,0,Sep,3,2,3,4,Returning_Visitor,FALSE,FALSE 2,241.6,0,0,28,920.7,0,0.015555556,0,0,Nov,1,8,3,20,Returning_Visitor,FALSE,FALSE 0,0,0,0,16,336.3,0.0125,0.025,0,0,Jul,2,2,7,1,Returning_Visitor,FALSE,FALSE 4,65.4,0,0,23,1954.4,0,0.006666667,15.13596148,0,Aug,2,2,3,2,Returning_Visitor,FALSE,FALSE 4,12,0,0,28,580.65,0,0.016666667,0,0,Jul,1,2,1,4,Returning_Visitor,FALSE,FALSE 0,0,0,0,13,323.3,0.084615385,0.102564103,0,0,June,3,2,1,1,Returning_Visitor,FALSE,FALSE 4,60.2,0,0,8,132.8,0,0.009090909,0,0,Oct,1,1,1,2,Returning_Visitor,FALSE,FALSE 7,56.3,0,0,11,353.9,0.025,0.04375,10.02470626,0,Oct,2,2,2,1,Returning_Visitor,FALSE,FALSE 3,117.8,0,0,62,2032.39696,0.005230769,0.01881429,0,0,Sep,3,2,6,4,Returning_Visitor,FALSE,FALSE 0,0,0,0,6,66.9,0,0.02,0,0,Nov,3,2,1,2,New_Visitor,FALSE,FALSE 0,0,0,0,20,1413.768571,0.025,0.038472222,0,0,June,1,2,3,1,Returning_Visitor,TRUE,FALSE 9,179.3785714,0,0,8,131.9785714,0,0.0025,0,0,Sep,2,2,1,5,New_Visitor,FALSE,FALSE 0,0,3,135,32,804.1899134,0.029946524,0.03546327,4.027998418,0,Oct,1,1,6,2,Returning_Visitor,TRUE,FALSE 3,98.6,0,0,7,131.8,0,0.02,88.84365062,0,Oct,2,2,7,2,Returning_Visitor,FALSE,TRUE 3,29.44,1,0,4,15.24,0,0.039583333,0,0,Sep,2,2,8,2,Returning_Visitor,FALSE,TRUE 6,158.6704505,1,430.2,146,5602.006712,0.002671513,0.00935218,6.711290592,0,Nov,2,10,7,2,Returning_Visitor,FALSE,TRUE 2,24.2,0,0,8,530,0,0.044444444,0,0,Aug,1,8,1,2,Returning_Visitor,FALSE,FALSE 1,27.2,0,0,71,1722.721127,0.005555556,0.034027778,0,0,Jul,2,2,7,1,Returning_Visitor,TRUE,FALSE 2,20.2,0,0,7,114.5333333,0,0.05,0,0,Oct,2,2,1,1,Returning_Visitor,FALSE,FALSE 0,0,0,0,22,657.42,0.021212121,0.046969697,0,0,Jul,3,2,3,4,Returning_Visitor,TRUE,FALSE 3,356.6,0,0,39,589.2510658,0.001282051,0.010133221,0,0,Jul,1,1,4,4,Returning_Visitor,FALSE,FALSE 2,24,0,0,37,1391.105556,0.001315789,0.01505848,0,0,Sep,1,1,2,2,Returning_Visitor,FALSE,FALSE 0,0,0,0,4,67.6,0.125,0.133333333,0,0,Jul,3,2,1,4,Returning_Visitor,FALSE,FALSE 1,249.8,2,722.9,8,2219.583333,0,0,0,0,Nov,2,4,4,2,New_Visitor,FALSE,FALSE 8,315.845679,4,382.4,31,1314.112346,0.014285714,0.013518007,37.53469014,0,Oct,1,1,1,4,Returning_Visitor,FALSE,TRUE 1,0,2,6,9,195.1666667,0,0.066666667,0,0,June,4,2,3,4,Returning_Visitor,FALSE,FALSE 1,15.1,0,0,9,248.05,0,0.01875,0,0,Sep,3,2,4,4,New_Visitor,FALSE,FALSE 4,19.8,0,0,2,10,0.0125,0.034166667,0,0,Sep,1,1,6,5,New_Visitor,TRUE,TRUE 0,0,0,0,6,0,0.2,0.2,0,0,June,3,2,7,13,Returning_Visitor,FALSE,FALSE 0,0,0,0,37,1530.006667,0,0.005555556,0,0,Oct,2,2,2,2,Returning_Visitor,TRUE,FALSE 0,0,0,0,47,1423,0.004347826,0.013768116,0,0,Sep,2,2,7,1,Returning_Visitor,FALSE,FALSE 2,34.2,0,0,5,63.4,0,0.028571429,0,0,Jul,4,1,2,2,New_Visitor,FALSE,FALSE 4,143.0666667,1,6.5,74,1294.405,0.002666667,0.010666667,0,0,Aug,2,2,3,1,Returning_Visitor,TRUE,FALSE 4,65.68,1,42.4,12,285.68,0.015384615,0.038461538,0,0,Aug,2,6,4,3,Returning_Visitor,TRUE,FALSE 2,115.2,0,0,7,211.1,0,0,0,0,Jul,2,2,2,2,New_Visitor,TRUE,FALSE 0,0,0,0,4,159.2,0.05,0.066666667,0,0,June,3,2,5,13,Returning_Visitor,TRUE,FALSE 1,0,3,69,15,352.875,0,0.055555556,0,0,Aug,2,2,3,2,Returning_Visitor,FALSE,FALSE 7,140.9,1,18.2,20,318.7666667,0,0.009876543,0,0,Oct,2,2,3,2,New_Visitor,FALSE,FALSE 1,11,0,0,2,51.06666667,0,0,0,0,June,2,2,9,6,Returning_Visitor,FALSE,FALSE 3,32,0,0,15,276.3666667,0,0,55.82099528,0,Sep,2,4,1,2,New_Visitor,FALSE,FALSE 3,66.33333333,0,0,12,368.68,0.04,0.084444444,21.24191985,0,Nov,2,2,1,2,Returning_Visitor,FALSE,TRUE 2,12.1,0,0,4,38.3,0.00952381,0.066666667,0,0,Nov,1,2,1,5,Returning_Visitor,FALSE,FALSE 7,98.73333333,2,229.8,151,4249.12,0.013402778,0.041166667,5.88595629,0,Jul,2,5,3,3,Returning_Visitor,FALSE,FALSE 0,0,0,0,62,944.4055556,0,0.015591398,0,0,June,1,1,7,3,Returning_Visitor,FALSE,FALSE 0,0,0,0,4,72,0,0,35.34967016,0,Sep,3,2,3,2,Returning_Visitor,TRUE,TRUE 0,0,0,0,2,107.8,0,0.1,0,0,Aug,2,2,5,13,Returning_Visitor,FALSE,FALSE 1,64.4,0,0,22,2364.65,0,0.043478261,0,0,Oct,2,2,1,2,Returning_Visitor,FALSE,FALSE 3,145.2,0,0,28,425.0969048,0,0.002380952,0,0,Oct,3,2,7,11,New_Visitor,TRUE,FALSE 0,0,0,0,19,528.25,0,0.005263158,0,0,Sep,2,2,5,2,Returning_Visitor,FALSE,FALSE 0,0,0,0,14,276.35,0,0.007142857,0,0,June,1,1,1,2,Returning_Visitor,FALSE,FALSE 2,24.2,0,0,3,52.4,0,0.04,0,0,Sep,2,2,3,5,New_Visitor,FALSE,FALSE 0,0,0,0,32,905.1,0,0,83.80837784,0,Oct,2,2,6,2,New_Visitor,FALSE,TRUE 0,0,0,0,12,274.75,0,0.030555556,0,0,Nov,2,2,1,1,Returning_Visitor,FALSE,FALSE 1,8,0,0,47,1459.576667,0.010416667,0.017638889,0,0,Oct,1,1,3,3,Returning_Visitor,FALSE,FALSE 2,58.4,0,0,84,3528.69125,0.01577381,0.039900794,0,0,June,2,2,1,1,Returning_Visitor,FALSE,FALSE 0,0,0,0,6,351.4,0.066666667,0.1,0,0,June,2,2,1,3,Returning_Visitor,FALSE,FALSE 3,184.7333333,0,0,106,4590.381905,0.001851852,0.00287037,3.96093872,0,Aug,2,2,9,2,Returning_Visitor,FALSE,TRUE 0,0,0,0,3,60.4,0,0.066666667,0,0,Sep,1,1,1,5,Returning_Visitor,FALSE,FALSE 5,75.66349206,0,0,4,35.92777778,0,0.056481481,0,0,Oct,2,5,7,2,Returning_Visitor,FALSE,FALSE 2,49.4,0,0,45,1526.14119,0,0.004444444,117.6165932,0,Sep,2,2,3,2,Returning_Visitor,TRUE,TRUE 1,50.4,0,0,49,1463.48,0.004081633,0.015646259,24.68624612,0,June,2,2,9,1,Returning_Visitor,FALSE,FALSE 4,123.4,0,0,38,585.37,0.017948718,0.033333333,0,0,Jul,1,1,3,3,Returning_Visitor,TRUE,FALSE 0,0,0,0,7,211.6333333,0,0,28.80119616,0,Aug,1,1,1,5,New_Visitor,FALSE,TRUE 1,10,0,0,23,271.5785714,0,0.008333333,0,0,Aug,2,2,4,1,Returning_Visitor,FALSE,FALSE 6,432.2,0,0,30,363.677619,0.003030303,0.014018978,12.1958841,0,Oct,1,1,3,2,Returning_Visitor,FALSE,TRUE 2,139,0,0,5,274.7,0,0.014285714,0,0,Jul,1,1,5,2,New_Visitor,FALSE,FALSE 11,251.3090909,2,1655.4,80,1249.048358,0.003571429,0.013743735,8.517221252,0,Sep,3,2,4,2,Returning_Visitor,TRUE,FALSE 0,0,0,0,13,261.8,0.015384615,0.084615385,0,0,Sep,1,1,1,3,Returning_Visitor,FALSE,FALSE 0,0,0,0,5,89.86666667,0.04,0.06,0,0,Jul,3,2,5,4,Returning_Visitor,TRUE,FALSE 2,36.4,0,0,3,28.2,0,0,55.89978908,0,Sep,1,1,3,2,New_Visitor,FALSE,FALSE 0,0,0,0,3,54.4,0.066666667,0.133333333,0,0,Jul,3,2,4,3,Returning_Visitor,TRUE,FALSE 3,113.1,0,0,17,237.6066667,0,0.033333333,94.10360412,0,Oct,1,1,2,4,Returning_Visitor,FALSE,TRUE 3,38.3,0,0,18,929.2,0,0.010884354,0,0,Jul,2,2,5,2,Returning_Visitor,TRUE,FALSE 2,43.73333333,0,0,19,158.9333333,0,0.003741497,0,0,June,1,1,6,2,Returning_Visitor,FALSE,FALSE 10,131.34,2,38.2,102,6684.520952,0.002181818,0.017363636,4.215363542,0,Sep,2,2,5,1,Returning_Visitor,FALSE,FALSE 7,136.5,1,42.4,120,7604.397831,0.015762274,0.030675226,3.996742423,0,Jul,2,2,2,1,Returning_Visitor,TRUE,TRUE 4,275.1,2,43.4,31,1108.653333,0.006060606,0.012121212,0,0,Sep,3,2,1,13,Returning_Visitor,TRUE,FALSE 0,0,0,0,31,2285.2,0.006451613,0.04516129,0,0,Nov,2,2,9,1,Returning_Visitor,FALSE,FALSE 11,385.5666667,0,0,54,1256.293333,0.010344828,0.014367816,0,0,Oct,2,2,7,3,Returning_Visitor,FALSE,FALSE 4,59.53333333,0,0,13,183.65,0,0.011764706,0,0,Sep,2,2,9,2,New_Visitor,FALSE,FALSE 0,0,0,0,8,232,0,0.025,0,0,Jul,1,2,1,2,Returning_Visitor,TRUE,FALSE 1,54.4,0,0,1,101.2727273,0,0.007042254,0,0,Sep,2,2,4,2,Returning_Visitor,FALSE,FALSE 2,64.4,0,0,15,443.5666667,0,0.00625,166.3735531,0,June,1,1,1,5,New_Visitor,TRUE,FALSE 2,31.2,2,468.4,15,1388,0.003703704,0.063333333,12.1360201,0,Jul,2,2,5,3,Returning_Visitor,TRUE,FALSE 0,0,0,0,17,557.4,0,0.011111111,83.93274289,0,Sep,2,2,2,2,New_Visitor,TRUE,TRUE 3,16,0,0,24,1004.383333,0,0.023076923,0,0,June,2,2,4,13,Returning_Visitor,FALSE,FALSE 1,0,0,0,11,113.8,0.083333333,0.116666667,0,0,Nov,2,4,1,3,Returning_Visitor,FALSE,TRUE 0,0,0,0,9,249.41,0.022222222,0.005128205,0,0,Nov,1,2,1,4,Returning_Visitor,FALSE,FALSE 7,119.4,0,0,16,743.5314286,0.01,0.02,35.35631939,0,Sep,1,1,8,4,Returning_Visitor,FALSE,FALSE 3,23.2,0,0,24,809.4333333,0.0128,0.021333333,0,0,Aug,3,2,1,3,Returning_Visitor,TRUE,FALSE 1,22.2,2,44.4,37,400.8,0.005128205,0.002564103,0,0,Oct,2,10,1,2,Returning_Visitor,FALSE,FALSE 3,82.95714286,0,0,28,951.4412698,0.013793103,0.025860732,0,0,Oct,3,2,1,2,Returning_Visitor,FALSE,FALSE 1,2,0,0,5,86.6,0,0.1,0,0,Sep,3,2,3,2,Returning_Visitor,FALSE,FALSE 0,0,0,0,2,30.4,0,0.066666667,21.47899475,0,Oct,1,8,6,20,Returning_Visitor,TRUE,TRUE 0,0,0,0,6,339.5,0,0.016666667,124.9045858,0,Nov,2,6,9,20,New_Visitor,FALSE,TRUE 0,0,0,0,27,1010.633333,0,0.012962963,0,0,June,2,2,1,2,Returning_Visitor,FALSE,FALSE 1,68.6,1,29.2,97,4318.668082,0.002520046,0.013784185,0,0,Jul,2,4,2,2,Returning_Visitor,TRUE,FALSE 1,4,1,10,274,5876.459165,0.002431163,0.013240553,0,0,Oct,2,2,9,2,Returning_Visitor,FALSE,FALSE 3,60.4,0,0,4,25.8,0,0.04,0,0,Jul,1,1,3,4,New_Visitor,FALSE,FALSE 2,89.33333333,0,0,22,502.5083333,0,0.018695652,0,0,Sep,3,2,2,2,Returning_Visitor,TRUE,FALSE 2,66.26666667,1,348.6,6,233.1666667,0.044444444,0.046666667,11.12205139,0,Oct,3,2,3,7,Returning_Visitor,FALSE,FALSE 7,98.46666667,0,0,13,257.9333333,0,0.016,0,0,Sep,2,2,3,4,Returning_Visitor,FALSE,FALSE 0,0,0,0,4,236.8,0,0.05,36.26141924,0,Oct,2,2,3,1,Returning_Visitor,FALSE,TRUE 7,667.25,0,0,4,101.8,0,0.01,0,0,Jul,3,2,6,2,Returning_Visitor,TRUE,FALSE 0,0,0,0,13,266.8,0,0.015384615,0,0,Jul,2,5,7,4,Returning_Visitor,FALSE,FALSE 6,206.03,0,0,129,3778.345,0.010687023,0.024611656,2.169469761,0,Oct,2,2,1,3,Returning_Visitor,FALSE,TRUE 3,82.6,0,0,33,598.7611111,0,0.007843137,0,0,Sep,2,2,2,1,Returning_Visitor,FALSE,FALSE 13,498.9,0,0,221,3111.362121,0,0.006020558,5.268190163,0,Oct,2,5,4,2,Returning_Visitor,FALSE,FALSE 2,109.8,0,0,32,897.3619048,0.005882353,0.035620915,0,0,Sep,2,10,4,2,Returning_Visitor,FALSE,FALSE 8,115.7,0,0,24,2159.5,0,0.022857143,0,0,Sep,2,2,5,2,Returning_Visitor,FALSE,FALSE 15,149.0316092,2,1529.2,23,2128.704568,0.005714286,0.020425941,16.19731984,0,Oct,2,2,2,20,Returning_Visitor,FALSE,FALSE 2,20.2,0,0,14,1261.316667,0,0.009183673,0,0,Aug,2,2,1,5,Returning_Visitor,FALSE,FALSE 3,86.1,2,28.4,2,5,0,0.033333333,0,0,Jul,2,2,1,5,New_Visitor,FALSE,FALSE 0,0,0,0,1,0,0.2,0.2,0,0,Aug,2,2,1,4,Returning_Visitor,TRUE,FALSE 0,0,0,0,2,48.4,0,0,34.03997536,0,Sep,2,2,4,1,Returning_Visitor,FALSE,TRUE 11,903.7,1,26.2,109,3297.491311,0.006140351,0.010281148,3.870588042,0,Oct,3,2,6,1,Returning_Visitor,FALSE,FALSE 0,0,0,0,7,431.5333333,0,0.007142857,0,0,Oct,2,2,1,2,Returning_Visitor,FALSE,FALSE 1,73.6,1,31.2,52,1761.156667,0.003703704,0.018312757,0,0,Sep,2,2,1,1,Returning_Visitor,FALSE,FALSE 0,0,0,0,1,0,0.2,0.2,0,0,Oct,1,1,1,1,New_Visitor,TRUE,FALSE 2,28.2,0,0,12,271,0,0.014285714,0,0,Aug,1,8,4,6,Returning_Visitor,FALSE,FALSE 9,236.8,0,0,240,5523.926505,0,0.001197045,20.62387076,0,Oct,2,5,4,20,Returning_Visitor,FALSE,TRUE 2,24.13333333,0,0,19,263.7128571,0,0.00125,0,0,Oct,3,2,1,2,Returning_Visitor,FALSE,FALSE 3,23,0,0,3,1225.2,0,0.04,10.99901844,0,Sep,2,2,1,1,Returning_Visitor,TRUE,TRUE 0,0,0,0,4,98.6,0,0.05,0,0,Jul,2,2,5,1,Returning_Visitor,FALSE,FALSE 3,80.6,2,112.8,10,390.2,0,0.015384615,0,0,Oct,2,5,3,20,Returning_Visitor,FALSE,FALSE 0,0,0,0,8,272.9,0.05,0.075,0,0,Oct,1,1,1,1,New_Visitor,FALSE,FALSE 6,82.01111111,2,211.6,162,6117.814359,0,0.003964551,22.36033441,0,Sep,2,2,1,6,Returning_Visitor,FALSE,FALSE 14,416.2,0,0,90,8024.509197,0.008247423,0.032048254,10.31930868,0,Sep,3,2,8,2,Returning_Visitor,FALSE,FALSE 0,0,0,0,4,18.2,0.15,0.15,0,0,Sep,3,2,1,13,Returning_Visitor,FALSE,FALSE 7,125.4533333,1,44.4,54,2240.996667,0.027118644,0.043050847,21.74067473,0,Jul,2,2,6,1,Returning_Visitor,FALSE,TRUE 3,319.4,1,251.8,53,2525.307766,0,0.010606061,52.97841866,0,Jul,2,2,9,4,Returning_Visitor,FALSE,TRUE 4,117.8,0,0,12,267.8,0.025,0.04375,0,0,Sep,3,2,3,13,Returning_Visitor,FALSE,FALSE 2,126,1,6,47,1101.803333,0,0.002721088,4.406578162,0,Nov,2,2,1,4,Returning_Visitor,FALSE,FALSE 1,4,0,0,28,877.6666667,0,0.003571429,0,0,Nov,2,2,9,2,New_Visitor,TRUE,FALSE 0,0,0,0,19,401.9,0.056140351,0.092982456,0,0,Aug,1,1,2,1,Returning_Visitor,FALSE,FALSE 7,114.2,0,0,26,360.1816667,0,0.008800253,0,0,Nov,1,1,1,2,Returning_Visitor,FALSE,FALSE 0,0,0,0,19,923.22,0,0,0,0,Aug,1,1,1,4,Returning_Visitor,FALSE,FALSE 2,165.2,0,0,36,983.425,0,0.010526316,0,0,Aug,2,4,8,6,New_Visitor,TRUE,FALSE 0,0,0,0,8,41.13333333,0.025,0.0625,0,0,Jul,2,2,9,1,Returning_Visitor,FALSE,FALSE 2,191.4,0,0,7,789.5333333,0.025,0.0375,0,0,Nov,3,3,3,5,Returning_Visitor,TRUE,FALSE 14,484.4615789,2,14,33,898.4915789,0.004269006,0.03466118,0,0,Sep,1,8,1,3,Returning_Visitor,FALSE,FALSE 15,419.1795254,0,0,110,10278.90421,0.0128,0.02480939,14.4030575,0,Oct,2,2,3,3,Returning_Visitor,FALSE,FALSE 4,149.95,0,0,1,57.4,0,0.04,0,0,Jul,1,8,1,5,Returning_Visitor,FALSE,FALSE 5,36.86666667,0,0,123,3886.823333,0.002090592,0.015563298,0,0,June,2,2,1,4,Returning_Visitor,FALSE,FALSE 3,79.6,0,0,28,627.6542857,0.007142857,0.008214286,0,0,Sep,1,2,2,2,Returning_Visitor,FALSE,FALSE 0,0,0,0,2,16.2,0,0.1,0,0,Oct,1,1,1,2,New_Visitor,FALSE,FALSE 5,69.6,3,53,6,92.7,0,0.02,0,0,Jul,2,6,1,3,New_Visitor,TRUE,FALSE 2,58.4,0,0,54,3708.392857,0.002631579,0.020467836,11.7031641,0,Sep,4,2,9,13,Returning_Visitor,FALSE,TRUE 3,202.1333333,0,0,23,721.1833333,0,0.008,0,0,Sep,1,1,1,2,New_Visitor,FALSE,FALSE 0,0,0,0,3,132.4,0,0.033333333,0,0,June,3,2,1,1,Returning_Visitor,FALSE,FALSE 1,34.3,0,0,22,722.1333333,0.018181818,0.045,0,0,Sep,2,2,8,1,Returning_Visitor,FALSE,FALSE 0,0,3,27.6,11,1167.187143,0.030769231,0.040384615,0,0,June,3,2,2,2,Returning_Visitor,FALSE,FALSE 3,70.93333333,3,560,16,810.8333333,0,0.028070175,9.163038423,0,Jul,2,2,1,2,Returning_Visitor,FALSE,FALSE 13,243.65,0,0,20,234.0166667,0.016,0.022,0,0,Oct,2,2,1,2,Returning_Visitor,FALSE,FALSE 0,0,0,0,6,252.3,0,0,22.15399132,0,Nov,1,1,7,3,Returning_Visitor,FALSE,TRUE 10,170.15,0,0,11,71.35,0,0.002352941,0,0,Aug,2,2,1,2,New_Visitor,FALSE,FALSE 0,0,0,0,83,1543.423333,0.02804878,0.057723577,10.15253138,0,Sep,2,6,4,1,Returning_Visitor,FALSE,FALSE 1,134,0,0,21,1429.616667,0,0.00952381,0,0,Nov,1,1,2,2,New_Visitor,FALSE,FALSE 0,0,0,0,16,2214.666667,0,0.003921569,92.9041786,0,Oct,2,2,1,2,New_Visitor,FALSE,TRUE 2,67.5,2,100.2,17,1770.8,0.026315789,0.035087719,0,0,Nov,2,2,2,2,Returning_Visitor,FALSE,FALSE 0,0,0,0,1,0,0.2,0.2,0,0,June,2,4,9,3,Returning_Visitor,FALSE,FALSE 1,30.2,0,0,20,293.0333333,0.019047619,0.025396825,9.775216594,0,Nov,1,2,1,1,Returning_Visitor,FALSE,FALSE 3,49.2,0,0,17,300.7666667,0.004761905,0.022222222,0,0,Jul,1,1,3,4,Returning_Visitor,FALSE,FALSE 2,81.6,1,0,32,2230.600476,0.0125,0.023125,0,0,Jul,2,2,9,1,Returning_Visitor,FALSE,FALSE 3,98.1,2,28.2,130,4994.848002,0.019799499,0.036825949,0,0,Sep,2,2,4,1,Returning_Visitor,TRUE,FALSE 2,67,0,0,8,533.3,0,0.016666667,0,0,Sep,3,2,9,2,New_Visitor,FALSE,FALSE 10,706.5566667,0,0,53,1572.816667,0.007017544,0.024948268,0,0,Nov,3,2,1,1,Returning_Visitor,FALSE,FALSE 0,0,2,5,5,122.4,0.028571429,0.042857143,0,0,Jul,3,2,2,4,Returning_Visitor,TRUE,FALSE 0,0,0,0,18,426.0666667,0,0.033333333,0,0,Jul,2,2,1,3,Returning_Visitor,FALSE,FALSE 0,0,0,0,16,161.15,0.1,0.125,0,0,Aug,3,2,3,13,Returning_Visitor,FALSE,FALSE 2,88.6,0,0,59,458.61,0.006779661,0.007605389,0,0,Aug,3,2,1,4,Returning_Visitor,TRUE,FALSE 7,155,0,0,20,252.9833333,0.008333333,0.025,0,0,Oct,2,2,1,3,Returning_Visitor,FALSE,FALSE 0,0,0,0,135,7091.697778,0.019259259,0.037964727,0,0,Sep,2,2,2,3,Returning_Visitor,FALSE,FALSE 5,49.2,4,379,5,74.6,0,0.018181818,8.326728149,0,Sep,2,2,8,2,New_Visitor,FALSE,FALSE 2,84.6,0,0,43,674.5333333,0,0.005319149,45.63345378,0,Oct,2,2,1,2,New_Visitor,FALSE,TRUE 1,85.1,0,0,8,194.2285714,0.075,0.078125,0,0,Nov,3,3,1,1,Returning_Visitor,TRUE,FALSE 0,0,0,0,22,1076.65,0.023809524,0.036507937,10.7869918,0,Aug,2,2,2,1,Returning_Visitor,FALSE,TRUE 0,0,0,0,3,41.4,0,0.066666667,0,0,Sep,2,2,7,2,Returning_Visitor,FALSE,FALSE 2,24.2,0,0,46,1376.3,0.008163265,0.03537415,6.012671082,0,Sep,2,4,3,1,Returning_Visitor,FALSE,FALSE 0,0,2,91.2,24,1017.775758,0.052564103,0.049224806,0,0,Nov,2,2,9,20,Returning_Visitor,TRUE,FALSE 1,31.2,0,0,31,824.86,0,0.008333333,0,0,Sep,1,1,1,3,New_Visitor,FALSE,FALSE 2,91.9,0,0,6,273.8,0,0.005,0,0,Sep,3,2,1,2,New_Visitor,TRUE,FALSE 2,31.6,0,0,4,75.6,0,0.028571429,0,0,Aug,2,2,7,4,Returning_Visitor,FALSE,FALSE 2,99.2,0,0,7,106.9666667,0.025,0.015,0,0,Oct,3,2,1,2,Returning_Visitor,TRUE,FALSE 6,175.1,0,0,3,27.6,0,0.011111111,0,0,Jul,2,10,3,5,New_Visitor,FALSE,FALSE 0,0,0,0,13,338.2,0.030769231,0.069230769,0,0,Oct,1,1,4,4,Returning_Visitor,TRUE,FALSE 0,0,0,0,21,987.77,0.015238095,0.067619048,0,0,Jul,2,4,3,1,Returning_Visitor,FALSE,FALSE 10,80.06666667,1,812,113,3282.583983,0.007377049,0.016691039,45.55270796,0,Aug,2,2,3,1,Returning_Visitor,FALSE,FALSE 4,70.5,0,0,3,51.3,0,0.00952381,0,0,Sep,3,3,4,5,New_Visitor,FALSE,FALSE 6,128.5,1,0,32,796.7066667,0.027027027,0.041441441,0,0,Nov,2,4,1,1,Returning_Visitor,TRUE,FALSE 0,0,0,0,26,587.9,0.023076923,0.037179487,0,0,June,3,2,5,13,Returning_Visitor,FALSE,FALSE 0,0,0,0,1,0,0.2,0.2,0,0,June,3,2,3,13,Returning_Visitor,FALSE,FALSE 1,23.2,0,0,0,0,0,0.033333333,0,0,Oct,2,2,6,3,Returning_Visitor,FALSE,TRUE 4,11,0,0,44,2149.190195,0.02705314,0.048201119,0,0,Aug,1,1,1,3,Returning_Visitor,FALSE,FALSE 0,0,0,0,51,507.5,0.007843137,0.011671335,12.01221257,0,Oct,3,2,1,1,Returning_Visitor,TRUE,TRUE 5,173.9666667,0,0,17,930.3666667,0.018181818,0.017272727,0,0,Aug,2,2,3,4,Returning_Visitor,FALSE,FALSE 5,174.3,0,0,39,794.7131148,0.032954545,0.056528671,0,0,Sep,1,2,1,1,Returning_Visitor,FALSE,FALSE 1,7,0,0,27,1739.575,0.038095238,0.06037037,0,0,Jul,2,2,7,1,Returning_Visitor,FALSE,FALSE 5,415.55,0,0,115,4424.621667,0.004273504,0.023036223,13.29131586,0,Aug,2,4,4,1,Returning_Visitor,TRUE,TRUE 2,21.2,0,0,7,897.8,0,0.055555556,0,0,Aug,2,2,1,1,Returning_Visitor,FALSE,FALSE 1,59.4,0,0,73,891.568795,0,0,258.5498732,0,Sep,2,2,3,2,New_Visitor,FALSE,TRUE 5,115.8,0,0,60,1516.73,0.007936508,0.01982092,0,0,Aug,2,2,4,3,Returning_Visitor,TRUE,FALSE 5,399.2,2,34.3,42,1713.21619,0.062222222,0.090962963,3.16569129,0,June,2,2,6,13,Returning_Visitor,FALSE,FALSE 5,106.9,0,0,211,7442.388149,0.012735849,0.028901336,0,0,Aug,2,2,3,13,Returning_Visitor,FALSE,FALSE 0,0,0,0,7,245.4,0.025,0.08125,3.27121463,0,Sep,3,7,3,4,Returning_Visitor,FALSE,FALSE 0,0,0,0,42,922.3,0.019047619,0.034920635,0,0,Aug,3,2,8,2,Returning_Visitor,FALSE,FALSE 5,32,0,0,30,425.0333333,0,0.0125,0,0,Oct,2,2,7,1,Returning_Visitor,FALSE,FALSE 4,54.4,0,0,68,2889.946154,0.002898551,0.008789401,0,0,June,4,1,4,1,Returning_Visitor,FALSE,FALSE 2,217.8666667,0,0,15,614.6409524,0,0.027777778,0,0,Nov,1,1,2,2,Returning_Visitor,FALSE,FALSE 0,0,0,0,2,150.6,0,0.033333333,0,0,Oct,2,2,3,5,Returning_Visitor,FALSE,FALSE 3,212.6,0,0,87,3898.816667,0,0.00411985,0,0,Sep,2,2,7,1,Returning_Visitor,FALSE,FALSE 1,160.2,0,0,13,381.6166667,0,0.011904762,0,0,Oct,1,2,3,2,New_Visitor,FALSE,FALSE 8,85.48223443,1,5.5,227,3818.308629,0.007264957,0.018863427,0.154821253,0,Oct,2,2,1,13,Returning_Visitor,FALSE,FALSE 0,0,0,0,11,169.1666667,0,0.036363636,0,0,Oct,2,2,1,1,Returning_Visitor,FALSE,FALSE 1,169.2,0,0,24,1433.163333,0.008333333,0.006944444,0,0,Aug,3,2,3,4,Returning_Visitor,TRUE,TRUE 8,125.7333333,0,0,7,81.66666667,0.033333333,0.024166667,0,0,Sep,2,2,1,1,Returning_Visitor,FALSE,FALSE 1,22.2,0,0,9,348.54,0,0.005194805,16.1722885,0,Nov,2,2,3,3,Returning_Visitor,FALSE,TRUE 0,0,0,0,19,447.7166667,0,0.011111111,0,0,Sep,3,2,1,2,Returning_Visitor,TRUE,FALSE 3,86.6,0,0,41,1678.4,0.031060606,0.046893939,0,0,June,2,2,1,1,Returning_Visitor,FALSE,FALSE 10,154.0229167,0,0,50,1388.052366,0.008611111,0.02038584,18.19675574,0,Sep,3,2,3,4,Returning_Visitor,FALSE,FALSE 3,132.92,4,212.8,36,1335.773333,0.014285714,0.034484127,11.11383981,0,Nov,2,7,2,2,Returning_Visitor,FALSE,FALSE 14,260.7361111,3,186.5,51,2921.902778,0.013661202,0.02989071,8.339171985,0,Nov,4,2,7,1,Returning_Visitor,TRUE,TRUE 0,0,0,0,18,535.3995238,0,0.02962963,0,0,June,1,1,1,2,Returning_Visitor,FALSE,FALSE 0,0,0,0,18,650.1333333,0.05,0.07962963,0,0,Jul,2,2,3,4,Returning_Visitor,FALSE,FALSE 9,162.625,0,0,37,841.3583333,0.000724638,0.0125,0,0,Sep,2,10,1,1,Returning_Visitor,FALSE,FALSE 3,72.6,0,0,17,544.1,0,0.002,0,0,Sep,2,2,9,2,New_Visitor,FALSE,FALSE 3,92.6,0,0,9,626.6,0,0.024242424,0,0,Nov,2,2,5,2,Returning_Visitor,TRUE,FALSE 0,0,0,0,86,5332.547778,0.013882353,0.0654082,0,0,Aug,2,2,4,1,Returning_Visitor,FALSE,FALSE 2,4,0,0,2,44.4,0,0.05,0,0,Sep,2,10,3,5,New_Visitor,FALSE,FALSE 5,43,0,0,36,725.3333333,0.005714286,0.017142857,0,0,Sep,2,2,3,13,Returning_Visitor,FALSE,FALSE 0,0,0,0,5,0,0.2,0.2,0,0,Jul,3,2,3,1,Returning_Visitor,TRUE,FALSE 0,0,0,0,23,297.3,0.013636364,0.037207792,10.95661974,0,Sep,2,2,3,1,Returning_Visitor,FALSE,TRUE 4,81.6,0,0,45,1096.42,0.041945289,0.072659574,0,0,Jul,3,2,6,13,Returning_Visitor,FALSE,FALSE 0,0,2,39.4,6,169.9666667,0,0.00625,0,0,Jul,2,2,3,2,Returning_Visitor,TRUE,FALSE 0,0,0,0,42,2084.479524,0.004761905,0.014920635,0,0,Oct,1,1,1,2,Returning_Visitor,FALSE,FALSE 2,287.5,2,12.5,9,979,0.01025641,0.041025641,0,0,Oct,1,1,4,3,Returning_Visitor,FALSE,FALSE 6,189.8,0,0,9,208.8333333,0,0.015384615,70.31635146,0,Nov,2,2,3,2,New_Visitor,FALSE,TRUE 8,129.9,0,0,70,1438.71,0.011351351,0.033766459,0,0,Jul,2,2,7,1,Returning_Visitor,FALSE,FALSE 3,50.06666667,0,0,159,2184.484938,0.001851852,0.005588342,0,0,Oct,2,2,4,4,Returning_Visitor,TRUE,FALSE 0,0,0,0,28,803.5833333,0.007142857,0.033333333,0,0,Jul,3,2,3,15,Returning_Visitor,FALSE,FALSE 1,3,0,0,9,133.3666667,0.054545455,0.042424242,4.760328465,0,Jul,3,2,4,1,Returning_Visitor,FALSE,TRUE 3,35.2,0,0,44,1535.666667,0.012765957,0.014184397,17.21022568,0,Jul,1,1,1,3,Returning_Visitor,FALSE,FALSE 5,31.7,3,239.2,3,78.5,0,0.008333333,0,0,Sep,2,2,1,5,New_Visitor,FALSE,FALSE 6,159.7,1,72.6,44,531.72,0,0.003921569,0,0,Oct,2,2,2,5,New_Visitor,FALSE,FALSE 5,246.6,0,0,46,1805.146667,0.012244898,0.02122449,0,0,June,2,2,8,1,Returning_Visitor,FALSE,FALSE 2,227.6,0,0,115,5284.816825,0,0.002849003,0,0,Oct,2,2,4,4,Returning_Visitor,FALSE,FALSE 9,215.5,1,12,12,245.6,0,0.0125,0,0,Oct,2,2,4,5,New_Visitor,FALSE,FALSE 0,0,0,0,24,950.0166667,0.009090909,0.027056277,0,0,Oct,2,2,1,20,Returning_Visitor,FALSE,FALSE 1,28.2,1,37.3,7,70.46666667,0.058333333,0.086904762,0,0,Oct,2,2,1,20,Returning_Visitor,FALSE,FALSE 8,173.4115512,0,0,162,5721.800123,0.001142857,0.009090249,17.79812649,0,Oct,2,2,1,1,Returning_Visitor,FALSE,FALSE 1,0,0,0,9,336.8833333,0,0.04,30.21969637,0,Sep,1,2,6,4,Returning_Visitor,FALSE,TRUE 8,217.4,2,67.4,24,428.3,0,0.007142857,0,0,Sep,2,4,4,5,New_Visitor,FALSE,FALSE 2,22.2,0,0,10,163.9,0,0.018181818,0,0,Nov,2,2,1,2,Returning_Visitor,FALSE,FALSE 0,0,0,0,4,44.4,0,0.05,0,0,June,2,10,1,3,Returning_Visitor,FALSE,FALSE 18,608.14,6,733.8,168,4948.398759,0.006631763,0.013527508,10.15064372,0,Aug,2,2,3,1,Returning_Visitor,TRUE,FALSE 3,444.7,0,0,59,3296.55,0.006666667,0.0265,0,0,Jul,2,2,4,13,Returning_Visitor,FALSE,FALSE 8,376.3333333,3,69.4,161,7263.957157,0.003592814,0.007729114,3.104019139,0,Jul,1,1,3,3,Returning_Visitor,FALSE,FALSE 1,81.6,0,0,10,107.2,0.018181818,0.027272727,0,0,Oct,2,2,3,20,Returning_Visitor,FALSE,FALSE 2,31.1,3,31.2,18,792.7333333,0.013043478,0.048115942,8.611221424,0,June,2,5,3,20,Returning_Visitor,FALSE,FALSE 0,0,0,0,28,1555.601212,0,0.009511278,0,0,Nov,3,2,2,2,Returning_Visitor,FALSE,FALSE 9,294.6587879,2,97.64,107,3173.210417,0.003138528,0.00912201,5.866248908,0,June,1,1,1,2,Returning_Visitor,FALSE,FALSE 2,23.2,0,0,11,119.2,0,0.015384615,0,0,Sep,2,2,2,2,New_Visitor,TRUE,FALSE 0,0,0,0,26,2021.4,0.007692308,0.030769231,0,0,Jul,3,3,1,4,Returning_Visitor,FALSE,FALSE 3,47.2,0,0,2,12,0,0.04,0,0,Sep,2,5,4,2,New_Visitor,FALSE,FALSE 0,0,0,0,8,65.4,0.05,0.15,0,0,Aug,1,1,1,3,Returning_Visitor,FALSE,FALSE 0,0,0,0,31,493.58,0,0.006451613,0,0,Sep,2,2,3,2,Returning_Visitor,TRUE,FALSE 16,314.505,0,0,37,1048.663333,0,0.008695652,87.25396293,0,Nov,1,1,3,2,New_Visitor,FALSE,TRUE 0,0,0,0,55,1023.506667,0,0.009090909,45.71146255,0,Aug,2,2,1,4,Returning_Visitor,FALSE,TRUE 0,0,0,0,5,44.8,0,0.033333333,50.36347405,0,Jul,4,1,3,1,Returning_Visitor,FALSE,TRUE 0,0,2,17.2,93,5496.25,0.003191489,0.014078014,0.139200623,0,Jul,2,2,1,1,Returning_Visitor,FALSE,TRUE 0,0,0,0,20,526.8,0,0.010526316,104.2012273,0,Sep,2,2,1,2,Returning_Visitor,FALSE,TRUE 2,140.1666667,4,244.3,14,307.4435055,0.044444444,0.067397661,0,0,Sep,3,2,1,1,Returning_Visitor,FALSE,FALSE 10,515.6133333,3,85.14,50,1558.62,0.003636364,0.011878788,12.12586885,0,Aug,3,2,4,4,Returning_Visitor,TRUE,TRUE 0,0,0,0,30,469.1,0,0.015,0,0,Jul,2,4,4,4,Returning_Visitor,FALSE,FALSE 2,20.2,0,0,16,357.4,0,0.011111111,0,0,Oct,2,2,2,2,New_Visitor,TRUE,FALSE 1,19.2,0,0,42,2197,0,0.013636364,0,0,Oct,2,2,1,1,Returning_Visitor,FALSE,FALSE 1,47.4,0,0,6,146.4,0.022222222,0.062962963,29.65880372,0,Oct,2,2,1,2,Returning_Visitor,FALSE,FALSE 0,0,0,0,15,150.4,0,0.013333333,0,0,Nov,2,2,7,20,Returning_Visitor,FALSE,FALSE 7,168.7,0,0,34,1062.704762,0.005405405,0.013719827,0,0,Sep,2,2,3,2,New_Visitor,FALSE,FALSE 0,0,0,0,16,566.4866667,0,0.039365079,18.30205576,0,Sep,3,2,2,1,Returning_Visitor,FALSE,TRUE 5,178.92,1,6,33,1017.576667,0.013157895,0.011842105,0,0,Nov,3,2,1,3,Returning_Visitor,FALSE,FALSE 0,0,0,0,16,690.3133333,0.028571429,0.057142857,0,0,Oct,2,2,1,1,Returning_Visitor,FALSE,FALSE 2,26.2,0,0,4,487.6,0,0.025,0,0,Sep,2,2,1,4,Returning_Visitor,FALSE,FALSE 0,0,0,0,1,0,0.2,0.2,0,0,Jul,3,2,1,4,Returning_Visitor,TRUE,FALSE 7,46.3,0,0,2,9,0,0.025,0,0,Sep,2,4,1,2,New_Visitor,FALSE,FALSE 0,0,0,0,3,24,0,0.066666667,0,0,Sep,2,2,3,3,Returning_Visitor,FALSE,FALSE 5,209.2,0,0,18,447.1666667,0.008695652,0.041304348,0,0,Aug,1,1,1,1,Returning_Visitor,FALSE,FALSE 0,0,0,0,22,1190.941667,0,0.003174603,0,0,Nov,3,2,2,6,New_Visitor,TRUE,FALSE 2,55.4,2,96.8,26,600.5733333,0.013793103,0.022413793,2.812474051,0,Aug,2,2,1,2,Returning_Visitor,FALSE,FALSE 0,0,0,0,3,34.2,0.066666667,0.1,0,0,Oct,3,2,5,13,Returning_Visitor,FALSE,FALSE 0,0,0,0,28,527.8333333,0.02962963,0.048148148,0,0,Oct,2,2,2,13,Returning_Visitor,FALSE,FALSE 7,103.6,0,0,72,1933.559259,0.005333333,0.026377261,7.806338478,0,Sep,2,2,6,2,Returning_Visitor,FALSE,FALSE 2,182.4,0,0,27,1165.944762,0,0.007407407,0,0,Nov,2,4,6,11,New_Visitor,TRUE,FALSE 15,190.55,2,6,21,280.65,0.031313131,0.043198653,0,0,Oct,1,1,7,3,Returning_Visitor,FALSE,FALSE 5,161.1076923,5,108.3,44,2982.063248,0.016352201,0.037389344,5.721365099,0,Nov,1,1,4,1,Returning_Visitor,FALSE,TRUE 2,33.2,0,0,1,7,0,0,0,0,Oct,2,5,3,2,New_Visitor,TRUE,FALSE 0,0,0,0,9,152,0.111111111,0.155555556,0,0,Jul,2,2,7,1,Returning_Visitor,TRUE,FALSE 8,308.5859649,0,0,67,1784.755219,0.000352113,0.001928521,0.384720286,0,Sep,3,2,1,2,Returning_Visitor,FALSE,FALSE 4,1660.3,0,0,2,45.7,0,0.016666667,113.9293434,0,Sep,2,10,1,2,Returning_Visitor,FALSE,TRUE 2,36.2,0,0,22,1805.083333,0,0.008695652,3.586636448,0,Sep,2,2,1,2,Returning_Visitor,TRUE,TRUE 13,913.5222222,3,141.88,29,1293.652222,0.002631579,0.020570175,1.483746347,0,Aug,2,2,4,2,Returning_Visitor,TRUE,FALSE 0,0,0,0,20,616.5,0,0.01,0,0,Nov,3,3,1,3,New_Visitor,FALSE,FALSE 6,180.0666667,0,0,3,111.8,0,0.007142857,0,0,Aug,2,2,9,5,New_Visitor,TRUE,FALSE 1,20.2,0,0,77,3107.173333,0.002597403,0.017041847,18.74860165,0,Sep,2,2,7,1,Returning_Visitor,FALSE,TRUE 0,0,0,0,15,478.26,0,0.015384615,0,0,Nov,2,2,8,20,Returning_Visitor,FALSE,FALSE 0,0,0,0,15,598.6,0,0.033333333,0,0,Jul,2,2,1,2,Returning_Visitor,FALSE,FALSE 0,0,0,0,41,747.9077778,0.027526132,0.048583043,0,0,June,1,1,7,3,Returning_Visitor,FALSE,FALSE 5,74.4,0,0,140,2810.797158,0.001909722,0.016226074,0,0,Sep,2,2,3,4,Returning_Visitor,TRUE,FALSE 0,0,0,0,29,453.9,0.101149425,0.128275862,0,0,Jul,1,1,1,3,Returning_Visitor,FALSE,FALSE 3,94.6,0,0,1,4,0,0.05,0,0,Jul,2,2,1,2,New_Visitor,FALSE,FALSE 0,0,1,105.1333333,17,526.3761905,0.052941176,0.045751634,0,0,June,3,2,6,13,Returning_Visitor,FALSE,FALSE 2,73.95833333,0,0,17,864.9983333,0.025,0.0375,27.83116147,0,Sep,3,2,4,1,Returning_Visitor,FALSE,TRUE 0,0,0,0,2,252.8,0,0.1,0,0,Oct,2,5,9,13,Returning_Visitor,TRUE,FALSE 0,0,0,0,15,269.4,0.053333333,0.064444444,0,0,Aug,2,2,3,1,Returning_Visitor,FALSE,FALSE 9,177.2,0,0,34,817.4333333,0,0.011111111,0,0,Nov,2,2,3,2,Returning_Visitor,FALSE,FALSE 0,0,0,0,40,589.5,0.018333333,0.034416667,0,0,Aug,3,2,1,1,Returning_Visitor,TRUE,FALSE 4,94.86666667,0,0,22,812.1357143,0.008333333,0.042708333,18.08644865,0,Jul,3,2,2,4,Returning_Visitor,FALSE,TRUE 2,33.4,0,0,35,1795.925,0,0.01,0,0,Nov,2,2,1,3,Returning_Visitor,FALSE,FALSE 2,143.8,2,94.8,23,890.1952381,0,0.004166667,0,0,Jul,2,2,2,1,New_Visitor,FALSE,FALSE 7,327.2357143,4,260,42,443.4990476,0,0.022666667,18.50321705,0,Jul,2,4,6,1,Returning_Visitor,FALSE,TRUE 2,16.2,0,0,4,60.6,0.025,0.075,0,0,Sep,2,4,1,3,Returning_Visitor,FALSE,TRUE 0,0,0,0,13,748.2333333,0,0,0,0,Oct,1,1,1,2,Returning_Visitor,FALSE,FALSE 0,0,0,0,1,0,0.2,0.2,0,0,June,3,2,1,1,Returning_Visitor,TRUE,FALSE 5,57.4,0,0,21,423.5,0,0.008695652,0,0,Sep,2,2,1,2,Returning_Visitor,FALSE,FALSE 7,186.6666667,7,790.0066667,57,1720.444748,0.000980392,0.007866316,3.801048407,0,Sep,3,2,6,2,Returning_Visitor,TRUE,FALSE 3,54.8,1,28.2,24,203.0677778,0,0.008,44.35972562,0,June,2,2,1,4,Returning_Visitor,FALSE,TRUE 1,27.2,0,0,9,396,0,0.02,0,0,Nov,2,4,1,2,Returning_Visitor,FALSE,FALSE 19,341.8166667,1,51.4,429,9661.585763,0.00304414,0.012547206,3.459384236,0,Sep,2,2,9,1,Returning_Visitor,FALSE,FALSE 4,80.6,0,0,39,639.4933333,0,0.005,0,0,Nov,1,2,1,20,Returning_Visitor,FALSE,FALSE 0,0,1,0,65,934.8704274,0.008080808,0.036336088,0,0,Oct,1,1,3,2,Returning_Visitor,TRUE,FALSE 0,0,0,0,54,3371.05,0.003773585,0.022012579,0,0,Jul,2,4,1,3,Returning_Visitor,FALSE,FALSE 3,240.8,1,4,13,279.8333333,0,0.00625,0,0,Nov,3,2,2,2,Returning_Visitor,FALSE,FALSE 1,0,0,0,3,58.6,0,0.05,113.3238614,0,Oct,2,2,9,2,Returning_Visitor,TRUE,TRUE 3,16,0,0,63,3908.754762,0,0.001587302,1.298232174,0,Aug,4,1,6,4,Returning_Visitor,FALSE,FALSE 0,0,1,105.8,44,868.1636364,0,0.036803966,9.550627398,0,Sep,2,6,4,2,Returning_Visitor,FALSE,FALSE 1,52.4,0,0,16,176.1,0,0.011764706,0,0,Aug,1,1,1,5,Returning_Visitor,TRUE,FALSE 1,8,0,0,22,699.2671429,0.028571429,0.037698413,10.79250883,0,Jul,3,2,1,1,Returning_Visitor,FALSE,TRUE 20,199.4562728,7,299.0333333,686,23342.08205,0.009853301,0.022771363,0.150650498,0,Aug,2,2,1,1,Returning_Visitor,FALSE,FALSE 3,666.2,5,173.54,27,1581.48831,0,0.010419753,35.22837227,0,Oct,3,6,3,20,Returning_Visitor,FALSE,FALSE 3,5,0,0,16,246.3933333,0.044444444,0.075925926,0,0,Oct,3,2,1,4,Returning_Visitor,TRUE,FALSE 6,446.3833333,0,0,28,789.1266667,0.01,0.012860238,37.5474164,0,Nov,2,2,1,2,Returning_Visitor,FALSE,FALSE 3,106.6,0,0,11,203.98,0,0.004444444,0,0,Jul,2,2,1,3,New_Visitor,FALSE,FALSE 12,221.7279167,4,144.2,136,5944.387373,0.011252599,0.031851593,2.045137389,0,Oct,2,5,1,1,Returning_Visitor,FALSE,FALSE 0,0,0,0,1,0,0.2,0.2,0,0,Jul,3,2,4,1,Returning_Visitor,FALSE,FALSE 18,167,0,0,37,1113.898538,0.016666667,0.030296143,0,0,Nov,2,2,3,2,Returning_Visitor,FALSE,FALSE 1,5,0,0,6,119.6,0,0.014285714,0,0,Jul,2,2,1,2,New_Visitor,FALSE,TRUE 10,141.6333333,0,0,13,386.3,0,0.023529412,0,0,Sep,2,2,1,3,Returning_Visitor,FALSE,FALSE 2,35.3,0,0,2,20.1,0,0,40.27815244,0,Sep,2,2,1,6,New_Visitor,FALSE,TRUE 1,26.2,0,0,92,2451.346667,0.007692308,0.021318681,0,0,Jul,4,1,3,1,Returning_Visitor,FALSE,FALSE 2,46.4,0,0,8,349,0.02,0.08,0,0,Aug,4,1,1,1,Returning_Visitor,FALSE,FALSE 0,0,1,13.1,62,1918.966667,0.01031746,0.022246642,0,0,Aug,2,4,1,4,Returning_Visitor,FALSE,FALSE 0,0,2,11.4,43,763.6614286,0.004545455,0.021212121,0,0,Nov,2,2,1,1,Returning_Visitor,FALSE,FALSE 2,137,0,0,6,185.4,0,0.028571429,44.89345937,0,Oct,2,2,7,4,Returning_Visitor,FALSE,TRUE 0,0,0,0,1,0,0.2,0.2,0,0,Oct,2,5,1,20,Returning_Visitor,FALSE,TRUE 0,0,0,0,14,317.0666667,0.035714286,0.05,0,0,Aug,1,1,9,3,Returning_Visitor,FALSE,FALSE 0,0,0,0,4,72.4,0.1,0.125,0,0,Jul,1,1,3,3,Returning_Visitor,FALSE,FALSE 3,103.6,0,0,39,1003.38,0.068292683,0.08699187,0,0,Aug,1,1,1,3,Returning_Visitor,FALSE,FALSE 0,0,0,0,4,90.8,0,0.025,0,0,Oct,1,1,3,2,Returning_Visitor,TRUE,FALSE 0,0,0,0,91,4157.633333,0.01,0.028013468,0,0,Jul,2,2,1,13,Returning_Visitor,FALSE,FALSE 6,141,0,0,47,830.3411669,0.004421769,0.028456454,0,0,Aug,1,1,4,2,Returning_Visitor,FALSE,FALSE 4,391.3,0,0,24,1015.3,0,0.004166667,61.91792063,0,Aug,2,2,3,2,New_Visitor,FALSE,TRUE 6,73.3,1,0,120,2872.719031,0.000226757,0.011948854,5.915342917,0,Aug,2,2,2,1,Returning_Visitor,FALSE,FALSE 4,40.2,1,16.13333333,9,229.8833333,0,0.014285714,0,0,Oct,2,2,6,2,New_Visitor,FALSE,FALSE 2,178.3,2,148.1,39,2237.775,0.004761905,0.012857143,26.41021956,0,Oct,1,1,3,2,Returning_Visitor,FALSE,FALSE 4,176.9133333,1,40.8,72,1548.194444,0,0.007887882,1.891943485,0,Aug,2,4,3,5,Returning_Visitor,FALSE,TRUE 3,26.7,2,49.4,16,703.3,0,0.01,0,0,Aug,2,4,3,4,Returning_Visitor,FALSE,FALSE 12,178.65,0,0,77,999.0133333,0.013636364,0.026893939,5.292014182,0,June,2,2,4,13,Returning_Visitor,FALSE,FALSE 4,72.2952381,1,103.8,27,1211.066667,0.022580645,0.064516129,0,0,Aug,2,2,3,13,Returning_Visitor,FALSE,FALSE 6,130,2,14.2,30,2472.24,0.03537037,0.05723545,0,0,June,1,1,3,3,Returning_Visitor,FALSE,FALSE 0,0,0,0,71,1057.2,0.122222222,0.129580026,0,0,June,3,2,2,13,Returning_Visitor,TRUE,FALSE 0,0,0,0,7,1721.666667,0.038095238,0.038571429,0,0,Nov,1,1,3,3,Returning_Visitor,FALSE,FALSE 0,0,0,0,1,0,0.2,0.2,0,0,Aug,2,5,7,1,Returning_Visitor,FALSE,FALSE 7,351.0833333,1,131,53,3271.030448,0,0.005527012,13.78150822,0,Nov,1,1,2,4,Returning_Visitor,FALSE,FALSE 0,0,0,0,17,601.0333333,0.062745098,0.092156863,18.87145759,0,Aug,3,2,3,1,Returning_Visitor,FALSE,TRUE 0,0,0,0,25,1017.966667,0.008333333,0.017361111,0,0,Aug,3,2,4,1,Returning_Visitor,TRUE,FALSE 3,113.8,0,0,15,336.1383333,0,0.011764706,0,0,Nov,2,4,9,3,New_Visitor,FALSE,FALSE 0,0,0,0,39,1858.566667,0,0.002564103,0,0,Nov,2,2,3,2,New_Visitor,FALSE,FALSE 7,143.28,0,0,27,787.2433333,0.005882353,0.044117647,0,0,Sep,1,2,3,1,Returning_Visitor,TRUE,FALSE 5,141.14,0,0,12,305.24,0,0.013333333,0,0,June,2,2,9,3,Returning_Visitor,FALSE,FALSE 7,135.2833333,3,92.33333333,31,892.7244444,0,0.003468013,31.14097898,0,Oct,3,2,1,5,Returning_Visitor,FALSE,FALSE 10,519,0,0,89,2577.960349,0.012852234,0.03729051,0,0,Jul,3,2,7,1,Returning_Visitor,FALSE,FALSE 0,0,2,594.1333333,39,922.4006061,0,0.001709402,154.0955389,0,Sep,3,2,1,2,New_Visitor,FALSE,TRUE 2,76.6,0,0,38,3872.716667,0,0.002702703,0,0,Aug,2,2,3,2,New_Visitor,FALSE,FALSE 2,49.4,2,7,4,53.2,0.028571429,0.042857143,0,0,Nov,3,2,1,2,Returning_Visitor,FALSE,FALSE 0,0,0,0,14,260.8666667,0,0.028571429,0,0,June,2,2,1,3,Returning_Visitor,FALSE,FALSE 1,29.53333333,0,0,22,814.7093939,0.004,0.021333333,14.92784091,0,Nov,2,2,1,2,Returning_Visitor,FALSE,FALSE 2,35.2,2,98.8,84,8365.602222,0.008914729,0.037209302,0,0,Sep,2,2,5,1,Returning_Visitor,FALSE,FALSE 4,40.2,1,0,12,2339.207143,0.011111111,0.036944444,0,0,Nov,1,1,1,2,Returning_Visitor,FALSE,FALSE 0,0,0,0,28,2102.75,0.058928571,0.078333333,0,0,Jul,3,2,3,13,Returning_Visitor,FALSE,FALSE 1,24.2,0,0,18,1014.2,0,0.02,22.9772856,0,Nov,2,2,2,13,Returning_Visitor,FALSE,TRUE 1,216.6,0,0,34,1463.8,0.006060606,0.007575758,0,0,Nov,1,1,8,2,Returning_Visitor,FALSE,FALSE 0,0,0,0,53,1121.433333,0.007843137,0.019607843,24.57349606,0,Jul,2,4,9,1,Returning_Visitor,FALSE,TRUE 4,98.6,0,0,10,63.6,0.090909091,0.090909091,0,0,Sep,1,1,4,3,Returning_Visitor,FALSE,FALSE 3,79.4,0,0,11,394,0,0.005555556,0,0,Aug,3,2,3,1,Returning_Visitor,TRUE,FALSE 9,122.5171429,0,0,167,3890.865924,0.002339181,0.009653998,0,0,Jul,2,2,1,2,Returning_Visitor,FALSE,FALSE 4,107.8,3,1236.2,27,1483.457143,0.022222222,0.036111111,29.71496014,0,Nov,2,2,9,2,Returning_Visitor,FALSE,FALSE 0,0,0,0,1,0,0.2,0.2,0,0,June,3,2,1,4,Returning_Visitor,FALSE,FALSE 5,69.8,0,0,87,1764.180047,0.007007576,0.032261103,0,0,June,2,6,1,1,Returning_Visitor,FALSE,FALSE 0,0,5,194.4,8,197.9,0.036363636,0.054545455,0,0,Aug,1,1,3,4,Returning_Visitor,FALSE,TRUE 0,0,0,0,20,676.6,0.008421053,0.026315789,0,0,Sep,3,2,1,1,Returning_Visitor,FALSE,FALSE 0,0,0,0,18,860.5603175,0,0.009705882,0,0,Oct,3,2,6,1,Returning_Visitor,FALSE,FALSE 6,202.1,0,0,47,3633.953333,0.00130719,0.017287582,16.14887273,0,Oct,4,2,8,1,Returning_Visitor,FALSE,FALSE 5,85.16666667,4,365.9666667,31,538.72,0.014035088,0.029699248,0,0,Oct,4,1,3,1,Returning_Visitor,FALSE,FALSE 2,27.2,0,0,47,1299.833333,0.008333333,0.032638889,0,0,Oct,2,2,3,1,Returning_Visitor,FALSE,FALSE 1,92.6,0,0,30,1126.392821,0,0.006666667,67.18378365,0,Nov,2,2,1,6,Returning_Visitor,FALSE,TRUE 4,136.5,0,0,130,3507.365152,0.022807018,0.042471564,0,0,Sep,2,4,2,1,Returning_Visitor,FALSE,FALSE 3,55.05,0,0,18,4916.583333,0.013636364,0.018636364,20.08902391,0,Nov,2,6,5,1,Returning_Visitor,FALSE,TRUE 1,16.2,1,8.1,17,1180.366667,0.010526316,0.052631579,0,0,Aug,2,2,3,4,Returning_Visitor,FALSE,FALSE 0,0,0,0,1,0,0.2,0.2,0,0,Aug,3,2,2,3,Returning_Visitor,FALSE,FALSE 4,21.1,2,341.6,92,2668.712121,0.018556202,0.047858483,0,0,Aug,2,2,1,3,Returning_Visitor,TRUE,FALSE 12,430.1570175,14,622.4,85,3684.855282,0.000934579,0.009959744,34.45943549,0,Nov,3,2,1,2,Returning_Visitor,TRUE,FALSE 0,0,0,0,1,0,0.2,0.2,0,0,Jul,1,1,1,2,Returning_Visitor,FALSE,FALSE 1,71.6,0,0,28,559.8666667,0.003448276,0.013793103,0,0,Nov,2,2,1,1,Returning_Visitor,TRUE,FALSE 5,53.3,0,0,4,102.2,0,0.025,31.44363136,0,Oct,2,2,6,2,Returning_Visitor,FALSE,TRUE 1,7,0,0,11,98.1,0.035555556,0.058412698,0,0,June,1,1,1,20,Returning_Visitor,FALSE,FALSE 1,175.48,0,0,98,12449.9,0.002291667,0.031102504,0,0,June,2,2,1,5,Returning_Visitor,FALSE,FALSE 4,96.4,2,53.4,9,338.34,0,0.013333333,0,0,Oct,1,1,3,5,New_Visitor,TRUE,FALSE 8,287.7,0,0,20,531.56,0,0.012121212,0,0,Oct,2,2,9,20,New_Visitor,FALSE,FALSE 12,150.1333333,0,0,17,231,0.016,0.039333333,0,0,Sep,3,2,3,3,Returning_Visitor,FALSE,FALSE 2,70.6,0,0,23,2244.416667,0,0.009090909,0,0,Aug,2,2,3,3,Returning_Visitor,FALSE,FALSE 3,42.2,0,0,18,721.8,0.01,0.02,0,0,Oct,3,2,1,1,Returning_Visitor,FALSE,TRUE 0,0,0,0,9,372.6,0,0.022222222,55.89978907,0,Jul,2,2,1,4,Returning_Visitor,FALSE,TRUE 2,83.4,2,385.8,41,1194.988132,0,0.018043478,10.25950957,0,Oct,1,1,3,2,Returning_Visitor,FALSE,FALSE 13,140.55,1,24.7,149,8147.111565,0.011111111,0.050691712,0,0,Nov,2,2,3,2,Returning_Visitor,FALSE,FALSE 9,449.8166667,0,0,17,200.95,0,0.024603175,0,0,Sep,2,2,1,2,Returning_Visitor,FALSE,FALSE 7,123.4333333,0,0,7,132.8833333,0,0.006060606,0,0,Oct,1,1,3,2,New_Visitor,FALSE,FALSE 3,67.4,0,0,4,51.3,0,0.028571429,0,0,Oct,2,2,1,2,New_Visitor,TRUE,FALSE 0,0,0,0,32,707.7,0.025,0.084375,0,0,June,2,2,6,13,Returning_Visitor,FALSE,FALSE 1,78.6,0,0,5,744.8,0,0,0,0,Sep,2,2,1,4,New_Visitor,FALSE,FALSE 6,52.7,0,0,88,1568.533333,0,0.005128205,0,0,June,2,5,1,2,Returning_Visitor,TRUE,FALSE 7,76.15,0,0,67,1553.696667,0.008571429,0.019047619,0.68291229,0,June,2,2,2,13,Returning_Visitor,FALSE,FALSE 10,81.36666667,0,0,7,35.16666667,0,0.006060606,0,0,Nov,2,7,8,3,Returning_Visitor,FALSE,FALSE 3,61.65,7,380.9571429,102,3025.290325,0.014102564,0.019800052,11.43180835,0,Sep,3,2,1,2,Returning_Visitor,FALSE,FALSE 8,187.7,0,0,21,881.35,0,0.008641975,23.42271616,0,Jul,2,4,1,2,Returning_Visitor,FALSE,FALSE 10,579.1333333,0,0,22,539.8357143,0,0.004761905,25.5330039,0,Oct,2,2,3,2,New_Visitor,FALSE,FALSE 1,146,0,0,7,221.4,0.025,0.0225,0,0,Nov,1,1,4,3,Returning_Visitor,FALSE,FALSE 0,0,0,0,5,280.2,0,0.033333333,19.50605707,0,Aug,1,1,4,2,New_Visitor,FALSE,TRUE 0,0,0,0,8,32.2,0.125,0.15,0,0,June,1,1,1,3,Returning_Visitor,FALSE,FALSE 4,20.06666667,1,189.4,10,297.1666667,0,0.028571429,0,0,Nov,2,2,1,4,Returning_Visitor,FALSE,FALSE 5,79.7,0,0,27,1051.55,0.00862069,0.016896552,9.128607511,0,Oct,2,5,1,13,Returning_Visitor,FALSE,FALSE 8,161.6685714,0,0,518,11976.72135,3.83E-05,0.003837261,0,0,Oct,4,2,9,2,Returning_Visitor,FALSE,FALSE 3,267.5,0,0,4,8,0.085714286,0.085714286,0,0,Sep,4,6,4,13,Returning_Visitor,FALSE,FALSE 3,285.2,0,0,23,619.48,0.007692308,0.034615385,8.342755887,0,Jul,2,4,2,4,Returning_Visitor,FALSE,FALSE 1,28.2,0,0,18,536.0857143,0,0.011111111,75.62994948,0,Aug,2,2,4,4,New_Visitor,FALSE,TRUE 13,246.5333333,0,0,26,892,0,0.01127451,0,0,Sep,2,6,1,3,Returning_Visitor,FALSE,FALSE 5,113.7,0,0,6,181.2666667,0.018181818,0.018181818,0,0,Nov,1,1,8,3,Returning_Visitor,FALSE,FALSE 10,215.244507,2,245.1714286,101,3558.576942,0.008636364,0.012040588,30.87227217,0,Sep,3,2,1,2,Returning_Visitor,TRUE,TRUE 2,29.86666667,2,99.6,13,534.4833333,0,0.004166667,0,0,Oct,1,1,2,2,New_Visitor,FALSE,FALSE 0,0,0,0,2,0,0.2,0.2,0,0,Oct,3,2,1,1,Returning_Visitor,TRUE,FALSE 0,0,0,0,55,872.5916667,0,0.001212121,0,0,Aug,2,4,1,4,New_Visitor,TRUE,FALSE 4,53.4,2,113.8,256,13453.25103,0,0.003240964,0,0,Nov,2,2,1,2,Returning_Visitor,FALSE,FALSE 2,128.8,2,49.3,13,700.55,0,0.005882353,0,0,Jul,2,2,6,2,New_Visitor,FALSE,FALSE 3,213,0,0,2,338,0,0,112.4141272,0,Oct,1,1,1,5,Returning_Visitor,FALSE,TRUE 1,82.6,0,0,25,243.4666667,0.016,0.024,0,0,Aug,2,2,2,2,New_Visitor,FALSE,FALSE 1,0,0,0,1,74.6,0.1,0.1,0,0,Oct,3,2,1,4,Returning_Visitor,FALSE,FALSE 14,337.58,0,0,29,443.5,0,0.027027027,0,0,Oct,2,2,1,3,Returning_Visitor,FALSE,FALSE 5,69.9,0,0,62,1846.666667,0.006060606,0.018939394,0,0,Aug,2,2,1,3,Returning_Visitor,FALSE,FALSE 3,93.8,0,0,40,526.6853968,0.015609756,0.020412311,2.321264079,0,Aug,1,1,4,2,Returning_Visitor,FALSE,FALSE 2,76.6,0,0,16,368.4666667,0,0.021568627,0,0,Oct,4,1,4,1,Returning_Visitor,TRUE,FALSE 1,46.4,0,0,2,689.1,0,0.016666667,0,0,Aug,1,1,1,5,New_Visitor,FALSE,TRUE 1,18.2,0,0,44,725.2,0.008888889,0.031111111,24.83718656,0,June,2,2,4,13,Returning_Visitor,FALSE,TRUE 0,0,0,0,13,167.5,0,0.035897436,0,0,Jul,2,2,1,1,Returning_Visitor,FALSE,FALSE 10,214.7833333,3,30.2,52,1968.942963,0.004166667,0.023015892,17.75267237,0,June,3,3,3,2,Returning_Visitor,TRUE,TRUE 0,0,0,0,1,0,0.2,0.2,0,0,Sep,2,2,6,1,Returning_Visitor,TRUE,FALSE 3,420.1,2,14.2,32,591.72,0.032432432,0.043745174,0,0,Oct,1,1,1,3,Returning_Visitor,TRUE,FALSE 0,0,1,2,33,2601.231408,0.005882353,0.011764706,177.7714536,0,Nov,2,2,7,20,Returning_Visitor,FALSE,TRUE 0,0,0,0,5,109.8,0.12,0.16,0,0,Aug,1,1,4,3,Returning_Visitor,FALSE,FALSE 0,0,0,0,11,1197.8,0,0.01,0,0,Oct,2,2,3,20,Returning_Visitor,FALSE,FALSE 6,88.6,0,0,4,59.4,0,0.028571429,0,0,Nov,2,2,7,2,Returning_Visitor,FALSE,FALSE 1,156.2,0,0,3,50.4,0,0,92.97624144,0,Oct,2,2,7,2,Returning_Visitor,TRUE,TRUE 4,264.1333333,1,152.2,47,906.1166667,0,0.002614379,0,0,Aug,3,2,3,2,New_Visitor,TRUE,FALSE 0,0,0,0,10,204.6,0,0.02,0,0,Jul,2,2,1,1,Returning_Visitor,FALSE,FALSE 0,0,0,0,258,3637.985674,0.00078125,0.008679315,0,0,June,2,2,3,3,Returning_Visitor,FALSE,FALSE 13,255.9159289,0,0,255,8467.584379,0.017351104,0.025016422,0.685335799,0,June,3,2,1,3,Returning_Visitor,FALSE,FALSE 0,0,0,0,30,1372.455556,0.012903226,0.021505376,21.14016404,0,Sep,2,2,7,1,Returning_Visitor,FALSE,FALSE 5,61.4,1,0,8,32.1,0.028571429,0.064285714,0,0,Nov,3,2,3,1,Returning_Visitor,FALSE,FALSE 1,12,0,0,29,919.8995238,0,0.017091837,7.36509031,0,Aug,2,5,4,2,Returning_Visitor,FALSE,TRUE 19,230.3948365,3,48.3,111,3276.190541,0.0088,0.011027644,0.447038663,0,Jul,1,1,3,3,Returning_Visitor,FALSE,FALSE 0,0,3,146,23,1249.216667,0.015384615,0.029102564,15.33867021,0,Aug,3,2,3,4,Returning_Visitor,FALSE,FALSE 1,19.2,0,0,11,663.5,0,0,0,0,Jul,2,2,6,13,Returning_Visitor,FALSE,FALSE 0,0,0,0,8,198.9,0.0875,0.116666667,0,0,Jul,1,1,1,3,Returning_Visitor,FALSE,FALSE 1,21.2,1,141,7,307.8666667,0,0,0,0,Aug,2,2,1,3,Returning_Visitor,FALSE,FALSE 1,20.2,0,0,6,1561.2,0.028571429,0.057142857,0,0,Nov,4,2,3,1,Returning_Visitor,TRUE,FALSE 0,0,0,0,1,0,0.2,0.2,0,0,Jul,3,2,1,13,Returning_Visitor,FALSE,FALSE 11,734.6333333,0,0,67,3523.331111,0.002666667,0.012088889,22.2931906,0,Aug,2,2,1,4,Returning_Visitor,FALSE,FALSE 0,0,0,0,45,1387.267857,0.036507937,0.059221273,0,0,Jul,3,2,9,13,Returning_Visitor,FALSE,FALSE 11,104.9733333,2,41.2,27,572.8733333,0,0.011111111,23.5504394,0,Oct,2,4,1,1,Returning_Visitor,FALSE,FALSE 3,57.5,2,66.13333333,95,2772.278333,0.024722222,0.030104167,0,0,Oct,3,2,1,1,Returning_Visitor,FALSE,FALSE 1,23.2,0,0,23,290,0,0.033333333,0,0,Jul,2,2,8,3,Returning_Visitor,FALSE,FALSE 1,15.2,2,62.6,84,4941.698611,0.017647059,0.038705882,0,0,Sep,1,8,6,1,Returning_Visitor,FALSE,FALSE 0,0,2,232.7333333,23,1669.024978,0,0.016333333,0,0,June,3,2,4,2,Returning_Visitor,FALSE,FALSE 9,132.05,4,137,56,1775.89,0,0.025520833,4.365181293,0,June,4,2,1,1,Returning_Visitor,FALSE,FALSE 0,0,0,0,10,1186.6,0.02,0.08,0,0,Aug,2,5,1,3,Returning_Visitor,FALSE,FALSE 0,0,1,7,10,672.5142857,0,0.018181818,60.71287168,0,Sep,1,1,7,2,Returning_Visitor,FALSE,TRUE 3,58.4,0,0,12,197,0,0.028571429,0,0,Nov,2,2,2,2,Returning_Visitor,FALSE,FALSE 5,224.8,0,0,30,974.9,0.00625,0.0140625,25.11150884,0,Nov,2,2,4,4,Returning_Visitor,FALSE,FALSE 4,49.3,0,0,12,524.9692308,0,0.0125,0,0,Sep,2,2,5,2,New_Visitor,FALSE,FALSE 4,242.8,0,0,7,238.6,0.018181818,0.063636364,0,0,Oct,2,2,1,2,Returning_Visitor,TRUE,FALSE 1,56.4,1,7,72,2163.569524,0.015492958,0.032253521,4.286743658,0,June,2,2,1,13,Returning_Visitor,FALSE,FALSE 2,91.65,0,0,11,553.3433333,0.015384615,0.023589744,11.62063524,0,Oct,1,1,3,4,Returning_Visitor,TRUE,FALSE 0,0,0,0,24,985,0.070833333,0.0875,0,0,June,3,2,6,13,Returning_Visitor,FALSE,FALSE 8,86.06190476,2,186.2333333,49,1088.636905,0.015456989,0.029589443,0,0,June,1,1,3,3,Returning_Visitor,FALSE,FALSE 6,231.9,0,0,8,188.3,0,0.014285714,12.15713406,0,Sep,2,2,1,4,Returning_Visitor,FALSE,FALSE 4,151.8,0,0,10,265.4,0,0.007692308,0,0,Jul,1,1,1,5,New_Visitor,FALSE,FALSE 0,0,1,0,43,367.8166667,0.043181818,0.078787879,0,0,June,2,4,1,3,Returning_Visitor,FALSE,FALSE 11,1305.114737,4,629.5142857,27,1161.479023,0.009803922,0.022538246,12.76490187,0,Oct,1,2,3,20,Returning_Visitor,FALSE,TRUE 0,0,0,0,18,490.3,0.011111111,0.022222222,0,0,Sep,2,2,1,13,Returning_Visitor,FALSE,FALSE 5,109.4,4,93.6,10,427.0666667,0,0.003125,0,0,Nov,3,2,2,6,Returning_Visitor,FALSE,FALSE 4,76.8,0,0,59,2050.292399,0.010055755,0.022337803,0,0,Jul,2,6,2,7,Returning_Visitor,FALSE,FALSE 0,0,0,0,1,0,0.2,0.2,0,0,June,2,2,1,1,Returning_Visitor,FALSE,FALSE 0,0,0,0,15,171.8,0.013333333,0.046666667,0,0,June,2,2,1,2,Returning_Visitor,FALSE,FALSE 1,6,0,0,97,1643.365907,0.005666667,0.015870587,24.21760276,0,Jul,2,2,1,1,Returning_Visitor,FALSE,FALSE 0,0,0,0,5,42.2,0,0.04,0,0,June,2,2,3,1,Returning_Visitor,FALSE,FALSE 3,120.6,3,183.8,85,1813.753466,0,0.005699436,0.858361456,0,Aug,2,2,3,4,Returning_Visitor,FALSE,FALSE 4,110.5666667,0,0,28,1203.72,0.00625,0.0125,0,0,Nov,2,2,5,2,New_Visitor,TRUE,FALSE 8,146.6,1,18.2,198,8006.695205,0.002722772,0.01852759,0,0,Jul,2,2,4,1,Returning_Visitor,FALSE,FALSE 5,147.4,0,0,12,1226.1,0.022222222,0.037301587,5.034409251,0,Sep,3,2,2,1,Returning_Visitor,FALSE,TRUE 3,47.8,0,0,33,451.1890072,0,0.00240967,0,0,Aug,1,1,2,3,Returning_Visitor,TRUE,FALSE 0,0,0,0,20,600.3966667,0.023333333,0.04,0,0,Aug,3,2,2,13,Returning_Visitor,TRUE,FALSE 2,37,0,0,46,3043.916667,0.030434783,0.05,0,0,Nov,2,2,3,2,Returning_Visitor,FALSE,FALSE 0,0,0,0,2,64.4,0,0.1,0,0,Aug,3,2,6,20,Returning_Visitor,FALSE,FALSE 11,213.9722222,4,92.3,86,3908.03349,0.015178571,0.023856289,5.659271218,0,Nov,1,2,7,1,Returning_Visitor,FALSE,FALSE 10,1427.025,1,99.8,26,1446.520523,0.019362745,0.025837468,10.59553121,0,Aug,1,1,1,4,Returning_Visitor,FALSE,FALSE 14,295.2,3,56.9,80,1242.063095,0.004545455,0.020707071,0,0,Oct,2,2,3,1,Returning_Visitor,FALSE,FALSE 8,196.55,0,0,27,422.5333333,0,0.009195402,0,0,Sep,2,4,1,2,Returning_Visitor,FALSE,FALSE 2,19.1,0,0,8,137.7,0,0.01,0,0,Aug,3,2,1,2,New_Visitor,FALSE,TRUE 6,32.06666667,0,0,41,1921.10307,0.014074074,0.032987654,5.290262526,0,Jul,2,2,3,1,Returning_Visitor,FALSE,FALSE 0,0,0,0,53,952.1598198,0.015839126,0.037802052,0,0,Jul,3,2,8,1,Returning_Visitor,TRUE,FALSE 1,22.2,0,0,34,606.2,0,0.001388889,17.92983378,0,Oct,2,2,7,3,New_Visitor,TRUE,FALSE 4,253.15,0,0,81,1839.13,0.01468254,0.018380231,0,0,Sep,3,2,3,13,Returning_Visitor,FALSE,FALSE 8,293,0,0,12,399.0666667,0.003030303,0.010606061,37.86167396,0,Nov,4,1,3,3,Returning_Visitor,FALSE,TRUE 3,46.4,0,0,8,168.2,0,0.033333333,4.78307258,0,Oct,2,7,7,20,Returning_Visitor,TRUE,TRUE 0,0,0,0,9,145.9,0.022222222,0.074074074,0,0,June,1,1,2,3,Returning_Visitor,TRUE,FALSE 1,4,0,0,8,244,0.02,0.04,15.45198077,0,Sep,2,4,1,1,Returning_Visitor,FALSE,TRUE 1,10,0,0,14,624.4333333,0,0.015238095,0,0,Nov,2,2,1,1,Returning_Visitor,FALSE,FALSE 9,275.74,1,64.4,16,273.7066667,0,0.0084,16.37978199,0,Oct,2,2,1,2,Returning_Visitor,FALSE,FALSE 5,327.4,0,0,86,2806.846825,0.001532567,0.004061303,0,0,Oct,3,2,9,3,Returning_Visitor,TRUE,FALSE 4,183.6,0,0,19,457.9,0,0.022222222,0,0,Nov,1,1,1,4,Returning_Visitor,TRUE,FALSE 7,132,4,256.0666667,125,3674.803673,0.023134328,0.032705748,5.613708292,0,Aug,3,2,3,13,Returning_Visitor,FALSE,FALSE 6,101.2,2,438.7,53,1692.694155,0.012068966,0.022197723,0,0,Aug,3,2,4,1,Returning_Visitor,FALSE,FALSE 1,12.6,0,0,6,140.7,0,0.00952381,15.89480154,0,Jul,2,10,3,2,Returning_Visitor,TRUE,TRUE 3,59.5,0,0,2,13.1,0,0.008333333,0,0,Sep,1,1,4,5,New_Visitor,FALSE,FALSE 5,28.58333333,0,0,45,649.8632035,0.006122449,0.02755102,0,0,Jul,3,2,6,5,Returning_Visitor,FALSE,FALSE 3,10,0,0,2,8,0,0.033333333,0,0,Sep,2,2,1,2,New_Visitor,FALSE,TRUE 11,92.10151515,0,0,69,2183.007024,0.008552632,0.032956586,0,0,Jul,2,2,5,6,Returning_Visitor,FALSE,FALSE 8,279.28,0,0,17,835.4180952,0,0.018571429,35.89140755,0,Aug,3,2,3,2,New_Visitor,FALSE,TRUE 9,410.55,0,0,57,2791.060635,0,0.006917563,13.09195948,0,Oct,2,2,3,2,Returning_Visitor,FALSE,FALSE 4,131,0,0,30,548.96,0.005882353,0.026470588,6.929826318,0,Nov,2,2,2,4,Returning_Visitor,FALSE,TRUE 0,0,0,0,14,825.3333333,0,0,50.4111604,0,Aug,2,2,8,4,Returning_Visitor,FALSE,TRUE 1,20.2,0,0,6,204.4,0,0.028571429,0,0,Oct,2,7,3,5,New_Visitor,TRUE,FALSE 11,155.6,0,0,12,248.6,0,0.008823529,0,0,Aug,2,7,1,5,New_Visitor,FALSE,FALSE 14,1097.016667,1,291.2,68,4100.255455,0.007631579,0.025614035,18.42740587,0,Aug,1,1,3,3,Returning_Visitor,FALSE,FALSE 0,0,1,108.8,10,322.3666667,0,0.009090909,61.80843596,0,Nov,2,5,4,2,Returning_Visitor,FALSE,TRUE 2,253.0666667,1,36.2,67,2568.675541,0.039047619,0.071984127,4.001173545,0,Aug,2,4,2,1,Returning_Visitor,FALSE,TRUE 1,5,0,0,2,15.2,0,0.04,0,0,Aug,1,1,6,3,Returning_Visitor,FALSE,FALSE 3,83.6,0,0,6,314.4,0,0.025,0,0,Oct,1,1,4,2,New_Visitor,FALSE,FALSE 1,0,0,0,24,991.4222222,0,0.008333333,17.62211147,0,Nov,2,2,4,2,New_Visitor,TRUE,TRUE 4,102.2,0,0,10,226.4,0.015384615,0.076923077,0,0,Oct,3,2,1,13,Returning_Visitor,FALSE,FALSE 17,96.16060606,5,174.7,75,2549.529127,0.011818182,0.047380485,0,0,Jul,2,2,4,4,Returning_Visitor,FALSE,FALSE 5,176.68,3,249.8,23,700.58,0.014285714,0.019047619,9.352780048,0,Oct,1,1,4,3,Returning_Visitor,FALSE,TRUE 1,54.9,0,0,4,111.1333333,0,0,0,0,Aug,3,2,3,1,Returning_Visitor,FALSE,FALSE 0,0,2,20.2,11,473.8333333,0,0.025,0,0,June,3,2,8,3,Returning_Visitor,FALSE,FALSE 1,150.2,1,31.7,7,93.9,0,0.005555556,0,0,Oct,1,2,3,2,New_Visitor,TRUE,FALSE 1,27.2,0,0,91,1596.412698,0.001098901,0.007912088,33.61787649,0,Sep,2,2,2,1,Returning_Visitor,FALSE,FALSE 7,257.4,0,0,7,244.7333333,0.018181818,0.018181818,0,0,Nov,1,1,4,2,New_Visitor,FALSE,FALSE 2,10.77142857,0,0,70,2094.138889,0.023611111,0.05224537,0,0,Jul,2,2,3,1,Returning_Visitor,FALSE,FALSE 1,88.7,0,0,34,620.13,0.054285714,0.083095238,0,0,Oct,3,2,6,13,Returning_Visitor,FALSE,FALSE 3,251.8142857,2,101.8,37,1722.814286,0.001025641,0.007997558,4.683075961,0,Oct,1,1,1,2,Returning_Visitor,TRUE,FALSE 2,119.8,1,22.2,15,643.5,0,0.005882353,0,0,Sep,3,2,1,5,Returning_Visitor,TRUE,FALSE 0,0,0,0,8,316.1333333,0,0.05,0,0,June,2,2,3,13,Returning_Visitor,FALSE,FALSE 1,9,0,0,11,269.96,0,0.006666667,0,0,Jul,3,2,3,4,Returning_Visitor,FALSE,FALSE 6,287,0,0,80,5046.056667,0.011084337,0.025771658,0.579785745,0,Oct,2,2,4,3,Returning_Visitor,FALSE,FALSE 3,35.4,0,0,27,473.0833333,0,0.028965517,0,0,Nov,2,2,6,1,Returning_Visitor,FALSE,FALSE 0,0,0,0,15,340.1,0,0.04,0,0,Aug,2,2,1,1,Returning_Visitor,FALSE,FALSE 11,344.1,2,477.6,57,1475.608427,0.01641791,0.030868963,10.45977002,0,Oct,1,1,3,1,Returning_Visitor,FALSE,FALSE 0,0,0,0,15,956.3333333,0.013333333,0.046666667,20.13907622,0,Nov,2,2,3,1,Returning_Visitor,FALSE,FALSE 0,0,0,0,4,0,0.2,0.2,0,0,Aug,2,2,2,4,Returning_Visitor,FALSE,FALSE 3,119.0295082,2,696.2,37,1305.527286,0.015384615,0.022135467,6.324477083,0,Aug,1,2,8,2,Returning_Visitor,TRUE,TRUE 4,133.8,0,0,10,299.5,0.072222222,0.063095238,0,0,Jul,1,8,7,1,Returning_Visitor,FALSE,FALSE 0,0,0,0,39,706.2,0.074774775,0.09009009,0,0,Sep,3,2,1,13,Returning_Visitor,FALSE,FALSE 5,62.6,0,0,15,272.5,0,0.011111111,0,0,Oct,1,1,1,2,New_Visitor,TRUE,FALSE 0,0,0,0,48,1510.383333,0.008510638,0.030927052,16.61211742,0,Aug,2,4,3,1,Returning_Visitor,FALSE,FALSE 0,0,0,0,1,0,0.2,0.2,0,0,June,2,2,1,1,Returning_Visitor,FALSE,FALSE 4,62,1,87.6,88,3634.585714,0.00556391,0.035701754,1.435224011,0,Oct,2,2,1,3,Returning_Visitor,FALSE,FALSE 0,0,0,0,64,1298.20326,0.003125,0.017573302,0,0,Jul,1,1,3,4,Returning_Visitor,FALSE,FALSE 2,68.6,0,0,10,184.6,0,0.016666667,0,0,Sep,2,2,3,2,New_Visitor,FALSE,FALSE 10,78.9,0,0,15,718.8,0,0.015789474,0,0,Nov,2,2,4,2,New_Visitor,FALSE,FALSE 5,85.16666667,1,8,95,3715.777564,0.01010101,0.011851852,0,0,Jul,2,4,4,1,Returning_Visitor,FALSE,FALSE 1,19.6,0,0,0,0,0,0.088888889,0,0,Oct,3,5,1,5,Returning_Visitor,FALSE,FALSE 14,88.19047619,5,218.65,168,4316.262171,0.003575419,0.018670927,11.71746761,0,Jul,2,2,3,1,Returning_Visitor,FALSE,FALSE 4,104.2,0,0,19,456.9,0,0.0125,0,0,Aug,2,2,8,6,New_Visitor,TRUE,FALSE 3,90.6,0,0,21,608.3033333,0,0.026666667,0,0,Sep,2,2,3,2,Returning_Visitor,FALSE,FALSE 8,456.9,0,0,35,1113.783333,0,0.012017544,12.1225775,0,Oct,2,5,2,2,Returning_Visitor,FALSE,TRUE 0,0,0,0,14,487.3209524,0,0.002380952,47.32661324,0,Oct,3,2,1,2,New_Visitor,FALSE,TRUE 13,428.0222222,2,11,133,10338.67177,0.00027604,0.017657266,22.97507587,0,June,2,2,1,4,Returning_Visitor,FALSE,FALSE 13,205.7,0,0,10,157.1,0.011764706,0.033333333,0,0,Oct,2,6,2,20,Returning_Visitor,TRUE,TRUE 0,0,0,0,37,565.38,0.024324324,0.054954955,0,0,Jul,2,2,1,1,Returning_Visitor,FALSE,FALSE 5,39.7,0,0,14,253.6,0,0.018333333,0,0,Sep,2,2,4,4,Returning_Visitor,FALSE,FALSE 1,51.4,0,0,41,883.1395238,0.017948718,0.033333333,48.7880688,0,Jul,2,2,3,3,Returning_Visitor,FALSE,TRUE 0,0,0,0,37,2960.971429,0.036936937,0.062012012,0,0,Oct,1,1,1,1,Returning_Visitor,FALSE,FALSE 0,0,0,0,3,66.4,0.066666667,0.1,0,0,June,3,2,6,11,Returning_Visitor,TRUE,FALSE 8,221.33,2,70.6,63,5245.277619,0.002739726,0.020091324,0,0,Nov,2,2,3,1,Returning_Visitor,FALSE,FALSE 3,46.9,0,0,26,561.4,0,0.007142857,53.88782441,0,Nov,2,5,1,5,New_Visitor,FALSE,TRUE 9,124.4266667,0,0,19,720.6766667,0,0.018933333,0,0,Oct,2,2,7,4,Returning_Visitor,FALSE,FALSE 4,82,0,0,17,269,0.034782609,0.046376812,0,0,Jul,3,2,1,13,Returning_Visitor,FALSE,FALSE 0,0,0,0,60,2072.605556,0.013793103,0.016833856,0,0,Aug,2,2,1,2,Returning_Visitor,FALSE,FALSE 0,0,0,0,14,199.4904762,0,0,17.36252324,0,Sep,2,2,2,4,New_Visitor,FALSE,TRUE 4,31.6,0,0,9,1760.7,0,0.003333333,0,0,Nov,4,2,9,2,Returning_Visitor,FALSE,FALSE 0,0,0,0,7,138.8733333,0,0,179.307293,0,Nov,2,2,4,2,New_Visitor,TRUE,TRUE 4,659.5066667,3,92.7,160,2006.59312,0.002453988,0.013361447,9.453449065,0,Aug,2,2,7,2,Returning_Visitor,FALSE,FALSE 9,135.1,0,0,23,620.0333333,0.022222222,0.030246914,0,0,Aug,2,5,5,1,Returning_Visitor,FALSE,FALSE 6,360.9166667,4,114.2,17,806.65,0.01,0.015,0,0,June,2,10,3,5,New_Visitor,TRUE,FALSE 16,445.35,0,0,103,2091.037588,0.007894737,0.028165804,7.224419132,0,Oct,3,2,1,2,Returning_Visitor,TRUE,FALSE 3,49.4,0,0,11,285.6,0,0.021428571,0,0,Oct,2,2,7,2,Returning_Visitor,FALSE,FALSE 4,76.6,7,89.53333333,87,4131.156044,0.006593407,0.037080939,0,0,Sep,2,2,1,3,Returning_Visitor,FALSE,FALSE 3,37.2,1,0,3,14,0,0.033333333,0,0,Sep,2,7,7,2,New_Visitor,TRUE,FALSE 3,53.4,0,0,21,216.7,0,0.008695652,0,0,Sep,2,2,1,2,New_Visitor,FALSE,FALSE 1,0,1,34.2,69,3051.525518,0.000714286,0.016949586,0,0,Oct,2,4,9,3,Returning_Visitor,FALSE,FALSE 3,22.2,0,0,2,836.2,0,0.04,0,0,Sep,2,2,8,2,New_Visitor,TRUE,FALSE 2,43.2,0,0,27,264.4,0.044444444,0.081481481,0,0,Jul,2,2,7,13,Returning_Visitor,FALSE,FALSE 2,58.6,0,0,42,906.2333333,0,0.006976744,0,0,Nov,2,2,4,20,New_Visitor,FALSE,FALSE 1,16.6,0,0,39,581.9419883,0,0.000732601,68.32050059,0,Aug,2,2,5,2,New_Visitor,FALSE,TRUE 13,219.475,0,0,79,1828.884048,0,0.01252331,5.329743627,0,Oct,2,2,6,2,Returning_Visitor,FALSE,FALSE 1,0,0,0,28,219.8,0,0.037931034,0,0,Nov,2,2,1,1,Returning_Visitor,TRUE,FALSE 6,168.85,0,0,22,383.2,0.002666667,0.0096,0,0,Jul,2,2,1,2,Returning_Visitor,FALSE,FALSE 4,92.1,2,28.2,29,852,0.00625,0.022395833,0,0,Aug,2,5,8,5,Returning_Visitor,FALSE,TRUE 1,3.5,0,0,26,433.6366667,0,0.007692308,0,0,Jul,2,5,3,3,New_Visitor,TRUE,FALSE 0,0,2,5,13,354.2833333,0.035294118,0.037647059,68.75226072,0,Sep,3,2,4,4,Returning_Visitor,TRUE,FALSE 0,0,0,0,3,43.4,0.066666667,0.066666667,0,0,Sep,1,1,3,4,Returning_Visitor,FALSE,FALSE 0,0,0,0,12,1653.6,0.016666667,0.027777778,0,0,Jul,3,2,4,3,Returning_Visitor,FALSE,FALSE 0,0,0,0,7,169.9866667,0.0125,0.033333333,21.27271783,0,Jul,2,2,7,2,New_Visitor,FALSE,TRUE 0,0,0,0,17,649.02,0,0.017647059,0,0,June,2,4,3,3,Returning_Visitor,FALSE,FALSE 9,299.45,2,48.3,54,2802.367821,0.009289968,0.025442703,16.96885494,0,Aug,3,2,7,1,Returning_Visitor,FALSE,FALSE 0,0,0,0,39,1429.501111,0.005263158,0.018947368,0,0,June,2,2,1,3,Returning_Visitor,FALSE,FALSE 2,337.6,0,0,2,8,0,0.04,0,0,Nov,2,2,1,2,New_Visitor,FALSE,TRUE 4,120.9,0,0,7,534,0,0.02962963,0,0,Aug,2,2,7,2,Returning_Visitor,FALSE,FALSE 3,61.26666667,0,0,7,216.3333333,0,0.00952381,0,0,Sep,3,2,1,2,Returning_Visitor,FALSE,FALSE 1,162.2,0,0,6,156.2,0.014285714,0.1,0,0,Jul,3,2,3,1,Returning_Visitor,FALSE,FALSE 2,43.4,0,0,5,278,0,0.033333333,0,0,Aug,2,2,1,2,New_Visitor,TRUE,FALSE 0,0,0,0,63,2593.808333,0.036507937,0.066666667,0,0,Jul,3,2,8,20,Returning_Visitor,FALSE,FALSE 2,57.4,2,69.4,13,1316.533333,0.011764706,0.05,0,0,Aug,2,2,3,4,Returning_Visitor,TRUE,FALSE 0,0,0,0,27,925.2933333,0.014814815,0.030687831,0,0,June,1,1,1,4,Returning_Visitor,FALSE,FALSE 7,109.5,0,0,25,349.6969697,0,0.019047619,0,0,Oct,1,1,1,2,Returning_Visitor,FALSE,FALSE 0,0,0,0,3,40.4,0,0.066666667,0,0,June,2,2,1,4,Returning_Visitor,TRUE,FALSE 3,67,0,0,5,141.2,0,0.008333333,0,0,Oct,3,2,7,1,New_Visitor,FALSE,FALSE 13,314.6107143,0,0,358,9760.690488,0.000830955,0.004094211,0,0,June,3,2,5,1,Returning_Visitor,FALSE,FALSE 1,26.2,1,6,28,677.5333333,0,0.019472789,0,0,Jul,1,1,6,1,Returning_Visitor,TRUE,TRUE 0,0,0,0,1,0,0.2,0.2,0,0,Aug,3,2,1,13,Returning_Visitor,FALSE,FALSE 5,113.6666667,0,0,25,733.1666667,0.006896552,0.041609195,0,0,Sep,1,8,1,1,Returning_Visitor,FALSE,FALSE 3,97.8,0,0,5,60.4,0,0.025,0,0,Oct,2,2,9,2,New_Visitor,FALSE,FALSE 3,49.4,4,321.4,91,6418.819048,0.018181818,0.046632997,13.83791777,0,Jul,2,2,1,3,Returning_Visitor,FALSE,FALSE 10,549,5,608.45,213,9151.494242,0.003111111,0.013391698,2.568483894,0,Oct,2,2,1,2,Returning_Visitor,FALSE,TRUE 4,70.4,2,47.4,8,97.9,0.05,0.058333333,0,0,June,1,2,4,2,Returning_Visitor,FALSE,FALSE 0,0,0,0,4,39.2,0,0.05,0,0,Nov,1,2,1,2,New_Visitor,FALSE,FALSE 0,0,0,0,5,137,0,0.04,0,0,Oct,2,4,9,2,Returning_Visitor,FALSE,FALSE 0,0,0,0,26,409.3,0.007692308,0.038461538,0,0,Jul,1,1,1,1,Returning_Visitor,FALSE,FALSE 5,52.46666667,1,34.8,6,106.3,0,0.02,0,0,Oct,2,2,3,2,New_Visitor,TRUE,FALSE 1,0,0,0,8,1340.3,0,0.025,0,0,Sep,1,2,3,4,Returning_Visitor,FALSE,FALSE 0,0,0,0,2,0,0.2,0.2,0,0,Oct,1,2,2,5,Returning_Visitor,FALSE,FALSE 3,59.5,0,0,3,47.2,0.033333333,0.05,0,0,Aug,1,1,1,4,New_Visitor,FALSE,FALSE 10,593.4666667,1,21.2,39,1110.966667,0.004255319,0.010992908,112.4406212,0,Sep,4,2,7,2,Returning_Visitor,FALSE,FALSE 0,0,0,0,19,346.1333333,0,0.039473684,0,0,Aug,2,2,1,3,Returning_Visitor,FALSE,FALSE 3,76.5,0,0,55,2846.295305,0.010344828,0.028962492,0,0,Jul,2,2,1,13,Returning_Visitor,FALSE,FALSE 5,44.2,5,801.8,12,235.3,0.002777778,0.035925926,0,0,Aug,2,2,2,2,Returning_Visitor,FALSE,FALSE 7,398,0,0,27,770.3133333,0.006060606,0.03030303,10.05190252,0,Oct,1,8,3,3,Returning_Visitor,FALSE,TRUE 0,0,0,0,27,1544.533333,0.048148148,0.080246914,0,0,June,1,1,3,3,Returning_Visitor,FALSE,FALSE 5,20,0,0,72,3220.887143,0.024937343,0.061688596,0.975803268,0,Oct,4,1,2,1,Returning_Visitor,FALSE,FALSE 0,0,0,0,19,621.7266667,0.010526316,0.024035088,0,0,Jul,1,1,1,2,Returning_Visitor,FALSE,FALSE 4,54.6,2,22.13333333,44,1100.789082,0.008510638,0.018561297,0,0,Oct,2,2,1,1,Returning_Visitor,FALSE,FALSE 0,0,0,0,1,19.2,0,0.1,0,0,Aug,1,1,1,1,Returning_Visitor,TRUE,FALSE 1,43.4,0,0,30,775.8883333,0,0.006896552,0,0,Oct,2,2,4,2,New_Visitor,FALSE,FALSE 2,74.4,0,0,42,1481.866667,0.004761905,0.004761905,44.73610411,0,Oct,2,2,7,2,New_Visitor,TRUE,TRUE 0,0,0,0,7,49.86666667,0.085714286,0.092857143,0,0,Sep,1,1,9,3,Returning_Visitor,FALSE,FALSE 10,128.1142857,0,0,43,811.3809524,0.006122449,0.026328773,14.92797811,0,Oct,2,2,5,2,Returning_Visitor,FALSE,FALSE 0,0,0,0,33,889.72,0.012121212,0.044444444,0,0,Nov,2,2,3,1,Returning_Visitor,FALSE,FALSE 2,287.2,0,0,16,473.2833333,0,0.002222222,0,0,Oct,2,2,1,2,New_Visitor,TRUE,FALSE 8,43.95,1,56.4,31,577.65,0.011764706,0.029411765,0,0,Aug,2,2,3,3,Returning_Visitor,FALSE,FALSE 1,29.2,0,0,66,1364.873333,0.0171875,0.036488095,16.1347001,0,Jul,1,1,8,1,Returning_Visitor,TRUE,FALSE 0,0,0,0,43,778.6437229,0.010232558,0.023197087,0,0,Jul,4,1,1,3,Returning_Visitor,TRUE,FALSE 2,70.6,0,0,27,213.0157143,0,0.007407407,79.51302667,0,Oct,2,2,1,3,New_Visitor,TRUE,TRUE 2,46.4,1,29.2,12,303.2464286,0,0.015384615,0,0,Oct,1,1,3,2,New_Visitor,TRUE,FALSE 1,5,0,0,5,95.1,0.066666667,0.077777778,0,0,Oct,1,1,4,4,Returning_Visitor,FALSE,FALSE 2,46.3,0,0,2,48.4,0,0.05,0,0,Oct,1,1,1,3,Returning_Visitor,FALSE,FALSE 8,66.2,3,276,25,641.8,0.006451613,0.013978495,0,0,Oct,2,2,1,2,Returning_Visitor,FALSE,FALSE 0,0,0,0,18,345.12,0.044444444,0.072222222,0,0,Aug,2,2,3,13,Returning_Visitor,FALSE,FALSE 3,51.48,0,0,16,164.8133333,0.038095238,0.05991342,0,0,Oct,1,1,6,5,Returning_Visitor,TRUE,FALSE 2,39.2,0,0,6,86.4,0,0.028571429,0,0,Nov,1,1,3,3,New_Visitor,FALSE,FALSE 0,0,0,0,23,373.5,0.019047619,0.050793651,0,0,June,3,2,1,1,Returning_Visitor,FALSE,FALSE 0,0,0,0,58,1076.073649,0,0.022653941,1.81635021,0,Sep,2,2,3,2,Returning_Visitor,FALSE,FALSE 13,345.0166667,0,0,90,1816.555008,0,0.004861111,86.31034266,0,Aug,4,2,3,4,Returning_Visitor,FALSE,FALSE 1,119.8,0,0,10,175.35,0,0.009090909,0,0,Jul,1,1,5,3,Returning_Visitor,FALSE,FALSE 5,218.8,0,0,5,71.4,0,0.02,11.21330466,0,Oct,2,4,7,2,New_Visitor,FALSE,TRUE 11,373.62,9,322.8,191,3677.258176,0.009105691,0.024303938,0,0,Aug,2,2,6,1,Returning_Visitor,TRUE,FALSE 4,193.4,0,0,3,364.7,0,0.028571429,0,0,Oct,2,2,4,4,Returning_Visitor,FALSE,FALSE 3,318.2,0,0,7,414.8666667,0,0.01,0,0,Nov,1,1,6,2,Returning_Visitor,FALSE,FALSE 0,0,0,0,26,234.8,0.05,0.119230769,0,0,Jul,2,2,7,13,Returning_Visitor,FALSE,FALSE 0,0,0,0,3,0,0.2,0.2,0,0,Jul,1,1,1,1,Returning_Visitor,TRUE,FALSE 2,24,0,0,16,445.3333333,0.004444444,0.022222222,0,0,Jul,2,2,9,2,Returning_Visitor,FALSE,FALSE 11,225.22,2,106.8,68,1607.173333,0.011392405,0.033333333,0,0,Sep,2,2,1,3,Returning_Visitor,FALSE,TRUE 0,0,0,0,14,343.52,0,0.028571429,0,0,Jul,3,2,8,1,Returning_Visitor,TRUE,FALSE 3,49.4,2,9,18,330.5333333,0,0.009090909,0,0,Sep,2,2,8,4,New_Visitor,TRUE,FALSE 3,63.4,0,0,10,70.93333333,0,0.016666667,0,0,Sep,2,2,1,5,New_Visitor,FALSE,FALSE 1,7,0,0,26,2064.5,0,0.002,0,0,Oct,2,2,3,4,New_Visitor,TRUE,FALSE 0,0,0,0,47,1475.366667,0.010638298,0.02674772,0,0,Nov,1,1,4,1,Returning_Visitor,TRUE,FALSE 0,0,0,0,7,999.8,0.00952381,0.031428571,0,0,Sep,3,2,1,4,Returning_Visitor,FALSE,FALSE 2,141.6,0,0,10,728.18,0.018181818,0.065367965,14.28629744,0,Sep,3,2,1,1,Returning_Visitor,TRUE,FALSE 11,240.1,1,0,33,668.0333333,0,0.018119658,0,0,Aug,2,4,7,2,New_Visitor,TRUE,FALSE 1,0,3,62.4,23,708.1666667,0,0.007407407,0,0,Sep,2,2,6,4,Returning_Visitor,FALSE,FALSE 6,109.2,0,0,31,663.2777778,0.005882353,0.007058824,0,0,Oct,1,1,1,3,Returning_Visitor,FALSE,FALSE 2,75.6,2,68.4,2,44.66666667,0,0.033333333,0,0,Oct,2,2,7,2,New_Visitor,TRUE,FALSE 3,346,1,15.2,29,1107.216667,0.006060606,0.006700972,69.91439226,0,Nov,2,2,5,2,Returning_Visitor,FALSE,FALSE 3,60.73333333,0,0,27,696.9,0,0.014814815,0,0,Sep,2,2,6,4,Returning_Visitor,FALSE,FALSE 8,114.08,0,0,37,747.48,0.041463415,0.052845528,0,0,Jul,2,10,3,3,Returning_Visitor,FALSE,FALSE 0,0,0,0,10,447.1333333,0,0.018181818,17.29676376,0,Oct,2,2,5,2,New_Visitor,TRUE,TRUE 5,91.31666667,0,0,29,1352.976722,0.01125,0.024522672,7.91345877,0,Nov,1,1,6,1,Returning_Visitor,FALSE,FALSE 0,0,0,0,8,159.9,0,0,0,0,Oct,2,2,2,6,New_Visitor,FALSE,FALSE 10,170.9263566,0,0,282,4339.924841,0.002040816,0.011461095,2.395283135,0,Sep,2,2,4,1,Returning_Visitor,FALSE,FALSE 0,0,0,0,128,2787.692619,0.003125,0.014175592,0,0,Jul,2,4,4,1,Returning_Visitor,FALSE,FALSE 9,146.3,4,137.8166667,7,197.0666667,0,0.012631579,0,0,June,2,5,2,2,Returning_Visitor,TRUE,FALSE 2,35.8,0,0,27,785.05,0.044047619,0.069642857,0,0,Jul,2,2,4,13,Returning_Visitor,TRUE,FALSE 2,72.6,0,0,9,198.7,0,0.018181818,0,0,Sep,2,2,7,6,Returning_Visitor,TRUE,FALSE 1,23.2,0,0,71,3044.356667,0,0.013380282,0,0,Sep,2,2,3,2,Returning_Visitor,FALSE,FALSE 5,153.5666667,0,0,16,742.2095238,0,0.015789474,22.89972198,0,Nov,1,1,3,5,New_Visitor,FALSE,TRUE 0,0,0,0,22,987.6,0.009090909,0.015151515,0,0,Oct,2,2,1,4,Returning_Visitor,TRUE,FALSE 2,23.2,0,0,3,0,0.08,0.12,0,0,Aug,2,2,3,5,New_Visitor,FALSE,FALSE 2,174.6,0,0,12,251.2,0.061904762,0.080612245,0,0,Aug,3,2,6,1,Returning_Visitor,FALSE,FALSE 16,178.7995495,2,47.64,193,5946.275777,0,0.008886586,9.836180753,0,June,2,2,4,2,Returning_Visitor,FALSE,FALSE 1,128,2,136.1714286,13,847.6142857,0.013333333,0.021666667,0,0,Oct,3,2,8,4,Returning_Visitor,TRUE,FALSE 0,0,0,0,64,3038.38,0.017534722,0.038333333,0,0,Sep,2,2,1,1,Returning_Visitor,FALSE,FALSE 0,0,0,0,22,347.6666667,0.021212121,0.059848485,0,0,June,4,1,1,3,Returning_Visitor,FALSE,FALSE 0,0,0,0,26,710,0.003846154,0.017521368,0,0,Aug,3,3,4,4,Returning_Visitor,FALSE,FALSE 0,0,0,0,3,19,0,0.066666667,0,0,Jul,2,2,3,3,Returning_Visitor,FALSE,FALSE 5,390.87,2,29.2,39,2251.116667,0.006818182,0.020887446,0,0,Sep,2,2,9,1,Returning_Visitor,FALSE,FALSE 10,291.2357143,0,0,36,940.1044266,0,0.002845528,87.30695843,0,Aug,2,2,2,2,Returning_Visitor,FALSE,TRUE 2,60.5,0,0,26,1145.17,0,0.003703704,0,0,Nov,1,1,2,2,New_Visitor,TRUE,FALSE 4,218.9666667,0,0,12,672.7,0,0.004444444,83.10114264,0,Nov,2,2,3,2,Returning_Visitor,TRUE,TRUE 0,0,0,0,2,47.4,0,0.066666667,26.85210163,0,Oct,3,2,7,4,Returning_Visitor,FALSE,TRUE 0,0,0,0,54,4749.25,0.004938272,0.029938272,8.285119366,0,Oct,2,2,8,1,Returning_Visitor,TRUE,TRUE 0,0,0,0,22,494.6333333,0,0.004545455,42.29306752,0,Nov,2,2,1,2,New_Visitor,FALSE,TRUE 8,101.8511111,4,161.9666667,52,591.1542063,0,0.011448413,8.052496178,0,Jul,2,2,3,3,Returning_Visitor,FALSE,FALSE 7,405.55,1,23.53333333,21,568.9633333,0,0.001282051,0,0,Oct,3,2,6,20,Returning_Visitor,TRUE,FALSE 3,75.4,0,0,18,1051.42,0.00952381,0.019047619,0,0,Oct,2,5,2,2,New_Visitor,FALSE,FALSE 3,75.6,0,0,122,1655.57,0,0.0064,17.0952864,0,Aug,2,10,4,3,Returning_Visitor,FALSE,TRUE 6,85.96666667,1,2,76,1464.209618,0.009803094,0.01785746,0,0,Aug,3,2,8,4,Returning_Visitor,FALSE,FALSE 8,192.4333333,2,53.4,45,1372.875238,0.004166667,0.00625,0,0,Sep,4,1,1,3,New_Visitor,FALSE,FALSE 5,136.1,0,0,45,1424.804887,0,0.008333333,10.19295165,0,Nov,2,2,6,2,Returning_Visitor,TRUE,TRUE 2,40.2,2,26.1,10,186.7,0,0.021428571,0,0,Oct,2,2,3,3,New_Visitor,FALSE,FALSE 5,473.1666667,2,195.4,111,4022.812148,0.003571429,0.012351425,0,0,Aug,3,2,9,5,Returning_Visitor,FALSE,FALSE 0,0,0,0,17,1338.8,0.058823529,0.094117647,0,0,Aug,2,2,3,13,Returning_Visitor,FALSE,FALSE 4,341.6,0,0,3,77,0,0.022222222,0,0,Oct,3,2,8,2,New_Visitor,TRUE,FALSE 2,25.2,0,0,13,238.5333333,0,0.009375,36.70369311,0,Oct,2,2,1,20,Returning_Visitor,FALSE,TRUE 0,0,5,924,45,3198.186667,0.004,0.016363636,32.41773364,0,Oct,2,6,4,2,Returning_Visitor,FALSE,TRUE 0,0,0,0,62,1875.103333,0.003278689,0.027868852,12.71367924,0,Sep,2,4,1,1,Returning_Visitor,FALSE,FALSE 1,41.4,0,0,30,684.6,0.019354839,0.041935484,0,0,Aug,2,2,5,1,Returning_Visitor,FALSE,FALSE 3,79.93333333,0,0,17,348.85,0,0.023684211,0,0,Nov,2,2,1,20,Returning_Visitor,FALSE,FALSE 0,0,0,0,28,319.2771429,0.042857143,0.09265873,0,0,Aug,2,2,1,2,Returning_Visitor,FALSE,FALSE 6,202.4,1,0,22,2006.046667,0.007142857,0.032738095,8.052231522,0,Nov,6,5,4,2,Returning_Visitor,FALSE,FALSE 0,0,0,0,2,12,0,0.1,0,0,Aug,1,1,1,4,Returning_Visitor,TRUE,FALSE 0,0,0,0,74,1146.469048,0.021621622,0.031306306,0.408238132,0,Nov,2,2,1,1,Returning_Visitor,FALSE,FALSE 8,134.3733333,3,256.75,40,1773.610152,0.004255319,0.008355899,13.49047237,0,Nov,1,1,8,2,Returning_Visitor,TRUE,TRUE 0,0,0,0,52,1218.1,0.001282051,0.030448718,0,0,Sep,2,2,3,1,Returning_Visitor,FALSE,FALSE 0,0,0,0,37,4339.366667,0.009009009,0.036036036,13.6630847,0,Sep,2,2,4,1,Returning_Visitor,FALSE,FALSE 2,20.2,0,0,45,580.9357143,0.004444444,0.005,0,0,Aug,2,2,1,2,New_Visitor,FALSE,FALSE 4,93.6,0,0,8,69.4,0.090909091,0.103030303,0,0,Sep,2,2,1,3,Returning_Visitor,FALSE,FALSE 0,0,0,0,54,2593.1,0.018181818,0.039393939,0,0,Aug,2,2,1,3,Returning_Visitor,FALSE,FALSE 2,168.4,0,0,10,208,0,0.008333333,0,0,Nov,3,2,5,2,New_Visitor,FALSE,FALSE 6,109.6,0,0,17,495.1,0,0.01,0,0,Oct,2,4,8,2,New_Visitor,FALSE,FALSE 5,56.73333333,1,14.6,37,1123.993333,0.000696864,0.022648084,0,0,Oct,2,2,3,2,Returning_Visitor,FALSE,FALSE 8,111.93,0,0,13,860.77,0.020952381,0.056017316,0,0,Jul,1,1,1,2,Returning_Visitor,FALSE,FALSE 0,0,0,0,57,2163.96,0.039285714,0.061313776,0,0,Jul,3,2,6,3,Returning_Visitor,FALSE,FALSE 1,28.2,0,0,20,679.5666667,0,0.006349206,0,0,Nov,2,2,2,3,New_Visitor,FALSE,FALSE 4,338,0,0,9,724.76,0,0.022380952,0,0,Sep,3,2,2,4,Returning_Visitor,TRUE,FALSE 2,180.9,0,0,26,1030.06,0,0.005952381,0,0,Aug,3,2,6,2,New_Visitor,FALSE,FALSE 0,0,0,0,30,717.4666667,0.006666667,0.02,0,0,Jul,2,5,1,3,Returning_Visitor,FALSE,FALSE 10,337.9,1,6,123,2396.272456,0.000902151,0.008565782,1.952553388,0,June,2,2,1,2,Returning_Visitor,FALSE,FALSE 4,130.3,2,5,74,1903.45,0.005,0.019107143,0,0,Aug,2,2,9,13,Returning_Visitor,FALSE,FALSE 1,0,0,0,14,503.6333333,0.013333333,0.033333333,0,0,Aug,2,2,9,2,Returning_Visitor,FALSE,FALSE 3,48.35,2,358.6,35,676.2666667,0.005128205,0.013675214,0,0,Oct,2,2,2,2,New_Visitor,FALSE,FALSE 0,0,0,0,22,337.1,0,0.027272727,0,0,Aug,2,4,1,3,Returning_Visitor,FALSE,FALSE 2,30.2,0,0,29,630.3266667,0.006666667,0.023333333,0,0,Sep,2,2,6,1,Returning_Visitor,FALSE,FALSE 3,177.88,0,0,12,311.6133333,0.05,0.052380952,0,0,Oct,3,2,2,13,Returning_Visitor,FALSE,FALSE 7,234.4333333,0,0,85,6743.39876,0.013768116,0.033907028,0,0,Oct,2,2,8,13,Returning_Visitor,FALSE,FALSE 10,380.4333333,0,0,39,1526.213333,0.004651163,0.014728682,0,0,Nov,2,6,5,2,Returning_Visitor,FALSE,FALSE 6,102.4,2,44.4,33,688.3142857,0,0.022222222,0,0,Sep,2,2,3,3,Returning_Visitor,FALSE,FALSE 1,149.2,0,0,13,272.0083333,0,0.014285714,82.021436,0,Oct,1,1,1,20,Returning_Visitor,FALSE,TRUE 0,0,0,0,47,1153.54,0.003140097,0.039979296,0,0,Jul,1,1,6,2,Returning_Visitor,FALSE,FALSE 1,122,0,0,71,1096.876822,0,0.003014129,0,0,Nov,3,2,1,6,Returning_Visitor,FALSE,FALSE 15,382.3466667,2,21.6,32,1718.387778,0.004761905,0.01038961,0,0,Aug,3,2,1,2,Returning_Visitor,TRUE,FALSE 6,266.7,0,0,18,843.0166667,0,0.015151515,9.542515706,0,Sep,2,2,1,4,Returning_Visitor,FALSE,FALSE 4,504.3,0,0,3,7,0.05,0.0625,0,0,Nov,3,2,3,3,Returning_Visitor,FALSE,FALSE 0,0,0,0,12,1012.2,0,0.033333333,0,0,Jul,2,2,7,4,Returning_Visitor,FALSE,FALSE 1,23.2,0,0,4,52.4,0,0.02,0,0,Oct,2,4,2,1,New_Visitor,FALSE,TRUE 1,82.6,0,0,28,1375.133333,0,0.020689655,4.818947334,0,Oct,4,1,3,20,Returning_Visitor,FALSE,TRUE 9,87.8,1,12,120,2016.564401,0.006451613,0.012613585,33.41173784,0,Jul,2,2,7,13,Returning_Visitor,FALSE,TRUE 0,0,0,0,1,0,0.2,0.2,0,0,Sep,1,8,1,1,Returning_Visitor,FALSE,FALSE 3,250.8,2,270.6,20,1424.4,0,0,31.72080812,0,Nov,2,2,5,5,New_Visitor,FALSE,TRUE 5,170.8333333,0,0,79,1193.517172,0,0.002821869,0,0,Oct,4,1,1,2,Returning_Visitor,TRUE,FALSE 5,256.1,2,99.7,4,417.4,0,0.01,0,0,Sep,1,1,6,2,New_Visitor,FALSE,FALSE 7,312.8,1,4,155,2374.966713,0.025906525,0.039077841,0,0,Jul,3,2,4,13,Returning_Visitor,FALSE,FALSE 6,259.1833333,0,0,54,784.7342857,0.004511278,0.018114342,0,0,Oct,3,2,3,2,Returning_Visitor,FALSE,FALSE 7,301.0777778,0,0,27,1538.056725,0.013333333,0.036382031,11.15668928,0,Jul,3,2,3,2,Returning_Visitor,FALSE,FALSE 1,106.3,0,0,32,2331.15,0.03125,0.048958333,0,0,Aug,3,2,4,1,Returning_Visitor,FALSE,FALSE 5,116.2,0,0,38,1057.033333,0.030769231,0.05,0,0,Nov,1,8,1,1,Returning_Visitor,FALSE,FALSE 2,39.4,0,0,62,2021.747727,0.003076923,0.015641026,0,0,Oct,2,2,1,4,Returning_Visitor,FALSE,FALSE 0,0,0,0,1,0,0.2,0.2,0,0,June,2,2,1,1,Returning_Visitor,FALSE,FALSE 5,81.2,0,0,23,269.9018182,0,0.008,0,0,Nov,1,1,3,2,New_Visitor,FALSE,FALSE 3,41.3,0,0,4,55.9,0,0.014285714,0,0,June,3,2,8,2,New_Visitor,FALSE,FALSE 12,98.85,1,129,5,22.05,0,0.015384615,0,0,Oct,2,2,1,4,Returning_Visitor,FALSE,FALSE 1,30.7,0,0,33,1148.333333,0,0.020833333,0,0,Aug,2,5,6,1,Returning_Visitor,FALSE,FALSE 2,61.5,0,0,6,99,0,0.008333333,0,0,Oct,1,1,6,3,Returning_Visitor,FALSE,FALSE 0,0,0,0,22,562.1666667,0.018181818,0.053030303,0,0,June,2,2,3,3,Returning_Visitor,TRUE,FALSE 1,38.2,1,9,21,1290.5,0,0,86.12351528,0,Nov,2,2,9,2,New_Visitor,FALSE,TRUE 0,0,1,0,20,581.5,0.015,0.051666667,0,0,Sep,2,2,1,1,Returning_Visitor,FALSE,FALSE 15,831.6222222,0,0,23,845.2788889,0,0.011944444,1.533539275,0,Nov,2,2,7,20,Returning_Visitor,TRUE,FALSE 0,0,0,0,1,0,0.2,0.2,0,0,Jul,1,1,3,4,Returning_Visitor,TRUE,FALSE 2,73.6,0,0,38,813.6016667,0.004878049,0.037398374,0,0,Nov,1,1,3,1,Returning_Visitor,FALSE,FALSE 0,0,0,0,4,59.2,0,0.05,0,0,Aug,2,4,9,4,Returning_Visitor,FALSE,FALSE 9,292.3,0,0,117,1953.537512,0.002892562,0.007295031,2.767055125,0,Aug,1,1,1,4,Returning_Visitor,TRUE,FALSE 2,25.6,0,0,29,1199.916667,0,0.008888889,23.66358807,0,Jul,3,2,3,4,Returning_Visitor,FALSE,FALSE 1,10,0,0,25,583.35,0,0.016,71.07435633,0,Oct,1,1,1,2,Returning_Visitor,FALSE,TRUE 5,335.8,0,0,20,910.41,0,0.003333333,0,0,Nov,2,2,4,2,New_Visitor,TRUE,FALSE 12,203.28,4,241.7,47,1975.125,0.003636364,0.015186147,0,0,Nov,4,2,4,2,Returning_Visitor,FALSE,FALSE 7,302.9,0,0,42,651.665,0.028787879,0.060270563,0,0,Aug,1,1,1,3,Returning_Visitor,TRUE,FALSE 2,20.2,0,0,27,2129.8,0.012903226,0.051612903,6.476760366,0,Sep,2,6,3,3,Returning_Visitor,FALSE,TRUE 0,0,0,0,60,1063.666667,0.014166667,0.049722222,0,0,Aug,2,2,2,1,Returning_Visitor,FALSE,FALSE 3,47.4,0,0,21,961.52,0.030434783,0.053623188,0,0,Aug,3,2,4,4,Returning_Visitor,FALSE,FALSE 1,61.4,0,0,27,779.6066667,0,0.007142857,0,0,Oct,2,2,7,2,Returning_Visitor,FALSE,FALSE 8,353.7,6,181.95,23,959.9,0.005882353,0.016666667,4.428052445,0,Nov,1,1,6,3,Returning_Visitor,TRUE,TRUE 4,105.2666667,0,0,35,655.6847619,0,0.005128205,0,0,Nov,2,10,7,2,New_Visitor,TRUE,FALSE 9,185.8,0,0,32,631.2766667,0.005714286,0.016095238,0,0,Sep,2,4,1,1,Returning_Visitor,FALSE,FALSE 6,35.1,0,0,138,6440.235256,0.001891253,0.027316655,0,0,June,2,2,3,3,Returning_Visitor,FALSE,FALSE 5,145.8833333,0,0,9,112.0833333,0.023529412,0.043137255,2.001640344,0,Jul,3,2,4,1,Returning_Visitor,FALSE,FALSE 2,116.8,0,0,5,170.4,0,0.028571429,88.06848401,0,Nov,2,2,7,2,New_Visitor,FALSE,TRUE 0,0,0,0,9,153.2,0.02,0.06,27.8892456,0,Aug,2,2,7,13,Returning_Visitor,FALSE,TRUE 6,174.75,2,123,33,773.2566667,0.010810811,0.022972973,31.74373458,0,Nov,3,2,1,2,Returning_Visitor,FALSE,TRUE 2,146.9,0,0,45,6707.54,0.011363636,0.019859307,4.023323663,0,Oct,2,2,8,2,Returning_Visitor,FALSE,FALSE 1,108.8,0,0,15,387.4,0,0.004166667,0,0,June,2,2,1,2,New_Visitor,FALSE,FALSE 0,0,0,0,11,631.4,0.054545455,0.048484848,0,0,Aug,1,1,1,3,Returning_Visitor,FALSE,FALSE 0,0,0,0,4,0,0.2,0.2,0,0,June,2,4,3,3,Returning_Visitor,FALSE,FALSE 9,433.8,2,6,28,728.7,0,0.009803922,0,0,Oct,2,10,1,3,Returning_Visitor,FALSE,FALSE 1,29.2,0,0,7,75.6,0,0.025,0,0,Aug,1,1,4,2,New_Visitor,TRUE,FALSE 2,11,0,0,38,938.9333333,0.039473684,0.06622807,0,0,June,3,2,2,3,Returning_Visitor,FALSE,FALSE 3,95.6,0,0,10,337.1,0.008333333,0.029166667,0,0,Jul,1,1,9,3,Returning_Visitor,FALSE,FALSE 0,0,0,0,13,353.7866667,0,0.016666667,36.33496365,0,June,1,1,1,2,New_Visitor,FALSE,TRUE 0,0,0,0,6,67,0.016666667,0.05,0,0,Jul,2,4,1,1,Returning_Visitor,FALSE,FALSE 7,204.1,0,0,5,128.7,0,0.0125,0,0,Sep,2,7,1,2,New_Visitor,FALSE,FALSE 4,32.33333333,0,0,18,374.6,0,0.010526316,0,0,Sep,2,2,3,3,New_Visitor,FALSE,FALSE 8,400.65,0,0,27,952.2833333,0,0.00137931,0,0,Aug,1,8,1,6,New_Visitor,FALSE,TRUE 3,118.55,0,0,34,1948.088333,0.027027027,0.041643072,0,0,June,3,2,1,13,Returning_Visitor,TRUE,FALSE 2,152,0,0,13,241.5833333,0.013333333,0.013333333,0,0,Sep,1,1,1,2,New_Visitor,FALSE,FALSE 9,257.0933333,2,39.4,98,3364.64045,0.029570957,0.034691241,35.03953094,0,Sep,3,2,8,13,Returning_Visitor,FALSE,FALSE 2,33.86666667,4,32.7,109,1759.686519,0,0.009734513,48.73081038,0,Oct,4,1,3,6,Returning_Visitor,FALSE,TRUE 0,0,0,0,48,476.3600939,0.000694444,0.028639403,0,0,Aug,1,1,1,2,Returning_Visitor,FALSE,FALSE 3,84,0,0,59,1720.422222,0.026984127,0.038231522,0,0,Oct,3,2,1,3,Returning_Visitor,TRUE,FALSE 2,9,0,0,23,1516.2,0.016,0.028,17.72722287,0,June,2,2,5,2,Returning_Visitor,FALSE,TRUE 2,14.2,0,0,53,2016.566667,0.003636364,0.036363636,8.154361332,0,June,2,2,3,3,Returning_Visitor,FALSE,TRUE 8,97.6,0,0,24,498.9,0,0.01875,5.818151241,0,Oct,2,2,1,2,Returning_Visitor,FALSE,TRUE 1,47.4,0,0,5,238.2,0,0.033333333,0,0,Sep,2,2,5,4,New_Visitor,TRUE,FALSE 0,0,1,0,26,653.4,0.041333333,0.067142857,0,0,Jul,2,2,2,1,Returning_Visitor,TRUE,FALSE 9,158.3555639,3,177.8,96,4162.538005,0.012378641,0.025287479,3.582981195,0,Sep,3,2,5,13,Returning_Visitor,FALSE,FALSE 3,264,0,0,20,769.9,0.018181818,0.027272727,0,0,Nov,2,10,7,1,Returning_Visitor,FALSE,FALSE 1,0,0,0,29,1085.9,0,0.007407407,30.54596337,0,Nov,2,2,1,4,New_Visitor,FALSE,TRUE 0,0,7,269.6666667,24,518.85,0.019139785,0.025371224,0,0,Jul,1,1,6,2,Returning_Visitor,FALSE,FALSE 0,0,0,0,2,0,0.2,0.2,0,0,Jul,1,1,3,1,Returning_Visitor,FALSE,FALSE 3,90.6,0,0,6,166,0,0.025,0,0,Jul,2,2,8,6,New_Visitor,FALSE,TRUE 0,0,0,0,5,291.2,0.18,0.186666667,0,0,Oct,3,2,8,20,Returning_Visitor,FALSE,FALSE 0,0,0,0,29,2064.766667,0.020689655,0.037356322,0,0,June,2,4,3,1,Returning_Visitor,FALSE,FALSE 1,10.1,0,0,10,256.1857143,0,0.007407407,46.84856664,0,Aug,1,1,2,2,New_Visitor,TRUE,TRUE 0,0,1,217.6,77,1690.489222,0.011842105,0.027846816,0,0,Sep,3,2,6,1,Returning_Visitor,TRUE,FALSE 0,0,0,0,20,436.1,0.04,0.07,0,0,June,1,1,4,3,Returning_Visitor,FALSE,FALSE 0,0,0,0,11,315.58,0,0.054545455,0,0,Jul,2,2,4,1,Returning_Visitor,TRUE,FALSE 0,0,0,0,66,1203.817143,0.0015625,0.004166667,0,0,Sep,2,2,4,4,Returning_Visitor,TRUE,FALSE 4,296.2,0,0,33,920.4,0,0.013513514,0,0,Oct,1,1,1,5,Returning_Visitor,FALSE,FALSE 1,55.4,0,0,9,949.6,0,0.046666667,0,0,Oct,1,8,5,11,Returning_Visitor,FALSE,FALSE 1,229.8,0,0,41,1083.716667,0,0.006666667,0,0,June,2,5,8,2,Returning_Visitor,FALSE,FALSE 1,34.26666667,2,21.2,8,2398.766667,0,0.022222222,0,0,June,4,1,1,3,Returning_Visitor,FALSE,FALSE 4,34.93333333,0,0,2,22.8,0,0.028571429,0,0,Oct,2,2,4,2,New_Visitor,FALSE,FALSE 3,294.2,2,108.9,60,1994.023778,0.022153846,0.037014927,0,0,Sep,3,2,6,2,Returning_Visitor,FALSE,FALSE 6,205,0,0,20,221.78,0,0.001515152,0,0,Nov,1,1,1,2,Returning_Visitor,FALSE,FALSE 11,280.3366667,0,0,20,337.8533333,0,0.016296296,0,0,Sep,2,2,2,5,New_Visitor,FALSE,FALSE 9,179.4533333,7,277.6333333,37,829.1871429,0.002325581,0.007674419,9.098216282,0,Nov,2,2,3,2,Returning_Visitor,TRUE,FALSE 0,0,0,0,3,146.4,0,0.016666667,0,0,Aug,3,2,1,3,Returning_Visitor,FALSE,FALSE 7,36.05,0,0,27,344.45,0,0.035714286,0,0,Jul,2,2,7,4,Returning_Visitor,FALSE,FALSE 4,46.4,0,0,3,25.2,0,0,0,0,Oct,3,2,1,20,Returning_Visitor,FALSE,FALSE 0,0,0,0,13,1001.3,0,0.057142857,0,0,June,2,2,9,1,Returning_Visitor,FALSE,FALSE 0,0,0,0,39,794.16,0,0.012820513,0,0,Sep,4,2,1,4,Returning_Visitor,FALSE,FALSE 5,260.1,0,0,17,341.6166667,0,0.00627451,0,0,Jul,1,1,2,4,Returning_Visitor,TRUE,FALSE 1,18.2,0,0,22,624.76,0.009090909,0.015909091,0,0,Oct,2,2,7,3,Returning_Visitor,FALSE,FALSE 18,198.99,2,28.2,53,2387.596667,0,0.007692308,0,0,Oct,2,2,1,2,Returning_Visitor,FALSE,FALSE 1,109.8,1,518.8,55,3987.386667,0.005357143,0.011607143,0,0,Sep,1,1,4,2,Returning_Visitor,FALSE,FALSE 0,0,0,0,13,157,0,0.015384615,0,0,Nov,2,2,5,2,Returning_Visitor,FALSE,FALSE 0,0,0,0,29,2159.4,0.010344828,0.02183908,0,0,Oct,3,2,1,13,Returning_Visitor,FALSE,FALSE 0,0,0,0,85,1558.529048,0.006024096,0.024257028,0,0,Aug,2,4,1,3,Returning_Visitor,FALSE,FALSE 13,407.57,0,0,146,2650.780526,0,0.011184211,0,0,Oct,4,1,1,2,Returning_Visitor,TRUE,FALSE 0,0,0,0,10,145.04,0,0.023333333,0,0,Nov,4,2,4,1,Returning_Visitor,FALSE,FALSE 0,0,0,0,16,687.3848485,0,0.005208333,29.4628439,0,Nov,3,2,3,2,New_Visitor,TRUE,FALSE 2,44.4,0,0,109,1926.701905,0,0.000262123,0,0,Aug,2,2,1,2,New_Visitor,TRUE,FALSE 1,14.2,1,26.2,3,25.2,0,0.04,0,0,Nov,6,2,9,2,Returning_Visitor,FALSE,FALSE 5,171.7333333,0,0,93,2087.64987,0.013829787,0.023551165,0,0,Oct,3,2,1,4,Returning_Visitor,TRUE,FALSE 7,145.98,0,0,36,646.1533333,0,0.017719298,22.08032319,0,Aug,2,2,2,2,Returning_Visitor,FALSE,FALSE 1,23.2,1,73.6,17,566.5666667,0,0,0,0,Jul,1,1,1,2,Returning_Visitor,FALSE,FALSE 1,66.4,0,0,22,641.4809524,0,0.004761905,0,0,Oct,3,2,4,6,New_Visitor,FALSE,FALSE 2,71.1,0,0,11,529.8,0,0.004545455,0,0,Oct,2,2,2,2,Returning_Visitor,FALSE,FALSE 0,0,0,0,9,980.5666667,0,0.011111111,78.56959864,0,Sep,2,2,5,2,New_Visitor,FALSE,TRUE 4,77.1,0,0,4,41.7,0,0.028571429,0,0,Sep,2,2,4,6,Returning_Visitor,TRUE,FALSE 2,117.8,0,0,17,586.1,0,0.011111111,0,0,Aug,3,2,7,13,Returning_Visitor,FALSE,FALSE 0,0,0,0,2,12,0,0.1,0,0,Jul,2,10,2,6,Returning_Visitor,FALSE,FALSE 2,112.8,0,0,11,375.8,0,0.015384615,0,0,Sep,2,2,9,5,New_Visitor,TRUE,FALSE 0,0,0,0,6,118.3,0.011111111,0.077777778,0,0,June,2,2,1,2,Returning_Visitor,FALSE,FALSE 3,36.2,0,0,16,166.7062706,0,0.014705882,0,0,Aug,2,2,6,5,New_Visitor,FALSE,FALSE 2,11,0,0,36,474.3066667,0,0.015714286,47.65907425,0,June,4,1,2,4,Returning_Visitor,FALSE,FALSE 10,187.7166667,1,11,29,277.35,0,0.021904762,0,0,Sep,3,2,9,5,Returning_Visitor,FALSE,FALSE 3,42.4,0,0,20,331.8914286,0,0.004761905,0,0,Aug,2,2,8,1,Returning_Visitor,TRUE,FALSE 2,135,0,0,25,1684.375,0.014814815,0.052674897,0,0,Jul,1,1,8,3,Returning_Visitor,TRUE,FALSE 5,106.8,0,0,20,470.8666667,0.027272727,0.045454545,0,0,Oct,2,2,1,13,Returning_Visitor,FALSE,FALSE 0,0,0,0,39,1348.45,0.01025641,0.02039072,0,0,Jul,1,2,1,4,Returning_Visitor,FALSE,FALSE 3,420.0666667,3,230.7,23,1251.6,0.055172414,0.079310345,0,0,Nov,1,1,3,2,Returning_Visitor,FALSE,FALSE 2,153.2,1,24.2,22,615.0666667,0.008,0.036,0,0,Oct,2,2,1,1,Returning_Visitor,FALSE,FALSE 0,0,0,0,1,0,0.2,0.2,0,0,Aug,2,2,3,1,Returning_Visitor,FALSE,FALSE 0,0,0,0,1,0,0.2,0.2,0,0,Oct,2,2,6,2,Returning_Visitor,FALSE,FALSE 4,265,0,0,22,616.2033333,0.003846154,0.010470085,0,0,Nov,3,2,6,2,Returning_Visitor,TRUE,FALSE 3,423,0,0,24,1203.533333,0,0.011111111,0,0,Oct,4,1,1,4,New_Visitor,FALSE,FALSE 0,0,0,0,12,309.3666667,0.014285714,0.019047619,14.37785831,0,Sep,3,2,1,6,New_Visitor,FALSE,TRUE 7,138.15,0,0,9,232.31,0,0.028717949,0,0,Oct,2,2,5,2,Returning_Visitor,TRUE,FALSE 0,0,0,0,16,318.0666667,0.025,0.033333333,0,0,Jul,3,2,3,1,Returning_Visitor,FALSE,FALSE 14,512.5805785,4,211,61,2362.724563,0.005615082,0.024507322,27.61615655,0,Oct,1,1,3,1,Returning_Visitor,FALSE,FALSE 2,6,0,0,37,501.7666667,0.01025641,0.020512821,0,0,Sep,2,2,1,1,Returning_Visitor,FALSE,FALSE 1,9,0,0,22,1889.25,0,0.024603175,0,0,Aug,2,2,7,6,Returning_Visitor,FALSE,FALSE 0,0,0,0,2,0,0.2,0.2,0,0,Jul,3,2,9,13,Returning_Visitor,FALSE,FALSE 0,0,0,0,1,0,0.2,0.2,0,0,Jul,3,2,4,1,Returning_Visitor,TRUE,FALSE 1,0,0,0,7,82.2,0.057142857,0.114285714,0,0,June,3,2,2,4,Returning_Visitor,FALSE,FALSE 0,0,0,0,14,320.25,0,0.013333333,52.17313647,0,Oct,2,2,1,3,Returning_Visitor,TRUE,TRUE 0,0,0,0,5,43.9,0,0.02,0,0,Sep,2,2,1,2,Returning_Visitor,FALSE,FALSE 7,144.8,1,96.2,34,1374.630952,0.019973545,0.028024691,0,0,Jul,1,1,3,4,Returning_Visitor,FALSE,FALSE 2,187.9,4,100.1,29,2107.033333,0,0.024242424,0,0,Oct,1,1,3,1,Returning_Visitor,FALSE,FALSE 6,56.89,0,0,40,1296.916144,0,0.022634921,1.342134935,0,Sep,2,2,7,1,Returning_Visitor,FALSE,FALSE 5,127.8,0,0,15,766.8238095,0,0,65.4726506,0,Aug,1,8,4,1,Returning_Visitor,FALSE,TRUE 6,352.3666667,0,0,11,605.6166667,0,0.008888889,0.714623214,0,June,2,2,1,2,Returning_Visitor,FALSE,TRUE 1,0,0,0,1,17.2,0.05,0.15,0,0,Aug,2,2,1,5,Returning_Visitor,FALSE,FALSE 4,36.3,2,11,71,3287.216667,0.031506849,0.055342466,0,0,Sep,4,1,9,3,Returning_Visitor,TRUE,FALSE 3,32.1,0,0,3,21.1,0.028571429,0.042857143,0,0,June,3,2,6,5,New_Visitor,FALSE,FALSE 0,0,0,0,1,0,0.2,0.2,0,0,Jul,2,2,4,13,Returning_Visitor,FALSE,FALSE 4,118.7,0,0,3,89.5,0,0.047619048,0,0,Nov,3,2,1,4,Returning_Visitor,FALSE,FALSE 13,266.047619,9,985.4833333,179,11492.20838,0.020689931,0.028728531,0,0,June,1,1,1,3,Returning_Visitor,FALSE,FALSE 1,383.8,0,0,11,92.4,0,0.016666667,0,0,Jul,2,2,9,2,New_Visitor,FALSE,FALSE 5,91,2,232.8,246,9227.371429,0.006425703,0.024959839,0,0,Oct,4,1,4,3,Returning_Visitor,TRUE,FALSE 4,365.0333333,0,0,9,461.7333333,0.026666667,0.032991453,33.60744027,0,Aug,2,2,1,2,Returning_Visitor,FALSE,FALSE 8,98.4,0,0,16,156.3333333,0,0.007843137,0,0,Oct,2,2,3,1,Returning_Visitor,FALSE,FALSE 3,78.6,0,0,2,18.2,0,0.02,0,0,Jul,3,2,3,2,New_Visitor,TRUE,TRUE 2,13,0,0,66,2061.926508,0.013235294,0.014202682,0,0,Jul,3,2,6,3,Returning_Visitor,FALSE,FALSE 0,0,0,0,4,226.8,0.04,0.06,12.83702398,0,Nov,1,1,6,2,Returning_Visitor,FALSE,TRUE 0,0,0,0,5,116.6666667,0.04,0.05,0,0,Oct,3,2,1,3,Returning_Visitor,TRUE,FALSE 1,109.8,0,0,17,418.8233333,0,0.001388889,0,0,Oct,2,4,1,3,New_Visitor,FALSE,FALSE 1,5,2,31.7,6,104.25,0,0.007407407,0,0,June,2,5,7,11,New_Visitor,FALSE,FALSE 10,163.425,0,0,73,1077.511667,0,0.005833333,0,0,Nov,2,2,5,4,Returning_Visitor,FALSE,FALSE 0,0,0,0,17,1926.133333,0.011764706,0.027647059,0,0,Nov,2,2,4,1,Returning_Visitor,FALSE,FALSE 6,184.32,0,0,8,163.22,0,0.018181818,0,0,Nov,1,1,1,2,New_Visitor,FALSE,FALSE 2,13.1,0,0,62,5556.932698,0,0.020625,4.964521194,0,Sep,2,2,3,2,Returning_Visitor,TRUE,FALSE 2,132.4,0,0,11,296.3,0,0.015384615,0,0,Sep,2,4,3,2,New_Visitor,FALSE,FALSE 4,39.48,0,0,33,549.78,0,0.005555556,0,0,Sep,3,2,4,1,New_Visitor,FALSE,FALSE 11,211.0333333,3,173.2333333,62,1340.676667,0.000391389,0.019726027,17.42324027,0,Aug,2,2,7,2,Returning_Visitor,FALSE,FALSE 3,51.8,0,0,23,864.0071429,0,0.008,13.52088614,0,Nov,2,4,5,2,Returning_Visitor,FALSE,FALSE 15,309.6333333,2,45.8,50,2230.986667,0,0.004371585,40.77935433,0,Nov,2,2,4,2,Returning_Visitor,FALSE,TRUE 1,67.4,0,0,14,590.5533333,0,0.007142857,38.06520001,0,Aug,2,2,1,5,New_Visitor,FALSE,TRUE 1,28.2,0,0,1,0,0,0.05,0,0,Sep,1,1,1,5,New_Visitor,FALSE,TRUE 1,15.2,1,13,39,1706.119697,0.010365854,0.030371661,5.251288882,0,June,2,2,2,2,Returning_Visitor,TRUE,FALSE 5,46.8,0,0,33,743.2666667,0.005,0.021666667,0,0,Nov,2,2,4,1,Returning_Visitor,TRUE,FALSE 15,584.2,0,0,73,2693.963452,0,0.001298701,0,0,Nov,3,2,4,13,Returning_Visitor,FALSE,FALSE 9,356.7464198,4,1109.533333,81,6847.936896,0.015227273,0.022982896,5.046329077,0,Oct,1,1,3,1,Returning_Visitor,FALSE,FALSE 2,32.2,0,0,11,222.5,0,0.015384615,0,0,Aug,2,2,1,4,Returning_Visitor,TRUE,TRUE 0,0,0,0,4,123.3,0,0,67.98927956,0,Oct,1,1,1,2,Returning_Visitor,FALSE,TRUE 0,0,0,0,2,0,0.2,0.2,0,0,June,1,1,3,2,Returning_Visitor,FALSE,FALSE 11,604.73,0,0,63,1276.843158,0,0.006712329,0,0,Jul,2,2,8,6,New_Visitor,FALSE,TRUE 4,464.5,0,0,9,635.7238095,0,0.002777778,0,0,Jul,1,1,3,2,New_Visitor,FALSE,FALSE 6,240.9,2,201.4,237,9437.769068,0.010204082,0.030380952,1.453831135,0,June,2,4,3,1,Returning_Visitor,FALSE,TRUE 1,32.2,0,0,17,876.4285714,0,0.0040625,0,0,Jul,3,2,1,3,Returning_Visitor,FALSE,TRUE 6,102.5,0,0,23,351.0333333,0,0.015517241,0,0,Oct,1,1,1,4,Returning_Visitor,FALSE,FALSE 1,16.2,0,0,42,1369.7,0,0.01969697,11.33037122,0,Jul,2,5,3,3,Returning_Visitor,FALSE,FALSE 3,77.6,0,0,3,8,0.033333333,0.066666667,0,0,June,2,2,6,2,Returning_Visitor,FALSE,FALSE 2,150.6,0,0,86,2554.562381,0.023255814,0.041100372,0,0,Sep,2,2,1,1,Returning_Visitor,FALSE,FALSE 3,94,2,44.2,56,1430.208205,0.02,0.030388889,0,0,Nov,2,2,9,3,Returning_Visitor,FALSE,FALSE 1,4.333333333,0,0,3,14.33333333,0.006666667,0.074444444,0,0,June,4,2,1,5,Returning_Visitor,TRUE,TRUE 5,72.57333333,10,208.6,27,1091.323333,0.005,0.029166667,0,0,Sep,2,2,1,2,Returning_Visitor,FALSE,TRUE 4,85.6,0,0,1,5,0,0.033333333,0,0,Sep,2,4,1,5,New_Visitor,FALSE,TRUE 9,233.6224044,0,0,35,1941.066667,0.01255814,0.02917153,0,0,Aug,3,2,7,2,Returning_Visitor,FALSE,FALSE 0,0,0,0,5,190.6,0,0.02,0,0,Jul,2,2,5,1,Returning_Visitor,TRUE,FALSE 0,0,0,0,40,1161.9,0.055,0.08,0,0,Sep,1,8,6,1,Returning_Visitor,FALSE,FALSE 4,102.6,0,0,34,2633.4,0.018974359,0.020989011,0,0,Sep,1,1,3,4,Returning_Visitor,FALSE,FALSE 5,109.6,1,3,29,1980.266667,0.006060606,0.026262626,0,0,Jul,2,2,1,4,Returning_Visitor,FALSE,TRUE 1,6,0,0,27,1496.466667,0.04691358,0.069982363,7.468469311,0,Jul,3,2,3,1,Returning_Visitor,TRUE,FALSE 0,0,0,0,21,344.1714286,0.007017544,0.007017544,13.59007196,0,Oct,1,1,3,2,Returning_Visitor,FALSE,TRUE 3,35.4,0,0,38,937.8075758,0.01025641,0.039316239,0,0,Jul,3,2,1,1,Returning_Visitor,FALSE,FALSE 4,46.3,0,0,2,22.83333333,0,0.033333333,0,0,Nov,2,2,6,2,New_Visitor,TRUE,TRUE 7,611.0333333,2,13,81,1719.375714,0.004494382,0.009737828,6.49444108,0,Oct,1,1,6,2,Returning_Visitor,TRUE,FALSE 5,95.1,4,116.8,53,1696.567619,0.003508772,0.017602339,0,0,Oct,2,2,5,2,Returning_Visitor,FALSE,FALSE 0,0,0,0,24,445.9,0.058333333,0.079166667,0,0,Aug,2,4,1,1,Returning_Visitor,FALSE,FALSE 4,50.9,0,0,60,2686.402922,0.016666667,0.022713675,0,0,Oct,2,4,5,4,Returning_Visitor,FALSE,FALSE 0,0,0,0,13,852.2,0.164230769,0.18034188,0,0,Aug,3,2,4,13,Returning_Visitor,FALSE,FALSE 1,81.6,0,0,42,2017.520714,0,0.023696145,0,0,Oct,2,2,1,2,Returning_Visitor,TRUE,FALSE 0,0,0,0,25,1978.7,0.032,0.06,0,0,Aug,2,2,1,3,Returning_Visitor,FALSE,FALSE 2,43.4,2,16.2,31,1279.806667,0.006857143,0.02952381,0,0,Sep,1,1,3,2,Returning_Visitor,TRUE,FALSE 0,0,0,0,2,65.4,0,0.1,0,0,Jul,2,2,9,13,Returning_Visitor,TRUE,FALSE 5,74.6,3,43.4,67,1207.653333,0.020833333,0.047826279,0,0,Aug,2,2,6,1,Returning_Visitor,FALSE,FALSE 4,822.7666667,0,0,17,1229.466667,0,0.041269841,0,0,June,2,2,1,16,Returning_Visitor,FALSE,TRUE 1,71.6,0,0,7,286.7,0,0.008333333,0,0,Aug,1,1,7,3,New_Visitor,FALSE,FALSE 0,0,0,0,7,71.6,0.028571429,0.071428571,0,0,Nov,1,1,9,4,Returning_Visitor,FALSE,FALSE 10,664.0866667,3,18.5,26,1494.053333,0.021212121,0.028542569,20.35484005,0,Nov,3,2,1,20,Returning_Visitor,FALSE,FALSE 1,19.2,0,0,22,443.8333333,0,0.002380952,0,0,Nov,1,1,2,3,Returning_Visitor,FALSE,FALSE 4,34.4,0,0,12,189.4333333,0,0.014285714,0,0,Sep,2,10,4,4,New_Visitor,FALSE,FALSE 0,0,0,0,22,817.9590698,0.056060606,0.079518398,0,0,Sep,3,2,3,1,Returning_Visitor,TRUE,FALSE 1,54.4,6,505.6,21,838.15,0,0,0,0,Nov,2,2,3,2,New_Visitor,TRUE,FALSE 1,0,3,80,125,3835.564121,0.022076923,0.039945665,1.002042069,0,Jul,1,1,1,3,Returning_Visitor,FALSE,TRUE 1,13,0,0,28,611.94,0,0.006896552,0,0,Sep,3,2,1,1,Returning_Visitor,FALSE,FALSE 0,0,0,0,2,0,0.2,0.2,0,0,Aug,1,2,8,1,New_Visitor,FALSE,FALSE 2,126.3333333,0,0,11,401.0133333,0,0.022222222,45.12532172,0,Nov,1,1,3,2,Returning_Visitor,TRUE,TRUE 13,869.1666667,2,28.2,97,2865.817778,0.009345794,0.024205607,0,0,Oct,2,6,1,1,Returning_Visitor,FALSE,FALSE 9,524.9333333,2,30.66666667,31,1157.444833,0.005698006,0.02881889,10.79674946,0,Nov,2,2,3,1,Returning_Visitor,TRUE,FALSE 3,44.22857143,0,0,13,311.79,0,0.002666667,34.03997536,0,June,2,2,1,5,New_Visitor,FALSE,TRUE 0,0,0,0,7,238.9333333,0,0.014285714,0,0,Sep,2,2,4,2,Returning_Visitor,FALSE,FALSE 3,105.2,0,0,25,830.9366667,0.007142857,0.004285714,70.2595851,0,Nov,2,2,1,2,Returning_Visitor,FALSE,FALSE 0,0,0,0,1,27.2,0,0,58.37208988,0,Nov,2,2,4,20,Returning_Visitor,FALSE,TRUE 1,0,0,0,28,2195.580952,0,0.021428571,8.391281758,0,Oct,1,1,3,2,Returning_Visitor,FALSE,TRUE 2,17,0,0,32,2026.2,0,0.029411765,1.332510752,0,Oct,2,2,3,1,Returning_Visitor,FALSE,FALSE 2,66.4,3,41.2,138,4031.561667,0.002857143,0.010238095,0,0,Nov,2,2,9,13,Returning_Visitor,FALSE,FALSE 8,466.6166667,5,81.4,25,1220.175,0.02,0.040444444,0,0,Aug,3,2,3,2,Returning_Visitor,TRUE,FALSE 3,63,0,0,2,14.6,0,0.016666667,0,0,Oct,1,1,1,2,New_Visitor,FALSE,FALSE 6,93.9,0,0,19,323.3740476,0.024242424,0.032223645,19.39995042,0,Sep,2,2,3,1,Returning_Visitor,TRUE,FALSE 2,79.6,0,0,24,433.36,0.008333333,0.009722222,0,0,Aug,2,2,1,1,Returning_Visitor,FALSE,FALSE 1,159.2,0,0,21,526.5,0,0.009090909,0,0,Aug,3,2,1,1,Returning_Visitor,TRUE,FALSE 2,34.4,0,0,18,379.6166667,0.035,0.059166667,0,0,Jul,2,2,6,1,Returning_Visitor,FALSE,FALSE 16,825.9400176,1,120.8,109,2110.422354,0.005177873,0.014936612,2.491316021,0,Aug,2,2,7,1,Returning_Visitor,FALSE,FALSE 3,49.75,0,0,20,547.4007937,0,0.011688312,15.06586317,0,Nov,3,2,5,2,Returning_Visitor,FALSE,TRUE 0,0,3,12,21,330,0.069565217,0.091304348,0,0,June,2,2,4,13,Returning_Visitor,FALSE,FALSE 2,19.6,0,0,22,1089.5,0,0.026086957,0,0,Oct,2,5,1,3,Returning_Visitor,FALSE,FALSE 4,54.8,0,0,3,20.25714286,0,0.028571429,0,0,Aug,1,1,3,2,Returning_Visitor,FALSE,FALSE 8,92.93333333,0,0,78,2063.642844,0.007407407,0.011934156,0,0,Sep,3,2,6,13,Returning_Visitor,FALSE,FALSE 4,246.8,0,0,13,175.7666667,0.04,0.053333333,0,0,Sep,3,2,1,1,Returning_Visitor,FALSE,FALSE 12,263.46,7,145,71,1683.744444,0.004878049,0.016666667,42.14163315,0,Oct,2,10,1,4,Returning_Visitor,FALSE,FALSE 0,0,0,0,1,0,0,0.2,0,0,Jul,1,1,4,4,Returning_Visitor,FALSE,FALSE 0,0,0,0,41,4006.352857,0.009756098,0.014634146,48.99526482,0,Aug,2,2,7,2,Returning_Visitor,FALSE,TRUE 9,551.7944444,4,124.2,94,5579.315873,0,0.007335491,0,0,June,2,2,5,2,New_Visitor,FALSE,FALSE 2,56.4,0,0,45,3166.333333,0,0.004891304,4.859624751,0,Aug,2,2,2,1,Returning_Visitor,TRUE,FALSE 6,90.1,0,0,35,643.05,0,0.015789474,0,0,Oct,1,1,3,4,Returning_Visitor,TRUE,FALSE 4,62.9,5,482.4,4,128.5,0,0.018181818,0,0,Sep,2,2,1,5,New_Visitor,FALSE,FALSE 14,128.8095238,2,41.6,37,1584.770476,0.009302326,0.029296378,0,0,Oct,2,2,1,2,Returning_Visitor,FALSE,FALSE 10,148.9,0,0,25,692.22,0.038709677,0.039354839,0,0,Oct,1,1,7,1,Returning_Visitor,TRUE,FALSE 2,94.6,1,108.8,42,1157.783333,0,0.032222222,0,0,Oct,2,4,1,1,Returning_Visitor,TRUE,FALSE 6,90.6,0,0,10,384.8,0,0.015384615,0,0,Aug,1,2,3,2,New_Visitor,TRUE,FALSE 4,71.2,0,0,6,178.8,0,0.014814815,0,0,Aug,1,1,1,5,New_Visitor,FALSE,FALSE 4,45.7,1,0,13,94.6,0.041666667,0.070670996,0,0,Jul,3,2,2,4,Returning_Visitor,FALSE,TRUE 8,164.4666667,0,0,3,26.7,0.022222222,0.044444444,0,0,Oct,3,2,4,4,New_Visitor,TRUE,FALSE 9,352.2333333,0,0,49,2268.900677,0,0.017407407,0,0,Aug,1,2,9,2,Returning_Visitor,FALSE,FALSE 3,66.4,0,0,16,516.4785714,0.022222222,0.035555556,0,0,Sep,3,2,3,5,Returning_Visitor,FALSE,FALSE 7,194.4,0,0,5,46.2,0.0125,0.0375,0,0,Jul,2,2,4,5,New_Visitor,FALSE,FALSE 0,0,0,0,8,257.4,0,0.028571429,0,0,Jul,2,2,1,1,Returning_Visitor,TRUE,FALSE 3,1178.6,4,124.8,18,1716.373333,0.008333333,0.016666667,0,0,Jul,2,5,6,1,Returning_Visitor,FALSE,FALSE 1,12,0,0,22,380.8066667,0.019047619,0.013333333,0,0,Jul,1,2,3,2,Returning_Visitor,FALSE,FALSE 0,0,0,0,30,1737.016667,0.034482759,0.048357964,1.444763592,0,June,3,2,4,3,Returning_Visitor,TRUE,FALSE 2,34.2,0,0,32,929.91,0.006060606,0.026262626,0,0,Aug,1,1,3,3,Returning_Visitor,FALSE,FALSE 0,0,0,0,5,158.6,0,0.013333333,0,0,Jul,1,1,4,4,Returning_Visitor,FALSE,FALSE 4,51.2,0,0,11,1297.25,0.014285714,0.054761905,18.65904914,0,Oct,2,2,2,4,Returning_Visitor,FALSE,FALSE 14,181.0733333,3,58.4,18,450.5066667,0,0.011111111,0,0,Oct,2,2,1,3,Returning_Visitor,TRUE,FALSE 0,0,0,0,2,0,0.2,0.2,0,0,Jul,3,2,1,1,Returning_Visitor,FALSE,FALSE 7,169.35,2,182.2,29,1612.983333,0.005882353,0.013697479,0,0,Nov,2,2,8,2,Returning_Visitor,FALSE,FALSE 5,175.1,0,0,26,615.5595238,0,0.002083333,62.77367187,0,Sep,1,1,1,3,New_Visitor,TRUE,TRUE 4,52.5,0,0,12,234.4,0,0.013333333,0,0,Oct,2,2,1,2,New_Visitor,FALSE,FALSE 5,269.8,0,0,8,312.3333333,0,0.01,0,0,Nov,1,1,9,4,Returning_Visitor,FALSE,FALSE 11,369.8333333,4,109.8,150,5962.205556,0.005345912,0.015283019,0,0,June,2,2,5,2,Returning_Visitor,FALSE,FALSE 1,5,0,0,15,228.8,0.01875,0.06875,0,0,Nov,2,2,1,1,Returning_Visitor,FALSE,FALSE 8,107.7,0,0,8,135.0666667,0,0.005128205,0,0,June,2,2,1,5,New_Visitor,FALSE,FALSE 8,237.8326007,0,0,84,2759.135058,0.005681818,0.02926548,4.477838682,0,June,1,1,2,2,Returning_Visitor,FALSE,FALSE 1,6,0,0,10,506.4,0,0.02,0,0,Sep,1,1,3,5,New_Visitor,TRUE,FALSE 6,93.3,0,0,70,2678.055238,0,0.005868545,0,0,Jul,2,2,1,2,Returning_Visitor,TRUE,FALSE 0,0,0,0,15,337,0.02,0.032222222,0,0,Jul,1,8,1,1,Returning_Visitor,FALSE,FALSE 3,343.0666667,0,0,63,1108.369762,0,0.002786596,7.826836353,0,Aug,2,10,1,2,Returning_Visitor,FALSE,TRUE 5,73.8,0,0,5,82.5,0,0.028571429,0,0,Aug,2,5,6,5,New_Visitor,FALSE,FALSE 6,161.52,0,0,14,295.6974359,0,0.026315789,0,0,Nov,3,2,8,2,Returning_Visitor,FALSE,FALSE 15,292.88,1,48.35,130,3645.909048,0.001459854,0.008515815,10.41358118,0,Nov,2,4,8,2,Returning_Visitor,FALSE,FALSE 4,83.6,0,0,42,3333.076068,0.011627907,0.027506986,0,0,Aug,1,2,1,2,Returning_Visitor,FALSE,FALSE 0,0,0,0,12,749.8,0,0.015384615,23.00459544,0,Jul,2,2,1,4,New_Visitor,FALSE,TRUE 0,0,0,0,75,2532.960656,0.004266667,0.030748538,0,0,Aug,1,6,1,1,Returning_Visitor,FALSE,FALSE 1,55.4,0,0,28,630.6466667,0,0.016666667,12.58722248,0,Sep,2,4,7,4,Returning_Visitor,TRUE,TRUE 0,0,0,0,4,0,0.2,0.2,0,0,Jul,3,2,1,13,Returning_Visitor,FALSE,FALSE 1,17.6,0,0,12,1192.866667,0,0.019230769,0,0,Sep,2,2,1,3,Returning_Visitor,FALSE,FALSE 6,81.12,0,0,26,2134.103333,0,0.014583333,13.38353821,0,Jul,2,2,1,6,Returning_Visitor,FALSE,FALSE 1,139,3,871.4,137,7672.152381,0.00177305,0.011631206,37.72031898,0,Oct,2,2,4,2,Returning_Visitor,FALSE,TRUE 2,50.4,0,0,14,497.8,0.011458333,0.018333333,0,0,Sep,3,6,1,2,Returning_Visitor,FALSE,FALSE 1,18.6,2,54.4,45,1390.40359,0.017021277,0.023262411,0,0,Jul,3,2,7,13,Returning_Visitor,TRUE,FALSE 0,0,0,0,2,0,0.2,0.2,0,0,Aug,1,1,3,1,Returning_Visitor,FALSE,FALSE 1,36.3,0,0,1,0,0.053333333,0.093333333,0,0,Sep,3,2,3,6,Returning_Visitor,FALSE,TRUE 4,184.3666667,0,0,3,52.26666667,0,0.021212121,0,0,Oct,3,2,1,5,Returning_Visitor,FALSE,FALSE 0,0,3,114.8,22,583.0933333,0,0.015942029,2.749716646,0,Sep,1,1,3,1,Returning_Visitor,FALSE,TRUE 0,0,0,0,2,35.2,0,0.1,0,0,June,2,2,2,3,Returning_Visitor,TRUE,FALSE 1,18.2,0,0,0,0,0,0.066666667,0,0,Oct,2,4,1,5,New_Visitor,FALSE,FALSE 8,185,0,0,12,368.9,0.00625,0.02375,0,0,Oct,3,2,1,13,Returning_Visitor,TRUE,FALSE 1,15.2,2,55.4,52,3019.805397,0,0.002724518,0,0,Nov,2,6,1,1,Returning_Visitor,FALSE,FALSE 19,902.16,2,39.2,27,1171.471429,0,0.019152047,16.43725303,0,Nov,2,5,1,5,Returning_Visitor,FALSE,FALSE 0,0,0,0,2,0,0.2,0.2,0,0,June,2,2,2,3,Returning_Visitor,FALSE,FALSE 4,131.1,0,0,13,777.05,0,0.003921569,0,0,Oct,2,2,1,2,Returning_Visitor,FALSE,FALSE 2,124,0,0,2,112.8,0,0.025,0,0,Aug,2,2,1,1,New_Visitor,FALSE,FALSE 2,17,0,0,6,35.2,0.025,0.075,0,0,Aug,2,2,7,1,Returning_Visitor,FALSE,FALSE 3,100.92,0,0,9,168.02,0,0.045454545,0,0,Oct,3,2,1,1,Returning_Visitor,TRUE,FALSE 11,268.2,2,71.1,49,1758.25,0,0.020562771,0,0,Sep,2,5,4,1,Returning_Visitor,FALSE,TRUE 0,0,0,0,20,381,0.045,0.075,0,0,June,1,1,2,3,Returning_Visitor,FALSE,FALSE 4,95.8,2,35.7,14,380.2666667,0,0.011111111,0,0,Oct,2,4,7,2,New_Visitor,FALSE,FALSE 2,182.4,1,0,19,555.4,0.027272727,0.053030303,0,0,Oct,3,2,1,2,Returning_Visitor,FALSE,FALSE 1,7,0,0,24,445.4333333,0.017391304,0.02173913,0,0,Aug,1,1,4,3,Returning_Visitor,TRUE,FALSE 2,41.2,0,0,51,820.4833333,0,0.008627451,0,0,Oct,4,1,1,1,Returning_Visitor,FALSE,FALSE 2,28.2,0,0,16,312.2,0.033333333,0.048148148,0,0,Oct,2,2,1,1,Returning_Visitor,FALSE,FALSE 1,5,0,0,7,1252.766667,0.025,0.021428571,0,0,Sep,1,2,7,4,Returning_Visitor,FALSE,FALSE 2,209.5,0,0,102,2436.287879,0.051797386,0.060539216,0,0,Oct,3,2,1,13,Returning_Visitor,FALSE,FALSE 3,204.4,0,0,4,117,0,0.028571429,0,0,Nov,2,2,6,2,New_Visitor,FALSE,FALSE 4,46.4,0,0,5,61.4,0,0.014285714,0,0,Oct,2,2,4,4,New_Visitor,FALSE,FALSE 4,45.4,0,0,14,339,0,0.010526316,0,0,Jul,2,2,1,5,New_Visitor,FALSE,FALSE 2,27.2,0,0,62,976.3333333,0.009375,0.015625,0,0,Nov,2,5,7,1,Returning_Visitor,FALSE,FALSE 1,45.4,0,0,15,114.9,0,0.025,0,0,Jul,2,2,3,4,New_Visitor,FALSE,FALSE 0,0,0,0,1,0,0.2,0.2,0,0,June,1,8,3,4,Returning_Visitor,TRUE,FALSE 0,0,0,0,33,1985.933333,0,0.02969697,0,0,Jul,2,2,1,2,Returning_Visitor,FALSE,FALSE 4,47.8,0,0,7,183,0.018181818,0.072727273,0,0,Jul,3,3,3,4,Returning_Visitor,FALSE,FALSE 1,23.2,0,0,24,704.5666667,0,0.011538462,14.22049566,0,Aug,2,2,2,1,Returning_Visitor,FALSE,FALSE 3,80.5,0,0,62,2337.196667,0.008196721,0.023911007,0,0,Aug,2,2,5,1,Returning_Visitor,FALSE,FALSE 4,124.7,5,88.9,11,577.6,0.010526316,0.010526316,0,0,June,3,2,3,2,New_Visitor,FALSE,FALSE 4,155,0,0,31,2130.693837,0.025,0.032482778,0,0,Nov,3,2,8,3,Returning_Visitor,FALSE,FALSE 0,0,0,0,7,96,0,0.025,54.20146753,0,Nov,2,2,1,2,New_Visitor,FALSE,TRUE 4,282.1,0,0,7,107.6,0,0.011111111,0,0,Aug,1,1,2,5,New_Visitor,FALSE,FALSE 7,148.6,3,99.6,8,144.9,0,0.021052632,27.14408757,0,Sep,2,2,1,2,Returning_Visitor,FALSE,TRUE 6,83.4,1,102.3,34,970.6266667,0.008823529,0.014313725,0,0,Nov,1,1,6,2,Returning_Visitor,FALSE,FALSE 0,0,0,0,104,3528.060714,0.006213018,0.035602227,0,0,Jul,2,2,1,1,Returning_Visitor,FALSE,FALSE 0,0,0,0,23,798.3,0.026086957,0.047826087,0,0,Aug,2,2,5,1,Returning_Visitor,FALSE,FALSE 0,0,0,0,1,0,0.2,0.2,0,0,Nov,3,2,4,1,New_Visitor,FALSE,FALSE 0,0,0,0,6,286.2,0,0.022222222,0,0,Aug,2,2,6,1,Returning_Visitor,FALSE,FALSE 9,228.2,1,0,7,186.4,0.02,0.03,0,0,Nov,2,2,1,20,Returning_Visitor,FALSE,FALSE 4,271.2,0,0,4,144.6,0,0.014285714,0,0,Oct,3,2,1,2,Returning_Visitor,FALSE,FALSE 7,140.5733333,1,108.8,43,1912.751905,0.008333333,0.007777778,19.64189593,0,Nov,3,2,6,5,Returning_Visitor,FALSE,FALSE 0,0,0,0,1,0,0.2,0.2,0,0,Aug,2,2,1,3,Returning_Visitor,FALSE,FALSE 3,31.2,1,42.4,13,134.45,0,0.0125,0,0,Nov,2,2,6,2,New_Visitor,FALSE,FALSE 0,0,0,0,53,2121.311111,0.020754717,0.060754717,0,0,June,2,2,1,1,Returning_Visitor,FALSE,FALSE 6,229,2,40.02,71,2576.432121,0.01027027,0.045526366,0,0,Oct,1,1,2,1,Returning_Visitor,TRUE,FALSE 4,68.4,0,0,14,337.5333333,0,0.023529412,0,0,Aug,2,2,7,2,New_Visitor,FALSE,FALSE 7,165.1,0,0,63,1422.103333,0,0.008766234,0,0,June,4,2,2,3,Returning_Visitor,FALSE,FALSE 1,15.2,0,0,30,515.5333333,0,0.016666667,0,0,Sep,2,2,1,2,Returning_Visitor,FALSE,FALSE 1,18.2,0,0,44,1310.5,0.042222222,0.065925926,0,0,Oct,2,2,1,13,Returning_Visitor,FALSE,FALSE 0,0,0,0,12,460.2,0.061111111,0.111111111,0,0,Jul,2,2,6,4,Returning_Visitor,FALSE,FALSE 0,0,3,201.9,27,393.3,0,0.006666667,3.92827844,0,Sep,2,2,1,4,Returning_Visitor,FALSE,TRUE 0,0,0,0,14,390.9,0.013333333,0.066666667,0,0,Aug,2,2,6,3,Returning_Visitor,FALSE,FALSE 4,237,3,87.7,16,379.3,0.021212121,0.057575758,0,0,Oct,7,1,1,3,Returning_Visitor,FALSE,FALSE 3,145.2,0,0,11,1646.366667,0,0.027777778,0,0,June,2,2,2,1,Returning_Visitor,FALSE,FALSE 14,286.5333333,2,118.8,31,835.71,0,0.007317073,33.05509961,0,Nov,2,2,7,2,Returning_Visitor,FALSE,TRUE 0,0,0,0,10,150.8666667,0,0.02,0,0,Sep,1,1,6,2,Returning_Visitor,FALSE,FALSE 0,0,0,0,27,652.7,0,0.02962963,0,0,Jul,2,2,7,1,Returning_Visitor,TRUE,FALSE 0,0,0,0,3,62.4,0,0.05,23.79060609,0,Aug,1,1,8,3,New_Visitor,FALSE,TRUE 6,44.4,1,0,38,2001.7,0.006342495,0.030232558,0,0,June,2,2,2,3,Returning_Visitor,FALSE,FALSE 4,75.86666667,0,0,72,1822.866667,0.006756757,0.024774775,11.34860076,0,June,2,2,7,1,Returning_Visitor,FALSE,FALSE 1,68.6,0,0,73,2265.492857,0.011111111,0.00716821,0,0,Nov,3,2,3,13,Returning_Visitor,FALSE,FALSE 4,47.3,1,0,32,1870.966667,0,0.037037037,0,0,Aug,2,2,9,2,Returning_Visitor,FALSE,FALSE 3,180.4,0,0,12,2646.4,0.042857143,0.052380952,0,0,Oct,2,2,2,20,Returning_Visitor,FALSE,FALSE 2,172.4,3,134,15,569.4933333,0,0.025,0,0,Sep,2,2,1,2,Returning_Visitor,TRUE,FALSE 23,335.1309524,5,399.8,88,1839.098106,0.015758703,0.031587556,0,0,Aug,3,2,3,2,Returning_Visitor,TRUE,FALSE 2,210.6,0,0,4,97.6,0,0.016666667,0,0,Nov,3,2,7,6,New_Visitor,TRUE,FALSE 2,152.2,0,0,28,494.88,0,0.013793103,0,0,Sep,1,8,7,1,New_Visitor,FALSE,FALSE 1,15.2,0,0,11,201.6333333,0.005555556,0.05,31.49043616,0,June,2,2,5,2,Returning_Visitor,FALSE,TRUE 8,352.4,1,49.73333333,12,309.7,0.011764706,0.014705882,0,0,Sep,3,2,1,4,Returning_Visitor,FALSE,FALSE 3,167,0,0,42,1246.12,0,0.008888889,0,0,Aug,2,4,1,3,Returning_Visitor,FALSE,FALSE 3,40.2,0,0,3,41.3,0,0.011111111,0,0,Oct,2,2,1,2,Returning_Visitor,TRUE,FALSE 4,174.8,0,0,16,237.5,0,0.011111111,0,0,Sep,3,2,4,2,New_Visitor,FALSE,FALSE 0,0,0,0,13,472.4333333,0,0.004395604,0,0,Jul,2,2,3,2,Returning_Visitor,FALSE,FALSE 0,0,0,0,36,641.4083333,0.060925926,0.081375661,0,0,Jul,3,2,2,13,Returning_Visitor,FALSE,FALSE 0,0,0,0,3,335,0,0.133333333,0,0,Aug,3,2,3,4,Returning_Visitor,TRUE,FALSE 7,236.4,0,0,14,341.7,0.059649123,0.084210526,0,0,June,3,2,4,3,Returning_Visitor,FALSE,FALSE 0,0,0,0,24,607.9666667,0,0.004166667,19.47465798,0,Nov,4,1,1,2,New_Visitor,FALSE,TRUE 1,0,2,30.26666667,33,835.3,0.005714286,0.028571429,2.159096645,0,Oct,2,5,1,1,Returning_Visitor,TRUE,TRUE 3,154,0,0,8,422.08,0,0.035185185,27.93478267,0,Jul,1,1,4,4,Returning_Visitor,FALSE,TRUE 2,22.8,0,0,81,1342.75,0,0.008860759,0,0,Sep,1,1,1,3,Returning_Visitor,FALSE,TRUE 2,171.2,0,0,16,218,0.011111111,0.033333333,0,0,Oct,1,2,6,4,Returning_Visitor,FALSE,FALSE 0,0,0,0,56,1294.637143,0.007407407,0.03345679,0,0,Aug,1,1,3,3,Returning_Visitor,FALSE,FALSE 3,86.6,0,0,35,596.0333333,0,0.005263158,0,0,Oct,2,5,1,2,New_Visitor,FALSE,FALSE 10,367.6333333,5,74.5,181,4287.960606,0,0.012467279,4.529635712,0,Oct,2,2,4,1,Returning_Visitor,FALSE,FALSE 1,15.2,0,0,19,348.6,0,0.005555556,0,0,Aug,2,2,7,3,Returning_Visitor,FALSE,TRUE 1,5,1,7,31,561.0940141,0.034375,0.052507716,0,0,Jul,3,2,3,13,Returning_Visitor,FALSE,FALSE 3,196.4,0,0,9,397,0.027272727,0.045454545,0,0,Sep,3,2,6,13,Returning_Visitor,FALSE,FALSE 5,83.16666667,0,0,18,585.5666667,0.010526316,0.052631579,0,0,Jul,2,2,2,13,Returning_Visitor,TRUE,FALSE 3,70.4,0,0,9,144.0666667,0,0,0,0,Oct,1,1,1,3,New_Visitor,FALSE,FALSE 0,0,0,0,48,3177.366667,0.008510638,0.044680851,0,0,Jul,2,2,5,1,Returning_Visitor,FALSE,FALSE 0,0,0,0,45,1970,0.019259259,0.037925926,0,0,June,4,1,3,3,Returning_Visitor,FALSE,FALSE 4,138.1,0,0,20,1708.98,0.0125,0.057936508,0,0,Sep,3,3,4,4,Returning_Visitor,TRUE,FALSE 3,93.10666667,4,87.6,32,810.0816667,0.005128205,0.015099715,17.27062073,0,Nov,1,1,5,2,Returning_Visitor,FALSE,TRUE 3,36.7032967,4,42.2,51,1187.737297,0.024223602,0.066119363,6.854149002,0,Oct,3,2,1,1,Returning_Visitor,FALSE,FALSE 2,6,0,0,9,89.5,0,0.058333333,0,0,Sep,1,1,4,4,Returning_Visitor,TRUE,TRUE 4,66.76,0,0,10,267.9,0,0.002777778,0,0,Aug,2,2,7,2,New_Visitor,FALSE,FALSE 0,0,0,0,8,471.4,0,0.075,0,0,June,2,7,2,1,Returning_Visitor,FALSE,FALSE 4,60.2,0,0,13,409.8371212,0,0.005882353,20.10126693,0,Oct,1,1,4,2,New_Visitor,FALSE,TRUE 6,245.9333333,2,15.2,27,1333.8,0.012903226,0.029032258,4.945717395,0,Nov,2,2,9,6,Returning_Visitor,FALSE,FALSE 7,285.7,0,0,28,587.9228571,0,0.00962963,10.57326688,0,Nov,1,1,6,3,Returning_Visitor,FALSE,TRUE 0,0,0,0,23,997.3142857,0,0.039130435,0,0,June,1,1,3,15,Returning_Visitor,TRUE,FALSE 5,81.6,0,0,16,308.7,0.00952381,0.019047619,0,0,Sep,2,4,2,5,New_Visitor,FALSE,FALSE 6,262.02,0,0,25,688.4533333,0,0.025904762,2.368830117,0,Sep,2,2,3,2,Returning_Visitor,FALSE,FALSE 2,126,0,0,2,14.35,0,0.05,0,0,Aug,3,2,6,5,New_Visitor,TRUE,FALSE 1,22.2,0,0,34,781.8,0.017142857,0.028571429,0,0,Oct,2,2,6,1,Returning_Visitor,TRUE,FALSE 4,76.4,0,0,2,49.4,0,0.033333333,16.77416803,0,Oct,2,2,6,2,Returning_Visitor,FALSE,TRUE 3,94.7,0,0,20,366.6166667,0.009090909,0.004545455,0,0,Jul,1,1,2,4,New_Visitor,FALSE,FALSE 3,149.0666667,0,0,19,285.6166667,0.019047619,0.019047619,0,0,Nov,3,2,1,2,Returning_Visitor,FALSE,FALSE 0,0,0,0,9,101.6,0.044444444,0.077777778,0,0,June,2,2,3,1,Returning_Visitor,FALSE,FALSE 6,132.22,3,18,22,418.8444444,0,0.001020408,0,0,Oct,2,2,1,2,New_Visitor,TRUE,FALSE 7,421.25,0,0,59,1945.896667,0.012239583,0.010578704,28.52919049,0,June,2,6,1,20,Returning_Visitor,FALSE,TRUE 3,73.2,5,124.2666667,10,239.7,0,0.0025,0,0,Sep,2,2,5,1,New_Visitor,FALSE,FALSE 7,115.7272727,0,0,43,2660.283333,0.030612245,0.055782313,0,0,Jul,2,2,2,13,Returning_Visitor,FALSE,FALSE 6,473.7,1,42.4,47,2101.691541,0.007435897,0.02061552,0,0,Jul,3,3,8,1,Returning_Visitor,TRUE,FALSE 0,0,0,0,3,60.4,0.066666667,0.133333333,0,0,June,3,2,2,1,Returning_Visitor,FALSE,FALSE 0,0,0,0,14,406.8666667,0.021428571,0.079761905,0,0,Jul,4,5,3,3,Returning_Visitor,FALSE,FALSE 8,203.6,1,0,42,1052.4,0,0.004545455,37.35725827,0,Nov,2,2,5,2,Returning_Visitor,FALSE,TRUE 3,72.4,0,0,3,86.6,0,0.033333333,0,0,June,1,1,7,3,Returning_Visitor,TRUE,TRUE 3,140,0,0,26,1629.438095,0,0.004938272,0,0,Nov,4,5,2,2,Returning_Visitor,TRUE,FALSE 7,107.75,1,1021.6,33,1454.75,0.004761905,0.00968254,40.18975207,0,Oct,3,2,1,4,Returning_Visitor,TRUE,FALSE 0,0,1,56.4,14,103.7579088,0,0.026666667,45.92878029,0,Nov,2,2,2,20,Returning_Visitor,FALSE,TRUE 3,31.2,0,0,4,36.2,0.028571429,0.057142857,0,0,June,2,2,3,2,Returning_Visitor,FALSE,FALSE 0,0,0,0,25,701.8833333,0,0.023333333,0,0,Sep,2,2,1,2,Returning_Visitor,FALSE,FALSE 16,590.925,0,0,54,2214.230303,0.009375,0.031076389,1.288569678,0,Oct,2,4,8,1,Returning_Visitor,FALSE,FALSE 0,0,0,0,7,728.8,0,0,58.9241766,0,Aug,4,2,1,2,Returning_Visitor,FALSE,TRUE 0,0,0,0,22,848.8407143,0,0.00952381,0,0,June,2,4,3,2,Returning_Visitor,FALSE,FALSE 5,46.5,0,0,94,1729.716667,0.005050505,0.025420875,6.322599996,0,Aug,2,2,8,1,Returning_Visitor,FALSE,FALSE 0,0,0,0,1,0,0.2,0.2,0,0,June,1,1,9,5,Returning_Visitor,FALSE,FALSE 0,0,0,0,2,29.2,0,0.05,0,0,Jul,1,1,4,3,Returning_Visitor,FALSE,FALSE 3,37.3,0,0,19,317.8,0.018181818,0.045454545,0,0,Jul,2,2,7,4,Returning_Visitor,FALSE,FALSE 4,49.2,0,0,3,22,0,0.025,0,0,Nov,2,2,3,2,New_Visitor,FALSE,TRUE 3,52.9,0,0,8,278.58,0,0.01,26.18180656,0,Jul,1,1,1,2,New_Visitor,FALSE,FALSE 9,201.85,2,20.6,43,1458.93619,0,0.00952381,0,0,Aug,3,2,3,1,New_Visitor,FALSE,FALSE 7,253.4,0,0,12,832.7,0,0.004761905,0,0,June,2,5,3,5,New_Visitor,FALSE,FALSE 7,131.08,1,124.9,159,4362.009752,0.001204819,0.008667383,6.047203519,0,Jul,2,10,2,2,Returning_Visitor,FALSE,FALSE 7,95.17,1,11.1,68,824.1573062,0,0.006666667,37.68626909,0,June,2,2,3,3,Returning_Visitor,FALSE,FALSE 14,325.48,6,22.6,24,359.63,0.024324324,0.042342342,0,0,Sep,1,1,2,4,Returning_Visitor,TRUE,FALSE 6,174.4571429,2,24.2,107,1688.2494,0,0.016407828,2.385969641,0,June,2,4,1,2,Returning_Visitor,TRUE,FALSE 0,0,0,0,8,170.5333333,0,0,37.96905976,0,Aug,2,5,1,5,New_Visitor,FALSE,TRUE 6,285.9,0,0,13,291.3,0,0.0125,53.11442085,0,Nov,1,1,4,2,New_Visitor,FALSE,TRUE 0,0,0,0,13,427.3,0,0.038461538,0,0,Sep,2,5,1,1,Returning_Visitor,FALSE,FALSE 5,247.1,0,0,8,1188.2,0,0.030769231,0,0,Oct,1,8,8,1,Returning_Visitor,TRUE,FALSE 0,0,0,0,8,251.8,0,0.025,0,0,Oct,1,1,1,2,New_Visitor,FALSE,FALSE 4,103.6,1,27.2,26,2609,0,0.002380952,47.12454606,0,Nov,2,6,1,2,Returning_Visitor,TRUE,TRUE 7,141.8388889,0,0,33,486.5600582,0.006666667,0.028784722,11.84059922,0,June,2,2,3,1,Returning_Visitor,FALSE,FALSE 1,45.4,0,0,41,1227.713333,0.019512195,0.041056911,0,0,Oct,1,1,3,3,Returning_Visitor,FALSE,FALSE 0,0,0,0,11,650.1666667,0.036363636,0.060606061,0,0,Oct,1,1,6,1,Returning_Visitor,FALSE,FALSE 2,30.2,0,0,32,776.8833333,0,0.003125,0,0,Jul,2,4,1,5,New_Visitor,FALSE,FALSE 1,90.6,0,0,11,424.5957143,0.053333333,0.055833333,0,0,Aug,3,2,8,1,Returning_Visitor,FALSE,FALSE 10,196.25,1,46.4,271,12292.46381,0.005995204,0.02001083,0.038034542,0,Jul,2,2,2,3,Returning_Visitor,TRUE,FALSE 5,163,0,0,5,25,0.02,0.04,0,0,Oct,2,2,2,20,New_Visitor,TRUE,FALSE 0,0,0,0,6,231.8,0.066666667,0.066666667,0,0,Oct,2,5,3,2,Returning_Visitor,FALSE,FALSE 2,9,0,0,26,623.0266667,0,0.009047619,36.0824516,0,Nov,2,2,4,20,Returning_Visitor,FALSE,FALSE 0,0,0,0,6,1183.2,0.033333333,0.066666667,0,0,Oct,2,6,2,1,Returning_Visitor,FALSE,FALSE 5,38.57777778,2,9,7,584.3777778,0,0.064351852,0,0,Jul,1,1,1,2,Returning_Visitor,TRUE,TRUE 0,0,0,0,4,39.8,0,0.05,0,0,Aug,1,1,1,3,New_Visitor,FALSE,FALSE 3,133.8666667,0,0,7,128,0,0.026666667,0,0,Oct,1,2,1,1,Returning_Visitor,TRUE,FALSE 3,69.1,0,0,24,368.6,0,0.008888889,0,0,Sep,2,2,1,2,New_Visitor,FALSE,FALSE 3,142.1333333,2,141.2,8,361.2333333,0.008333333,0.038888889,0,0,Oct,1,1,1,2,Returning_Visitor,TRUE,FALSE 4,206.6,0,0,6,282.8,0.025,0.025,0,0,Sep,3,2,3,5,Returning_Visitor,FALSE,FALSE 0,0,0,0,3,0,0.2,0.2,0,0,Nov,3,2,1,11,Returning_Visitor,FALSE,TRUE 0,0,0,0,1,0,0.2,0.2,0,0,Nov,2,2,1,20,Returning_Visitor,TRUE,FALSE 0,0,0,0,1,0,0.2,0.2,0,0,June,1,1,1,3,Returning_Visitor,FALSE,FALSE 0,0,3,62.8,90,2167.900476,0.002898551,0.013297101,0,0,Aug,1,1,3,4,Returning_Visitor,FALSE,FALSE 2,39.4,0,0,40,673.65,0,0.014634146,23.76322043,0,Nov,4,2,6,3,Returning_Visitor,TRUE,TRUE 0,0,0,0,67,771.4133333,0,0.004205128,0,0,Aug,1,8,9,1,Returning_Visitor,FALSE,FALSE 2,95.8,0,0,83,4052.358631,0.000340136,0.008176477,19.61979883,0,Sep,2,2,4,3,Returning_Visitor,FALSE,TRUE 15,182.7,0,0,60,632.25,0.005882353,0.011960784,0,0,Oct,4,2,1,3,Returning_Visitor,FALSE,FALSE 0,0,0,0,9,463.4,0,0.022222222,0,0,Oct,3,2,1,2,New_Visitor,FALSE,FALSE 1,21.2,0,0,41,1808.953333,0,0.012195122,0,0,Nov,2,2,3,3,Returning_Visitor,FALSE,FALSE 3,121.4,1,6,36,1418.377619,0.05787037,0.080562169,0,0,Jul,2,2,8,1,Returning_Visitor,TRUE,FALSE 5,171.9,4,145.1666667,171,4554.692744,0.002259887,0.004062416,0,0,Sep,2,2,3,2,Returning_Visitor,FALSE,FALSE 3,230.1,0,0,7,381.4,0,0.007407407,35.25541691,0,Nov,1,1,3,5,Returning_Visitor,FALSE,FALSE 1,19.2,0,0,55,3134.066667,0,0.006666667,5.767419658,0,Aug,2,4,1,1,Returning_Visitor,TRUE,FALSE 3,151,0,0,21,840.2863203,0,0.010763889,0,0,Oct,2,2,3,2,Returning_Visitor,FALSE,FALSE 6,342.1,0,0,82,895.1111111,0.002325581,0.004844961,0,0,Sep,2,2,1,3,Returning_Visitor,FALSE,FALSE 3,133.4666667,2,199.4,150,4935.536124,0.005664488,0.016427015,0,0,Oct,2,2,3,13,Returning_Visitor,TRUE,FALSE 6,72.04,2,206.6,24,709.4333333,0.007142857,0.016666667,15.70942935,0,Aug,2,2,2,2,Returning_Visitor,FALSE,TRUE 1,4,1,0,22,360.65,0,0.01037037,1.453922921,0,Oct,2,2,1,2,Returning_Visitor,FALSE,TRUE 3,44.3,0,0,17,468.85,0.01,0.006666667,64.2879309,0,Aug,1,2,1,2,Returning_Visitor,FALSE,TRUE 0,0,0,0,111,2805.038095,0.005405405,0.038192738,0,0,Aug,4,1,3,3,Returning_Visitor,TRUE,FALSE 1,5,2,33.3,16,521.5,0.010526316,0.021052632,0,0,Jul,2,2,7,1,Returning_Visitor,FALSE,FALSE 2,73.6,5,155.5666667,44,1993.396667,0.01254902,0.041246499,4.107661192,0,Sep,2,2,4,1,Returning_Visitor,FALSE,FALSE 8,82.85,0,0,84,2289.079157,0.002556818,0.013110506,0,0,June,1,1,7,3,Returning_Visitor,FALSE,FALSE 4,41.2,1,0,15,142.4,0.022222222,0.077777778,0,0,Jul,1,1,4,2,Returning_Visitor,FALSE,FALSE 5,70.20765027,0,0,89,2542.60431,0.001811594,0.012188596,5.887666731,0,Nov,2,2,4,2,Returning_Visitor,FALSE,TRUE 10,42.1,0,0,37,527.6666667,0.009090909,0.032954545,0,0,Aug,1,1,1,2,Returning_Visitor,FALSE,FALSE 1,63.4,0,0,6,137.6,0,0.013333333,0,0,Oct,2,2,3,1,Returning_Visitor,FALSE,FALSE 5,69.03333333,0,0,24,658.8466667,0,0.013793103,42.9845308,0,Oct,2,6,3,2,Returning_Visitor,FALSE,FALSE 3,81.6,0,0,13,443.9,0,0.0125,0,0,Sep,1,1,5,5,New_Visitor,FALSE,FALSE 1,508.8,0,0,7,379.8,0,0.05,0,0,Jul,1,1,1,1,Returning_Visitor,FALSE,FALSE 5,156.2,0,0,22,742,0,0.007407407,32.22252197,0,Sep,1,1,3,5,New_Visitor,TRUE,TRUE 2,65.4,0,0,79,2426.006667,0.001265823,0.017721519,0,0,Sep,3,2,1,3,Returning_Visitor,FALSE,FALSE 1,18.1,2,72.5,9,136.2833333,0.018181818,0.027272727,0,0,June,2,2,2,2,New_Visitor,FALSE,FALSE 0,0,0,0,20,1116.473333,0.033,0.027954545,0,0,Oct,3,2,6,1,Returning_Visitor,FALSE,FALSE 0,0,0,0,1,0,0.2,0.2,0,0,June,3,2,3,13,Returning_Visitor,FALSE,FALSE 0,0,0,0,39,835.6809524,0.032478632,0.028162393,0,0,Jul,1,1,6,3,Returning_Visitor,FALSE,FALSE 7,154.9333333,0,0,31,910.04,0.018181818,0.052020202,5.316109207,0,Jul,1,1,3,1,Returning_Visitor,FALSE,FALSE 0,0,0,0,10,232.4,0,0,0,0,Aug,1,1,3,4,Returning_Visitor,FALSE,FALSE 0,0,0,0,27,701.9,0.007407407,0.028395062,0,0,Aug,3,2,7,6,Returning_Visitor,FALSE,FALSE 4,82.7,0,0,26,405.2833333,0,0.001428571,0,0,Nov,2,2,1,2,New_Visitor,FALSE,FALSE 0,0,0,0,51,1219.054286,0,0.012113703,9.063088034,0,Aug,2,2,2,1,Returning_Visitor,FALSE,TRUE 0,0,0,0,7,0,0.2,0.2,0,0,June,2,2,6,13,Returning_Visitor,FALSE,FALSE 4,94.23333333,2,72,145,2935.255477,0.001360544,0.005708347,0.26809298,0,Aug,2,2,2,1,Returning_Visitor,FALSE,FALSE 0,0,0,0,72,5465.320175,0.033789954,0.06037786,0,0,Sep,2,5,2,1,Returning_Visitor,FALSE,FALSE 4,23.53333333,0,0,10,118,0.011111111,0.066666667,0,0,June,2,2,2,3,Returning_Visitor,TRUE,FALSE 7,216.4,0,0,37,825.205,0,0.004625745,0,0,Sep,2,2,1,1,Returning_Visitor,FALSE,FALSE 10,821.0754098,1,0,55,2400.782077,0,0.010717178,0.98110865,0,Nov,2,2,2,1,Returning_Visitor,FALSE,FALSE 1,7.6,1,0,3,14.6,0,0.08,0,0,Sep,2,2,1,2,Returning_Visitor,TRUE,FALSE 5,277.8,0,0,25,884.5,0,0.003571429,7.48051616,0,Jul,2,10,2,2,New_Visitor,TRUE,TRUE 1,62.13333333,0,0,79,3421.653333,0.004878049,0.022357724,4.127430681,0,Oct,2,4,1,2,Returning_Visitor,FALSE,TRUE 4,55.8,0,0,17,2023.2,0,0.011111111,0,0,Jul,2,2,1,2,New_Visitor,FALSE,FALSE 1,34.2,0,0,26,502.4533333,0,0.002564103,22.65617015,0,Oct,3,2,6,2,New_Visitor,TRUE,TRUE 0,0,1,37.2,37,2767.753333,0.005555556,0.015,0,0,Nov,2,2,2,2,Returning_Visitor,FALSE,FALSE 1,76.6,2,23.2,46,4598.433333,0.004255319,0.046808511,10.72449269,0,Jul,2,2,1,1,Returning_Visitor,FALSE,TRUE 4,64.66666667,0,0,50,936.81,0,0.003773585,0,0,Aug,2,2,4,2,New_Visitor,FALSE,FALSE 0,0,0,0,1,8,0,0,0,0,Sep,2,2,2,1,Returning_Visitor,FALSE,FALSE 1,8,0,0,11,701,0,0.016666667,0,0,Aug,2,2,1,2,Returning_Visitor,FALSE,FALSE 2,318.2,0,0,6,117,0.05,0.05,0,0,Nov,2,2,1,1,Returning_Visitor,FALSE,FALSE 0,0,0,0,15,176.5,0.04,0.12,0,0,Jul,2,2,1,3,Returning_Visitor,FALSE,FALSE 1,218.6,0,0,19,513.1622222,0.003333333,0.0075,0,0,Aug,3,2,1,2,Returning_Visitor,FALSE,FALSE 9,254.5,0,0,26,712.2714286,0.006060606,0.028282828,1.725866353,0,Sep,2,5,2,3,Returning_Visitor,FALSE,FALSE 10,1251.2,7,250,414,23888.81,0.009900332,0.027062016,1.033757336,0,Sep,2,2,4,13,Returning_Visitor,FALSE,FALSE 5,79.1,0,0,36,1081.108333,0.025,0.025972222,0,0,Sep,1,2,2,2,Returning_Visitor,TRUE,FALSE 0,0,2,53,12,318.5333333,0,0.014285714,0,0,Jul,1,1,2,4,New_Visitor,FALSE,FALSE 0,0,0,0,2,11,0,0.1,0,0,Aug,3,2,1,4,Returning_Visitor,FALSE,FALSE 2,94.6,1,74.6,16,199,0,0.015789474,0,0,Jul,1,2,9,2,New_Visitor,FALSE,FALSE 0,0,0,0,1,0,0.2,0.2,0,0,Sep,2,4,4,4,Returning_Visitor,FALSE,FALSE 4,60.4,0,0,7,207.6,0,0.033333333,30.84751033,0,Sep,2,2,9,3,Returning_Visitor,FALSE,FALSE 16,381.6731217,3,199.4,86,1618.40328,0.015067698,0.022043153,3.885233567,0,Jul,3,2,8,4,Returning_Visitor,FALSE,TRUE 0,0,1,68.5,58,1066.393635,0.000209249,0.034950263,0,0,June,3,2,3,6,Returning_Visitor,FALSE,FALSE 5,150.2,2,20,22,392.6,0.028,0.06,0,0,Oct,2,2,7,3,Returning_Visitor,FALSE,FALSE 4,75.1,0,0,7,102.7,0,0.011111111,0,0,Sep,2,4,6,2,Returning_Visitor,FALSE,TRUE 7,81.5,0,0,56,857.7355067,0,0.014259607,0,0,June,3,2,1,4,Returning_Visitor,FALSE,FALSE 0,0,0,0,11,660.8,0,0,33.83646892,0,June,2,2,6,1,New_Visitor,FALSE,TRUE 0,0,0,0,7,376.7,0,0,53.6853974,0,Aug,2,2,1,2,New_Visitor,FALSE,TRUE 2,303.2,0,0,13,181.0666667,0,0.016666667,0,0,Oct,1,1,3,2,Returning_Visitor,TRUE,FALSE 0,0,2,111.9,25,360.5111111,0.065384615,0.066783217,0,0,Jul,3,2,4,3,Returning_Visitor,FALSE,FALSE 3,21.2,0,0,23,1053.175758,0.002083333,0.014583333,0,0,Jul,3,2,3,4,Returning_Visitor,FALSE,FALSE 7,73.23333333,1,0,310,7710.731416,3.94E-05,0.00751105,0.098621403,0,Jul,4,1,1,4,Returning_Visitor,FALSE,TRUE 2,76.6,0,0,28,488.91,0.014814815,0.022222222,45.19820194,0,June,1,2,1,1,Returning_Visitor,TRUE,TRUE 2,7,0,0,40,906.4,0,0.015238095,0,0,Oct,2,2,9,4,Returning_Visitor,FALSE,FALSE 0,0,0,0,3,20.2,0,0.066666667,0,0,Jul,2,2,2,1,Returning_Visitor,FALSE,FALSE 0,0,0,0,8,883.9333333,0,0.014285714,0,0,Nov,1,1,1,4,New_Visitor,TRUE,FALSE 0,0,1,6,65,2349.101818,0.018785882,0.046505418,0,0,Jul,2,2,2,3,Returning_Visitor,FALSE,FALSE 0,0,0,0,3,15.2,0.066666667,0.133333333,0,0,Aug,2,4,1,1,Returning_Visitor,TRUE,FALSE 3,130.35,1,21.2,8,314.85,0,0.018181818,0,0,June,2,2,5,2,New_Visitor,FALSE,TRUE 1,23.31428571,0,0,15,124.9142857,0,0.016369048,4.90908873,0,Jul,2,2,3,2,Returning_Visitor,FALSE,FALSE 0,0,0,0,6,31,0,0.033333333,0,0,Oct,2,5,1,1,Returning_Visitor,FALSE,FALSE 14,574.4511905,0,0,41,2688.864762,0,0.011853002,36.96220452,0,Sep,3,2,3,4,Returning_Visitor,FALSE,TRUE 2,72.6,1,15.2,6,425,0,0.025,0,0,Jul,2,10,1,4,Returning_Visitor,TRUE,FALSE 1,24.2,0,0,0,0,0,0.066666667,0,0,Nov,1,1,1,3,Returning_Visitor,TRUE,TRUE 4,190.5,0,0,29,867.9915152,0.012903226,0.019354839,0,0,Sep,3,2,3,4,Returning_Visitor,FALSE,FALSE 1,4,0,0,7,417.5333333,0,0.027777778,0,0,Aug,3,2,6,2,Returning_Visitor,FALSE,FALSE 0,0,0,0,35,536.7333333,0,0.012222222,3.70979448,0,Jul,2,2,3,4,Returning_Visitor,FALSE,TRUE 4,120.7,0,0,5,74.4,0,0.022222222,0,0,Oct,2,2,4,5,New_Visitor,FALSE,FALSE 0,0,0,0,62,4391.666667,0.003174603,0.014814815,0,0,June,2,10,2,1,Returning_Visitor,FALSE,FALSE 3,83.4,0,0,43,2013.659524,0,0.009090909,0,0,Oct,2,2,1,20,Returning_Visitor,FALSE,FALSE 7,272.0333333,0,0,13,295.1,0.011764706,0.017647059,0,0,Nov,3,2,1,20,Returning_Visitor,FALSE,FALSE 2,76.76666667,2,9,17,1566.2,0.00952381,0.038095238,0,0,Sep,2,2,3,5,Returning_Visitor,FALSE,FALSE 0,0,0,0,16,213.8,0.00625,0.06875,0,0,Aug,2,2,1,4,Returning_Visitor,FALSE,TRUE 9,138.5666667,1,21.2,39,977.4128571,0.009756098,0.017615176,0,0,Oct,1,1,3,3,Returning_Visitor,FALSE,FALSE 0,0,0,0,24,1884.7,0.032,0.068,0,0,Sep,2,2,4,13,Returning_Visitor,FALSE,FALSE 7,199.8333333,0,0,27,669.9333333,0,0,0,0,Aug,2,5,1,2,New_Visitor,TRUE,FALSE 0,0,0,0,1,0,0.2,0.2,0,0,June,1,8,1,1,Returning_Visitor,FALSE,FALSE 1,43.4,0,0,29,432.0222222,0,0.006896552,0,0,Nov,1,1,1,3,New_Visitor,FALSE,FALSE 6,95.6,0,0,12,582.7333333,0.014285714,0.035714286,21.32413414,0,Sep,2,2,8,7,Returning_Visitor,FALSE,FALSE 0,0,1,242.8,73,1860.95,0.005405405,0.019527027,0,0,June,2,2,1,2,Returning_Visitor,FALSE,FALSE 2,630.5333333,0,0,33,357.8895238,0,0.007894737,0,0,Oct,3,2,7,4,Returning_Visitor,FALSE,FALSE 3,124,0,0,10,265.9333333,0,0.015277778,0,0,Sep,1,1,1,2,Returning_Visitor,FALSE,FALSE 0,0,0,0,4,313.8,0,0.05,14.53547145,0,Jul,2,6,3,5,Returning_Visitor,FALSE,FALSE 0,0,1,3,20,780.5,0.004761905,0.043809524,10.9112449,0,Jul,3,2,3,3,Returning_Visitor,FALSE,FALSE 0,0,0,0,5,131.8333333,0,0.013333333,36.13790493,0,June,1,1,1,5,New_Visitor,FALSE,TRUE 2,110.8,0,0,6,85.76666667,0,0.028571429,59.57182786,0,Aug,1,1,1,4,Returning_Visitor,FALSE,TRUE 5,125.1,2,34.6,25,887.75,0.013793103,0.023754789,0,0,Aug,2,2,1,1,Returning_Visitor,TRUE,FALSE 0,0,1,42.4,61,1140.796667,0,0.002833333,0,0,June,2,2,6,2,Returning_Visitor,FALSE,FALSE 0,0,0,0,10,122.8,0.11,0.133333333,0,0,Jul,3,2,3,13,Returning_Visitor,FALSE,FALSE 0,0,0,0,2,58.4,0,0.1,0,0,Oct,2,4,1,2,New_Visitor,FALSE,FALSE 5,110.3,1,21.8,78,3621.606667,0,0.006024096,18.78186098,0,Jul,2,4,4,1,Returning_Visitor,FALSE,FALSE 1,99.8,0,0,110,2056.48859,0,0.004892966,0,0,Jul,2,2,1,1,Returning_Visitor,FALSE,FALSE 3,83.8,0,0,20,798.3,0,0.009090909,0,0,Nov,1,1,1,3,Returning_Visitor,FALSE,FALSE 0,0,0,0,71,1094.310476,0,0.009285714,4.718931118,0,Oct,4,1,2,2,Returning_Visitor,FALSE,FALSE 0,0,1,22.2,16,240.6,0.035294118,0.062352941,0,0,Jul,3,2,9,13,Returning_Visitor,FALSE,FALSE 3,40.8,1,12,5,96,0.028571429,0.042857143,0,0,Sep,3,2,7,4,Returning_Visitor,TRUE,FALSE 2,98.6,0,0,11,1200.2,0.014285714,0.028571429,18.41056693,0,Nov,2,6,2,1,Returning_Visitor,TRUE,FALSE 3,92.3,3,68.2,4,81.7,0,0.03125,0,0,Jul,1,2,1,4,Returning_Visitor,FALSE,FALSE 1,21.2,0,0,53,2224.633333,0.011538462,0.03514652,0,0,Aug,2,2,3,3,Returning_Visitor,FALSE,FALSE 7,96.6,0,0,9,230.62,0.028571429,0.028571429,71.23156574,0,Nov,3,2,1,1,Returning_Visitor,FALSE,FALSE 11,157.3889922,3,338.4,46,1624.935625,0.005357143,0.021391443,0,0,Nov,3,2,3,3,Returning_Visitor,FALSE,FALSE 4,75.5,0,0,10,158.9733333,0.023076923,0.038461538,20.65287956,0,Oct,1,1,3,2,Returning_Visitor,TRUE,TRUE 2,371.8,0,0,5,255,0,0.028571429,0,0,Sep,3,2,4,2,New_Visitor,FALSE,FALSE 7,73.9,0,0,58,3441.806667,0,0.010752688,0,0,Oct,2,2,1,2,Returning_Visitor,FALSE,FALSE 2,216.6,0,0,36,937.8653488,0.040350877,0.069414101,0,0,Oct,3,2,6,1,Returning_Visitor,TRUE,FALSE 0,0,0,0,1,0,0.2,0.2,0,0,Sep,1,1,2,2,Returning_Visitor,FALSE,FALSE 2,16.6,1,18.46666667,32,783.2085714,0,0.005714286,0,0,Jul,2,4,2,5,New_Visitor,FALSE,TRUE 3,215.4,0,0,19,1221.7,0,0,0,0,June,2,2,1,2,New_Visitor,FALSE,FALSE 5,1415.5,0,0,34,588.3666667,0,0.030246914,8.041078329,0,Aug,2,5,1,1,Returning_Visitor,FALSE,TRUE 2,6,0,0,38,876.4766667,0,0.014529915,6.328693274,0,Oct,3,2,1,1,Returning_Visitor,FALSE,TRUE 8,121.0333333,0,0,9,230.8333333,0,0.035714286,0,0,Nov,2,2,1,3,Returning_Visitor,FALSE,FALSE 7,380.8,0,0,50,2653.196667,0.015686275,0.027908497,0,0,Aug,2,2,2,1,Returning_Visitor,FALSE,FALSE 0,0,0,0,11,1855.6,0.027272727,0.054545455,0,0,Sep,2,2,1,1,Returning_Visitor,FALSE,FALSE 0,0,0,0,27,228.9,0.014814815,0.037037037,0,0,Aug,4,1,1,1,Returning_Visitor,FALSE,FALSE 0,0,0,0,2,5,0,0.1,0,0,Nov,2,4,1,5,New_Visitor,FALSE,FALSE 7,132.0571429,3,207.6,29,1243.976108,0,0.007819795,39.88966398,0,Jul,3,2,4,4,Returning_Visitor,TRUE,FALSE 1,6,0,0,2,15,0,0.066666667,0,0,Oct,2,2,9,4,Returning_Visitor,FALSE,FALSE 0,0,0,0,1,0,0.2,0.2,0,0,Jul,3,2,1,1,Returning_Visitor,FALSE,FALSE 14,564.9,3,522.1666667,134,3410.697592,0.001495726,0.002839575,0,0,Nov,3,2,1,2,Returning_Visitor,FALSE,FALSE 1,250.8,2,194.4,2,194.4,0,0.05,0,0,Aug,2,2,1,6,New_Visitor,TRUE,FALSE 5,96.6,0,0,68,2664.416667,0,0.005633803,5.150260109,0,Sep,4,2,1,5,New_Visitor,TRUE,FALSE 7,89.95,0,0,37,552.04,0.006976744,0.017829457,0,0,Aug,1,1,4,3,Returning_Visitor,FALSE,FALSE 3,273,0,0,11,385.74,0,0.007692308,17.81649911,0,Oct,2,2,1,2,Returning_Visitor,FALSE,TRUE 3,63.4,1,0,26,1115.4,0.061494253,0.087060755,0,0,Aug,3,2,6,13,Returning_Visitor,TRUE,FALSE 0,0,0,0,53,1618.203333,0.023846154,0.047628205,0,0,Oct,1,8,1,1,Returning_Visitor,TRUE,FALSE 0,0,0,0,9,154.8,0.018181818,0.03030303,20.74935126,0,Nov,1,1,4,2,Returning_Visitor,FALSE,TRUE 0,0,0,0,20,1016.883333,0.035,0.052333333,0,0,Jul,3,2,4,4,Returning_Visitor,FALSE,FALSE 0,0,0,0,17,504.0833333,0.023529412,0.035294118,36.07906944,0,Oct,1,1,9,3,Returning_Visitor,FALSE,TRUE 0,0,3,55.4,36,1018.018025,0.01025641,0.038075929,0,0,Nov,2,2,1,2,Returning_Visitor,FALSE,FALSE 8,574.55,0,0,15,571.4238596,0.031578947,0.029645043,0,0,Oct,3,2,4,2,Returning_Visitor,FALSE,FALSE 11,289.6833333,0,0,41,678.4833333,0,0.032173913,13.57270341,0,Oct,4,1,3,1,Returning_Visitor,FALSE,TRUE 1,20.2,0,0,13,227.6,0,0,42.29306752,0,Jul,2,2,3,3,New_Visitor,FALSE,FALSE 0,0,0,0,22,343.0333333,0,0.027272727,0,0,June,2,5,6,1,Returning_Visitor,FALSE,FALSE 1,0,0,0,6,261.6666667,0.071428571,0.125714286,0,0,June,3,2,2,4,Returning_Visitor,FALSE,FALSE 13,245.4,0,0,61,3773.3,0.005714286,0.025021645,22.37276151,0,Oct,2,2,9,3,Returning_Visitor,FALSE,TRUE 15,266.25,0,0,172,4098.699853,0.003314917,0.016214742,2.90865324,0,Nov,2,2,7,2,Returning_Visitor,FALSE,FALSE 0,0,0,0,8,724.2,0.022222222,0.03968254,5.091914289,0,Nov,1,1,3,1,Returning_Visitor,FALSE,TRUE 1,67.4,0,0,18,297.5,0.031578947,0.040350877,0,0,Oct,2,2,3,4,Returning_Visitor,FALSE,FALSE 6,107.3666667,0,0,11,151.3,0.005555556,0.04,0,0,Nov,2,2,1,3,Returning_Visitor,FALSE,FALSE 7,46.2,0,0,22,280.0466667,0.00952381,0.012698413,0,0,Aug,2,2,1,3,New_Visitor,TRUE,FALSE 4,291.9600939,4,52.4,219,6747.414492,0.003604845,0.020793071,5.408935339,0,June,2,2,6,1,Returning_Visitor,FALSE,FALSE 3,81.4,0,0,94,2487.52,0,0.002083333,0,0,Oct,2,2,6,2,New_Visitor,FALSE,FALSE 3,49.4,0,0,176,6847.736667,0.003370787,0.011516854,20.44064077,0,Aug,2,4,9,4,Returning_Visitor,FALSE,FALSE 0,0,0,0,3,0,0.2,0.2,0,0,June,3,2,1,1,Returning_Visitor,FALSE,FALSE 0,0,0,0,42,602.8833333,0,0.004365079,21.26632526,0,Sep,6,2,3,1,Returning_Visitor,FALSE,FALSE 5,226.09,0,0,37,692.87,0,0.001666667,0,0,Oct,1,1,4,4,Returning_Visitor,FALSE,FALSE 6,75.5,0,0,3,66.05,0,0.022222222,21.47142017,0,Aug,2,2,7,6,Returning_Visitor,FALSE,TRUE 6,70.58,0,0,15,296.58,0,0.028571429,0,0,Nov,2,4,1,3,Returning_Visitor,FALSE,FALSE 3,64.3,0,0,25,2199.52,0,0.042307692,3.17966645,0,Oct,2,2,3,2,Returning_Visitor,FALSE,FALSE 0,0,0,0,26,372.3833333,0.019230769,0.059615385,0,0,Sep,2,1,1,2,Returning_Visitor,FALSE,FALSE 3,89.66666667,0,0,15,2022.117778,0.011764706,0.023529412,11.55399716,0,Sep,3,2,3,1,Returning_Visitor,FALSE,FALSE 1,53.9,0,0,34,422.5230769,0,0.006015038,36.31643173,0,Sep,1,1,2,2,Returning_Visitor,FALSE,TRUE 5,263.7333333,1,16.2,97,6079.82,0.002,0.017,3.454874142,0,Oct,2,2,2,3,Returning_Visitor,FALSE,FALSE 1,9.1,0,0,9,115.6,0,0.04,0,0,Oct,2,5,2,3,Returning_Visitor,FALSE,FALSE 2,109.8,0,0,55,1988.931429,0.007272727,0.005330144,5.79089525,0,Jul,1,1,1,2,Returning_Visitor,FALSE,FALSE 0,0,0,0,3,294.7,0,0,30.20357704,0,Nov,3,6,3,2,Returning_Visitor,TRUE,TRUE 1,69.6,0,0,46,1292.449762,0.004347826,0.005555556,0,0,Aug,3,2,1,2,Returning_Visitor,TRUE,FALSE 1,180.4,1,36.2,28,1907.08,0.006666667,0.015555556,19.24405095,0,Sep,2,2,1,1,Returning_Visitor,TRUE,TRUE 2,149.2,0,0,3,133.7333333,0,0.04,0,0,Oct,3,2,6,20,New_Visitor,FALSE,FALSE 6,122.8,0,0,7,175.4,0,0.018181818,0,0,Nov,2,5,5,2,New_Visitor,FALSE,FALSE 4,151,0,0,30,1547.233333,0,0.012121212,28.40448178,0,Sep,2,2,2,2,Returning_Visitor,TRUE,TRUE 8,375.12,0,0,13,288.7133333,0,0.003588517,0,0,Sep,3,2,4,3,New_Visitor,FALSE,FALSE 1,49.4,0,0,4,111.6,0.04,0.04,0,0,Jul,1,8,9,4,New_Visitor,FALSE,FALSE 4,226.6,2,7,33,849.7444444,0.021929825,0.042105263,0,0,Sep,3,2,1,13,Returning_Visitor,TRUE,FALSE 4,53.2,0,0,14,496.9666667,0,0.009803922,0,0,Oct,2,2,5,20,Returning_Visitor,FALSE,FALSE 2,141,0,0,10,606.6666667,0.008333333,0.026388889,36.672294,0,Aug,2,5,7,4,Returning_Visitor,FALSE,FALSE 2,41.4,0,0,28,1576.966667,0,0.018452381,0,0,Nov,2,2,9,2,Returning_Visitor,TRUE,FALSE 1,0,0,0,21,921.6233333,0,0.01,0,0,Sep,2,2,1,2,New_Visitor,FALSE,FALSE 8,157.3166667,0,0,48,691.95,0.012666667,0.021380952,6.887181233,0,Oct,2,2,1,1,Returning_Visitor,FALSE,FALSE 5,44.74,3,129.75,18,390.4566667,0,0.019230769,0,0,Aug,2,2,1,4,Returning_Visitor,FALSE,FALSE 7,94.83333333,2,38.9,262,6774.82,0.000425406,0.013069803,0,0,June,2,5,1,4,Returning_Visitor,FALSE,FALSE 2,11,0,0,4,15,0,0.04,0,0,Aug,2,10,1,1,Returning_Visitor,FALSE,FALSE 0,0,0,0,41,3389.8,0.032478632,0.05019425,0,0,Sep,2,4,3,1,Returning_Visitor,FALSE,FALSE 10,85.04,0,0,114,2139.156647,0,0.009414895,0,0,Nov,2,2,3,2,Returning_Visitor,FALSE,FALSE 6,803.47,0,0,30,634.6880952,0.08083779,0.106666667,0,0,Nov,3,2,8,13,Returning_Visitor,FALSE,FALSE 9,210.5666667,3,61.6,10,124.8666667,0.021052632,0.030526316,0,0,Aug,1,1,3,2,Returning_Visitor,FALSE,TRUE 6,132.025,3,79.7,45,1620.141667,0,0.009333333,0,0,Oct,1,1,1,2,Returning_Visitor,FALSE,FALSE 0,0,0,0,18,1110.866667,0,0.014814815,57.03508179,0,Aug,2,10,4,1,Returning_Visitor,FALSE,TRUE 0,0,0,0,11,292.7,0.036363636,0.090909091,0,0,June,1,1,4,3,Returning_Visitor,TRUE,FALSE 3,189.4,4,130.76,93,3856.82978,0.037525773,0.06218704,7.542144271,0,June,2,2,1,1,Returning_Visitor,FALSE,FALSE 0,0,0,0,154,4800.574762,0.014473684,0.032872807,0,0,Oct,2,2,1,1,Returning_Visitor,TRUE,FALSE 6,107.7,0,0,14,290.8,0,0.010526316,0,0,Sep,2,2,1,5,New_Visitor,FALSE,FALSE 1,4,0,0,26,242.1095238,0,0.004273504,0,0,June,4,1,2,1,Returning_Visitor,FALSE,FALSE 1,58.4,1,24.2,11,503.2666667,0.018181818,0.036363636,24.72209216,0,Oct,3,2,1,1,Returning_Visitor,FALSE,TRUE 8,130.55,4,214.1,18,385.75,0,0.013793103,0,0,Sep,2,2,6,4,New_Visitor,FALSE,FALSE 1,23.2,0,0,5,58,0,0.033333333,0,0,Oct,3,2,1,3,Returning_Visitor,FALSE,FALSE 4,59.4,0,0,19,157.2,0.010526316,0.021052632,0,0,Sep,2,5,3,2,New_Visitor,TRUE,FALSE 3,103.6,0,0,1,7,0,0.04,0,0,Nov,3,2,1,5,New_Visitor,FALSE,TRUE 0,0,0,0,13,70.4,0.138461538,0.143589744,0,0,Aug,2,2,2,13,Returning_Visitor,FALSE,FALSE 0,0,0,0,1,15.6,0,0,47.7917708,0,Sep,1,1,6,3,New_Visitor,FALSE,TRUE 4,97.8,0,0,53,1228.289524,0.02345679,0.042026749,0,0,Aug,1,1,2,3,Returning_Visitor,FALSE,FALSE 9,266.7433333,4,1312.4,26,743.4766667,0,0.021969697,9.284545518,0,Nov,2,7,1,2,Returning_Visitor,TRUE,TRUE 4,71.4,0,0,14,449.4,0.03,0.076666667,0,0,Aug,1,1,1,3,Returning_Visitor,FALSE,FALSE 6,105.6333333,2,105.95,8,246.3865079,0,0.008928571,0,0,Nov,3,2,2,3,Returning_Visitor,FALSE,FALSE 2,32.7,0,0,6,812.35,0.018181818,0.036709957,24.16451021,0,Aug,3,2,1,2,Returning_Visitor,FALSE,FALSE 0,0,0,0,4,75.4,0.1,0.125,0,0,Nov,3,3,1,3,Returning_Visitor,FALSE,FALSE 6,301.2,0,0,87,1836.983333,0.009782609,0.026449275,0,0,Sep,2,2,6,3,Returning_Visitor,FALSE,FALSE 2,48.4,0,0,2,37.2,0,0.05,0,0,June,1,1,1,2,Returning_Visitor,FALSE,FALSE 0,0,0,0,1,0,0.2,0.2,0,0,Aug,3,3,2,2,Returning_Visitor,TRUE,FALSE 0,0,0,0,1,381.8,0.1,0.133333333,0,0,June,3,2,1,4,Returning_Visitor,FALSE,FALSE 23,239.7445344,3,624.6,409,9100.402959,0.004301075,0.015934735,6.277683942,0,Jul,2,2,1,13,Returning_Visitor,TRUE,FALSE 4,243.68,0,0,12,1256,0,0.01875,0,0,June,2,2,3,1,New_Visitor,FALSE,FALSE 8,259.2836478,2,12,53,1854.242845,0.0004329,0.015548371,22.22165124,0,Nov,3,2,9,6,Returning_Visitor,FALSE,FALSE 0,0,0,0,54,941.4666667,0,0.003773585,26.67428219,0,Aug,4,5,1,1,Returning_Visitor,FALSE,TRUE 2,292.2,0,0,19,1391.383333,0,0.02,18.84993357,0,Oct,3,2,1,2,New_Visitor,FALSE,TRUE 3,52.4,0,0,25,324.2666667,0,0.032692308,0,0,Sep,2,2,1,5,Returning_Visitor,FALSE,FALSE 2,16.2,0,0,3,268,0,0.04,49.55482147,0,Nov,2,2,5,5,New_Visitor,FALSE,FALSE 3,94.6,0,0,17,265.5,0,0,0,0,Oct,2,2,1,2,New_Visitor,FALSE,FALSE 1,25.7,0,0,6,143.3,0,0.028571429,38.27331194,0,Jul,2,5,1,4,New_Visitor,FALSE,TRUE 0,0,0,0,13,218.6666667,0.107692308,0.111538462,0,0,Jul,2,2,1,1,Returning_Visitor,FALSE,FALSE 5,100.6,3,216.6,6,432,0,0.024242424,0,0,Sep,1,1,1,5,New_Visitor,FALSE,FALSE 1,0,0,0,3,121.2704225,0,0.04,36.14032284,0,Oct,2,2,1,4,Returning_Visitor,FALSE,TRUE 4,785.2166667,0,0,8,450.8,0.016666667,0.016666667,0,0,Sep,3,2,3,5,Returning_Visitor,FALSE,FALSE 6,124.2,0,0,5,94.6,0,0.022222222,0,0,Nov,1,1,2,5,New_Visitor,FALSE,TRUE 1,74.6,0,0,8,810,0,0.036666667,10.67716934,0,Nov,2,2,1,4,Returning_Visitor,FALSE,FALSE 9,252.8,0,0,36,1191.616667,0.013636364,0.029545455,14.75943613,0,Oct,2,2,2,13,Returning_Visitor,FALSE,FALSE 0,0,0,0,3,54.4,0,0.066666667,0,0,Aug,1,1,3,3,Returning_Visitor,TRUE,FALSE 0,0,0,0,28,683.3,0.028571429,0.089285714,0,0,June,2,2,3,1,Returning_Visitor,FALSE,FALSE 5,398.0666667,1,19.2,17,452,0.059064327,0.070737742,0,0,Aug,2,2,3,4,Returning_Visitor,FALSE,FALSE 0,0,0,0,2,0,0.2,0.2,0,0,June,3,2,3,13,Returning_Visitor,FALSE,FALSE 1,53.4,0,0,3,292,0,0,0,0,Nov,2,2,1,4,Returning_Visitor,FALSE,FALSE 1,112.8,0,0,8,543.7333333,0,0.011111111,28.69843548,0,Oct,1,1,6,2,New_Visitor,TRUE,TRUE 4,100.6,2,5,93,2002.76,0.008163265,0.026598639,0,0,Sep,2,2,3,13,Returning_Visitor,FALSE,FALSE 2,31.2,0,0,13,385.3,0.00625,0.027083333,0,0,Sep,1,1,1,5,Returning_Visitor,FALSE,FALSE 4,97.2,2,16,26,690.5666667,0,0.003703704,59.33984611,0,Jul,2,4,8,2,Returning_Visitor,FALSE,TRUE 1,51.4,0,0,7,562.3,0,0,36.65735004,0,Jul,1,1,6,2,New_Visitor,TRUE,TRUE 2,107.4,2,96.7,32,833.325,0.024509804,0.035882353,0,0,Jul,3,2,4,1,Returning_Visitor,FALSE,TRUE 4,102.2,0,0,3,72,0,0.016666667,0,0,Jul,2,2,6,5,New_Visitor,FALSE,FALSE 8,580.2,1,145.6,48,2734.966667,0,0.007692308,27.60143046,0,Oct,2,2,4,5,New_Visitor,FALSE,TRUE 10,471.06,0,0,171,5830.91,0.001098901,0.005494505,0,0,Oct,2,10,4,2,Returning_Visitor,FALSE,TRUE 3,146.5,3,277.1333333,31,700.0466667,0,0.01712963,0,0,Aug,1,1,5,2,Returning_Visitor,TRUE,FALSE 6,378.4666667,0,0,13,950.6666667,0.022222222,0.054444444,0,0,Oct,1,1,1,3,Returning_Visitor,FALSE,FALSE 3,173.8,2,124.8,7,411.6,0,0.017272727,0,0,Aug,1,1,1,4,Returning_Visitor,TRUE,FALSE 4,115.8,0,0,46,2557.35,0,0.01372549,44.35848718,0,Oct,2,2,1,2,Returning_Visitor,FALSE,TRUE 1,114.8,0,0,10,951.4333333,0,0.018181818,0,0,Sep,3,2,6,1,Returning_Visitor,FALSE,FALSE 0,0,0,0,2,0,0.2,0.2,0,0,June,3,2,4,13,Returning_Visitor,FALSE,FALSE 8,310.0666667,1,49.4,71,1441.974535,0.003013699,0.009218133,48.30461627,0,Oct,1,1,2,20,Returning_Visitor,TRUE,TRUE 0,0,0,0,1,0,0.2,0.2,0,0,Jul,3,2,1,4,Returning_Visitor,FALSE,FALSE 2,33.2,0,0,9,162.2333333,0,0.018181818,0,0,Sep,2,2,1,5,Returning_Visitor,FALSE,FALSE 0,0,0,0,9,166.12,0,0.022222222,0,0,Jul,2,2,1,1,Returning_Visitor,FALSE,FALSE 0,0,0,0,53,1624.320952,0.009803922,0.01735761,0,0,Sep,3,2,4,3,Returning_Visitor,FALSE,FALSE 4,101.7,0,0,40,708.5547619,0.009302326,0.016434109,0,0,Aug,2,2,3,13,Returning_Visitor,FALSE,FALSE 1,191.4,0,0,13,679.9033333,0,0.002564103,34.86423061,0,Nov,3,2,3,2,Returning_Visitor,TRUE,TRUE 13,83.9952381,2,99.8,24,364.9619048,0,0.018181818,0,0,Nov,2,2,1,2,Returning_Visitor,FALSE,FALSE 2,118.9,0,0,30,1216.726667,0,0.008602151,26.13036122,0,Nov,4,2,6,2,New_Visitor,TRUE,TRUE 3,109.7,0,0,16,852.3,0.011764706,0.043137255,0,0,Oct,2,2,1,3,Returning_Visitor,FALSE,FALSE 11,293.6222222,2,42.2,64,1831.942222,0,0.015238095,16.35155923,0,Nov,2,5,3,4,Returning_Visitor,TRUE,FALSE 1,15.2,4,104.8,31,2725.708571,0.01047619,0.026482993,20.14579261,0,Nov,2,2,3,1,Returning_Visitor,FALSE,TRUE 5,66.9,0,0,12,86.63333333,0,0.025,42.93595137,0,Oct,4,1,1,7,Returning_Visitor,TRUE,TRUE 2,341.6,0,0,7,177.9,0.047619048,0.062857143,0,0,Aug,3,2,1,1,Returning_Visitor,FALSE,FALSE 10,130.55,1,23.2,7,128.2333333,0.006666667,0.015238095,0,0,Oct,2,2,6,5,New_Visitor,FALSE,FALSE 0,0,0,0,3,31.2,0.066666667,0.133333333,0,0,June,3,2,1,1,Returning_Visitor,FALSE,FALSE 0,0,0,0,8,846.6,0.022222222,0.022222222,24.61778468,0,Nov,1,1,7,5,New_Visitor,FALSE,TRUE 1,30.2,0,0,5,323.4,0.066666667,0.083333333,0,0,Jul,2,4,1,3,Returning_Visitor,TRUE,FALSE 3,25,0,0,1,8,0,0.033333333,0,0,Oct,2,5,1,5,New_Visitor,TRUE,FALSE 3,75.6,0,0,14,178.59,0.0125,0.00625,0,0,Aug,3,2,1,4,Returning_Visitor,TRUE,FALSE 4,134.39,1,191.9,24,1592.533333,0.003703704,0.013580247,5.245868726,0,Oct,3,2,1,5,Returning_Visitor,FALSE,FALSE 0,0,0,0,4,103.8,0.1,0.15,0,0,Jul,1,1,1,1,Returning_Visitor,FALSE,FALSE 0,0,0,0,1,0,0.2,0.2,0,0,June,2,2,1,13,Returning_Visitor,FALSE,FALSE 0,0,0,0,18,1257.946667,0,0.003921569,33.50211155,0,Oct,2,2,3,2,Returning_Visitor,TRUE,TRUE 2,108.8,0,0,15,542,0,0.029411765,0,0,Nov,2,2,1,3,Returning_Visitor,FALSE,FALSE 3,70.6,0,0,11,93.5,0.1,0.107142857,0,0,Oct,1,1,1,3,Returning_Visitor,FALSE,FALSE 4,56.63333333,7,309.7333333,58,1280.149821,0.016923077,0.035377102,0,0,Aug,2,2,6,1,Returning_Visitor,TRUE,FALSE 0,0,0,0,11,192.45,0.036363636,0.040909091,27.31510469,0,Oct,3,2,1,1,Returning_Visitor,TRUE,TRUE 5,181.6,2,722.52,98,1717.873278,0.014150943,0.023942797,0,0,Aug,3,2,1,4,Returning_Visitor,FALSE,FALSE 12,184.4173601,1,13,159,6137.14491,0.011276738,0.031489963,18.23386055,0,June,2,4,3,2,Returning_Visitor,FALSE,FALSE 4,100.0666667,0,0,30,310.3355556,0,0.000480769,28.52395644,0,Jul,2,2,5,2,Returning_Visitor,TRUE,TRUE 5,27.4,0,0,5,51.2,0,0.005,0,0,Aug,2,2,2,2,New_Visitor,FALSE,TRUE 0,0,0,0,96,1231.79046,0.00212766,0.01026897,0,0,Jul,2,4,3,1,Returning_Visitor,FALSE,FALSE 3,23,0,0,19,723.4833333,0.003508772,0.022807018,17.68865669,0,Jul,2,2,2,2,Returning_Visitor,FALSE,FALSE 5,93.4,0,0,38,689.65,0,0.004878049,34.48748308,0,Oct,2,2,2,2,New_Visitor,TRUE,TRUE 5,93.8,0,0,45,2139.7,0,0.004081633,20.2755559,0,Sep,2,4,2,2,New_Visitor,FALSE,TRUE 11,383.3,2,256.3,189,15454.43857,0.007286432,0.019882747,0,0,Aug,2,10,2,1,Returning_Visitor,FALSE,FALSE 0,0,0,0,38,643.9,0.026315789,0.057894737,0,0,June,2,4,9,3,Returning_Visitor,FALSE,FALSE 1,15.2,1,43.4,13,94.2,0.042857143,0.05,21.58031621,0,Jul,2,2,3,1,Returning_Visitor,FALSE,FALSE 2,81.6,0,0,1,27.2,0,0.04,0,0,Oct,3,2,3,5,New_Visitor,FALSE,TRUE 5,77.1,6,60.3,5,944.8,0,0.015384615,0,0,Sep,2,2,3,2,New_Visitor,FALSE,FALSE 3,103.6,0,0,30,440.5333333,0.006451613,0.021505376,0,0,Aug,2,2,2,1,Returning_Visitor,TRUE,FALSE 0,0,0,0,6,98.73333333,0,0.033333333,0,0,Sep,3,2,1,2,Returning_Visitor,TRUE,FALSE 0,0,0,0,12,402.6,0,0.008333333,0,0,Oct,2,2,3,4,Returning_Visitor,FALSE,FALSE 4,39.8,0,0,9,132.4,0,0.006666667,0,0,Aug,2,2,1,5,New_Visitor,FALSE,TRUE 0,0,0,0,62,3564.590227,0.021980198,0.052175571,0,0,Aug,3,2,4,2,Returning_Visitor,FALSE,FALSE 3,155.7,0,0,18,595,0,0.010526316,0,0,Nov,1,1,1,2,New_Visitor,FALSE,FALSE 11,200.42,0,0,31,697.5866667,0.0075,0.0325,0,0,June,2,2,2,2,Returning_Visitor,FALSE,FALSE 0,0,0,0,4,741.4,0.05,0.075,0,0,Oct,2,2,7,1,Returning_Visitor,FALSE,FALSE 0,0,0,0,31,502.9666667,0.012903226,0.04516129,0,0,Oct,2,2,2,1,Returning_Visitor,FALSE,FALSE 5,94.6,0,0,69,2222.419048,0.005092593,0.01496142,0,0,Oct,1,1,3,2,Returning_Visitor,FALSE,FALSE 6,104.4,1,0,32,879.75,0,0.014285714,20.503488,0,Nov,2,4,1,1,Returning_Visitor,FALSE,TRUE 0,0,2,35.2,16,275.1,0,0.027777778,0,0,Aug,2,2,3,3,Returning_Visitor,TRUE,FALSE 0,0,2,15.2,19,1227.153333,0.00952381,0.04399093,0,0,Sep,1,1,1,2,Returning_Visitor,TRUE,FALSE 2,83.6,0,0,43,1582.7,0.004545455,0.014393939,0,0,Nov,3,2,3,1,Returning_Visitor,FALSE,FALSE 1,19.2,0,0,3,60.6,0,0.033333333,0,0,Oct,2,2,6,6,New_Visitor,FALSE,FALSE 2,63.5,0,0,9,99.06666667,0.036363636,0.072727273,0,0,Oct,1,1,1,4,Returning_Visitor,FALSE,FALSE 0,0,0,0,3,0,0.2,0.2,0,0,Jul,1,1,3,3,Returning_Visitor,FALSE,FALSE 0,0,0,0,30,360.4733333,0.013333333,0.036666667,0,0,Jul,1,1,1,3,Returning_Visitor,FALSE,FALSE 0,0,0,0,47,636.7,0,0,0,0,Aug,2,4,1,2,New_Visitor,FALSE,FALSE 7,98.8,0,0,5,69.4,0.022222222,0.033333333,0,0,Jul,3,2,2,5,New_Visitor,TRUE,FALSE 12,504.2533333,2,1665.066667,243,8768.477228,0.014137484,0.028099523,1.827470744,0,Jul,2,2,1,1,Returning_Visitor,FALSE,TRUE 1,30.2,2,30.7,25,348.0611111,0.014814815,0.03037037,0,0,Oct,3,2,7,1,Returning_Visitor,FALSE,FALSE 9,275.8,1,39.68,46,1354.8,0.010909091,0.041212121,0,0,Aug,2,2,6,3,Returning_Visitor,FALSE,FALSE 0,0,0,0,1,0,0.2,0.2,0,0,Jul,1,1,1,1,Returning_Visitor,TRUE,FALSE 1,30.2,0,0,30,613.9933333,0.025287356,0.035139573,0,0,Oct,1,1,1,1,Returning_Visitor,TRUE,FALSE 2,78.64980843,0,0,117,4185.096695,0.024119241,0.04298103,2.427453927,0,Jul,2,2,1,1,Returning_Visitor,FALSE,TRUE 0,0,0,0,5,73.1,0,0.02,0,0,Sep,1,1,1,4,Returning_Visitor,FALSE,FALSE 3,67.4,0,0,34,544.6833333,0,0.01754386,0,0,Sep,1,1,2,2,Returning_Visitor,FALSE,FALSE 1,54.4,0,0,19,351.4,0.065,0.125,0,0,Jul,1,1,3,3,Returning_Visitor,FALSE,FALSE 0,0,0,0,2,8,0,0.1,0,0,June,2,4,6,3,Returning_Visitor,FALSE,FALSE 2,136.4,0,0,41,1275.26927,0,0.013461742,0,0,Nov,3,2,4,3,Returning_Visitor,TRUE,FALSE 2,31.2,0,0,17,2077.166667,0,0.011764706,0,0,Nov,2,2,8,2,Returning_Visitor,TRUE,FALSE 2,59.4,0,0,86,3334.506667,0.025581395,0.050387597,0,0,Aug,2,2,7,3,Returning_Visitor,FALSE,FALSE 5,143.4428571,1,19.6,184,3579.7859,0,0.00381931,138.3208342,0,Oct,2,2,3,20,Returning_Visitor,FALSE,FALSE 0,0,0,0,20,593.7019802,0,0.005364455,20.82613173,0,Nov,2,5,2,5,Returning_Visitor,TRUE,TRUE 6,135.15,0,0,52,1199.22619,0.013095238,0.020257937,0,0,Sep,3,2,3,13,Returning_Visitor,FALSE,FALSE 3,127.1,0,0,36,2590.8,0,0.026315789,0,0,Oct,2,2,1,1,Returning_Visitor,FALSE,FALSE 8,410.9,5,406.9,24,1220.257143,0.011764706,0.024215686,8.887671892,0,Oct,3,2,2,2,Returning_Visitor,FALSE,TRUE 0,0,0,0,8,117.8,0.15,0.15,0,0,June,1,1,3,3,Returning_Visitor,TRUE,FALSE 10,398.5727115,1,132,24,985.0802412,0.00859375,0.028987132,0,0,Sep,3,2,1,3,Returning_Visitor,TRUE,FALSE 0,0,0,0,32,2479.342857,0,0.010967742,0,0,Nov,2,2,3,7,Returning_Visitor,FALSE,FALSE 6,171.1190476,0,0,22,472.752381,0.028571429,0.029761905,0,0,Nov,3,2,2,4,Returning_Visitor,FALSE,FALSE 0,0,0,0,2,7,0,0.1,0,0,Aug,2,2,6,13,Returning_Visitor,FALSE,FALSE 2,70.6,0,0,26,571.35,0,0.007142857,0,0,Nov,2,2,9,5,New_Visitor,FALSE,FALSE 0,0,0,0,16,344.4,0.023529412,0.052941176,0,0,Jul,2,2,2,4,Returning_Visitor,FALSE,FALSE 3,326,0,0,19,364.5333333,0,0.027,0,0,Nov,1,1,3,2,Returning_Visitor,FALSE,FALSE 1,6,0,0,21,1046.32,0.019565217,0.053623188,0,0,Sep,4,1,5,1,Returning_Visitor,TRUE,FALSE 0,0,0,0,5,883.6,0,0,125.8939091,0,Nov,2,2,4,2,Returning_Visitor,TRUE,TRUE 12,248.2285714,0,0,66,1158.782857,0.009722222,0.022569444,0,0,Aug,3,2,1,3,Returning_Visitor,FALSE,FALSE 0,0,0,0,15,89.38333333,0,0.0125,39.6497508,0,Oct,2,2,6,2,New_Visitor,TRUE,TRUE 1,28.53333333,0,0,61,2006.688158,0.003278689,0.015460019,2.039270228,0,Aug,2,5,3,2,Returning_Visitor,FALSE,FALSE 3,117.85,0,0,9,492.9,0,0.028571429,29.4620883,0,Aug,4,1,1,1,Returning_Visitor,FALSE,FALSE 3,180.1333333,1,34.2,27,242.45,0.090322581,0.122427035,0,0,Oct,3,2,1,13,Returning_Visitor,FALSE,FALSE 1,12.1,5,493,50,1572.486399,0,0.019939881,0,0,Nov,2,2,1,2,Returning_Visitor,FALSE,FALSE 3,53.5,0,0,82,3801.680397,0.010714286,0.03015873,0.906375967,0,Sep,2,2,1,4,Returning_Visitor,FALSE,FALSE 2,124.4,2,7,7,294.6,0,0.02,0,0,June,3,2,6,11,New_Visitor,FALSE,FALSE 3,64.4,0,0,5,47.3,0,0.022222222,45.82834797,0,Sep,2,2,1,3,Returning_Visitor,FALSE,TRUE 0,0,0,0,4,570.2,0,0.05,0,0,Jul,2,2,9,1,Returning_Visitor,FALSE,FALSE 2,46.4,0,0,16,463.36,0,0.011111111,46.33991252,0,Sep,2,2,7,2,Returning_Visitor,FALSE,TRUE 0,0,4,112.6,93,2299.97,0.002777778,0.0109375,16.95907848,0,Aug,2,2,1,4,Returning_Visitor,FALSE,TRUE 3,95.83333333,6,303.65,13,438.3833333,0,0.016666667,0,0,June,4,1,1,2,Returning_Visitor,TRUE,FALSE 2,36.25,0,0,29,585.7,0,0.003448276,0,0,Oct,2,2,9,2,New_Visitor,TRUE,FALSE 3,54,4,32,4,46,0,0.022222222,0,0,Sep,1,1,1,5,New_Visitor,FALSE,FALSE 2,103.3,0,0,38,1649.766667,0,0.011965812,0,0,Oct,1,1,3,3,Returning_Visitor,FALSE,FALSE 0,0,1,13,27,1620.024762,0.05,0.085714286,0,0,Sep,5,11,4,1,Returning_Visitor,FALSE,FALSE 4,177.3333333,1,69.6,10,402.6166667,0,0.005555556,0,0,Nov,1,1,3,5,New_Visitor,FALSE,FALSE 17,626.4933333,0,0,76,1985.894203,0.015630252,0.031102029,0,0,Oct,2,2,1,1,Returning_Visitor,FALSE,FALSE 0,0,0,0,5,120.79,0,0.00952381,11.80891356,0,June,2,2,1,2,Returning_Visitor,FALSE,TRUE 4,77.6,0,0,39,1477.154762,0.038157895,0.073976608,0.775318113,0,Nov,1,1,4,3,Returning_Visitor,FALSE,FALSE 7,356.675,0,0,72,1636.258333,0.005333333,0.007733333,0,0,Oct,2,2,4,4,Returning_Visitor,FALSE,FALSE 6,142.9111111,0,0,13,264.3396825,0,0.034509804,0,0,Sep,1,1,3,5,Returning_Visitor,FALSE,FALSE 12,975.6466667,4,276.08,53,2822.623603,0.006666667,0.017015392,0,0,Sep,3,2,8,2,Returning_Visitor,TRUE,FALSE 4,89.6,2,12,13,182.5,0,0.014814815,0,0,Sep,2,2,1,2,Returning_Visitor,FALSE,FALSE 6,265.4,0,0,6,161.6,0,0.022222222,0,0,Aug,1,1,3,3,New_Visitor,FALSE,FALSE 3,284.2,0,0,22,2366.6,0.036,0.064,0,0,Oct,1,1,8,1,Returning_Visitor,TRUE,FALSE 3,52.53333333,0,0,16,2059.3,0,0.027777778,26.18852293,0,Jul,5,11,1,1,Returning_Visitor,FALSE,TRUE 2,17.13333333,0,0,20,1078.2,0.02,0.051166667,0,0,Oct,1,1,1,3,Returning_Visitor,FALSE,FALSE 0,0,0,0,1,0,0.2,0.2,0,0,Dec,2,2,2,1,Returning_Visitor,FALSE,FALSE 0,0,5,663.9166667,47,2059.7,0.005769231,0.033243423,0,0,Dec,3,2,3,6,Returning_Visitor,FALSE,FALSE 2,15,0,0,44,1412.270833,0.004347826,0.015483092,0,0,Dec,2,2,1,2,New_Visitor,FALSE,FALSE 0,0,0,0,14,561.6666667,0,0.025,0,0,Dec,2,2,9,10,Returning_Visitor,FALSE,FALSE 0,0,0,0,6,157.5,0,0.016666667,0,0,Nov,2,2,7,13,Returning_Visitor,FALSE,FALSE 0,0,0,0,15,438.6666667,0.023076923,0.039487179,0,0,Nov,1,1,1,3,Returning_Visitor,FALSE,FALSE 0,0,6,521.9791667,27,733.2871212,0.02020202,0.031564383,0,0,Nov,2,2,7,2,Returning_Visitor,FALSE,FALSE 4,106.5,0,0,12,806.25,0.0125,0.029166667,0,0,Dec,2,2,1,2,Returning_Visitor,FALSE,FALSE 0,0,0,0,14,242.0833333,0.014285714,0.023809524,0,0,Nov,1,1,1,2,Returning_Visitor,FALSE,TRUE 7,113.25,2,38.5,92,4066.207476,0.001754386,0.013603668,18.04114817,0,Nov,2,6,6,13,Returning_Visitor,TRUE,FALSE 1,22,0,0,5,180.5,0,0.033333333,0,0,Dec,3,2,6,20,New_Visitor,FALSE,FALSE 0,0,0,0,20,821.8333333,0.05,0.076666667,0,0,Nov,3,2,4,10,Returning_Visitor,TRUE,FALSE 5,209.625,2,118.75,21,398,0.006896552,0.02183908,0,0,Dec,2,2,4,2,New_Visitor,FALSE,FALSE 0,0,1,68.5,25,927,0.007692308,0.034615385,0,0,Nov,2,2,3,2,Returning_Visitor,TRUE,FALSE 0,0,0,0,64,3025.058333,0.003125,0.018958333,0,0,Nov,3,2,2,2,Returning_Visitor,FALSE,FALSE 0,0,0,0,3,80,0.166666667,0.177777778,0,0,Nov,1,1,6,1,Returning_Visitor,FALSE,FALSE 6,93.125,0,0,29,424.125,0,0.020689655,0,0,Dec,1,8,1,1,Returning_Visitor,FALSE,FALSE 3,60,0,0,66,2339.805556,0.012121212,0.034848485,0,0,Nov,2,2,1,2,Returning_Visitor,FALSE,FALSE 0,0,0,0,14,275,0,0.023076923,0,0,Nov,2,2,9,20,Returning_Visitor,TRUE,FALSE 0,0,0,0,10,73.5,0.12,0.15,0,0,Dec,3,2,1,13,Returning_Visitor,TRUE,FALSE 0,0,0,0,7,168.75,0,0.00952381,0,0,Nov,3,2,1,2,Returning_Visitor,FALSE,FALSE 3,77.33333333,2,7,24,1051.291667,0,0.014814815,0,0,Dec,2,13,9,20,Returning_Visitor,FALSE,FALSE 3,139,0,0,35,631.9333333,0.005263158,0.015789474,0,0,Nov,1,2,1,2,New_Visitor,FALSE,FALSE 0,0,0,0,33,1132.791667,0.009090909,0.047474747,0,0,Dec,4,1,3,1,Returning_Visitor,FALSE,FALSE 5,446.25,0,0,18,815.25,0,0.0025,0,0,Dec,8,13,9,20,Other,FALSE,FALSE 0,0,0,0,10,351.75,0.033333333,0.05,0,0,Dec,2,2,4,1,Returning_Visitor,TRUE,FALSE 0,0,0,0,28,4446.625,0.030952381,0.07047619,0,0,Nov,2,5,1,3,Returning_Visitor,FALSE,FALSE 1,65.5,1,38.5,131,3902.664774,0.008888889,0.02287037,0,0,Nov,3,2,3,1,Returning_Visitor,FALSE,FALSE 0,0,0,0,84,2524.194444,0,0.013730159,0,0,Nov,2,2,1,3,Returning_Visitor,FALSE,TRUE 0,0,0,0,1,0,0.2,0.2,0,0,Dec,2,2,7,1,Returning_Visitor,TRUE,FALSE 1,4,0,0,33,947.4166667,0.012121212,0.045165945,0,0,Nov,2,2,1,2,Returning_Visitor,TRUE,FALSE 1,5,0,0,8,203,0,0.00952381,0,0,Dec,2,4,1,2,New_Visitor,FALSE,FALSE 0,0,0,0,12,410.25,0,0.016666667,0,0,Nov,2,2,3,2,Returning_Visitor,FALSE,FALSE 1,6.5,0,0,10,511.25,0,0.007407407,7.848538778,0,Dec,2,5,3,8,New_Visitor,TRUE,TRUE 2,21,1,802.5,48,2073.916667,0.003921569,0.02627451,0,0,Nov,2,2,3,2,Returning_Visitor,TRUE,FALSE 2,83,2,40,241,5194.79857,0.003101093,0.014304381,0.468406088,0,Dec,2,4,1,1,Returning_Visitor,FALSE,FALSE 0,0,0,0,24,610.6333333,0,0.020833333,0,0,Nov,1,1,3,2,Returning_Visitor,TRUE,TRUE 0,0,0,0,2,9,0,0.1,0,0,Dec,2,2,1,3,Returning_Visitor,FALSE,FALSE 0,0,0,0,58,2174.708333,0.004022989,0.015229885,0,0,Nov,1,2,1,1,Returning_Visitor,TRUE,TRUE 0,0,0,0,1,0,0.2,0.2,0,0,Nov,4,1,1,10,Returning_Visitor,TRUE,FALSE 1,32.3,1,26.5,51,1068.357692,0.01372549,0.043697479,7.570470024,0,Nov,2,10,2,2,Returning_Visitor,FALSE,FALSE 1,0,0,0,15,541.0625,0.052380952,0.093809524,0,0,Dec,2,4,5,1,Returning_Visitor,FALSE,FALSE 0,0,0,0,20,562.0071429,0,0.00125,0,0,Nov,1,1,1,3,Returning_Visitor,FALSE,FALSE 0,0,0,0,13,390.5,0.038461538,0.053846154,0,0,Dec,2,2,1,13,Returning_Visitor,FALSE,FALSE 9,441.85,0,0,122,2577.449603,0,0.010463332,75.27712913,0,Nov,1,1,1,2,Returning_Visitor,FALSE,TRUE 0,0,2,116,11,419.75,0,0.038461538,0,0,Dec,2,2,1,2,Returning_Visitor,FALSE,FALSE 8,106.8333333,1,96,16,373.4583333,0,0.028333333,0,0,Nov,2,2,3,2,Returning_Visitor,FALSE,FALSE 0,0,0,0,4,202.75,0,0.025,0,0,Nov,3,2,1,13,Returning_Visitor,FALSE,FALSE 1,49.5,0,0,65,3804.2875,0,0.0203125,0,0,Nov,2,5,3,13,Returning_Visitor,TRUE,FALSE 0,0,0,0,20,655.0416667,0,0.023928571,0,0,Dec,1,1,4,2,Returning_Visitor,FALSE,FALSE 0,0,0,0,23,462.2678571,0,0.004347826,0,0,Nov,1,1,1,10,Returning_Visitor,TRUE,FALSE 7,97.33333333,2,447.5,16,967.625,0,0.012727273,0,0,Dec,1,1,8,2,Returning_Visitor,FALSE,FALSE 0,0,0,0,5,219,0,0.04,0,0,Nov,1,8,7,6,Returning_Visitor,FALSE,FALSE 2,38.5,0,0,28,770.3095238,0.026666667,0.035396825,0,0,Nov,1,1,1,3,Returning_Visitor,FALSE,FALSE 6,539.5,0,0,11,602.5833333,0.008888889,0.02,0,0,Dec,3,2,1,1,Returning_Visitor,FALSE,FALSE 0,0,0,0,28,475.025,0,0.000446429,0,0,Nov,1,1,6,10,New_Visitor,FALSE,FALSE 3,21,0,0,19,750.2083333,0,0.019047619,60.51988521,0,Nov,2,2,2,3,Returning_Visitor,FALSE,TRUE 0,0,0,0,36,5300.291667,0,0.005714286,0,0,Nov,2,4,6,2,Returning_Visitor,FALSE,FALSE 9,188.5322344,3,197.4166667,76,1758.912393,0.004901961,0.016913178,22.86584543,0,Dec,3,2,2,6,Returning_Visitor,FALSE,FALSE 5,179.5,1,43.5,12,482.75,0,0.007692308,0,0,Dec,2,2,4,2,Returning_Visitor,FALSE,FALSE 0,0,0,0,5,147.5,0,0.04,0,0,Dec,2,2,2,8,Returning_Visitor,FALSE,FALSE 0,0,0,0,12,279.547619,0,0.019047619,0,0,Nov,3,2,1,3,Returning_Visitor,FALSE,FALSE 4,64.66666667,0,0,38,1445.666667,0,0.005263158,52.11335786,0,Nov,2,2,1,2,New_Visitor,FALSE,TRUE 4,77.66666667,0,0,8,159.75,0.016666667,0.038888889,0,0,Nov,3,2,3,2,Returning_Visitor,FALSE,TRUE 1,11,0,0,23,835.5833333,0.009090909,0.022727273,0,0,Nov,2,2,1,10,Returning_Visitor,TRUE,FALSE 0,0,0,0,5,99.5,0,0.04,0,0,Nov,1,1,4,2,Returning_Visitor,FALSE,FALSE 0,0,0,0,5,720.5,0.16,0.18,0,0,Nov,3,2,2,13,Returning_Visitor,FALSE,FALSE 2,36.5,0,0,32,770.375,0,0.01372549,0,0,Dec,2,2,3,2,Returning_Visitor,FALSE,FALSE 1,43.5,3,230.1666667,93,3773.715179,0.004123711,0.018162523,17.77043431,0,Nov,2,2,1,2,Returning_Visitor,FALSE,TRUE 2,33,0,0,91,2023.75,0.004731183,0.018752216,0,0,Nov,2,2,3,3,Returning_Visitor,FALSE,FALSE 0,0,0,0,0,0,0.2,0.2,0,0,Nov,1,1,9,2,Returning_Visitor,FALSE,FALSE 7,286.8333333,0,0,49,1493.695238,0.005390836,0.015750591,0,0,Dec,3,2,3,10,Returning_Visitor,TRUE,FALSE 5,56.6,0,0,55,1058.338095,0,0.007142857,37.21183634,0,Nov,2,2,1,3,Returning_Visitor,FALSE,TRUE 0,0,0,0,5,60,0,0.053333333,0,0,Dec,2,2,1,20,Returning_Visitor,FALSE,FALSE 3,82.5,1,56.5,54,2295,0.007272727,0.014545455,0,0,Dec,1,1,1,10,Returning_Visitor,TRUE,FALSE 0,0,0,0,9,198.1333333,0,0.053333333,0,0,Nov,1,1,9,2,Returning_Visitor,FALSE,FALSE 0,0,0,0,35,729.2916667,0,0.009387755,0,0,Dec,2,2,3,2,Returning_Visitor,FALSE,FALSE 4,49.5,0,0,4,180.5,0.028571429,0.028571429,0,0,Dec,3,2,6,10,Returning_Visitor,FALSE,FALSE 0,0,0,0,38,1185.986275,0,0.009501385,0,0,Nov,1,1,4,3,Returning_Visitor,FALSE,FALSE 0,0,0,0,70,1473.701515,0.008695652,0.032370528,0,0,Nov,1,1,1,2,Returning_Visitor,FALSE,FALSE 1,43.5,0,0,25,1055.583333,0.007407407,0.022839506,0,0,Dec,1,1,1,2,Returning_Visitor,FALSE,FALSE 0,0,0,0,29,1211.166667,0,0.006896552,0,0,Nov,2,2,4,2,New_Visitor,FALSE,TRUE 0,0,0,0,1,0,0.2,0.2,0,0,Dec,2,2,1,13,New_Visitor,FALSE,FALSE 0,0,0,0,6,82.5,0,0.033333333,0,0,Dec,2,2,1,2,Returning_Visitor,FALSE,FALSE 11,524.75,3,258.625,197,6309.427034,0.006113821,0.011878229,3.364060957,0,Nov,2,10,1,2,Returning_Visitor,FALSE,TRUE 0,0,0,0,8,163.5,0.1,0.125,0,0,Nov,3,2,1,10,Returning_Visitor,FALSE,FALSE 1,26.5,0,0,71,1260.441758,0.001449275,0.008792271,3.74015068,0,Nov,2,2,1,1,Returning_Visitor,FALSE,TRUE 0,0,0,0,1,0,0.2,0.2,0,0,Dec,3,2,4,2,Returning_Visitor,FALSE,FALSE 1,23,0,0,16,281.75,0,0.014285714,0,0,Nov,8,2,1,2,Returning_Visitor,FALSE,FALSE 5,3398.75,6,2549.375,449,63973.52223,0.000764406,0.02770134,0,0,Dec,2,2,1,2,Returning_Visitor,FALSE,FALSE 0,0,0,0,15,372.0833333,0,0.04,0,0,Nov,2,2,1,2,New_Visitor,TRUE,FALSE 3,65.92857143,0,0,17,557.6869048,0,0.009803922,25.27760515,0,Dec,2,2,2,2,Returning_Visitor,FALSE,TRUE 0,0,0,0,3,0,0.2,0.2,0,0,Dec,3,2,6,1,Returning_Visitor,FALSE,FALSE 0,0,0,0,11,170.5,0,0.018181818,0,0,Nov,1,8,1,11,Returning_Visitor,TRUE,FALSE 3,15,0,0,102,4021.172222,0,0.01,0,0,Nov,2,2,1,2,Returning_Visitor,TRUE,FALSE 0,0,0,0,5,81.08333333,0.04,0.05,0,0,Dec,3,2,1,2,New_Visitor,FALSE,FALSE 2,83,0,0,15,630,0,0.003333333,42.23025869,0,Dec,4,1,3,1,Returning_Visitor,TRUE,TRUE 0,0,0,0,1,0,0.2,0.2,0,0,Dec,1,2,1,1,Returning_Visitor,FALSE,FALSE 1,12,0,0,63,2138.383333,0,0.012282691,0,0,Dec,2,2,7,1,Returning_Visitor,FALSE,FALSE 0,0,0,0,143,4977.850893,0.008732394,0.038977353,0,0,Nov,2,2,3,1,Returning_Visitor,FALSE,FALSE 0,0,0,0,26,1312.333333,0.036538462,0.054230769,0,0,Nov,3,2,1,13,Returning_Visitor,FALSE,FALSE 0,0,0,0,3,61.75,0,0.066666667,0,0,Nov,2,10,1,3,Returning_Visitor,FALSE,FALSE 0,0,0,0,14,1131.208333,0,0.015384615,94.19490268,0,Nov,3,2,3,11,Returning_Visitor,TRUE,TRUE 0,0,0,0,2,17,0,0.1,0,0,Dec,2,2,1,10,Returning_Visitor,FALSE,FALSE 0,0,0,0,2,57.5,0,0.1,0,0,Dec,1,1,4,2,Returning_Visitor,FALSE,FALSE 4,67.5,0,0,25,475.65,0.014285714,0.042857143,0,0,Nov,1,1,3,3,Returning_Visitor,FALSE,FALSE 4,75.75,0,0,71,3534.298611,0.00890411,0.023832104,0,0,Nov,2,2,1,2,Returning_Visitor,FALSE,FALSE 0,0,0,0,18,1093.083333,0.011111111,0.011481481,0,0,Nov,3,2,3,1,Returning_Visitor,FALSE,FALSE 0,0,0,0,10,516.75,0.02,0.08,0,0,Dec,4,2,3,1,Returning_Visitor,FALSE,FALSE 2,90.17857143,2,88.5,19,1214.307692,0.022727273,0.031677489,15.52703405,0,Nov,1,1,4,2,Returning_Visitor,FALSE,TRUE 3,123.5,1,0,22,759.5833333,0,0.0096,46.6785275,0,Nov,1,1,3,2,Returning_Visitor,FALSE,TRUE 8,825,1,55.5,148,8629.783691,0.0095,0.019155126,1.60801626,0,Nov,1,1,2,2,Returning_Visitor,FALSE,FALSE 8,935.1,1,21,172,10524.35936,0.005046115,0.01576463,2.924161864,0,Nov,3,2,1,3,Returning_Visitor,FALSE,TRUE 2,1145.25,3,91.30357143,46,2756.19246,0,0.01765792,0,0,Nov,3,2,1,2,Returning_Visitor,TRUE,FALSE 1,35.75,0,0,7,1173,0,0.028571429,76.7809411,0,Nov,2,4,9,2,Returning_Visitor,FALSE,FALSE 6,65,0,0,4,40,0,0.014285714,0,0,Dec,2,2,1,2,New_Visitor,FALSE,FALSE 2,50.5,0,0,29,2147.375,0,0.006896552,0,0,Dec,2,2,7,2,New_Visitor,FALSE,FALSE 0,0,0,0,20,1175.488095,0,0.021052632,0,0,Dec,3,2,3,13,Returning_Visitor,FALSE,FALSE 0,0,0,0,5,404.5,0,0.05,0,0,Nov,2,2,1,13,Returning_Visitor,FALSE,FALSE 0,0,0,0,2,264.75,0,0.1,0,0,Nov,1,1,9,3,Returning_Visitor,FALSE,FALSE 7,169.5,0,0,50,1557.916667,0.003773585,0.013207547,0,0,Dec,1,1,3,2,Returning_Visitor,FALSE,FALSE 1,49.5,0,0,14,207.5416667,0,0.015384615,0,0,Dec,2,4,1,2,New_Visitor,FALSE,FALSE 0,0,0,0,8,135.25,0,0.05,0,0,Nov,2,2,9,1,Returning_Visitor,FALSE,FALSE 0,0,0,0,8,493.75,0,0.05,0,0,Nov,2,2,4,20,Other,FALSE,FALSE 2,256.5,0,0,10,117.25,0.110714286,0.130952381,0,0,Nov,2,2,7,11,Returning_Visitor,TRUE,TRUE 1,19,0,0,10,349.8333333,0,0.00625,0,0,Dec,3,2,8,8,New_Visitor,FALSE,FALSE 0,0,0,0,35,1579.333333,0.005714286,0.02,0,0,Nov,2,2,1,1,New_Visitor,FALSE,FALSE 0,0,1,6,71,1375.264286,0.005633803,0.017425336,0,0,Dec,2,2,3,2,New_Visitor,FALSE,TRUE 4,141.5,0,0,3,59.5,0,0.025,0,0,Dec,1,1,2,8,New_Visitor,FALSE,FALSE 1,90,0,0,37,592.8375,0.009817814,0.051919272,0,0,Nov,2,2,1,11,Returning_Visitor,FALSE,FALSE 0,0,0,0,11,499,0,0.018181818,0,0,Dec,4,1,1,2,Returning_Visitor,FALSE,FALSE 2,28,0,0,27,434.5,0,0.006896552,0,0,Nov,8,2,7,2,New_Visitor,FALSE,TRUE 0,0,0,0,23,1375.916667,0.039130435,0.093478261,0,0,Nov,3,2,8,13,Returning_Visitor,FALSE,FALSE 0,0,0,0,7,87,0,0.028571429,0,0,Dec,8,13,9,20,Other,FALSE,FALSE 5,150,0,0,135,4966.520661,0.003649635,0.011277952,0,0,Nov,1,1,2,10,Returning_Visitor,TRUE,FALSE 6,416,2,121,94,2881.684524,0.006122449,0.025340136,0,0,Nov,3,2,2,13,Returning_Visitor,FALSE,FALSE 1,20,0,0,21,442,0.008695652,0.020289855,42.28127491,0,Nov,2,2,1,13,Returning_Visitor,FALSE,FALSE 10,247.15,3,788,111,2556.373335,0.000423729,0.00814597,2.514977953,0,Nov,2,2,1,3,Returning_Visitor,FALSE,FALSE 9,333.8674242,4,121.625,29,1406.917424,0.017567568,0.021302211,20.74810067,0,Nov,3,2,6,1,Returning_Visitor,FALSE,FALSE 0,0,0,0,26,955.375,0,0.008333333,0,0,Dec,2,2,7,2,New_Visitor,TRUE,FALSE 9,192.9204545,2,114.5,60,1394.236526,0,0.003030303,63.64653548,0,Dec,2,2,7,2,Returning_Visitor,FALSE,TRUE 0,0,0,0,22,704,0,0.004761905,0,0,Nov,2,2,2,2,Returning_Visitor,FALSE,FALSE 0,0,0,0,7,197,0.028571429,0.042857143,0,0,Dec,3,2,9,2,Returning_Visitor,FALSE,FALSE 12,171.2916667,2,55.5,213,7443.407447,0.003958333,0.014633794,0,0,Nov,2,2,1,1,Returning_Visitor,FALSE,FALSE 0,0,0,0,10,455,0,0.022222222,25.7526389,0,Dec,1,1,2,2,New_Visitor,FALSE,TRUE 1,0,2,57.5,63,1980.238095,0,0.015025253,0,0,Nov,3,2,1,2,New_Visitor,TRUE,FALSE 5,46.5,0,0,3,26,0,0.028571429,0,0,Nov,2,2,1,2,Returning_Visitor,FALSE,TRUE 1,81,0,0,15,914.5,0,0.014285714,56.5802171,0,Nov,2,2,2,11,Returning_Visitor,TRUE,TRUE 0,0,0,0,20,804.25,0.015,0.028333333,0,0,Nov,3,2,8,1,Returning_Visitor,FALSE,FALSE 4,49.85714286,0,0,94,4804.834524,0.008843537,0.023561816,0,0,Nov,2,2,1,2,Returning_Visitor,FALSE,TRUE 0,0,0,0,14,536,0,0.01025641,0,0,Dec,1,1,3,10,Returning_Visitor,FALSE,FALSE 0,0,0,0,3,0,0.2,0.2,0,0,Nov,1,1,4,10,Returning_Visitor,TRUE,FALSE 2,41.75,2,30,25,596.075,0.001538462,0.02014652,0,0,Nov,2,2,3,1,Returning_Visitor,FALSE,FALSE 0,0,0,0,28,2076.966667,0.003846154,0.017948718,0,0,Dec,2,2,8,2,Returning_Visitor,FALSE,FALSE 2,126.75,0,0,22,2281.583333,0.02969697,0.043686869,0,0,Dec,3,2,2,3,Returning_Visitor,FALSE,FALSE 1,2,0,0,143,5149.434919,0.004195804,0.014489415,0,0,Nov,2,2,1,1,Returning_Visitor,TRUE,FALSE 0,0,1,41.5,98,2400.037843,0.000183655,0.011646771,0,0,Nov,2,2,1,2,Returning_Visitor,FALSE,TRUE 2,67,0,0,10,234.6384615,0.054545455,0.064935065,0,0,Nov,3,2,3,13,Returning_Visitor,TRUE,FALSE 0,0,0,0,21,947.3928571,0.01,0.033333333,0,0,Dec,1,1,1,10,Returning_Visitor,TRUE,FALSE 0,0,0,0,5,186.5,0,0.04,0,0,Nov,2,2,1,2,Returning_Visitor,FALSE,FALSE 0,0,1,62.5,67,2876.441667,0.015196078,0.027326203,0,0,Dec,2,2,1,1,Returning_Visitor,FALSE,FALSE 0,0,0,0,10,478.5,0,0.02,0,0,Nov,4,1,1,2,New_Visitor,FALSE,FALSE 3,29.75,0,0,12,398.9803922,0,0.005555556,50.51764193,0,Nov,2,2,2,2,New_Visitor,FALSE,TRUE 4,122.25,0,0,45,2007.522619,0.013043478,0.018657031,0,0,Nov,1,1,4,2,Returning_Visitor,FALSE,FALSE 7,60.29166667,0,0,8,108.5,0.013333333,0.048666667,0,0,Dec,2,2,2,6,Returning_Visitor,FALSE,FALSE 0,0,0,0,9,246.5,0.125925926,0.138888889,0,0,Nov,1,1,3,1,Returning_Visitor,FALSE,FALSE 7,131.8333333,0,0,63,2074.611111,0.010793651,0.02000962,0,0,Dec,3,2,3,10,Returning_Visitor,FALSE,FALSE 0,0,0,0,6,291,0,0.033333333,0,0,Nov,2,6,7,1,Returning_Visitor,FALSE,FALSE 1,4,4,123.3333333,79,2387.139881,0.028733963,0.046198341,0,0,Nov,3,2,1,13,Returning_Visitor,TRUE,FALSE 1,56.5,0,0,16,541.875,0,0.0125,15.14864831,0,Nov,2,4,9,8,New_Visitor,FALSE,TRUE 0,0,0,0,5,187.5,0,0.04,0,0,Dec,2,5,8,2,Returning_Visitor,TRUE,FALSE 0,0,0,0,28,341.4583333,0,0.007407407,0,0,Dec,2,4,3,10,New_Visitor,FALSE,FALSE 0,0,0,0,12,173.5,0,0.008333333,0,0,Dec,2,2,2,2,Returning_Visitor,FALSE,FALSE 2,84,0,0,30,597.6330532,0.006666667,0.005925926,0,0,Nov,1,8,1,11,Returning_Visitor,TRUE,FALSE 0,0,0,0,4,69,0,0.05,0,0,Nov,3,2,1,3,Returning_Visitor,FALSE,FALSE 3,548.5,0,0,9,117,0,0.016666667,0,0,Nov,2,4,1,2,Returning_Visitor,TRUE,FALSE 0,0,0,0,10,294.75,0,0.03,0,0,Dec,1,1,3,8,New_Visitor,FALSE,FALSE 3,19.25,0,0,8,713.5833333,0,0.046969697,0,0,Nov,3,2,1,10,Returning_Visitor,TRUE,FALSE 1,2,0,0,79,1319.547421,0.005128205,0.00962556,0,0,Nov,1,1,1,3,Returning_Visitor,FALSE,FALSE 5,63,0,0,27,458.5,0,0.005952381,0,0,Dec,2,2,6,2,New_Visitor,TRUE,FALSE 0,0,0,0,3,41.5,0.066666667,0.133333333,0,0,Nov,2,2,1,1,Returning_Visitor,TRUE,FALSE 0,0,0,0,14,267.25,0,0.021428571,0,0,Nov,3,2,1,11,New_Visitor,TRUE,FALSE 0,0,0,0,17,1059.575,0,0.011764706,62.76294758,0,Nov,2,2,1,2,New_Visitor,FALSE,TRUE 0,0,0,0,21,173,0.00952381,0.023809524,0,0,Dec,1,2,3,3,Returning_Visitor,FALSE,FALSE 0,0,0,0,1,0,0.2,0.2,0,0,Nov,2,2,1,1,Returning_Visitor,FALSE,FALSE 2,14,0,0,65,2130.447802,0.016923077,0.024589744,20.60235987,0,Nov,3,2,1,1,Returning_Visitor,FALSE,FALSE 2,58.5,0,0,37,1351.908333,0.005405405,0.018670099,5.464243549,0,Dec,2,2,3,1,Returning_Visitor,FALSE,FALSE 0,0,0,0,1,24.5,0,0.1,0,0,Dec,2,4,3,1,Returning_Visitor,FALSE,FALSE 0,0,0,0,8,561.5,0,0.05,0,0,Dec,2,4,7,20,Returning_Visitor,FALSE,FALSE 0,0,0,0,39,805.7916667,0.007692308,0.013846154,0,0,Dec,2,2,4,1,Returning_Visitor,FALSE,FALSE 5,140.5833333,2,87,69,1918.902722,0.003424658,0.021575342,0,0,Nov,2,2,3,2,Returning_Visitor,FALSE,FALSE 1,492.1666667,0,0,63,4518.297619,0.012698413,0.039761905,8.137274079,0,Nov,2,2,2,1,Returning_Visitor,FALSE,TRUE 0,0,0,0,33,2199.5,0,0.008080808,0,0,Dec,2,2,6,2,New_Visitor,FALSE,FALSE 4,63.875,1,0,43,1329.686508,0.015646259,0.036603499,0,0,Nov,3,2,1,2,Returning_Visitor,FALSE,TRUE 4,104,0,0,8,243.5,0,0.016666667,0,0,Dec,2,2,3,2,New_Visitor,FALSE,FALSE 0,0,0,0,8,166,0.0625,0.116666667,0,0,Nov,3,2,6,3,Returning_Visitor,FALSE,FALSE 0,0,0,0,8,184.375,0,0.013333333,0,0,Nov,1,2,9,2,Returning_Visitor,FALSE,FALSE 1,51.5,0,0,21,730.15,0,0.012121212,0,0,Nov,2,2,4,2,New_Visitor,FALSE,FALSE 0,0,0,0,1,35.25,0,0.066666667,0,0,Nov,1,1,3,2,Returning_Visitor,FALSE,FALSE 0,0,0,0,52,2180.217803,0,0.012515263,0,0,Dec,2,2,4,2,Returning_Visitor,FALSE,FALSE 6,109.6666667,0,0,7,86.16666667,0.006666667,0.051111111,0,0,Dec,2,5,7,2,Returning_Visitor,FALSE,TRUE 0,0,0,0,11,1027,0,0.054545455,0,0,Nov,2,2,2,3,Returning_Visitor,FALSE,FALSE 0,0,0,0,13,173.25,0.046153846,0.061538462,0,0,Dec,2,2,3,1,Returning_Visitor,FALSE,FALSE 4,588.2972222,0,0,13,631.0674603,0.007058824,0.033529412,0,0,Nov,2,12,7,2,Returning_Visitor,FALSE,FALSE 1,9,1,29.5,23,1157.583333,0.007692308,0.053846154,0,0,Nov,2,2,7,2,Returning_Visitor,FALSE,FALSE 0,0,0,0,4,129.5,0,0.05,0,0,Dec,8,13,9,20,Other,FALSE,FALSE 6,72.01960784,4,343,157,4461.28524,8.08E-05,0.007018826,31.85608032,0,Dec,2,4,3,2,Returning_Visitor,FALSE,TRUE 0,0,0,0,5,1377.25,0.18,0.186666667,0,0,Nov,3,2,6,1,Returning_Visitor,FALSE,FALSE 6,265.0416667,0,0,59,2934.236722,0.006666667,0.022333333,6.099899016,0,Nov,1,2,1,2,Returning_Visitor,FALSE,FALSE 2,35.5,0,0,29,3296.65,0.009677419,0.021935484,18.25059634,0,Dec,4,2,9,8,Returning_Visitor,FALSE,FALSE 0,0,0,0,37,1065.25641,0.020767196,0.054225865,0,0,Dec,1,1,8,1,Returning_Visitor,TRUE,FALSE 5,157.5,3,433.5,9,599.5,0,0.020833333,0,0,Nov,1,1,1,2,New_Visitor,FALSE,FALSE 4,537.5,0,0,12,748.9583333,0.0125,0.021875,0,0,Dec,2,5,7,2,Returning_Visitor,FALSE,FALSE 0,0,0,0,1,0,0.2,0.2,0,0,Dec,2,2,8,1,Returning_Visitor,FALSE,FALSE 0,0,0,0,25,566.8833333,0,0.035619048,0,0,Nov,2,2,3,1,Returning_Visitor,TRUE,FALSE 0,0,2,89,188,5728.673611,0.003409759,0.01638112,0,0,Nov,2,2,1,2,Returning_Visitor,FALSE,FALSE 0,0,0,0,7,203.25,0,0.028571429,0,0,Nov,1,1,3,3,Returning_Visitor,FALSE,FALSE 4,171.5,0,0,75,2416.060484,0.007699443,0.036664764,0,0,Nov,2,2,2,1,Returning_Visitor,FALSE,TRUE 2,21.25,0,0,15,305.2083333,0,0.010784314,0,0,Nov,2,4,7,2,Returning_Visitor,FALSE,FALSE 0,0,0,0,21,335.4166667,0,0.018170426,0,0,Nov,1,1,1,10,Returning_Visitor,FALSE,FALSE 0,0,0,0,33,960.125,0.006060606,0.019292929,0,0,Dec,2,6,1,3,Returning_Visitor,TRUE,FALSE 4,191.1666667,0,0,108,4656.331746,0.006456456,0.018326694,0,0,Nov,3,2,1,10,Returning_Visitor,FALSE,FALSE 2,98.75,0,0,24,683.1653846,0,0.011111111,0,0,Dec,2,2,1,2,Returning_Visitor,FALSE,FALSE 0,0,0,0,3,823.5,0,0.066666667,0,0,Dec,2,2,3,8,Returning_Visitor,FALSE,FALSE 2,386,0,0,72,712.2960784,0,0.000913242,0,0,Nov,2,2,4,2,New_Visitor,FALSE,FALSE 0,0,0,0,10,279.3333333,0.02,0.025,0,0,Nov,3,2,9,2,New_Visitor,FALSE,FALSE 0,0,0,0,33,2193.541667,0,0.008602151,0,0,Nov,2,2,2,6,Returning_Visitor,FALSE,FALSE 0,0,0,0,7,611.0833333,0,0.016666667,0,0,Dec,3,2,6,8,New_Visitor,FALSE,FALSE 0,0,0,0,2,14,0,0.1,0,0,Dec,2,2,2,2,Returning_Visitor,FALSE,FALSE 1,24.5,0,0,27,1792.466667,0,0.01469428,0,0,Dec,2,2,2,2,Returning_Visitor,FALSE,FALSE 8,162.4166667,2,102,25,357.1666667,0.007407407,0.015873016,0,0,Dec,4,1,3,2,Returning_Visitor,TRUE,FALSE 0,0,0,0,3,1666.5,0,0.033333333,0,0,Nov,2,5,3,3,Returning_Visitor,FALSE,FALSE 0,0,0,0,12,846.25,0,0.018181818,40.4014481,0,Dec,2,6,1,6,New_Visitor,FALSE,TRUE 0,0,0,0,6,129.5,0,0.033333333,0,0,Dec,2,2,1,11,New_Visitor,FALSE,FALSE 8,156.25,0,0,48,1481.108333,0.003773585,0.008176101,0,0,Dec,2,4,4,1,Returning_Visitor,FALSE,FALSE 1,95.33333333,0,0,16,819.3333333,0,0.015625,25.25564195,0,Dec,2,2,7,2,New_Visitor,FALSE,TRUE 1,51.5,0,0,12,549.25,0,0.015384615,0,0,Dec,2,2,2,2,New_Visitor,FALSE,FALSE 1,33.5,0,0,34,1181.85119,0,0.03515625,0,0,Nov,1,1,1,2,Returning_Visitor,FALSE,TRUE 0,0,0,0,9,355.5,0.022222222,0.044444444,0,0,Nov,3,2,1,3,Returning_Visitor,FALSE,FALSE 0,0,0,0,2,0,0.2,0.2,0,0,Nov,3,2,7,3,Returning_Visitor,FALSE,FALSE 0,0,0,0,18,230.1666667,0.033333333,0.038888889,0,0,Dec,2,2,1,1,Returning_Visitor,FALSE,FALSE 0,0,2,193.5,29,2141.902778,0.029032258,0.042319508,0,0,Dec,2,2,1,3,Returning_Visitor,FALSE,FALSE 0,0,0,0,1,0,0.2,0.2,0,0,Nov,1,1,1,3,Returning_Visitor,FALSE,FALSE 7,490.5,0,0,43,1286.916667,0,0.01037037,0,0,Dec,2,2,3,1,New_Visitor,FALSE,FALSE 5,191.25,0,0,27,1109.25,0.013793103,0.024137931,9.022741507,0,Nov,2,6,1,1,Returning_Visitor,FALSE,FALSE 0,0,0,0,1,0,0.2,0.2,0,0,Nov,2,7,3,1,Returning_Visitor,FALSE,FALSE 4,60.16666667,3,164.5,159,5608.913545,0.002453988,0.018075125,2.702517858,0,Dec,2,2,3,1,Returning_Visitor,FALSE,TRUE 9,234.4333333,0,0,20,590.5833333,0.025925926,0.040922619,0,0,Nov,2,2,7,1,Returning_Visitor,FALSE,FALSE 7,82.76666667,1,47,391,15025.23302,0.001515152,0.010140622,1.271406629,0,Nov,2,2,1,1,Returning_Visitor,FALSE,FALSE 1,4,1,12,16,1012.964286,0.011764706,0.023529412,0,0,Dec,2,2,7,10,Returning_Visitor,FALSE,FALSE 5,70.33333333,0,0,29,1049.313889,0.025595238,0.054241071,0,0,Nov,3,2,3,2,Returning_Visitor,TRUE,FALSE 3,68.58333333,0,0,72,1781.191667,0.008219178,0.015174871,0,0,Nov,1,1,1,8,Returning_Visitor,TRUE,FALSE 6,294.0705128,6,1343.25,51,3472.870513,0.011866969,0.037170572,0,0,Nov,2,2,1,2,Returning_Visitor,FALSE,FALSE 7,147.1666667,0,0,10,146,0.053333333,0.068888889,0,0,Dec,1,1,2,1,Returning_Visitor,FALSE,FALSE 0,0,0,0,4,562,0.075,0.1,0,0,Dec,3,2,3,13,Returning_Visitor,FALSE,FALSE 0,0,0,0,37,1369.775,0,0.014814815,0,0,Dec,2,2,3,2,Returning_Visitor,FALSE,FALSE 15,317.0189394,3,126.75,238,9956.225533,0.0052,0.016955684,6.556237434,0,Nov,2,4,3,1,Returning_Visitor,TRUE,TRUE 0,0,0,0,7,16,0,0.033333333,0,0,Dec,1,1,1,2,Returning_Visitor,TRUE,FALSE 5,190,0,0,12,307.6666667,0,0.013333333,0,0,Nov,2,2,9,8,New_Visitor,FALSE,FALSE 0,0,0,0,20,2403.25,0.01,0.03,0,0,Dec,2,2,3,2,New_Visitor,FALSE,FALSE 0,0,0,0,1,0,0.2,0.2,0,0,Nov,2,6,2,1,Returning_Visitor,FALSE,FALSE 0,0,0,0,18,773.2,0,0.0125,46.4748023,0,Nov,1,1,3,1,New_Visitor,FALSE,TRUE 5,110.95,0,0,23,448.25,0.024,0.043333333,0,0,Nov,3,2,8,11,Returning_Visitor,FALSE,FALSE 3,224.3,5,487,74,1845.958333,0.002531646,0.017679325,34.67841487,0,Nov,2,2,1,10,Returning_Visitor,TRUE,TRUE 0,0,4,147.5,45,1424.839744,0.012244898,0.031739553,0,0,Dec,1,1,3,2,Returning_Visitor,TRUE,FALSE 0,0,0,0,1,0,0.2,0.2,0,0,Dec,1,1,1,1,Returning_Visitor,TRUE,FALSE 12,159.8958333,0,0,121,3648.5625,0.001927438,0.009409219,3.322866215,0,Nov,2,2,5,7,Returning_Visitor,FALSE,FALSE 0,0,0,0,7,300.5,0.085714286,0.114285714,0,0,Nov,2,2,1,13,Returning_Visitor,FALSE,FALSE 0,0,0,0,36,756.0416667,0.005555556,0.008950617,0,0,Nov,2,5,1,10,Returning_Visitor,FALSE,FALSE 0,0,0,0,8,642,0,0.028571429,109.1211555,0,Dec,2,2,3,2,New_Visitor,FALSE,TRUE 3,110.5357143,7,330.2666667,10,396.3452381,0.011111111,0.031834215,0,0,Nov,3,2,4,2,Returning_Visitor,FALSE,FALSE 1,79,0,0,22,1017.166667,0.013043478,0.022318841,0,0,Nov,1,1,1,2,Returning_Visitor,TRUE,FALSE 6,239.3333333,0,0,31,1875.169444,0,0.025324675,21.85958403,0,Nov,1,1,1,2,Returning_Visitor,FALSE,FALSE 2,16,0,0,43,821.275,0.015873016,0.024900794,0,0,Nov,1,1,1,3,Returning_Visitor,FALSE,FALSE 1,390,0,0,26,1325.833333,0.007692308,0.025641026,0,0,Dec,2,2,1,1,Returning_Visitor,TRUE,FALSE 0,0,0,0,16,228.625,0,0.015,0,0,Dec,2,2,1,2,New_Visitor,FALSE,FALSE 0,0,0,0,38,1517.416739,0,0.004122807,0,0,Nov,4,2,4,2,Returning_Visitor,FALSE,FALSE 0,0,0,0,4,44.5,0,0.05,0,0,Dec,2,2,8,2,Returning_Visitor,FALSE,FALSE 4,69.66666667,1,23,161,6524.035369,0.005050505,0.013944826,45.41882207,0,Nov,2,2,3,2,Returning_Visitor,FALSE,TRUE 0,0,0,0,1,0,0,0.1,0,0,Nov,2,6,1,3,Returning_Visitor,FALSE,FALSE 4,47.5,0,0,37,839.2083333,0.005263158,0.017105263,12.96520279,0,Nov,1,1,1,2,Returning_Visitor,FALSE,TRUE 0,0,0,0,16,426.325,0,0.011904762,0,0,Nov,1,1,4,3,Returning_Visitor,FALSE,FALSE 1,11.16666667,3,63.5,28,2245.333333,0.002580645,0.028494624,36.2003121,0,Nov,4,2,1,11,Returning_Visitor,FALSE,TRUE 0,0,0,0,8,428.5,0.025,0.0375,0,0,Dec,2,2,1,2,Returning_Visitor,TRUE,FALSE 3,82.5,0,0,12,221.125,0.015384615,0.005128205,0,0,Nov,1,2,3,8,Returning_Visitor,FALSE,FALSE 0,0,0,0,4,67,0,0.05,0,0,Dec,2,2,9,2,Returning_Visitor,FALSE,FALSE 0,0,0,0,14,731.5,0.014285714,0.042857143,0,0,Nov,2,2,4,2,New_Visitor,FALSE,TRUE 11,752.3833333,3,14,108,4575.477552,0.016945141,0.026110286,3.401438792,0,Nov,1,1,1,2,Returning_Visitor,FALSE,FALSE 3,66.5,3,65.75,152,3684.321458,0.005194805,0.010451453,12.64606692,0,Nov,2,2,1,2,Returning_Visitor,FALSE,TRUE 0,0,0,0,21,882.75,0,0.020408163,0,0,Dec,2,2,2,2,New_Visitor,TRUE,FALSE 0,0,2,6.333333333,16,301.3333333,0.033333333,0.088888889,0,0,Nov,2,2,3,2,Returning_Visitor,FALSE,FALSE 3,29,0,0,2,11,0.075,0.075,0,0,Dec,1,1,4,8,Returning_Visitor,FALSE,FALSE 11,233.475,0,0,118,4251.208187,0,0.012659622,14.67198629,0,Nov,2,2,1,2,Returning_Visitor,FALSE,TRUE 0,0,0,0,4,157.5,0,0.05,0,0,Nov,2,4,9,2,Returning_Visitor,FALSE,FALSE 0,0,0,0,12,148.75,0.02,0.056666667,0,0,Nov,1,1,1,3,Returning_Visitor,FALSE,FALSE 5,134.75,3,16,165,6323.853465,0.010654414,0.02804138,3.512852934,0,Nov,1,1,1,2,Returning_Visitor,FALSE,TRUE 4,93.5,0,0,293,12268.24477,0.002837838,0.01542982,2.164809711,0,Nov,2,2,1,2,Returning_Visitor,FALSE,TRUE 2,34,2,9,174,4994.24915,0.006025974,0.015842941,0,0,Nov,2,2,1,8,Returning_Visitor,FALSE,FALSE 5,46.5,0,0,17,219.5,0.011111111,0.022222222,0,0,Dec,3,2,2,13,Returning_Visitor,FALSE,FALSE 11,365.1166667,4,65.5,149,6082.873739,0.011520436,0.027243522,0,0,Nov,1,1,3,2,Returning_Visitor,FALSE,FALSE 8,111.5416667,3,226,68,1664.14881,0.002702703,0.021846847,6.693658292,0,Dec,2,1,1,2,Returning_Visitor,FALSE,TRUE 0,0,0,0,2,35,0,0.05,0,0,Nov,2,2,1,10,New_Visitor,FALSE,FALSE 11,118.1785714,2,91.5,162,5399.231269,0.004859086,0.014667832,14.77221948,0,Nov,2,2,4,10,Returning_Visitor,FALSE,TRUE 0,0,0,0,6,50.83333333,0.033333333,0.05,0,0,Nov,1,1,3,2,New_Visitor,FALSE,FALSE 0,0,0,0,68,1723.753711,0.011940299,0.023681592,0,0,Nov,3,2,5,2,Returning_Visitor,FALSE,FALSE 4,643.8333333,0,0,17,508.25,0.031578947,0.052631579,0,0,Nov,2,4,1,1,Returning_Visitor,FALSE,FALSE 0,0,0,0,3,65.5,0.066666667,0.1,0,0,Nov,3,2,1,13,Returning_Visitor,FALSE,FALSE 0,0,0,0,20,968.75,0.015789474,0.018421053,73.98253901,0,Dec,1,2,1,1,Returning_Visitor,FALSE,TRUE 1,56.5,0,0,19,1089.75,0,0.003508772,24.872085,0,Dec,2,2,7,2,Returning_Visitor,FALSE,TRUE 7,165.9166667,2,150.5,42,3127.078788,0.009614512,0.021287825,0,0,Nov,1,1,3,2,Returning_Visitor,FALSE,FALSE 0,0,0,0,15,418.375,0,0.013333333,13.18554515,0,Dec,3,2,1,2,New_Visitor,TRUE,TRUE 0,0,0,0,53,2052.125,0,0.003921569,52.49862029,0,Nov,2,4,2,2,New_Visitor,TRUE,TRUE 0,0,2,12,0,0,0,0.1,0,0,Dec,2,2,1,2,Returning_Visitor,FALSE,FALSE 0,0,0,0,18,545.5,0,0.011111111,0,0,Nov,2,2,1,3,Returning_Visitor,FALSE,FALSE 3,37,0,0,62,1193,0.003076923,0.015384615,21.6018009,0,Nov,2,2,1,1,Returning_Visitor,FALSE,TRUE 3,143,0,0,17,331.8375,0.033333333,0.051234568,0,0,Dec,2,2,1,1,Returning_Visitor,TRUE,FALSE 0,0,0,0,36,2107.583333,0.014814815,0.03287037,0,0,Dec,3,2,1,2,Returning_Visitor,FALSE,FALSE 0,0,0,0,6,222,0,0.04,87.9029606,0,Dec,8,13,9,20,Other,FALSE,TRUE 1,13,1,28.5,39,1028.775,0,0.010436508,0,0,Dec,2,2,3,2,Returning_Visitor,FALSE,FALSE 8,993.0833333,1,28.5,90,2776.013582,0.002380952,0.017256944,0,0,Nov,3,2,3,2,Returning_Visitor,TRUE,FALSE 2,95,0,0,24,360.5416667,0.011538462,0.017948718,0,0,Dec,1,1,1,1,Returning_Visitor,FALSE,FALSE 0,0,2,50.5,95,4379.863158,0.006314433,0.033908935,0,0,Nov,2,2,1,2,Returning_Visitor,TRUE,FALSE 0,0,0,0,4,118.25,0,0.016666667,0,0,Dec,3,2,3,2,New_Visitor,FALSE,FALSE 0,0,0,0,19,347,0,0.010526316,0,0,Dec,2,2,2,1,Returning_Visitor,FALSE,FALSE 4,159,2,27.75,33,821.6825397,0.000617284,0.019343434,0,0,Dec,2,2,1,2,Returning_Visitor,FALSE,TRUE 0,0,0,0,1,0,0.2,0.2,0,0,Dec,1,1,1,3,New_Visitor,FALSE,FALSE 27,853.735949,2,126.5,584,24844.1562,0.00209938,0.009346937,4.511100422,0,Nov,2,4,3,8,Returning_Visitor,FALSE,FALSE 4,91,0,0,12,197,0,0.0375,0,0,Nov,2,2,1,2,New_Visitor,FALSE,FALSE 0,0,0,0,4,26,0,0.05,0,0,Nov,2,5,6,11,Returning_Visitor,TRUE,FALSE 0,0,3,75.5,60,2050.191803,0.008758782,0.028381703,0,0,Nov,3,2,1,3,Returning_Visitor,FALSE,FALSE 4,84.75,0,0,46,962.9011905,0.004444444,0.01212963,0,0,Dec,1,1,6,2,Returning_Visitor,FALSE,FALSE 1,183,0,0,28,586.3722222,0,0.004021164,0,0,Dec,3,2,6,10,Returning_Visitor,FALSE,TRUE 0,0,0,0,8,284.5,0,0.025,0,0,Dec,2,2,3,1,Returning_Visitor,FALSE,FALSE 0,0,0,0,1,0,0.2,0.2,0,0,Nov,3,2,4,3,Returning_Visitor,FALSE,FALSE 0,0,0,0,1,0,0.2,0.2,0,0,Nov,3,2,3,3,Returning_Visitor,FALSE,FALSE 1,0,0,0,65,732.0916667,0.00625,0.0203125,2.734351538,0,Dec,2,2,1,2,Returning_Visitor,TRUE,TRUE 0,0,0,0,2,0,0.2,0.2,0,0,Nov,1,1,1,3,Returning_Visitor,TRUE,FALSE 0,0,0,0,3,59,0,0.066666667,0,0,Dec,1,1,3,2,Other,FALSE,FALSE 2,70.5,0,0,20,855.6666667,0,0.013157895,0,0,Nov,2,2,9,20,New_Visitor,FALSE,FALSE 1,111,0,0,13,274.5833333,0,0.016666667,0,0,Nov,1,1,1,2,New_Visitor,FALSE,FALSE 0,0,0,0,5,111,0,0.04,0,0,Nov,2,2,1,2,New_Visitor,FALSE,FALSE 1,4,0,0,37,1828.375,0,0.013888889,8.258088766,0,Nov,4,1,2,3,Returning_Visitor,FALSE,TRUE 0,0,0,0,5,82,0,0.04,0,0,Nov,2,2,9,8,Returning_Visitor,FALSE,FALSE 0,0,0,0,8,247.5,0,0.00952381,0,0,Nov,2,2,9,2,New_Visitor,FALSE,TRUE 1,4,0,0,13,2123.5,0.015384615,0.030769231,42.74590431,0,Nov,2,2,1,3,Returning_Visitor,FALSE,TRUE 6,189.25,0,0,25,635.4916667,0.006666667,0.010222222,10.58098677,0,Nov,3,2,3,8,Returning_Visitor,TRUE,TRUE 5,60.5,0,0,5,50,0,0.025,0,0,Dec,8,13,9,20,Other,TRUE,TRUE 1,18,0,0,24,1332.25,0,0.008695652,0,0,Dec,2,2,1,2,New_Visitor,FALSE,FALSE 8,121.75,0,0,154,5253.069444,0.001273885,0.008832272,0,0,Nov,4,1,1,8,Returning_Visitor,TRUE,FALSE 0,0,0,0,19,593.75,0,0.014035088,0,0,Nov,2,2,1,2,New_Visitor,FALSE,FALSE 7,145,1,37.5,130,5252.861501,0.004411765,0.012009804,0.680988542,0,Nov,2,2,4,2,Returning_Visitor,FALSE,TRUE 1,33.5,0,0,18,1169,0,0.011764706,54.38258659,0,Dec,2,4,7,2,New_Visitor,FALSE,TRUE 0,0,0,0,21,987.9,0.01,0.016666667,0,0,Dec,2,4,2,1,Returning_Visitor,FALSE,FALSE 0,0,0,0,59,2886.841667,0,0.025574713,0,0,Nov,2,2,3,2,Returning_Visitor,FALSE,FALSE 3,30.75,0,0,17,473.1666667,0.013333333,0.0225,0,0,Nov,1,2,3,3,Returning_Visitor,FALSE,FALSE 2,3,4,161.5,137,4791.460889,0.004373522,0.020956372,5.482895824,0,Nov,2,2,1,2,Returning_Visitor,FALSE,TRUE 6,30.7,3,45,38,1481.783333,0.012244898,0.029387755,0,0,Nov,2,2,4,10,Returning_Visitor,FALSE,TRUE 3,65,0,0,29,386.2621212,0,0.000666667,0,0,Nov,8,2,9,2,Other,FALSE,FALSE 0,0,0,0,31,1144.972222,0.006666667,0.008333333,77.04199767,0,Dec,2,2,7,2,New_Visitor,FALSE,TRUE 7,44.29545455,0,0,44,2747.616288,0.024205128,0.062408964,0,0,Nov,2,2,7,1,Returning_Visitor,FALSE,FALSE 0,0,0,0,7,271.5,0,0.028571429,0,0,Nov,8,2,3,2,Returning_Visitor,FALSE,FALSE 4,70.25,1,139.5,58,3022.370406,0,0.009611111,0,0,Nov,1,1,2,2,New_Visitor,FALSE,TRUE 6,479.25,0,0,51,2156.525403,0,0.011878788,6.229994739,0,Nov,2,2,1,2,Returning_Visitor,FALSE,TRUE 2,123.75,0,0,21,938.3333333,0.01,0.016666667,218.3951915,0,Dec,1,1,3,2,Returning_Visitor,FALSE,FALSE 8,355,0,0,50,1039.599344,0.000925926,0.010164456,0,0,Nov,1,2,3,10,Returning_Visitor,FALSE,FALSE 0,0,0,0,8,721.4166667,0,0.00952381,0,0,Nov,2,5,3,8,New_Visitor,TRUE,FALSE 0,0,0,0,89,3480.811706,0.002247191,0.014700375,0,0,Nov,4,2,1,1,Returning_Visitor,TRUE,TRUE 22,438.3309524,3,703.5,178,6241.687398,0.002878526,0.016885589,2.038399449,0,Nov,2,2,1,10,Returning_Visitor,FALSE,TRUE 0,0,0,0,32,749.25,0,0.00625,0,0,Dec,2,2,3,8,New_Visitor,FALSE,FALSE 1,78,0,0,11,225.1666667,0.02,0.026666667,0,0,Nov,1,1,4,2,Returning_Visitor,FALSE,FALSE 1,40.5,0,0,49,1587.25,0,0.011538462,35.79969486,0,Nov,2,2,3,13,Returning_Visitor,FALSE,TRUE 0,0,0,0,9,1397.666667,0.022222222,0.027777778,0,0,Nov,3,2,4,2,Returning_Visitor,FALSE,FALSE 3,201.15,0,0,17,506.1833333,0,0.001666667,0,0,Dec,1,8,3,11,New_Visitor,FALSE,FALSE 0,0,1,14,104,7181.877778,0.008843537,0.023737786,0,0,Nov,2,2,1,2,Returning_Visitor,FALSE,FALSE 1,75.16666667,0,0,20,1173.417614,0,0.002,21.41943708,0,Nov,3,2,8,11,Returning_Visitor,FALSE,TRUE 0,0,1,36.5,17,318.25,0.011111111,0.05,17.96069044,0,Nov,2,5,1,1,Returning_Visitor,TRUE,TRUE 2,11,0,0,51,2855.166667,0,0.012745098,0,0,Nov,2,2,4,3,Returning_Visitor,FALSE,FALSE 0,0,0,0,27,1165.95,0.025925926,0.044444444,0,0,Dec,2,5,3,1,Returning_Visitor,TRUE,FALSE 1,31.25,0,0,31,1470.208333,0,0.012222222,0,0,Nov,3,2,3,8,New_Visitor,FALSE,TRUE 6,160,0,0,55,1707.666667,0,0.005357143,24.10206377,0,Dec,2,2,1,2,New_Visitor,FALSE,TRUE 0,0,0,0,26,532,0.030769231,0.044230769,0,0,Nov,2,2,1,1,Returning_Visitor,FALSE,FALSE 0,0,0,0,90,2636.138889,0.023333333,0.045777778,0,0,Nov,2,2,1,2,Returning_Visitor,FALSE,FALSE 0,0,0,0,24,452.4166667,0,0.020833333,0,0,Nov,2,2,3,2,Returning_Visitor,TRUE,TRUE 0,0,0,0,20,447.047619,0.01,0.028888889,0,0,Dec,1,2,3,2,New_Visitor,FALSE,TRUE 5,125.75,0,0,44,1113.309524,0.008333333,0.015625,18.9160732,0,Dec,3,2,1,1,Returning_Visitor,FALSE,FALSE 0,0,0,0,1,0,0.2,0.2,0,0,Dec,2,2,4,8,New_Visitor,FALSE,FALSE 5,1652,0,0,15,446.375,0,0.005882353,0,0,Dec,8,13,9,20,Other,FALSE,FALSE 0,0,0,0,25,952.6777778,0,0.023541667,23.57537117,0,Nov,2,2,3,11,Returning_Visitor,FALSE,TRUE 0,0,0,0,12,276,0,0.016666667,0,0,Dec,2,2,8,2,New_Visitor,FALSE,FALSE 18,505.4,1,10,105,4434.281746,0.005042017,0.029692564,4.528329755,0,Dec,2,2,1,1,Returning_Visitor,FALSE,FALSE 3,155.5,2,121,8,311.25,0,0.01,0,0,Nov,2,2,3,10,Returning_Visitor,FALSE,FALSE 1,2,0,0,16,323.2333333,0,0.013333333,0,0,Dec,3,2,8,8,Returning_Visitor,FALSE,FALSE 0,0,4,102,93,2835.417641,0.00754386,0.037316087,0,0,Dec,2,2,4,1,Returning_Visitor,FALSE,FALSE 9,223,1,93,76,2893.940476,0.002409639,0.014859438,1.935491302,0,Nov,1,2,3,8,Returning_Visitor,FALSE,FALSE 2,48.66666667,4,155.3333333,20,540.75,0,0.004166667,38.88816225,0,Nov,2,2,3,2,Returning_Visitor,FALSE,TRUE 5,98.75,0,0,126,6085.002381,0,0.004264323,28.79847144,0,Nov,2,2,1,2,Returning_Visitor,FALSE,TRUE 0,0,0,0,22,1390.083333,0,0.004761905,0,0,Nov,2,2,4,8,New_Visitor,TRUE,FALSE 2,95,0,0,20,1031.083333,0,0.021052632,68.20038032,0,Dec,2,2,1,10,Returning_Visitor,FALSE,FALSE 0,0,0,0,19,539.15,0.010526316,0.003947368,0,0,Nov,3,2,3,3,Returning_Visitor,FALSE,FALSE 4,112.5,0,0,26,1657,0.007407407,0.020987654,5.237224754,0,Nov,2,2,1,13,Returning_Visitor,FALSE,FALSE 2,334.5,2,29,17,939.0833333,0,0.0225,18.22304387,0,Dec,2,2,1,6,Returning_Visitor,FALSE,TRUE 8,671.0833333,0,0,32,1403.828355,0,0.003153153,37.05600958,0,Nov,2,2,3,2,Returning_Visitor,FALSE,TRUE 0,0,0,0,3,46,0,0.033333333,0,0,Dec,1,2,3,2,Returning_Visitor,FALSE,FALSE 5,83,0,0,5,29.5,0.044444444,0.059259259,0,0,Dec,1,1,4,2,Returning_Visitor,TRUE,FALSE 1,28.25,0,0,30,1862.333333,0,0.026436782,0,0,Nov,3,2,1,13,Returning_Visitor,FALSE,FALSE 0,0,0,0,18,894.5809524,0,0.007142857,0,0,Dec,3,2,4,6,Returning_Visitor,FALSE,FALSE 0,0,0,0,15,475.6666667,0.013333333,0.013333333,0,0,Dec,1,1,3,8,New_Visitor,FALSE,FALSE 12,356.125,0,0,44,2187.338725,0.005102041,0.029042386,0,0,Dec,1,1,8,1,Returning_Visitor,FALSE,FALSE 7,144.4615385,4,167.1666667,111,2312.337853,0.005128205,0.013887559,8.191922892,0,Nov,1,1,1,2,Returning_Visitor,FALSE,TRUE 0,0,0,0,70,1652.486111,0,0.008695652,0,0,Nov,1,2,3,8,New_Visitor,FALSE,FALSE 1,0,2,6.5,63,5826.333333,0.000793651,0.026719577,0,0,Nov,3,2,1,3,Returning_Visitor,TRUE,FALSE 0,0,0,0,6,198.75,0.016666667,0.023333333,0,0,Nov,2,5,7,3,Returning_Visitor,TRUE,FALSE 0,0,0,0,7,197,0,0.042857143,0,0,Dec,2,2,3,2,New_Visitor,FALSE,FALSE 0,0,0,0,22,464.3833333,0,0.002272727,0,0,Nov,2,2,1,8,New_Visitor,FALSE,FALSE 0,0,0,0,22,266.1666667,0.009090909,0.018181818,0,0,Nov,2,4,1,2,Returning_Visitor,FALSE,FALSE 7,113.75,1,28.5,15,1047,0.010526316,0.019298246,0,0,Dec,1,1,3,2,New_Visitor,FALSE,TRUE 5,74.75,0,0,14,512.5,0,0.023529412,0,0,Dec,2,7,7,8,New_Visitor,TRUE,FALSE 1,18,0,0,29,765.3928571,0,0.000892857,0,0,Nov,2,2,8,3,New_Visitor,FALSE,FALSE 0,0,0,0,10,314.75,0.006666667,0.046666667,0,0,Nov,4,2,9,3,Returning_Visitor,FALSE,FALSE 0,0,0,0,30,1682.275,0,0.014285714,41.13336886,0,Dec,2,2,4,2,Returning_Visitor,FALSE,TRUE 0,0,0,0,21,580.0833333,0.00952381,0.023809524,0,0,Nov,2,2,3,11,Returning_Visitor,TRUE,FALSE 0,0,0,0,36,1388.2,0.005555556,0.027777778,0,0,Nov,2,2,3,2,Returning_Visitor,TRUE,FALSE 0,0,0,0,26,470.8166667,0.005128205,0.042307692,0,0,Nov,3,2,4,11,Returning_Visitor,TRUE,FALSE 0,0,0,0,44,1184.269841,0.004545455,0.023295455,0,0,Nov,1,2,1,2,Returning_Visitor,FALSE,FALSE 10,301.625,3,9,122,6919.899495,0.004615385,0.022008376,0,0,Dec,2,2,1,2,Returning_Visitor,FALSE,FALSE 0,0,0,0,12,303.5,0.016666667,0.016666667,0,0,Dec,1,1,7,2,Returning_Visitor,TRUE,FALSE 6,42,0,0,41,1074.333333,0,0.019047619,0,0,Dec,2,2,7,3,Returning_Visitor,FALSE,FALSE 0,0,0,0,18,588.5,0.051851852,0.08962963,0,0,Dec,3,2,4,1,Returning_Visitor,FALSE,FALSE 0,0,0,0,3,21,0,0.066666667,0,0,Nov,2,4,2,2,Returning_Visitor,TRUE,TRUE 0,0,0,0,2,11,0,0.1,0,0,Dec,2,4,1,2,Returning_Visitor,FALSE,FALSE 14,173.55,0,0,164,4155.746883,0.002514286,0.01343784,13.76473644,0,Nov,2,2,3,2,Returning_Visitor,FALSE,TRUE 1,12.5,0,0,3,55.5,0,0.08,0,0,Nov,1,1,1,8,Returning_Visitor,TRUE,FALSE 0,0,0,0,8,64,0,0.05,0,0,Dec,2,2,3,8,New_Visitor,FALSE,FALSE 4,32,1,43.5,311,12500.35306,0.001387656,0.014687275,0,0,Nov,2,2,7,2,Returning_Visitor,FALSE,FALSE 6,276,0,0,24,1021.2,0.014285714,0.022619048,82.4326349,0,Dec,1,1,3,1,Returning_Visitor,TRUE,FALSE 9,156.6071429,5,156.75,83,2694.507143,0,0.009090909,0,0,Nov,2,4,1,2,Returning_Visitor,FALSE,TRUE 6,257.5,0,0,37,2533.333333,0.014634146,0.031707317,6.651334683,0,Nov,2,2,3,1,Returning_Visitor,FALSE,TRUE 0,0,0,0,32,1658.125,0,0.014791667,19.6055151,0,Nov,2,2,1,10,Returning_Visitor,FALSE,FALSE 0,0,0,0,19,1440.166667,0.026315789,0.040350877,0,0,Nov,1,1,1,1,Returning_Visitor,FALSE,FALSE 1,60.5,0,0,10,449.5,0.025,0.033333333,0,0,Dec,1,8,2,20,Returning_Visitor,FALSE,FALSE 0,0,0,0,60,1315.177778,0.009039548,0.031793785,0,0,Nov,2,2,3,2,Returning_Visitor,FALSE,TRUE 5,62,0,0,125,1935.335516,0,0.008031496,0,0,Nov,4,2,3,3,Returning_Visitor,FALSE,FALSE 0,0,0,0,140,2404.831006,0.001428571,0.003142857,0,0,Nov,1,1,4,2,Returning_Visitor,FALSE,FALSE 0,0,0,0,18,346.5666667,0.02962963,0.062962963,0,0,Nov,2,2,3,2,Returning_Visitor,FALSE,FALSE 0,0,0,0,1,695.5,0,0.1,0,0,Nov,2,2,4,1,Returning_Visitor,FALSE,FALSE 0,0,0,0,18,415.2738095,0.005882353,0.028921569,0,0,Nov,2,10,7,10,Returning_Visitor,FALSE,FALSE 3,21,0,0,17,256.95,0,0.02,0,0,Nov,2,4,1,8,New_Visitor,TRUE,TRUE 2,13.875,0,0,26,1939.155952,0.006451613,0.026352357,24.69983785,0,Nov,3,2,1,2,Returning_Visitor,TRUE,FALSE 0,0,0,0,1,0,0.2,0.2,0,0,Nov,3,2,7,13,Returning_Visitor,FALSE,FALSE 0,0,0,0,54,1975.455952,0.003703704,0.016049383,0,0,Nov,2,2,9,2,Returning_Visitor,FALSE,FALSE 0,0,0,0,5,28.25,0.08,0.093333333,0,0,Nov,2,2,1,2,Returning_Visitor,TRUE,FALSE 4,118.75,0,0,66,2556.220833,0.018732493,0.02229101,0,0,Nov,3,2,1,13,Returning_Visitor,FALSE,FALSE 1,0,0,0,0,0,0.2,0.2,0,0,Dec,3,2,1,3,Returning_Visitor,TRUE,FALSE 10,192.6654762,3,211,153,3812.161753,0.003012048,0.013769082,5.91438512,0,Dec,2,2,3,1,Returning_Visitor,FALSE,FALSE 1,67.5,0,0,12,592.25,0,0.018181818,218.8649096,0,Dec,1,1,5,3,New_Visitor,FALSE,TRUE 0,0,0,0,9,190.3333333,0,0.033333333,0,0,Dec,1,1,3,2,New_Visitor,FALSE,FALSE 8,147.175,7,254,340,10902.45826,0.001713207,0.015734957,0,0,Nov,2,2,1,2,Returning_Visitor,FALSE,FALSE 0,0,0,0,10,130.5,0.02,0.1,0,0,Dec,2,2,7,3,Returning_Visitor,FALSE,FALSE 0,0,0,0,9,273.25,0.066666667,0.077777778,0,0,Dec,3,3,5,1,Returning_Visitor,TRUE,FALSE 0,0,0,0,56,3543.809524,0.004464286,0.021785714,0,0,Nov,2,7,5,1,Returning_Visitor,TRUE,FALSE 1,0,0,0,39,1159.541667,0.00125,0.03125,0,0,Nov,2,2,6,2,Returning_Visitor,TRUE,TRUE 0,0,0,0,2,18,0,0.1,0,0,Nov,2,2,2,6,Returning_Visitor,TRUE,FALSE 4,47.25,0,0,2,24.25,0,0.04,0,0,Dec,2,2,1,2,New_Visitor,FALSE,FALSE 0,0,0,0,97,2300.891317,0,0.015730379,0,0,Nov,2,2,1,2,Returning_Visitor,FALSE,FALSE 6,221.8333333,1,51.5,39,841.7833333,0.005426357,0.017585825,0,0,Dec,1,2,8,1,Returning_Visitor,FALSE,FALSE 0,0,2,386,36,1609.939744,0,0.009323308,12.50325678,0,Nov,2,2,3,3,Returning_Visitor,FALSE,TRUE 0,0,0,0,14,424.3333333,0,0.007142857,0,0,Dec,2,2,2,13,Returning_Visitor,FALSE,FALSE 1,54.5,0,0,35,1267.0625,0.005555556,0.021296296,9.458902094,0,Dec,2,2,3,10,Returning_Visitor,TRUE,FALSE 1,33.5,0,0,37,1631.416667,0,0.009009009,13.82316952,0,Dec,2,2,1,1,Returning_Visitor,FALSE,FALSE 7,58.41666667,0,0,22,1043.583333,0.014285714,0.048809524,0,0,Nov,2,2,3,2,New_Visitor,FALSE,FALSE 6,112.5,0,0,86,2041.23355,0.000387597,0.00786268,5.911452178,0,Nov,2,4,1,2,Returning_Visitor,FALSE,TRUE 7,153.85,0,0,19,504.6,0,0.018518519,0,0,Dec,1,1,1,8,Returning_Visitor,FALSE,TRUE 9,328.75,0,0,108,2793.733084,0.001949318,0.019205066,0,0,Nov,2,2,1,3,Returning_Visitor,FALSE,TRUE 4,106,0,0,6,180.4166667,0,0.025,0,0,Nov,3,6,3,2,New_Visitor,FALSE,FALSE 0,0,0,0,21,530.5833333,0.019047619,0.021428571,0,0,Nov,3,2,4,3,Returning_Visitor,FALSE,FALSE 0,0,0,0,12,329.2142857,0.016666667,0.028703704,0,0,Nov,3,2,1,2,Returning_Visitor,FALSE,FALSE 6,110.625,1,106,30,1508.521825,0.04,0.043294931,21.01746974,0,Nov,3,2,3,13,Returning_Visitor,FALSE,FALSE 1,29,0,0,41,803.5077381,0,0.008424908,0,0,Dec,1,2,1,8,Returning_Visitor,TRUE,FALSE 2,98,0,0,15,947,0.00625,0.006666667,0,0,Dec,2,2,3,2,Returning_Visitor,TRUE,FALSE 0,0,0,0,3,2251.28,0.095833333,0.137426901,0,0,Nov,3,2,1,13,Returning_Visitor,FALSE,FALSE 0,0,0,0,6,48,0,0.066666667,0,0,Nov,2,2,4,1,Returning_Visitor,FALSE,FALSE 0,0,0,0,24,1286.5,0,0.015277778,0,0,Nov,2,2,9,8,Returning_Visitor,FALSE,FALSE 0,0,0,0,4,396.25,0,0.083333333,0,0,Nov,2,2,4,2,Returning_Visitor,FALSE,FALSE 0,0,0,0,18,4293.4,0.041176471,0.064705882,0,0,Nov,2,2,3,1,Returning_Visitor,FALSE,FALSE 1,27.9,0,0,64,3875.427778,0,0.019583333,5.698585477,0,Nov,2,2,1,2,Returning_Visitor,FALSE,FALSE 3,61,4,31,1,43,0,0.025,0,0,Dec,8,13,9,20,Other,FALSE,FALSE 0,0,1,8,21,936.6428571,0.004545455,0.008987603,0,0,Nov,1,1,3,2,Returning_Visitor,TRUE,FALSE 0,0,0,0,28,756.8611111,0.035714286,0.067857143,0,0,Dec,2,2,4,1,Returning_Visitor,FALSE,FALSE 0,0,0,0,2,46.5,0,0.05,0,0,Dec,1,1,2,2,New_Visitor,FALSE,FALSE 0,0,0,0,49,1385.85,0,0.013265306,0,0,Dec,2,2,4,3,Returning_Visitor,FALSE,FALSE 0,0,0,0,81,3197.755638,0.01,0.021511083,0,0,Nov,3,2,4,10,Returning_Visitor,TRUE,FALSE 0,0,0,0,39,1779.886905,0.01025641,0.028972694,0,0,Dec,1,1,4,2,Returning_Visitor,TRUE,FALSE 3,29,0,0,57,1191.8875,0.019298246,0.030732714,0,0,Nov,2,2,1,2,Returning_Visitor,TRUE,FALSE 0,0,0,0,4,51.5,0,0.05,0,0,Dec,2,2,1,2,New_Visitor,FALSE,FALSE 0,0,5,497.8333333,26,851.6666667,0.006451613,0.01827957,0,0,Nov,2,2,1,2,New_Visitor,TRUE,FALSE 0,0,0,0,90,3111.683333,0,0.013249672,0,0,Nov,2,4,1,2,Returning_Visitor,TRUE,FALSE 2,101.75,0,0,9,561.25,0.022222222,0.033333333,26.38074656,0,Dec,3,2,1,1,Returning_Visitor,FALSE,FALSE 6,110.5,1,20,137,5380.686996,0.005517241,0.021311846,1.963592426,0,Nov,2,4,3,10,Returning_Visitor,FALSE,TRUE 1,25.5,0,0,49,860.2083333,0.004081633,0.012380952,0,0,Dec,2,2,2,10,Returning_Visitor,TRUE,FALSE 2,75,0,0,26,2058.3,0,0.0125,8.524548919,0,Nov,1,1,9,8,New_Visitor,FALSE,TRUE 12,264.9,2,11,24,1032.3,0.020430108,0.036190476,0,0,Dec,1,1,9,1,Returning_Visitor,TRUE,FALSE 0,0,0,0,1,11,0,0.1,0,0,Dec,2,2,1,10,Other,FALSE,FALSE 0,0,0,0,2,0,0.2,0.2,0,0,Dec,2,2,1,13,Returning_Visitor,FALSE,FALSE 5,107,2,15,250,8622.38,0.005670471,0.02250017,0,0,Nov,3,2,1,1,Returning_Visitor,FALSE,TRUE 7,42.25714286,0,0,124,4067.010247,0.009230769,0.022888335,6.709440135,0,Nov,2,2,3,2,Returning_Visitor,FALSE,TRUE 6,513.8333333,0,0,33,2690.333333,0,0.01754386,30.21878011,0,Nov,2,4,1,2,Returning_Visitor,FALSE,TRUE 6,79.25,0,0,13,150.1666667,0,0.012631579,0,0,Dec,1,1,1,3,New_Visitor,FALSE,FALSE 0,0,0,0,7,267.5,0.028571429,0.064285714,0,0,Dec,2,2,6,3,Returning_Visitor,FALSE,FALSE 3,74,0,0,44,844.9416667,0.004444444,0.015925926,0,0,Dec,1,2,1,10,Returning_Visitor,FALSE,FALSE 0,0,0,0,1,0,0.2,0.2,0,0,Nov,2,2,1,3,Returning_Visitor,FALSE,FALSE 0,0,0,0,37,918.5416667,0.005405405,0.005315315,0,0,Nov,1,1,1,2,New_Visitor,TRUE,TRUE 0,0,0,0,2,11,0,0.1,0,0,Dec,2,2,2,13,Returning_Visitor,FALSE,FALSE 0,0,0,0,3,46.75,0,0.066666667,0,0,Nov,2,2,3,10,Returning_Visitor,FALSE,FALSE 0,0,0,0,7,121,0.028571429,0.057142857,0,0,Dec,2,2,7,3,Returning_Visitor,TRUE,FALSE 13,297.75,3,352.5,41,2422.375,0,0.016037736,72.43846009,0,Dec,2,2,1,8,Returning_Visitor,FALSE,TRUE 12,337.5416667,2,23,32,1249.866144,0.0055,0.02577466,0,0,Dec,2,2,1,2,Returning_Visitor,FALSE,FALSE 3,98.5,0,0,27,702.3333333,0.023214286,0.042261905,0,0,Dec,2,2,7,2,Returning_Visitor,FALSE,FALSE 1,12,0,0,99,4097.708838,0.002061856,0.013745704,0,0,Nov,2,2,3,2,Returning_Visitor,FALSE,FALSE 0,0,3,13,36,1042.507143,0.01025641,0.027136752,0,0,Dec,1,1,3,1,Returning_Visitor,FALSE,FALSE 0,0,0,0,14,169.9166667,0,0.076190476,0,0,Nov,2,2,4,2,Returning_Visitor,FALSE,TRUE 4,67.5,0,0,27,834.6666667,0,0.014285714,0,0,Nov,2,2,2,2,New_Visitor,TRUE,FALSE 1,33.5,1,15.75,28,1421.25,0.004597701,0.040229885,0,0,Dec,2,2,1,1,Returning_Visitor,TRUE,FALSE 1,5.5,0,0,103,2429.279762,0.034951923,0.044360501,0,0,Nov,3,2,1,13,Returning_Visitor,FALSE,FALSE 7,177.5,6,312,40,1123.050824,0.008680556,0.024695767,0,0,Dec,1,1,3,2,Returning_Visitor,FALSE,FALSE 3,171,0,0,31,441.1369048,0,0.008823529,0,0,Dec,2,2,1,20,Returning_Visitor,FALSE,FALSE 2,128.5,0,0,7,209,0.027777778,0.053439153,0,0,Nov,3,2,3,2,Returning_Visitor,FALSE,FALSE 0,0,0,0,33,1475.254167,0,0.015530303,9.429362483,0,Nov,3,2,4,2,Returning_Visitor,TRUE,FALSE 1,32.5,0,0,4,73.5,0,0.04,0,0,Nov,2,2,1,2,New_Visitor,FALSE,FALSE 9,98.26666667,3,144.5,37,1630.137415,0.019565217,0.038037998,0,0,Nov,3,2,7,3,Returning_Visitor,FALSE,TRUE 2,96.75,0,0,103,3216.920172,0.012190476,0.030754266,0,0,Dec,2,2,6,2,Returning_Visitor,TRUE,FALSE 0,0,0,0,3,32.5,0.066666667,0.133333333,0,0,Dec,2,2,9,3,Returning_Visitor,TRUE,FALSE 0,0,0,0,18,623.725,0.016666667,0.055396825,0,0,Dec,2,2,6,2,Returning_Visitor,FALSE,FALSE 3,52.5,0,0,139,3674.737578,0.002142857,0.011897444,17.50624004,0,Nov,2,2,2,10,Returning_Visitor,FALSE,TRUE 5,172.0833333,0,0,21,486.1472222,0,0.0105,48.0136549,0,Nov,2,2,4,1,Returning_Visitor,FALSE,TRUE 0,0,1,100,19,1645.679762,0.002222222,0.02962963,0,0,Dec,2,5,6,1,Returning_Visitor,FALSE,FALSE 6,77.5,0,0,71,1361.780952,0,0.014003044,0,0,Dec,2,2,9,2,Returning_Visitor,FALSE,FALSE 0,0,0,0,13,257,0.035714286,0.078571429,0,0,Nov,1,1,2,2,Returning_Visitor,FALSE,FALSE 8,223.375,0,0,37,3152.5,0,0.011965812,13.25001247,0,Dec,3,2,3,2,Returning_Visitor,FALSE,TRUE 0,0,0,0,6,339,0,0.033333333,0,0,Nov,1,1,1,3,Returning_Visitor,FALSE,FALSE 8,275.75,1,97,204,5209.820361,0.002571429,0.015411986,0,0,Nov,2,2,1,2,Returning_Visitor,FALSE,FALSE 0,0,0,0,28,558.4761905,0.016666667,0.016309524,0,0,Nov,1,1,2,2,Returning_Visitor,FALSE,FALSE 5,73.75,0,0,131,2244.616667,0.005343511,0.017506361,0,0,Nov,2,2,1,2,Returning_Visitor,FALSE,FALSE 0,0,0,0,47,2032.272727,0.017021277,0.023877069,0,0,Nov,3,2,7,1,Returning_Visitor,TRUE,FALSE 0,0,2,3,22,1904.183333,0.016666667,0.052777778,0,0,Nov,2,2,2,11,Returning_Visitor,FALSE,FALSE 0,0,0,0,38,883.55,0.026315789,0.03907496,0,0,Nov,2,2,1,3,Returning_Visitor,FALSE,FALSE 5,61.875,0,0,164,4389.887512,0.001183432,0.010151043,6.664239119,0,Nov,2,2,1,2,Returning_Visitor,FALSE,TRUE 0,0,0,0,4,214.3333333,0,0.05,0,0,Nov,1,1,9,3,Returning_Visitor,FALSE,FALSE 4,121.5,0,0,2,34,0,0.023809524,0,0,Nov,2,2,9,8,New_Visitor,FALSE,FALSE 0,0,2,7,18,598.8738095,0.003333333,0.025555556,0,0,Nov,2,2,1,10,Returning_Visitor,FALSE,FALSE 0,0,0,0,1,0,0.2,0.2,0,0,Dec,2,2,6,13,Returning_Visitor,FALSE,FALSE 1,73,0,0,32,593,0,0.013541667,0,0,Nov,2,2,3,6,Returning_Visitor,FALSE,FALSE 7,118.5,0,0,28,829.2083333,0,0.015151515,17.79456354,0,Dec,1,2,3,2,Returning_Visitor,FALSE,FALSE 0,0,0,0,9,183.3333333,0,0.022222222,0,0,Dec,2,10,4,1,Returning_Visitor,FALSE,FALSE 7,322.25,1,0,121,4875.106113,0.011806061,0.020025438,0,0,Nov,3,2,1,13,Returning_Visitor,FALSE,FALSE 0,0,0,0,1,0,0.2,0.2,0,0,Nov,4,1,3,3,Returning_Visitor,FALSE,FALSE 0,0,0,0,2,14,0,0.1,0,0,Dec,4,2,1,1,Returning_Visitor,TRUE,FALSE 0,0,0,0,25,1344.291667,0,0.008695652,0,0,Nov,2,4,1,8,Returning_Visitor,FALSE,FALSE 1,15,0,0,19,817.5,0.011111111,0.022222222,0,0,Nov,2,2,1,2,Returning_Visitor,FALSE,FALSE 4,28.5,0,0,20,500.5,0,0.029824561,0,0,Nov,4,1,2,1,Returning_Visitor,TRUE,FALSE 0,0,2,11,1,0,0.066666667,0.133333333,0,0,Nov,1,1,1,2,Returning_Visitor,FALSE,FALSE 4,78.5,1,32.75,50,1897.833333,0.0127,0.011746032,0,0,Dec,1,1,9,3,Returning_Visitor,TRUE,FALSE 5,112.1666667,0,0,60,1883.866667,0.017460317,0.042436975,0,0,Nov,2,2,1,2,Returning_Visitor,FALSE,FALSE 0,0,0,0,1,0,0.2,0.2,0,0,Nov,3,2,3,3,Returning_Visitor,FALSE,FALSE 0,0,0,0,59,3033.689449,0.00756193,0.015479685,0,0,Nov,1,2,3,2,Returning_Visitor,FALSE,FALSE 0,0,0,0,83,2922.497511,0.009756098,0.030081301,0,0,Nov,2,2,1,1,Returning_Visitor,FALSE,FALSE 5,135.25,0,0,26,794.9928571,0.007407407,0.026419753,33.44471217,0,Dec,1,1,1,2,Returning_Visitor,FALSE,FALSE 12,1106.389831,9,627.4583333,125,7444.938071,0.007701238,0.022385989,7.201209277,0,Nov,3,2,4,2,Returning_Visitor,FALSE,TRUE 0,0,0,0,1,0,0.2,0.2,0,0,Dec,3,2,7,2,Returning_Visitor,FALSE,FALSE 4,65.5,0,0,23,783.75,0.007692308,0.030769231,0,0,Nov,2,5,3,1,Returning_Visitor,TRUE,TRUE 1,0,0,0,30,1732.066667,0.025806452,0.050752688,13.13492027,0,Nov,2,2,9,10,Returning_Visitor,FALSE,FALSE 0,0,0,0,72,2304.15,0.008450704,0.020355466,3.178183427,0,Nov,3,2,1,2,Returning_Visitor,FALSE,TRUE 0,0,0,0,12,584.3333333,0,0.023076923,12.58590538,0,Nov,2,2,3,8,Returning_Visitor,FALSE,FALSE 0,0,0,0,20,678,0,0.03,0,0,Dec,2,2,3,10,Returning_Visitor,FALSE,FALSE 0,0,0,0,48,2492.166667,0,0.014583333,0,0,Nov,2,4,1,2,Returning_Visitor,FALSE,FALSE 0,0,0,0,38,1656.615043,0.018421053,0.04154591,0,0,Dec,3,2,2,2,Returning_Visitor,TRUE,FALSE 12,232.3535714,0,0,439,17550.58486,0.00562963,0.018437701,2.626909076,0,Nov,2,5,1,10,Returning_Visitor,FALSE,TRUE 0,0,1,118,14,224.3916667,0.047619048,0.063265306,0,0,Nov,3,2,1,2,Returning_Visitor,FALSE,FALSE 1,11,1,8,79,2693.582331,0,0.003430424,0,0,Dec,1,1,3,2,Returning_Visitor,FALSE,FALSE 6,125.25,2,89,146,3697.731633,0.008108108,0.015188129,73.35866939,0,Nov,2,2,1,2,Returning_Visitor,TRUE,TRUE 0,0,0,0,5,135.75,0.04,0.053333333,0,0,Dec,2,2,3,2,New_Visitor,FALSE,FALSE 1,0,0,0,4,795,0.066666667,0.111111111,0,0,Nov,3,2,9,6,Returning_Visitor,TRUE,FALSE 1,0,1,80.16666667,29,759.75,0,0.019354839,0,0,Nov,2,2,1,2,Returning_Visitor,TRUE,FALSE 0,0,1,21,25,525.1666667,0.007692308,0.030769231,0,0,Nov,2,2,3,2,New_Visitor,TRUE,FALSE 0,0,0,0,9,195.25,0.022222222,0.081481481,0,0,Nov,1,1,8,10,Returning_Visitor,FALSE,FALSE 0,0,0,0,21,499.75,0.00952381,0.033333333,0,0,Nov,2,4,3,10,Returning_Visitor,FALSE,TRUE 2,31,0,0,20,583.0694444,0,0.01,0,0,Dec,4,2,1,2,Returning_Visitor,FALSE,FALSE 0,0,0,0,12,231.75,0,0.016666667,0,0,Nov,1,1,2,1,Returning_Visitor,FALSE,FALSE 0,0,0,0,54,1441.636905,0.008461538,0.004290709,0,0,Nov,1,1,1,2,Returning_Visitor,FALSE,FALSE 13,662.3392857,0,0,81,4284.422619,0.002298851,0.022822804,2.647620068,0,Dec,1,1,6,2,Returning_Visitor,TRUE,TRUE 0,0,0,0,6,151,0,0.08,0,0,Dec,1,8,4,6,Returning_Visitor,FALSE,FALSE 0,0,0,0,18,909.75,0.022222222,0.048148148,0,0,Nov,3,2,1,3,Returning_Visitor,FALSE,FALSE 1,15,0,0,15,691.8333333,0,0.014285714,59.6118415,0,Nov,2,2,2,2,Returning_Visitor,FALSE,TRUE 6,368.75,0,0,46,3368.305556,0.003846154,0.011858974,0,0,Nov,2,2,1,2,New_Visitor,FALSE,FALSE 0,0,0,0,8,121.375,0,0.025,0,0,Dec,3,2,4,2,New_Visitor,FALSE,FALSE 0,0,0,0,10,386.5,0,0.01,0,0,Nov,2,4,6,8,Returning_Visitor,FALSE,FALSE 3,36,0,0,3,29.5,0,0.04,0,0,Nov,2,10,3,2,Other,FALSE,FALSE 0,0,0,0,18,2335.125,0,0.033333333,0,0,Nov,2,2,1,3,Returning_Visitor,FALSE,FALSE 4,64.46428571,0,0,29,1414.589286,0,0.0125,21.05431578,0,Dec,2,2,1,2,Returning_Visitor,FALSE,TRUE 1,0,0,0,59,2334.105159,0.018421053,0.042076023,0,0,Dec,2,2,3,3,Returning_Visitor,FALSE,FALSE 1,21,0,0,13,1474.5,0,0.016666667,69.46175766,0,Dec,2,2,4,3,Returning_Visitor,FALSE,TRUE 6,94.1,0,0,92,4212.141886,0.008333333,0.021875,6.664927724,0,Nov,1,1,1,2,Returning_Visitor,TRUE,TRUE 0,0,0,0,30,677.9166667,0.006666667,0.013333333,0,0,Nov,3,2,1,2,Returning_Visitor,TRUE,FALSE 6,507,2,36.25,11,431,0,0.013333333,0,0,Dec,3,2,2,2,Returning_Visitor,FALSE,FALSE 11,194.575,0,0,81,2126.077501,0.011728395,0.02129074,0,0,Nov,3,2,1,2,Returning_Visitor,TRUE,FALSE 10,229,0,0,27,760.7708333,0.011764706,0.012941176,41.2169154,0,Nov,2,2,3,10,Returning_Visitor,FALSE,TRUE 7,114.75,1,15.75,21,1615.660256,0,0.009937888,0,0,Dec,2,2,8,2,New_Visitor,TRUE,FALSE 9,474,0,0,19,805.65,0.001242236,0.019397993,0,0,Nov,1,1,1,2,Returning_Visitor,FALSE,FALSE 0,0,0,0,17,546.2291667,0.014814815,0.01957672,14.49431487,0,Dec,2,2,7,2,Returning_Visitor,FALSE,FALSE 1,0,0,0,20,809,0.00952381,0.028571429,25.75735474,0,Dec,2,5,1,1,Returning_Visitor,FALSE,FALSE 0,0,0,0,2,7,0,0.1,0,0,Nov,2,2,4,1,Returning_Visitor,FALSE,FALSE 0,0,0,0,6,604,0,0.04,0,0,Nov,1,1,2,2,New_Visitor,FALSE,FALSE 12,651.875,3,20,59,3066.695513,0,0.010460069,0,0,Nov,2,2,3,1,Returning_Visitor,FALSE,FALSE 8,199.625,2,10,81,4272.607937,0.002352941,0.018627451,0,0,Nov,2,2,1,2,Returning_Visitor,FALSE,FALSE 0,0,1,0,31,751,0.003125,0.0421875,0,0,Dec,2,2,1,6,Returning_Visitor,TRUE,FALSE 3,315.3333333,1,141.5,42,3009.0875,0.004021164,0.0323655,0,0,Nov,1,1,1,1,Returning_Visitor,FALSE,FALSE 0,0,0,0,10,242.5833333,0,0.026666667,0,0,Dec,2,2,7,2,New_Visitor,FALSE,FALSE 0,0,0,0,23,347.8522727,0.008695652,0.030434783,0,0,Nov,2,2,1,3,Returning_Visitor,FALSE,FALSE 0,0,0,0,5,27,0.04,0.08,0,0,Dec,2,2,1,1,Returning_Visitor,FALSE,FALSE 6,148.2083333,2,139.5,98,3714.352664,0.005,0.013429932,0,0,Nov,2,2,1,2,Returning_Visitor,FALSE,FALSE 0,0,0,0,29,738.8861111,0,0.002469136,0,0,Dec,3,2,6,2,Returning_Visitor,TRUE,FALSE 9,174.2083333,2,383.75,151,9018.123741,0.012820513,0.02459296,0,0,Nov,1,2,1,1,Returning_Visitor,TRUE,FALSE 0,0,0,0,9,1726.5,0,0.05,0,0,Dec,2,4,7,1,Returning_Visitor,FALSE,FALSE 0,0,0,0,24,1361.833333,0,0.029166667,0,0,Nov,2,2,2,1,Returning_Visitor,FALSE,FALSE 0,0,0,0,11,271,0.018181818,0.036363636,0,0,Nov,2,2,1,3,Returning_Visitor,FALSE,FALSE 0,0,0,0,13,546.9583333,0,0.033846154,0,0,Dec,2,2,1,3,Returning_Visitor,FALSE,FALSE 1,18.2,1,9,30,3959.45,0.024652778,0.060590278,0,0,Dec,2,2,6,3,Returning_Visitor,FALSE,FALSE 9,250.0833333,2,30.5,155,4643.283732,0.002073171,0.007466851,10.11599409,0,Nov,2,2,1,3,Returning_Visitor,FALSE,TRUE 7,268.4166667,0,0,41,1124.875,0,0.011904762,0,0,Nov,2,2,9,2,Returning_Visitor,FALSE,TRUE 0,0,0,0,3,98,0,0.066666667,0,0,Dec,1,1,9,2,Returning_Visitor,FALSE,FALSE 1,4,0,0,4,67.25,0,0.013333333,0,0,Dec,2,2,2,2,Returning_Visitor,TRUE,FALSE 6,101.625,0,0,140,7097.582291,0.003448276,0.009952433,21.88856759,0,Nov,2,2,3,13,Returning_Visitor,FALSE,TRUE 0,0,1,249.5,33,1458.97619,0.027459219,0.041167134,0,0,Dec,1,1,1,10,Returning_Visitor,FALSE,FALSE 2,20.14285714,0,0,4,33.14285714,0,0.039814815,0,0,Dec,3,2,1,10,Returning_Visitor,FALSE,FALSE 0,0,0,0,36,572.9,0.011111111,0.038888889,0,0,Dec,2,2,3,2,Returning_Visitor,FALSE,FALSE 0,0,0,0,10,208,0,0.022222222,125.961749,0,Dec,2,2,1,2,New_Visitor,FALSE,TRUE 0,0,0,0,10,824,0.06,0.09,0,0,Dec,1,1,1,3,Returning_Visitor,FALSE,FALSE 11,238.775641,1,9,200,7972.322431,0.002619048,0.014230973,21.83420125,0,Nov,2,2,1,2,Returning_Visitor,FALSE,TRUE 5,71.08333333,0,0,24,1249.708333,0,0.005128205,52.28491058,0,Dec,2,2,3,2,New_Visitor,FALSE,TRUE 0,0,0,0,30,480.6055556,0.028,0.041450216,0,0,Dec,3,2,3,3,Returning_Visitor,FALSE,FALSE 1,5,0,0,21,258.6166667,0,0.009090909,0,0,Dec,2,2,4,3,Returning_Visitor,FALSE,FALSE 0,0,2,23,4,57.75,0,0.066666667,0,0,Nov,2,5,3,1,Returning_Visitor,FALSE,FALSE 8,195,2,86,56,3408.266667,0.014516129,0.027557013,9.594846798,0,Nov,1,2,1,2,Returning_Visitor,FALSE,TRUE 0,0,0,0,4,73.5,0,0.05,0,0,Nov,1,1,3,1,Returning_Visitor,FALSE,FALSE 0,0,4,52.5,38,2533.008333,0,0.026190476,0,0,Dec,2,4,4,2,Returning_Visitor,FALSE,FALSE 0,0,0,0,6,85.75,0,0.05,0,0,Nov,2,4,1,2,Returning_Visitor,FALSE,FALSE 1,73,0,0,12,2156.5,0.018181818,0.027272727,68.88769355,0,Nov,3,2,1,2,New_Visitor,FALSE,TRUE 0,0,0,0,5,111.25,0,0.033333333,0,0,Nov,2,2,1,20,Returning_Visitor,FALSE,FALSE 15,677.1911765,0,0,152,3561.130212,0,0.008686559,5.731607803,0,Dec,2,2,6,1,Returning_Visitor,FALSE,TRUE 5,68.5,2,34.25,385,14505.72725,0.007309999,0.013519938,0,0,Nov,3,2,3,3,Returning_Visitor,FALSE,FALSE 1,8,1,15,58,2801.75119,0.010526316,0.016374269,7.267034211,0,Dec,3,2,4,2,Returning_Visitor,FALSE,FALSE 8,451.2836134,10,961.45,131,2460.323686,0.003082192,0.021249277,2.325663803,0,Nov,2,2,3,3,Returning_Visitor,FALSE,TRUE 2,530.5,2,9,9,452.25,0.026666667,0.053333333,0,0,Dec,1,1,7,2,Returning_Visitor,FALSE,FALSE 2,26.66666667,0,0,79,2619.109709,0.016315049,0.020747988,0,0,Nov,1,1,3,10,Returning_Visitor,TRUE,FALSE 0,0,0,0,44,784.8833333,0,0.004761905,360.9533839,0,Dec,8,13,9,20,Other,FALSE,TRUE 0,0,0,0,1,0,0.2,0.2,0,0,Nov,1,8,2,1,Returning_Visitor,FALSE,FALSE 2,62.5,0,0,17,775.3333333,0,0.045098039,0,0,Nov,2,2,3,1,Returning_Visitor,FALSE,TRUE 0,0,0,0,9,269.75,0.022222222,0.048148148,0,0,Dec,3,2,5,6,Returning_Visitor,FALSE,FALSE 1,20,0,0,38,1264,0,0.028947368,31.48142512,0,Dec,2,6,9,3,Returning_Visitor,FALSE,FALSE 0,0,0,0,34,508.558547,0.014705882,0.027528909,0,0,Dec,2,2,3,10,Returning_Visitor,FALSE,FALSE 0,0,0,0,1,0,0.2,0.2,0,0,Dec,3,2,7,1,Returning_Visitor,FALSE,FALSE 0,0,0,0,7,61.5,0.028571429,0.085714286,0,0,Dec,2,4,3,1,Returning_Visitor,FALSE,FALSE 0,0,0,0,2,12,0.166666667,0.166666667,0,0,Nov,1,1,1,10,Returning_Visitor,FALSE,FALSE 2,45.5,0,0,9,122.25,0,0.018181818,0,0,Dec,2,2,7,2,New_Visitor,FALSE,FALSE 2,147.5,7,209,48,698.6715201,0.004487179,0.01989011,68.91364255,0,Nov,3,2,7,11,Returning_Visitor,FALSE,TRUE 1,0,2,11,50,1799.666667,0.056410256,0.083012821,0,0,Dec,1,1,1,3,Returning_Visitor,FALSE,FALSE 3,36.5,0,0,11,496.25,0,0.026190476,0,0,Dec,2,2,1,8,Returning_Visitor,FALSE,FALSE 4,47,1,49.5,28,687.4670635,0.013978495,0.02734255,108.9089884,0,Nov,1,1,1,2,Returning_Visitor,TRUE,TRUE 3,49.5,0,0,22,458.25,0.02,0.056666667,0,0,Nov,1,1,1,3,Returning_Visitor,FALSE,FALSE 0,0,0,0,2,53.5,0.05,0.15,0,0,Dec,3,3,5,20,Returning_Visitor,FALSE,FALSE 0,0,0,0,3,0,0.2,0.2,0,0,Nov,3,2,6,13,Returning_Visitor,FALSE,FALSE 6,100.5,0,0,10,332.5833333,0,0.002197802,0,0,Dec,8,13,9,20,Other,FALSE,FALSE 0,0,0,0,12,749.75,0,0.016666667,0,0,Nov,8,2,1,10,Returning_Visitor,FALSE,FALSE 0,0,0,0,1,0,0.2,0.2,0,0,Nov,2,2,4,11,Returning_Visitor,TRUE,FALSE 17,346.7096405,3,207.25,292,8282.214508,0.000977199,0.005669849,13.91832377,0,Nov,2,2,1,2,Returning_Visitor,TRUE,FALSE 0,0,0,0,1,0,0.2,0.2,0,0,Dec,3,2,1,13,Returning_Visitor,FALSE,FALSE 0,0,0,0,6,142.125,0,0.033333333,0,0,Nov,3,2,6,2,Returning_Visitor,FALSE,FALSE 8,91,0,0,45,3007.6875,0.012765957,0.012907801,0,0,Dec,3,2,6,10,Returning_Visitor,FALSE,FALSE 1,126.5,1,0,52,1894.39384,0.005555556,0.016838538,0,0,Nov,3,2,3,11,Returning_Visitor,FALSE,FALSE 2,561.5,2,31,95,4817.924102,0.007496464,0.025637791,50.09121868,0,Nov,1,1,5,2,Returning_Visitor,FALSE,TRUE 0,0,0,0,2,0,0.2,0.2,0,0,Dec,2,2,3,1,Returning_Visitor,FALSE,FALSE 6,86,0,0,12,905.5,0,0.026666667,23.6393946,0,Dec,4,5,1,1,Returning_Visitor,FALSE,TRUE 0,0,0,0,37,1254.75,0.002631579,0.010175439,34.15414773,0,Nov,2,2,2,7,Returning_Visitor,FALSE,TRUE 4,209.0625,0,0,71,3401.050687,0.005128205,0.026238576,0,0,Nov,2,2,2,2,Returning_Visitor,FALSE,FALSE 4,67.41666667,0,0,17,523.5416667,0.023333333,0.057,30.68883081,0,Dec,2,2,5,1,Returning_Visitor,FALSE,FALSE 8,147,0,0,23,627,0.006896552,0.011264368,25.19297315,0,Dec,1,1,8,2,Returning_Visitor,FALSE,FALSE 0,0,0,0,56,1514.037879,0.016071429,0.0175,0,0,Dec,3,2,1,3,Returning_Visitor,FALSE,FALSE 0,0,0,0,2,0,0.2,0.2,0,0,Nov,2,10,2,10,Returning_Visitor,FALSE,FALSE 0,0,0,0,2,135.5,0,0.1,0,0,Dec,2,2,4,3,Returning_Visitor,FALSE,FALSE 5,72.5,0,0,35,2429.8125,0,0.023684211,102.4922188,0,Nov,2,2,2,1,Returning_Visitor,FALSE,TRUE 0,0,0,0,14,156.4047619,0.014285714,0.05,0,0,Nov,2,5,3,2,Returning_Visitor,FALSE,FALSE 5,173.1666667,2,24.5,30,1288.733333,0,0.013376623,54.121714,0,Nov,1,2,3,2,Returning_Visitor,TRUE,FALSE 4,598.375,0,0,126,4446.303175,0.001538462,0.015567766,3.333835543,0,Dec,2,2,3,2,Returning_Visitor,FALSE,FALSE 0,0,0,0,34,920.55,0.030588235,0.042913165,0,0,Dec,3,2,1,1,Returning_Visitor,TRUE,FALSE 0,0,0,0,8,885,0.04375,0.0375,49.72830378,0,Nov,3,2,1,8,Returning_Visitor,FALSE,TRUE 4,15,1,4,44,950.625,0,0.004444444,0,0,Nov,2,2,8,1,New_Visitor,FALSE,FALSE 5,42,0,0,232,5932.596554,0.002564103,0.014824296,0,0,Nov,2,2,1,2,Returning_Visitor,FALSE,TRUE 7,645.6666667,0,0,25,1358.166667,0.007407407,0.022222222,0,0,Nov,1,1,1,1,Returning_Visitor,TRUE,FALSE 0,0,0,0,6,89.66666667,0,0.033333333,0,0,Nov,2,10,3,3,Returning_Visitor,FALSE,FALSE 0,0,0,0,50,694.5166667,0.008163265,0.024489796,0,0,Dec,2,4,3,1,Returning_Visitor,FALSE,FALSE 4,66.83333333,0,0,125,3075.978608,0.00962963,0.022299824,0,0,Nov,2,5,3,1,Returning_Visitor,FALSE,TRUE 0,0,0,0,96,2857.712454,0.002393617,0.019742908,0,0,Nov,3,2,1,6,Returning_Visitor,TRUE,FALSE 7,190.4375,0,0,60,1967.780163,0.003809524,0.0154654,2.627347196,0,Dec,3,2,8,10,Returning_Visitor,TRUE,TRUE 0,0,0,0,1,0,0.2,0.2,0,0,Dec,2,2,8,11,Returning_Visitor,FALSE,FALSE 12,138.9166667,0,0,14,1352.75,0,0.023684211,0,0,Dec,2,2,1,2,Returning_Visitor,FALSE,FALSE 0,0,0,0,19,866.3333333,0,0.025,14.58941181,0,Dec,2,4,3,2,Returning_Visitor,TRUE,FALSE 8,258.1444444,1,0,81,1878.996825,0.002325581,0.019277493,0,0,Nov,2,2,1,2,Returning_Visitor,FALSE,FALSE 9,89.16666667,4,94.875,10,138.4166667,0,0.013333333,0,0,Nov,2,2,3,2,New_Visitor,FALSE,FALSE 0,0,1,24.5,46,1031.133333,0.004680851,0.034267293,0,0,Nov,2,2,3,2,Returning_Visitor,TRUE,FALSE 0,0,0,0,39,1208.333333,0,0.01025641,0,0,Nov,1,1,4,1,Returning_Visitor,TRUE,FALSE 4,68.5,0,0,152,5426.823764,0.005228758,0.018756793,0,0,Nov,2,2,4,10,Returning_Visitor,FALSE,FALSE 3,49.5,1,0,50,1570.362302,0.008959436,0.027766386,1.818480813,0,Nov,3,2,1,10,Returning_Visitor,FALSE,TRUE 0,0,0,0,1,0,0.2,0.2,0,0,Nov,1,1,4,2,Returning_Visitor,FALSE,FALSE 7,718,0,0,15,644.25,0.005263158,0.01754386,0,0,Nov,2,2,1,2,Returning_Visitor,FALSE,FALSE 1,38.5,0,0,28,705.1166667,0.003703704,0.017901235,0,0,Nov,1,1,7,2,Returning_Visitor,TRUE,FALSE 2,222.5,0,0,23,1183.9,0.04,0.058285714,0,0,Dec,2,2,6,2,Returning_Visitor,FALSE,FALSE 2,48.5,0,0,53,2438.075,0.005357143,0.017261905,29.04476648,0,Nov,2,2,1,6,Returning_Visitor,TRUE,TRUE 0,0,0,0,8,106.0833333,0.025,0.05625,0,0,Dec,1,1,2,1,Returning_Visitor,FALSE,FALSE 3,69,1,18,10,409.4666667,0.005128205,0.039010989,0,0,Dec,2,2,1,2,Returning_Visitor,FALSE,FALSE 0,0,0,0,1,0,0.2,0.2,0,0,Nov,8,13,9,20,Other,FALSE,FALSE 4,128.125,4,134.5,93,2697.17535,0.006060606,0.019274564,0,0,Nov,2,2,3,2,Returning_Visitor,TRUE,FALSE 0,0,0,0,10,166.1666667,0,0.05,0,0,Dec,2,2,1,2,Returning_Visitor,FALSE,FALSE 0,0,2,0,99,2622.600281,0.022,0.028519936,0,0,Nov,3,2,2,13,Returning_Visitor,FALSE,TRUE 0,0,0,0,69,5793.52381,0,0.007835821,0,0,Nov,2,2,1,8,New_Visitor,FALSE,FALSE 0,0,0,0,12,631.75,0.054545455,0.072727273,76.05518858,0,Nov,1,1,1,3,Returning_Visitor,FALSE,FALSE 2,327.5,0,0,7,570.5,0,0.028571429,0,0,Nov,2,2,2,2,Returning_Visitor,FALSE,FALSE 3,1715,0,0,20,536.6166667,0,0.002898551,0,0,Nov,1,1,1,8,New_Visitor,FALSE,FALSE 0,0,0,0,30,1661.833333,0.027222222,0.071666667,0,0,Dec,2,2,3,1,Returning_Visitor,FALSE,FALSE 3,53.5,1,18,42,2553.858333,0,0.022101449,0,0,Nov,2,2,2,1,Returning_Visitor,FALSE,FALSE 0,0,0,0,11,490.8333333,0,0.045454545,0,0,Dec,3,2,6,2,New_Visitor,FALSE,FALSE 0,0,0,0,13,334.75,0,0.030769231,0,0,Nov,2,2,1,2,Returning_Visitor,FALSE,FALSE 0,0,0,0,5,270.5833333,0.04,0.07,0,0,Dec,1,1,5,8,Returning_Visitor,TRUE,FALSE 0,0,0,0,3,61.5,0,0.033333333,0,0,Dec,1,1,9,2,Returning_Visitor,FALSE,FALSE 4,170.5,0,0,20,1052.833333,0.010526316,0.015789474,0,0,Nov,1,1,6,3,New_Visitor,FALSE,FALSE 0,0,0,0,2,301,0,0.033333333,0,0,Nov,1,1,1,3,Returning_Visitor,FALSE,FALSE 0,0,0,0,19,535.5,0.044444444,0.091666667,0,0,Nov,2,2,1,11,Returning_Visitor,FALSE,FALSE 0,0,0,0,11,667.9166667,0,0.009090909,0,0,Dec,2,5,1,2,Returning_Visitor,FALSE,TRUE 2,14.75,0,0,32,839.4305556,0.012903226,0.028602151,13.91276554,0,Nov,2,2,6,1,Returning_Visitor,FALSE,TRUE 0,0,0,0,102,4509,0.011764706,0.037679739,0,0,Nov,2,2,7,1,Returning_Visitor,FALSE,FALSE 0,0,0,0,19,273.7083333,0.021052632,0.035087719,0,0,Nov,3,2,4,11,Returning_Visitor,TRUE,TRUE 4,99.33333333,0,0,15,715.875,0.010526316,0.052631579,0,0,Dec,2,5,4,2,Returning_Visitor,FALSE,FALSE 0,0,0,0,6,903.3571429,0.054545455,0.054385965,0,0,Dec,3,3,1,3,Returning_Visitor,FALSE,FALSE 2,46.5,1,64.5,91,3309.448776,0.013285024,0.020476218,0,0,Nov,3,2,1,13,Returning_Visitor,TRUE,TRUE 0,0,0,0,6,339.5,0,0.033333333,0,0,Nov,2,2,1,3,Returning_Visitor,FALSE,FALSE 4,33.91666667,4,785.875,251,9297.575489,0.011608301,0.027881156,0,0,Nov,2,2,1,2,Returning_Visitor,FALSE,FALSE 0,0,0,0,140,4096.090152,0.003809524,0.011870748,0,0,Nov,2,5,7,1,Returning_Visitor,FALSE,FALSE 1,5,0,0,47,1480.233333,0.010416667,0.027824074,15.15643787,0,Nov,2,2,1,3,Returning_Visitor,TRUE,TRUE 2,47.5,0,0,30,735.5833333,0.006896552,0.024137931,23.51024736,0,Dec,2,2,2,1,Returning_Visitor,FALSE,FALSE 0,0,0,0,8,332,0,0.028571429,14.1273698,0,Nov,1,2,4,8,New_Visitor,TRUE,TRUE 0,0,0,0,37,581.0833333,0.005555556,0.026851852,0,0,Nov,2,2,2,10,Returning_Visitor,TRUE,FALSE 0,0,0,0,11,877.9166667,0,0.043636364,0,0,Nov,1,1,4,2,New_Visitor,FALSE,FALSE 0,0,0,0,1,0,0.2,0.2,0,0,Nov,3,2,3,10,Other,FALSE,FALSE 3,99.75,0,0,31,835.5833333,0,0.006451613,47.44850792,0,Nov,2,2,1,10,Returning_Visitor,FALSE,TRUE 5,104.875,0,0,39,805.625,0,0.020905923,2.217029402,0,Dec,2,2,2,2,Returning_Visitor,FALSE,FALSE 1,21,0,0,16,375.9166667,0,0.007142857,0,0,Dec,1,1,1,2,New_Visitor,FALSE,FALSE 0,0,0,0,13,555.5029412,0.036363636,0.020095694,47.99199256,0,Nov,4,2,4,2,New_Visitor,FALSE,TRUE 9,153.9166667,0,0,97,1935.887111,0.001168385,0.008928355,0,0,Dec,2,2,4,2,Returning_Visitor,FALSE,FALSE 4,378.75,1,0,15,638.9166667,0.010526316,0.031578947,0,0,Nov,2,2,1,2,New_Visitor,FALSE,TRUE 3,135.5,0,0,30,2040.166667,0,0.022727273,4.350513745,0,Dec,2,2,8,2,Returning_Visitor,FALSE,TRUE 2,169.5,2,105,35,990.4642857,0.010810811,0.027027027,14.78162714,0,Nov,1,1,1,2,Returning_Visitor,TRUE,TRUE 2,82,0,0,13,155.9833333,0,0.037777778,0,0,Dec,2,2,1,2,Returning_Visitor,FALSE,FALSE 0,0,0,0,1,0,0.2,0.2,0,0,Nov,2,2,2,2,Returning_Visitor,FALSE,FALSE 6,143.5,2,303,67,2199.976786,0.008333333,0.027665354,0,0,Nov,1,1,1,2,Returning_Visitor,TRUE,FALSE 0,0,0,0,5,44.5,0.12,0.16,0,0,Nov,1,1,1,3,Returning_Visitor,FALSE,FALSE 0,0,0,0,22,246.6458333,0.009090909,0.047727273,0,0,Nov,3,2,7,10,Returning_Visitor,TRUE,FALSE 0,0,0,0,47,2493.985714,0.017021277,0.032056738,0,0,Dec,2,4,9,1,Returning_Visitor,FALSE,TRUE 6,143.5142857,0,0,31,1359.957586,0.001612903,0.003655914,32.88667049,0,Dec,2,2,2,2,Returning_Visitor,FALSE,FALSE 0,0,0,0,4,46.75,0,0.05,0,0,Nov,1,1,3,3,Returning_Visitor,FALSE,FALSE 1,138.5,0,0,21,831,0,0.01,0,0,Dec,2,12,4,2,Returning_Visitor,FALSE,FALSE 1,52.5,0,0,19,812.875,0,0.003333333,0,0,Nov,3,3,5,11,Returning_Visitor,TRUE,FALSE 0,0,0,0,10,201.9166667,0,0.04,0,0,Nov,1,1,1,2,Returning_Visitor,FALSE,FALSE 0,0,0,0,4,17,0.1,0.15,0,0,Dec,2,2,1,1,Returning_Visitor,FALSE,FALSE 0,0,0,0,4,54.5,0.1,0.15,0,0,Nov,2,2,1,10,Returning_Visitor,FALSE,FALSE 0,0,0,0,17,722.0833333,0.011764706,0.029411765,0,0,Dec,2,2,2,1,Returning_Visitor,FALSE,FALSE 0,0,0,0,11,135.225,0,0.009090909,0,0,Nov,3,2,2,10,Returning_Visitor,FALSE,FALSE 9,125.2659091,2,115,146,3231.853672,0.007792208,0.016586922,8.597486055,0,Dec,2,2,9,2,Returning_Visitor,TRUE,FALSE 3,24.75,0,0,2,74.75,0,0.022222222,0,0,Nov,2,2,3,2,New_Visitor,FALSE,TRUE 2,41.5,0,0,43,4215.961111,0.009302326,0.022170543,0,0,Dec,2,10,2,1,Returning_Visitor,FALSE,FALSE 0,0,0,0,1,0,0.2,0.2,0,0,Dec,2,2,9,3,Returning_Visitor,FALSE,FALSE 0,0,0,0,38,624.5,0.005263158,0.021052632,0,0,Nov,4,1,9,3,Returning_Visitor,FALSE,FALSE 6,316,0,0,69,1618.532967,0.005555556,0.027698413,0,0,Nov,2,5,1,1,Returning_Visitor,FALSE,FALSE 0,0,0,0,44,1924.505952,0,0.004651163,59.21256014,0,Nov,2,2,3,6,Returning_Visitor,FALSE,TRUE 2,21.5,1,79,56,4899.626984,0.036904762,0.06360119,0,0,Nov,2,4,3,1,Returning_Visitor,TRUE,FALSE 1,18,1,6,122,7141.214642,0.012926829,0.01962467,0,0,Nov,3,2,1,2,Returning_Visitor,TRUE,FALSE 4,97.5,0,0,5,454.5,0,0.00625,0,0,Dec,2,2,7,2,New_Visitor,FALSE,FALSE 0,0,0,0,2,0,0.2,0.2,0,0,Nov,3,2,1,13,Returning_Visitor,FALSE,FALSE 0,0,0,0,5,23,0.11,0.15,0,0,Nov,2,2,3,13,Returning_Visitor,FALSE,FALSE 4,88.25,2,546.5,37,2304.5,0,0.005,14.54270238,0,Nov,2,2,9,2,New_Visitor,FALSE,TRUE 4,109.3333333,1,118,34,1573.895833,0.015,0.019055556,0,0,Nov,3,2,4,3,Returning_Visitor,TRUE,FALSE 15,290.2934078,2,302,49,2011.513765,0.020689655,0.024273772,10.74211393,0,Nov,1,1,2,2,Returning_Visitor,FALSE,FALSE 0,0,0,0,3,21,0,0.066666667,0,0,Nov,2,2,7,3,Returning_Visitor,FALSE,FALSE 0,0,2,6,50,3831.1,0,0.017307692,0,0,Nov,2,2,1,10,Returning_Visitor,FALSE,FALSE 0,0,0,0,11,326.5,0.036363636,0.077272727,0,0,Nov,1,1,4,3,Returning_Visitor,FALSE,FALSE 2,13,0,0,23,441,0.066666667,0.101449275,0,0,Nov,2,2,1,1,Returning_Visitor,FALSE,FALSE 0,0,0,0,3,164,0,0.066666667,0,0,Nov,1,1,1,3,Returning_Visitor,TRUE,FALSE 0,0,0,0,15,275.5,0.004444444,0.031111111,0,0,Nov,2,2,3,2,Returning_Visitor,FALSE,FALSE 2,248.5,5,192.25,9,1176.5,0,0.0125,0,0,Dec,1,1,3,2,New_Visitor,FALSE,FALSE 8,260.2083333,0,0,58,1436.690476,0,0.008231293,12.80819078,0,Dec,2,2,2,1,Returning_Visitor,TRUE,FALSE 3,97.75,1,46.5,12,257.4166667,0.006666667,0.027111111,0,0,Nov,3,2,6,3,Returning_Visitor,FALSE,FALSE 4,35.5,0,0,2,9.5,0,0.022222222,0,0,Dec,2,2,9,2,New_Visitor,FALSE,FALSE 0,0,0,0,2,10,0,0.1,0,0,Dec,1,1,2,2,New_Visitor,FALSE,FALSE 0,0,0,0,11,303.5,0,0.012121212,0,0,Nov,3,2,1,2,Returning_Visitor,FALSE,FALSE 0,0,0,0,7,1264.833333,0.073376623,0.075604396,0,0,Dec,3,2,1,13,Returning_Visitor,FALSE,FALSE 20,1307.675,3,132.6666667,517,27009.85943,0.004385217,0.014593571,8.4031637,0,Nov,1,1,1,2,Returning_Visitor,FALSE,TRUE 4,73.5,0,0,59,4679.029167,0.009617486,0.032389468,0,0,Dec,2,2,1,13,Returning_Visitor,FALSE,FALSE 0,0,0,0,28,1252.75,0.010714286,0.042857143,0,0,Nov,2,2,1,13,Returning_Visitor,FALSE,FALSE 5,116.1,3,97.66666667,170,5639.22176,0.002457143,0.01109801,0,0,Dec,2,2,7,2,Returning_Visitor,FALSE,FALSE 3,119.5,0,0,24,347.9166667,0,0.008333333,0,0,Dec,1,1,3,1,Returning_Visitor,FALSE,FALSE 3,96.5,0,0,8,323.5,0,0.02,0,0,Dec,1,1,6,2,Returning_Visitor,FALSE,FALSE 0,0,0,0,1,0,0.2,0.2,0,0,Nov,2,4,3,1,Returning_Visitor,FALSE,FALSE 4,72.5,0,0,95,2354.979058,0.00137457,0.005695387,5.214690992,0,Dec,3,2,1,2,Returning_Visitor,TRUE,FALSE 6,73.70833333,5,188,80,3313.073936,0.022621723,0.059636655,0,0,Nov,2,2,1,2,Returning_Visitor,TRUE,FALSE 0,0,0,0,15,147.5833333,0.014285714,0.028571429,0,0,Nov,3,2,7,3,Returning_Visitor,FALSE,FALSE 1,27.5,0,0,114,1403.720238,0,0.000589971,255.5691579,0,Nov,4,5,2,8,Returning_Visitor,FALSE,TRUE 1,0,0,0,2,66.5,0,0.1,0,0,Dec,2,2,9,2,Returning_Visitor,TRUE,FALSE 0,0,0,0,5,47.5,0.08,0.12,0,0,Dec,2,2,1,3,Returning_Visitor,FALSE,FALSE 2,13,0,0,3,200,0,0.05,0,0,Dec,2,2,1,8,New_Visitor,FALSE,FALSE 5,471.25,0,0,21,883,0,0.010666667,44.98081787,0,Nov,1,1,2,8,New_Visitor,TRUE,TRUE 5,136,1,13,85,2155.653752,0.005998168,0.019225201,3.920321611,0,Nov,1,1,1,2,Returning_Visitor,TRUE,TRUE 1,34.5,0,0,18,3026.374242,0,0.005263158,38.92648303,0,Dec,1,1,3,2,Returning_Visitor,FALSE,TRUE 1,0,0,0,1,0,0.2,0.2,0,0,Dec,1,1,1,10,Returning_Visitor,FALSE,FALSE 1,26.5,1,58.5,24,760.7632353,0.006153846,0.020512821,33.11000268,0,Nov,2,2,1,2,Returning_Visitor,FALSE,TRUE 8,487,0,0,28,839.5178571,0,0.003125,0,0,Dec,1,1,3,2,Returning_Visitor,TRUE,FALSE 5,51.04166667,0,0,14,837.375,0,0.011481481,0,0,Nov,1,1,2,8,New_Visitor,FALSE,TRUE 4,22.5,0,0,15,362.6333333,0.028571429,0.039285714,0,0,Dec,3,2,2,3,Returning_Visitor,FALSE,FALSE 0,0,0,0,31,1625.583333,0,0.009677419,0,0,Dec,4,2,1,8,New_Visitor,FALSE,FALSE 0,0,0,0,48,4152.083333,0.00625,0.028125,0,0,Nov,2,2,1,1,Returning_Visitor,TRUE,FALSE 7,155.9,0,0,25,1400.958333,0,0.029012346,14.11909834,0,Dec,2,4,7,1,Returning_Visitor,FALSE,FALSE 0,0,0,0,12,892.0833333,0.016666667,0.045833333,0,0,Dec,2,2,6,2,New_Visitor,FALSE,FALSE 0,0,0,0,4,75.5,0,0.05,0,0,Nov,2,2,1,8,New_Visitor,FALSE,FALSE 0,0,0,0,8,1066.25,0,0.028571429,9.0847678,0,Nov,1,8,4,11,Returning_Visitor,TRUE,TRUE 0,0,0,0,7,322.75,0.047619048,0.071428571,0,0,Nov,3,2,2,13,Returning_Visitor,FALSE,FALSE 0,0,0,0,29,873.75,0,0.031034483,0,0,Nov,2,2,5,2,Returning_Visitor,TRUE,TRUE 8,122.1,2,0,20,695.19375,0.012098765,0.058821329,0,0,Nov,1,1,8,2,Returning_Visitor,TRUE,TRUE 0,0,4,37,69,4112.394444,0.007123288,0.022355057,0,0,Dec,3,2,3,2,Returning_Visitor,FALSE,FALSE 13,210.427451,9,435.45,154,6515.45431,0.00867052,0.017328726,2.982410939,0,Nov,3,2,1,2,Returning_Visitor,FALSE,TRUE 0,0,0,0,26,1140.166667,0.032248521,0.032948718,0,0,Nov,3,2,6,13,Returning_Visitor,FALSE,FALSE 0,0,0,0,100,5212.708333,0.00952381,0.025768619,8.505586651,0,Nov,2,2,7,2,Returning_Visitor,TRUE,TRUE 0,0,1,0,13,1297.25,0.028571429,0.064285714,0,0,Dec,1,8,3,1,Returning_Visitor,TRUE,FALSE 6,407.25,4,67.5,14,195.4166667,0,0.030434783,0,0,Dec,3,2,8,1,Returning_Visitor,FALSE,FALSE 0,0,0,0,21,1535,0.01,0.048333333,0,0,Nov,2,2,7,3,Returning_Visitor,FALSE,FALSE 1,40.5,0,0,7,170,0,0.016666667,0,0,Dec,2,4,1,2,Returning_Visitor,FALSE,FALSE 0,0,0,0,4,53.625,0,0.05,0,0,Nov,3,2,1,11,Returning_Visitor,FALSE,FALSE 2,77.5,1,17,46,1066.566667,0.004255319,0.019148936,51.00308169,0,Dec,3,2,4,10,Returning_Visitor,FALSE,TRUE 5,135.5,0,0,86,2106.894048,0.002325581,0.008914729,0,0,Nov,2,2,3,2,Returning_Visitor,FALSE,FALSE 9,179.275,0,0,101,5276.594444,0,0.005545073,17.53927855,0,Dec,2,2,6,2,Returning_Visitor,TRUE,FALSE 3,36,2,763,9,163.5,0,0.014285714,0,0,Dec,8,2,1,2,New_Visitor,FALSE,TRUE 0,0,0,0,1,0,0.2,0.2,0,0,Nov,3,2,1,1,Returning_Visitor,FALSE,FALSE 0,0,0,0,18,849.5416667,0,0.00875,0,0,Nov,1,1,1,2,Returning_Visitor,FALSE,FALSE 11,1443.1,5,205,225,9595.649629,0.00455693,0.012050716,9.242286032,0,Nov,2,2,4,2,Returning_Visitor,FALSE,TRUE 0,0,2,105,194,6460.192638,0.003760684,0.017284446,1.79107248,0,Nov,2,2,3,2,Returning_Visitor,FALSE,TRUE 2,23,0,0,39,1828.966667,0.005128205,0.02008547,20.5924708,0,Nov,1,1,1,2,Returning_Visitor,TRUE,TRUE 15,543.3549065,7,243.2833333,169,6920.973243,0.005268892,0.016769542,0.742942737,0,Nov,1,1,3,2,Returning_Visitor,TRUE,FALSE 3,104,0,0,34,2991.319444,0.005555556,0.026111111,0,0,Dec,2,2,4,1,Returning_Visitor,FALSE,FALSE 0,0,0,0,6,78.75,0.033333333,0.066666667,0,0,Nov,2,2,9,3,Returning_Visitor,FALSE,FALSE 8,235.1861702,2,138.5,71,2512.51742,0.006349206,0.025572609,0,0,Nov,3,2,1,2,Returning_Visitor,TRUE,TRUE 0,0,0,0,11,335,0.036363636,0.060606061,0,0,Nov,3,2,1,13,Returning_Visitor,TRUE,FALSE 0,0,0,0,32,1297.75,0,0.0075,42.62274459,0,Nov,2,2,3,7,Returning_Visitor,FALSE,TRUE 3,115,0,0,6,132.75,0,0.022222222,0,0,Dec,1,1,7,2,New_Visitor,TRUE,FALSE 6,65.41666667,0,0,29,1904.541667,0.004848485,0.026060606,0,0,Nov,1,1,2,8,Returning_Visitor,FALSE,TRUE 0,0,0,0,7,270.75,0,0.057142857,0,0,Dec,1,1,4,2,Returning_Visitor,TRUE,FALSE 5,346.25,0,0,3,1692.25,0,0.014285714,0,0,Dec,2,2,9,2,New_Visitor,FALSE,FALSE 2,86.2,0,0,48,2259.736508,0.004166667,0.010138889,12.22605425,0,Dec,1,1,7,2,Returning_Visitor,FALSE,TRUE 0,0,0,0,75,1775.241667,0.001333333,0.019428571,0,0,Nov,2,6,5,3,Returning_Visitor,TRUE,FALSE 0,0,0,0,10,664.5,0,0.04,0,0,Nov,2,2,8,2,New_Visitor,TRUE,FALSE 0,0,3,1830.5,81,5000.739116,0.003294118,0.020433155,0,0,Nov,2,2,3,2,Returning_Visitor,TRUE,FALSE 0,0,0,0,2,69.5,0,0.1,0,0,Nov,4,5,9,11,New_Visitor,FALSE,FALSE 0,0,0,0,2,0,0.2,0.2,0,0,Nov,3,2,5,20,Returning_Visitor,FALSE,FALSE 0,0,0,0,57,2716.666667,0,0.005263158,0,0,Nov,2,2,2,3,Returning_Visitor,FALSE,TRUE 6,179.75,2,8,64,1876.333333,0.007246377,0.014251208,16.68711526,0,Dec,2,5,9,2,Returning_Visitor,FALSE,FALSE 0,0,0,0,8,95,0.025,0.058333333,0,0,Dec,2,4,7,1,Returning_Visitor,FALSE,FALSE 1,9,0,0,37,2368.111111,0.030555556,0.053240741,4.760548596,0,Nov,1,1,8,1,Returning_Visitor,FALSE,FALSE 0,0,0,0,11,229.9166667,0.018181818,0.040909091,0,0,Nov,3,2,4,1,Returning_Visitor,TRUE,FALSE 0,0,0,0,22,903.1634615,0.009090909,0.015757576,0,0,Nov,3,2,6,8,Returning_Visitor,TRUE,FALSE 6,197.25,0,0,51,3296.940476,0,0.008595388,3.507532249,0,Nov,1,1,1,3,Returning_Visitor,TRUE,FALSE 0,0,0,0,14,238.6666667,0,0.003846154,0,0,Dec,2,2,5,2,New_Visitor,FALSE,FALSE 0,0,2,19,65,2163.132326,0,0.011780384,0,0,Nov,2,2,8,2,New_Visitor,FALSE,FALSE 6,151.7,0,0,34,799.327381,0.017647059,0.023340336,0,0,Dec,3,2,1,2,Returning_Visitor,FALSE,FALSE 0,0,0,0,8,122.75,0,0.028571429,0,0,Nov,3,2,1,2,New_Visitor,FALSE,FALSE 0,0,0,0,62,2787.678571,0.003225806,0.015591398,57.48364657,0,Nov,4,2,1,1,Returning_Visitor,TRUE,FALSE 1,54.5,0,0,26,531.5,0.007692308,0.023076923,44.92362122,0,Nov,2,2,3,2,Returning_Visitor,FALSE,FALSE 0,0,0,0,10,165,0.03,0.046666667,0,0,Nov,1,1,1,2,Returning_Visitor,TRUE,TRUE 0,0,0,0,43,898.2388889,0.007317073,0.031707317,8.898618695,0,Nov,2,4,1,10,Returning_Visitor,FALSE,TRUE 1,8,0,0,10,109.1944444,0.04,0.06,0,0,Dec,1,1,7,3,Returning_Visitor,TRUE,FALSE 0,0,0,0,3,15,0.066666667,0.133333333,0,0,Nov,2,2,1,3,Returning_Visitor,FALSE,FALSE 5,231.75,0,0,30,805.7833333,0,0.0203125,0,0,Dec,2,2,4,2,Returning_Visitor,FALSE,FALSE 1,19,1,11,11,66.83333333,0,0.019230769,0,0,Nov,2,2,1,8,New_Visitor,FALSE,FALSE 0,0,0,0,1,0,0.2,0.2,0,0,Dec,1,1,3,3,New_Visitor,TRUE,FALSE 3,52.16666667,3,110.75,65,2019.769468,0.002941176,0.017081105,3.277744575,0,Nov,1,1,6,2,Returning_Visitor,TRUE,FALSE 10,163.6333333,1,294,80,1979.3,0.002325581,0.024224806,19.73335395,0,Nov,2,1,2,2,Returning_Visitor,TRUE,TRUE 0,0,0,0,28,1425.583333,0.014814815,0.026984127,14.59072178,0,Dec,2,4,4,1,Returning_Visitor,FALSE,FALSE 4,95.75,12,886.25,154,7838.556598,0.00376506,0.014926307,0,0,Nov,3,2,6,2,Returning_Visitor,TRUE,FALSE 0,0,0,0,1,0,0.2,0.2,0,0,Nov,1,1,3,3,Returning_Visitor,FALSE,FALSE 17,218.741707,4,808.5,74,3591.744196,0.019767442,0.015609868,0,0,Nov,3,2,1,2,Returning_Visitor,FALSE,FALSE 0,0,0,0,11,555.625,0.018181818,0.054545455,0,0,Nov,2,2,1,3,Returning_Visitor,FALSE,FALSE 0,0,0,0,2,242.5,0,0.05,0,0,Dec,8,13,9,20,Other,FALSE,FALSE 10,318.6410256,9,991.25,216,7761.034859,0.007542901,0.022135148,1.589329821,0,Nov,2,6,2,2,Returning_Visitor,TRUE,TRUE 7,78.35714286,3,210.6666667,309,12158.19639,0.007586207,0.025532176,0,0,Nov,2,2,1,2,Returning_Visitor,TRUE,TRUE 6,143,0,0,32,2289.1,0,0.001015625,62.33035594,0,Nov,3,2,9,3,New_Visitor,TRUE,FALSE 0,0,0,0,7,42,0.057142857,0.085714286,0,0,Dec,2,4,6,1,Returning_Visitor,TRUE,FALSE 0,0,0,0,3,138,0.1,0.111111111,0,0,Nov,1,1,1,3,Returning_Visitor,FALSE,FALSE 0,0,0,0,1,0,0.2,0.2,0,0,Dec,1,1,1,2,New_Visitor,FALSE,FALSE 1,36,1,74,98,7712.375,0,0.012431818,15.3498858,0,Dec,2,10,4,2,Returning_Visitor,FALSE,FALSE 0,0,0,0,9,436,0,0.025,68.1974018,0,Dec,1,2,2,2,New_Visitor,FALSE,TRUE 7,241.8611111,3,228,154,4755.224054,0.009808489,0.029142563,1.104440234,0,Nov,1,2,2,1,Returning_Visitor,FALSE,FALSE 0,0,0,0,12,465.25,0,0.016666667,0,0,Dec,3,2,6,2,Returning_Visitor,TRUE,FALSE 0,0,0,0,19,1210.1,0,0.005555556,0,0,Nov,1,1,3,10,Returning_Visitor,FALSE,FALSE 0,0,0,0,14,313.0178571,0,0.016071429,0,0,Nov,3,2,4,2,Returning_Visitor,FALSE,FALSE 1,34.5,0,0,22,279.8333333,0.008695652,0.013043478,0,0,Nov,2,2,1,2,Other,FALSE,FALSE 0,0,1,141.5,43,3044.047222,0,0.004545455,3.940422761,0,Dec,2,4,1,10,Returning_Visitor,FALSE,TRUE 0,0,0,0,30,946.5952381,0.013333333,0.031333333,0,0,Nov,2,2,1,2,Returning_Visitor,FALSE,FALSE 2,112.25,0,0,9,97.41666667,0.018181818,0.036363636,0,0,Dec,3,2,3,2,New_Visitor,FALSE,FALSE 1,60.5,0,0,31,1574.375,0,0.022580645,0,0,Dec,2,2,3,1,Returning_Visitor,FALSE,FALSE 2,121,0,0,28,1830.583333,0.015555556,0.015740741,111.5113802,0,Dec,1,1,6,2,Returning_Visitor,TRUE,FALSE 5,179.875,0,0,47,1011.319444,0.019607843,0.046744236,3.322660364,0,Nov,3,2,2,1,Returning_Visitor,FALSE,TRUE 0,0,0,0,16,385.5,0,0.0125,0,0,Dec,2,2,4,1,Returning_Visitor,FALSE,FALSE 1,0,0,0,17,669.4833333,0.038888889,0.057407407,0,0,Dec,3,2,2,1,Returning_Visitor,TRUE,FALSE 0,0,0,0,21,795.3611111,0.041269841,0.058571429,0,0,Dec,1,2,1,3,Returning_Visitor,FALSE,FALSE 9,71.2,2,72.125,23,1061.816667,0,0.022916667,6.064425698,0,Nov,2,2,9,2,Returning_Visitor,TRUE,FALSE 8,111,0,0,32,1206.833333,0.005263158,0.014473684,19.27621155,0,Nov,2,2,1,2,Returning_Visitor,FALSE,TRUE 13,110.7583333,1,3,34,858.9464286,0,0.011627907,17.78585727,0,Nov,2,2,5,10,Returning_Visitor,TRUE,FALSE 0,0,3,103.5,30,1093.175,0.0125,0.028125,0,0,Dec,2,2,1,2,Returning_Visitor,TRUE,FALSE 0,0,0,0,1,0,0.2,0.2,0,0,Nov,2,2,7,11,Returning_Visitor,TRUE,FALSE 1,0,1,13.83333333,65,3174.088455,0.017014925,0.040540739,0,0,Dec,3,2,2,13,Returning_Visitor,TRUE,FALSE 0,0,1,0,7,228,0,0.025,0,0,Dec,1,1,6,3,Returning_Visitor,FALSE,FALSE 5,286.4642857,1,29.5,64,2058.604545,0.008955224,0.009549977,6.49391045,0,Nov,3,2,3,10,Returning_Visitor,FALSE,TRUE 7,111,2,5,131,5396.264881,0.003928571,0.013640873,7.513426372,0,Nov,2,1,1,2,Returning_Visitor,FALSE,TRUE 1,284,0,0,18,2014.416667,0.0025,0.034166667,9.964444223,0,Nov,1,1,3,2,Returning_Visitor,TRUE,FALSE 0,0,0,0,41,1837.204167,0.027642276,0.066547107,0,0,Nov,2,2,1,3,Returning_Visitor,FALSE,FALSE 0,0,0,0,38,1326.430556,0.005263158,0.016666667,0,0,Nov,1,1,1,2,Returning_Visitor,FALSE,FALSE 0,0,0,0,39,2558.5,0,0.010810811,0,0,Nov,4,2,2,10,Returning_Visitor,FALSE,FALSE 0,0,0,0,1,0,0.2,0.2,0,0,Dec,8,13,9,20,Returning_Visitor,FALSE,FALSE 4,115.0833333,1,3,16,930.5,0,0.021428571,0,0,Nov,3,2,6,2,Returning_Visitor,FALSE,FALSE 0,0,0,0,1,0,0.2,0.2,0,0,Nov,4,1,1,3,Returning_Visitor,FALSE,FALSE 3,50.5,0,0,2,37.5,0,0.04,0,0,Nov,2,2,1,2,New_Visitor,FALSE,FALSE 3,142.5,0,0,48,1052.255952,0.004347826,0.013043478,0,0,Nov,1,8,6,11,Returning_Visitor,FALSE,FALSE 2,48.1875,4,121.5,51,1792.814481,0.003571429,0.01186886,60.07483189,0,Nov,3,2,1,2,Returning_Visitor,TRUE,TRUE 0,0,0,0,63,3301.222222,0.006451613,0.03016129,0,0,Nov,2,1,3,1,Returning_Visitor,TRUE,FALSE 4,75,2,312,98,1887.232143,0.002040816,0.025068027,0,0,Dec,2,2,8,2,Returning_Visitor,FALSE,FALSE 0,0,0,0,6,111.75,0,0.066666667,0,0,Dec,2,2,1,2,Returning_Visitor,FALSE,FALSE 5,115.375,0,0,20,473.5,0.026111111,0.028996212,14.88419318,0,Dec,3,2,1,1,Returning_Visitor,FALSE,TRUE 0,0,0,0,3,578,0,0.066666667,0,0,Nov,1,1,1,1,Returning_Visitor,FALSE,FALSE 0,0,0,0,5,251,0.04,0.08,0,0,Dec,1,1,1,3,Returning_Visitor,FALSE,FALSE 0,0,0,0,13,217.9166667,0.015384615,0.035897436,0,0,Dec,2,2,1,2,Returning_Visitor,TRUE,FALSE 12,196.8571429,0,0,59,1533.025834,0.00721393,0.0322986,6.048714136,0,Nov,2,2,1,1,Returning_Visitor,TRUE,FALSE 0,0,0,0,37,582.2666667,0,0.021959459,0,0,Nov,2,2,5,2,Returning_Visitor,FALSE,FALSE 0,0,0,0,200,6867.193847,0.006408856,0.024587384,0,0,Nov,2,2,1,2,Returning_Visitor,FALSE,FALSE 2,79,0,0,47,1366.058333,0.008510638,0.034751773,21.23960367,0,Dec,2,2,1,2,New_Visitor,FALSE,FALSE 6,69.125,2,13,227,6854.604365,0.005701754,0.020960781,0,0,Nov,2,2,1,1,Returning_Visitor,FALSE,FALSE 0,0,0,0,7,305.5,0,0.028571429,0,0,Nov,2,2,1,3,Returning_Visitor,FALSE,FALSE 0,0,0,0,28,859.3333333,0,0.007407407,17.50632106,0,Nov,4,1,6,2,New_Visitor,FALSE,TRUE 2,334.25,4,92.16666667,2,70.75,0,0.00952381,0,0,Dec,1,1,3,10,New_Visitor,TRUE,FALSE 2,19,0,0,4,88,0,0.02,0,0,Dec,1,1,1,3,Returning_Visitor,FALSE,FALSE 0,0,0,0,6,192.875,0,0.016666667,0,0,Dec,8,13,9,20,Other,FALSE,FALSE 0,0,1,33.75,72,1460.908333,0,0.014851055,0,0,Nov,1,1,1,2,Returning_Visitor,FALSE,TRUE 0,0,0,0,36,2070.325,0.008333333,0.030925926,0,0,Dec,1,1,1,2,Returning_Visitor,FALSE,FALSE 1,107,5,143.75,2,17,0.025,0.05,0,0,Dec,3,2,3,1,Returning_Visitor,FALSE,FALSE 0,0,1,0,2,0,0.2,0.2,0,0,Nov,1,1,1,15,Returning_Visitor,FALSE,FALSE 0,0,0,0,6,551.5,0.033333333,0.077777778,0,0,Nov,3,2,4,11,Returning_Visitor,TRUE,FALSE 0,0,0,0,19,295.8333333,0,0.010526316,0,0,Nov,2,2,2,2,New_Visitor,FALSE,FALSE 0,0,0,0,3,181,0.066666667,0.1,0,0,Dec,2,2,3,2,Returning_Visitor,FALSE,FALSE 2,452.5,0,0,18,762.2380952,0,0.002352941,0,0,Nov,1,1,6,2,New_Visitor,FALSE,FALSE 0,0,0,0,1,0,0.2,0.2,0,0,Dec,3,2,2,1,Returning_Visitor,TRUE,FALSE 1,19,0,0,4,296.9166667,0.04,0.04,0,0,Dec,3,2,1,6,Returning_Visitor,FALSE,FALSE 0,0,0,0,2,9,0,0.066666667,0,0,Nov,2,2,8,6,Returning_Visitor,FALSE,FALSE 0,0,0,0,84,2197.780952,0.01031746,0.014779942,0,0,Nov,1,1,3,2,Returning_Visitor,FALSE,TRUE 4,26,0,0,235,10046.80771,0.007670851,0.022527689,0,0,Nov,2,2,1,2,Returning_Visitor,FALSE,TRUE 3,19,1,106,17,475.6666667,0,0.023582766,0,0,Nov,1,10,3,2,Returning_Visitor,FALSE,FALSE 0,0,0,0,1,11,0,0.1,0,0,Dec,1,1,3,1,Returning_Visitor,FALSE,FALSE 0,0,0,0,5,54.5,0,0.04,0,0,Dec,2,5,1,2,New_Visitor,FALSE,FALSE 9,126.2631579,2,50.83333333,59,2004.371491,0.008695652,0.030961792,0,0,Dec,2,2,6,3,Returning_Visitor,FALSE,FALSE 4,55.75,0,0,74,3817.203846,0,0.020533333,0,0,Nov,2,2,1,2,Returning_Visitor,FALSE,FALSE 0,0,0,0,58,3178.5,0,0.015789474,0,0,Nov,2,2,9,2,Returning_Visitor,FALSE,TRUE 3,22,2,69.5,30,1671.583333,0,0.019354839,0,0,Nov,2,2,1,2,Returning_Visitor,FALSE,FALSE 0,0,0,0,13,1985,0.016666667,0.05,0,0,Nov,4,1,7,6,Returning_Visitor,FALSE,FALSE 1,6,0,0,15,762,0,0.014285714,0,0,Dec,8,13,9,20,Other,FALSE,FALSE 9,739.125,0,0,19,1655.190476,0,0.022666667,27.97007005,0,Dec,1,1,7,2,New_Visitor,FALSE,FALSE 0,0,0,0,33,1412.316667,0.012121212,0.051717172,0,0,Nov,2,5,3,1,Returning_Visitor,FALSE,TRUE 0,0,0,0,8,29.16666667,0,0.1,0,0,Dec,2,2,3,2,Returning_Visitor,FALSE,FALSE 0,0,0,0,16,306.5,0,0.015625,0,0,Dec,1,1,4,2,Returning_Visitor,TRUE,FALSE 0,0,0,0,54,3978.245833,0.003846154,0.024807692,0,0,Nov,2,2,6,1,Returning_Visitor,TRUE,FALSE 0,0,0,0,7,103.5,0,0.057142857,0,0,Nov,2,2,2,2,Returning_Visitor,FALSE,TRUE 0,0,0,0,4,47,0,0.05,0,0,Nov,2,2,3,11,Returning_Visitor,TRUE,FALSE 0,0,0,0,6,38.5,0.066666667,0.1,0,0,Nov,2,10,7,1,Returning_Visitor,FALSE,FALSE 11,334.1666667,2,34,140,4817.369546,0.002684564,0.009552573,11.97037051,0,Nov,2,2,9,2,Returning_Visitor,FALSE,FALSE 0,0,0,0,28,447,0,0.033333333,0,0,Nov,2,2,7,10,Returning_Visitor,FALSE,FALSE 0,0,0,0,1,0,0.2,0.2,0,0,Nov,1,1,4,1,Returning_Visitor,FALSE,FALSE 0,0,0,0,16,829.0833333,0,0.0125,41.67536194,0,Nov,2,4,1,3,New_Visitor,FALSE,TRUE 9,307.360119,4,77.91666667,95,2989.780952,0.005882353,0.014513583,12.74039337,0,Nov,3,2,4,1,Returning_Visitor,TRUE,FALSE 0,0,0,0,1,0,0,0.2,0,0,Dec,3,2,4,1,Returning_Visitor,FALSE,FALSE 0,0,0,0,1,0,0.2,0.2,0,0,Nov,3,2,1,13,Returning_Visitor,TRUE,FALSE 11,631.4166667,5,1037.15,501,21672.24425,0.003964723,0.014292461,9.131386805,0,Nov,2,2,1,2,Returning_Visitor,FALSE,TRUE 0,0,0,0,21,371,0,0.00952381,0,0,Nov,2,2,1,13,Returning_Visitor,FALSE,FALSE 0,0,2,45.75,42,1931.630952,0,0.008333333,0,0,Dec,1,1,6,1,Returning_Visitor,TRUE,FALSE 7,32.64285714,3,189,224,6095.481945,0.002623688,0.016400421,10.36159616,0,Nov,1,2,1,2,Returning_Visitor,FALSE,TRUE 7,184.9333333,4,277.5,70,3993.135714,0.006,0.014539683,32.09724943,0,Nov,3,2,1,2,Returning_Visitor,TRUE,TRUE 4,62.66666667,0,0,82,1805.277375,0,0.023295607,0,0,Dec,1,1,2,2,Returning_Visitor,FALSE,FALSE 0,0,0,0,3,0,0.2,0.2,0,0,Nov,2,2,1,1,Returning_Visitor,FALSE,FALSE 0,0,0,0,1,0,0.2,0.2,0,0,Nov,1,1,1,1,Other,FALSE,FALSE 4,55.575,0,0,4,42.575,0,0.011428571,0,0,Nov,1,1,1,8,New_Visitor,FALSE,FALSE 0,0,0,0,3,30,0,0.033333333,0,0,Dec,1,1,3,1,Returning_Visitor,FALSE,FALSE 0,0,0,0,23,566.8,0,0.007971014,0,0,Nov,3,2,1,13,Returning_Visitor,FALSE,FALSE 0,0,0,0,22,2189.5,0.00952381,0.028571429,0,0,Nov,4,2,1,10,Returning_Visitor,FALSE,FALSE 0,0,1,39.5,97,2811.47807,0.002749141,0.015832106,0,0,Nov,2,5,6,1,Returning_Visitor,TRUE,FALSE 5,107,0,0,34,777.2125,0,0.013513514,0,0,Dec,2,2,1,1,Returning_Visitor,TRUE,FALSE 0,0,0,0,22,876.25,0,0.004761905,0,0,Dec,2,2,2,10,Returning_Visitor,FALSE,FALSE 0,0,0,0,3,75.5,0,0.066666667,0,0,Dec,3,2,4,2,Returning_Visitor,TRUE,FALSE 0,0,0,0,21,1077.25,0.019047619,0.047619048,0,0,Nov,3,2,1,10,Returning_Visitor,FALSE,FALSE 4,104.25,0,0,38,1429.875,0,0.022807018,4.113391331,0,Nov,2,2,1,13,Returning_Visitor,FALSE,TRUE 1,9,0,0,15,688.1833333,0,0.011904762,43.03066045,0,Dec,1,1,3,2,New_Visitor,FALSE,TRUE 0,0,0,0,42,1022.384615,0.014634146,0.031939605,0,0,Nov,1,1,2,10,Returning_Visitor,TRUE,FALSE 0,0,0,0,1,0,0.2,0.2,0,0,Dec,1,1,3,3,Returning_Visitor,FALSE,FALSE 9,227.6,0,0,54,1935.06131,0.006557377,0.022222222,42.19732111,0,Dec,1,2,1,2,Returning_Visitor,FALSE,TRUE 0,0,0,0,7,354.5,0.028571429,0.04,0,0,Nov,2,2,4,8,Returning_Visitor,FALSE,FALSE 2,67.75,0,0,11,417.9892857,0.016666667,0.043148148,0,0,Nov,3,2,1,2,Returning_Visitor,FALSE,FALSE 11,554.75,0,0,34,1466.088462,0.007407407,0.024345679,10.73461716,0,Nov,1,1,3,8,New_Visitor,TRUE,TRUE 0,0,0,0,111,3971.515278,0.009009009,0.024725635,0,0,Nov,2,2,1,2,Returning_Visitor,FALSE,FALSE 0,0,0,0,11,124.2166667,0,0.075757576,0,0,Dec,1,1,3,1,Returning_Visitor,FALSE,FALSE 8,256.5,1,0,57,1319.366667,0.006774194,0.032699169,3.373062968,0,Nov,3,2,3,2,Returning_Visitor,TRUE,TRUE 0,0,0,0,2,0,0.2,0.2,0,0,Nov,2,2,9,13,Returning_Visitor,FALSE,FALSE 0,0,2,861,12,2250.35,0.014285714,0.030952381,0,0,Nov,1,1,3,2,Returning_Visitor,TRUE,FALSE 0,0,0,0,1,0,0.2,0.2,0,0,Nov,1,8,8,11,Returning_Visitor,TRUE,FALSE 2,3,2,63.5,118,5250.891667,0.001626016,0.014905149,0,0,Nov,2,2,1,2,Returning_Visitor,TRUE,FALSE 0,0,0,0,72,2494.8171,0.002777778,0.013842593,0,0,Nov,1,2,1,11,Returning_Visitor,FALSE,FALSE 5,249.8,2,50,229,8866.167386,0.007143733,0.024304152,0,0,Nov,2,2,1,1,Returning_Visitor,FALSE,FALSE 0,0,0,0,8,219,0,0.025,0,0,Dec,2,5,3,2,Returning_Visitor,FALSE,FALSE 0,0,0,0,3,0,0.2,0.2,0,0,Nov,2,2,5,1,Returning_Visitor,FALSE,FALSE 0,0,0,0,6,143.5,0,0.016666667,0,0,Dec,1,1,8,10,Returning_Visitor,FALSE,FALSE 0,0,0,0,21,1050.25,0,0.028571429,0,0,Nov,2,2,6,2,Returning_Visitor,TRUE,FALSE 0,0,0,0,27,520,0,0.003703704,0,0,Nov,8,2,3,20,New_Visitor,FALSE,TRUE 6,201,0,0,84,3646.083333,0.006896552,0.016803503,66.57412378,0,Nov,2,2,4,2,Returning_Visitor,FALSE,FALSE 0,0,0,0,8,141.6666667,0.04375,0.02875,0,0,Dec,1,2,1,1,Returning_Visitor,TRUE,FALSE 0,0,0,0,11,577.75,0.018181818,0.033333333,0,0,Nov,3,2,1,3,Returning_Visitor,FALSE,FALSE 3,89,0,0,26,655.5357143,0,0.0375,0,0,Nov,2,2,4,1,Returning_Visitor,FALSE,FALSE 10,149.013357,2,138,78,2207.366936,0.002380952,0.012458392,0,0,Dec,3,2,1,2,Returning_Visitor,FALSE,FALSE 4,113.25,0,0,91,1746.108683,0,0.00428729,0,0,Nov,2,2,8,8,New_Visitor,FALSE,FALSE 4,562,0,0,22,1945.5,0,0.008333333,0,0,Dec,2,2,1,2,Returning_Visitor,FALSE,FALSE 10,305.905303,1,89.95454545,101,6038.244769,0.001904762,0.019112158,51.36077038,0,Nov,2,4,3,2,Returning_Visitor,TRUE,TRUE 8,203.9166667,4,114.75,118,3903.167094,0.004098361,0.012520167,55.01131298,0,Nov,2,2,1,2,Returning_Visitor,FALSE,TRUE 1,7.625,0,0,21,1104.9,0,0.014603175,0,0,Dec,2,2,1,1,Returning_Visitor,FALSE,FALSE 1,4,0,0,48,1199.160606,0,0.00070922,0,0,Dec,2,2,1,2,New_Visitor,FALSE,FALSE 13,1230.333333,7,286.0833333,68,3147.245589,0.013684211,0.026801619,1.597803468,0,Dec,1,1,1,2,Returning_Visitor,FALSE,TRUE 0,0,0,0,34,1401.458333,0,0.023529412,0,0,Nov,2,2,9,11,New_Visitor,FALSE,TRUE 0,0,0,0,33,2861.966667,0,0.034375,0,0,Nov,2,2,9,2,Returning_Visitor,FALSE,FALSE 0,0,0,0,10,379.0833333,0,0.0125,0,0,Dec,3,12,1,1,Returning_Visitor,TRUE,FALSE 0,0,0,0,16,283.75,0.0125,0.025,0,0,Nov,2,2,1,11,Returning_Visitor,FALSE,FALSE 6,129.0208333,0,0,56,755.4178922,0.001724138,0.015842912,0,0,Nov,1,1,1,10,Returning_Visitor,TRUE,FALSE 1,5,1,70.5,125,3507.010877,0.005645161,0.013874153,0,0,Nov,4,1,4,2,Returning_Visitor,FALSE,FALSE 0,0,0,0,17,364.5416667,0,0.041666667,0,0,Dec,3,2,8,13,Returning_Visitor,FALSE,FALSE 4,45.75,0,0,18,1273.083333,0,0.031818182,6.750968245,0,Dec,1,1,1,1,Returning_Visitor,FALSE,TRUE 9,189.1098485,3,39,73,3059.013889,0.012345679,0.024897119,16.74209908,0,Nov,2,2,3,2,Returning_Visitor,TRUE,TRUE 3,156.5,0,0,9,143,0,0.005555556,0,0,Nov,1,1,1,2,New_Visitor,FALSE,FALSE 0,0,0,0,13,255.0833333,0,0.015384615,0,0,Dec,2,2,8,1,Returning_Visitor,FALSE,FALSE 0,0,0,0,56,2749.15,0.000595238,0.023809524,0,0,Nov,2,2,1,8,Returning_Visitor,FALSE,TRUE 6,89.0625,2,888,275,9139.496696,0.00625,0.018539498,0,0,Nov,2,2,1,2,Returning_Visitor,FALSE,FALSE 0,0,0,0,49,938.0416667,0.00375,0.010664683,0,0,Dec,1,1,7,2,Returning_Visitor,FALSE,FALSE 3,20.5,0,0,38,1806.558333,0.005128205,0.026495726,7.150073241,0,Nov,2,2,6,13,Returning_Visitor,FALSE,FALSE 0,0,0,0,3,58.5,0,0.066666667,0,0,Dec,2,4,1,2,Returning_Visitor,FALSE,FALSE 1,0,0,0,45,1296,0,0.013333333,0,0,Nov,2,5,2,3,Returning_Visitor,FALSE,FALSE 4,79.25,5,500.8,39,1080.875,0.010638298,0.024964539,20.06500939,0,Nov,2,5,1,2,Returning_Visitor,FALSE,TRUE 3,155.6078431,1,14,108,2942.021588,0.00025974,0.013002525,7.890931935,0,Dec,1,1,2,10,Returning_Visitor,FALSE,TRUE 5,175.9166667,0,0,12,276.25,0.013333333,0.046666667,0,0,Dec,3,2,1,3,Returning_Visitor,FALSE,TRUE 7,634.2666667,3,89.5,54,2864.633333,0.012295082,0.024020297,7.448242868,0,Nov,3,2,3,1,Returning_Visitor,FALSE,FALSE 2,49.25,2,17,77,4130.937879,0.011219512,0.029260777,43.99403085,0,Nov,4,1,1,2,Returning_Visitor,FALSE,FALSE 1,16,0,0,77,3586.833333,0,0.012575758,0,0,Dec,2,2,1,2,Returning_Visitor,FALSE,FALSE 0,0,0,0,2,0,0.2,0.2,0,0,Nov,3,2,9,3,Returning_Visitor,FALSE,FALSE 1,14,0,0,11,227.25,0.018181818,0.018181818,0,0,Nov,1,1,3,2,New_Visitor,TRUE,FALSE 0,0,0,0,3,52.5,0,0.083333333,0,0,Nov,1,1,9,2,Returning_Visitor,FALSE,FALSE 0,0,0,0,1,0,0.2,0.2,0,0,Nov,2,6,1,2,Returning_Visitor,FALSE,FALSE 10,214.6214286,1,114,36,673.6380952,0.019379845,0.032443035,0,0,Nov,1,1,1,2,Returning_Visitor,FALSE,FALSE 0,0,0,0,5,975.5,0.08,0.1,0,0,Nov,3,2,3,13,Returning_Visitor,FALSE,FALSE 10,137.5,1,32.5,145,6165.878774,0.001333333,0.020769697,0,0,Nov,2,2,6,2,Returning_Visitor,FALSE,TRUE 0,0,0,0,44,1502.291667,0.004545455,0.027272727,0,0,Dec,2,2,1,2,New_Visitor,FALSE,FALSE 6,237.5,3,347.5,22,740.5,0.008,0.02,87.94459488,0,Dec,3,2,1,2,Returning_Visitor,FALSE,FALSE 0,0,0,0,9,134.75,0.083333333,0.124444444,0,0,Nov,1,1,2,1,Returning_Visitor,FALSE,FALSE 0,0,2,162.5,49,572.061039,0.004,0.027944444,0,0,Dec,2,2,1,6,Returning_Visitor,FALSE,FALSE 6,62.75,0,0,46,1098.524802,0.007083333,0.016529882,0,0,Nov,1,1,4,8,Returning_Visitor,FALSE,FALSE 8,964.25,6,182.1666667,134,5889.730504,0.028910577,0.053431066,0,0,Nov,3,2,1,3,Returning_Visitor,FALSE,FALSE 2,131.5,2,99,96,3541.324493,0.010550214,0.020522711,0,0,Nov,3,2,1,2,Returning_Visitor,FALSE,FALSE 0,0,0,0,1,0,0.2,0.2,0,0,Dec,2,2,4,13,Returning_Visitor,FALSE,FALSE 2,19,0,0,11,658.9166667,0,0.015384615,0,0,Dec,2,2,1,2,Returning_Visitor,FALSE,FALSE 0,0,0,0,17,2641.416667,0.017647059,0.04,0,0,Nov,3,2,4,13,Returning_Visitor,FALSE,FALSE 0,0,0,0,12,364.75,0.025,0.025,0,0,Nov,2,6,3,2,Returning_Visitor,FALSE,FALSE 0,0,0,0,100,2781.125,0.012,0.037666667,0,0,Dec,2,6,3,1,Returning_Visitor,FALSE,FALSE 3,27,0,0,2,16,0,0.05,0,0,Nov,2,2,2,2,New_Visitor,TRUE,TRUE 0,0,4,115.8,95,2500.128052,0.005342081,0.011832634,0,0,Nov,1,1,3,2,Returning_Visitor,FALSE,FALSE 7,152.6964286,2,13,13,200.25,0.02,0.03,0,0,Dec,2,2,7,1,Returning_Visitor,TRUE,FALSE 0,0,0,0,2,19,0,0.1,0,0,Nov,1,1,1,2,New_Visitor,TRUE,FALSE 18,1012.5,6,114.3333333,106,3163.096825,0.011664747,0.021393867,1.307682317,0,Nov,3,2,6,13,Returning_Visitor,FALSE,FALSE 4,46.04166667,4,93.41666667,75,6057.928961,0.01042524,0.019667423,0,0,Nov,3,2,1,2,Returning_Visitor,FALSE,TRUE 2,17,0,0,30,1448.928571,0.043851852,0.069954545,0,0,Dec,3,2,1,13,Returning_Visitor,FALSE,FALSE 2,105.5,1,437.5,55,1984.025,0,0.003486395,0,0,Nov,3,2,4,6,Returning_Visitor,TRUE,FALSE 4,54.5,0,0,3,21,0,0.033333333,0,0,Nov,1,1,2,2,New_Visitor,FALSE,FALSE 1,44.5,0,0,31,739.6666667,0.01,0.052222222,0,0,Dec,1,8,3,1,Returning_Visitor,FALSE,FALSE 3,59.5,0,0,6,159,0,0.00625,0,0,Dec,3,2,8,6,New_Visitor,FALSE,FALSE 0,0,0,0,1,56.5,0,0.1,0,0,Nov,2,2,3,3,Returning_Visitor,FALSE,FALSE 1,5,0,0,37,2235.828084,0,0.010810811,50.07598981,0,Nov,2,5,7,10,Returning_Visitor,FALSE,TRUE 23,322.9416667,0,0,62,3142.941176,0.003037975,0.019685462,7.311892217,0,Nov,2,2,1,10,Returning_Visitor,TRUE,FALSE 0,0,0,0,11,1289.333333,0,0.022222222,0,0,Dec,1,1,6,2,New_Visitor,FALSE,FALSE 2,34.25,0,0,55,3227.483333,0.003636364,0.016363636,0,0,Nov,2,2,3,13,Returning_Visitor,FALSE,FALSE 1,6,0,0,14,1183,0,0.004761905,17.5336164,0,Nov,2,2,6,10,New_Visitor,TRUE,TRUE 11,151.525,6,435.25,72,5251.382738,0,0.009969093,13.77521629,0,Dec,2,2,4,2,Returning_Visitor,FALSE,FALSE 0,0,0,0,21,685.1744048,0.028571429,0.043174603,0,0,Nov,1,1,8,1,Returning_Visitor,TRUE,TRUE 1,22,0,0,29,2209.75,0,0.035057471,0,0,Nov,3,2,8,2,Returning_Visitor,FALSE,FALSE 0,0,0,0,8,1102.5,0.04,0.090625,0,0,Dec,1,1,6,2,Returning_Visitor,TRUE,FALSE 0,0,1,20,48,1686.354994,0.020408163,0.033747213,0,0,Dec,3,2,1,10,Returning_Visitor,TRUE,FALSE 0,0,0,0,8,704.1666667,0.0125,0.035416667,0,0,Nov,3,2,1,3,Returning_Visitor,FALSE,FALSE 1,5,0,0,30,1240.65,0,0.017708333,0,0,Nov,2,2,6,2,Returning_Visitor,FALSE,FALSE 1,103,0,0,20,1140.666667,0,0.005,19.18108767,0,Dec,3,2,3,2,New_Visitor,FALSE,TRUE 4,21,0,0,7,56.25,0.033333333,0.066666667,0,0,Nov,3,2,1,15,Returning_Visitor,TRUE,FALSE 3,44,0,0,15,310.25,0.0125,0.0125,0,0,Dec,3,2,3,2,Returning_Visitor,TRUE,FALSE 0,0,0,0,3,53,0.066666667,0.088888889,0,0,Nov,1,1,3,2,Returning_Visitor,TRUE,FALSE 2,12,0,0,8,91.25,0,0.02,0,0,Dec,2,2,6,10,Returning_Visitor,FALSE,FALSE 0,0,0,0,7,136.75,0,0.038095238,0,0,Dec,3,2,9,6,Returning_Visitor,FALSE,FALSE 3,116.5,0,0,15,320.25,0,0.005882353,0,0,Dec,1,1,4,6,Returning_Visitor,TRUE,FALSE 1,67.5,1,0,11,816.5,0,0.016666667,0,0,Dec,1,1,6,2,New_Visitor,FALSE,FALSE 0,0,0,0,95,3208.208333,0.00212766,0.010851064,5.364419268,0,Nov,2,2,1,1,Returning_Visitor,FALSE,TRUE 0,0,0,0,5,109.25,0,0.08,0,0,Nov,2,10,1,2,Returning_Visitor,FALSE,FALSE 6,169.7916667,0,0,32,1547.625,0,0.013157895,23.53105656,0,Dec,2,2,1,1,Returning_Visitor,FALSE,FALSE 0,0,0,0,3,139,0,0.066666667,127.3080148,0,Dec,2,10,1,2,Returning_Visitor,FALSE,TRUE 6,113.25,2,19,23,2049.75,0.022222222,0.033333333,95.35469341,0,Dec,1,1,2,2,Returning_Visitor,FALSE,TRUE 22,474.4764124,3,167.1428571,92,2324.820965,0.014613095,0.020072361,7.935285933,0,Nov,3,2,2,13,Returning_Visitor,FALSE,TRUE 0,0,0,0,2,17,0,0.1,0,0,Nov,1,1,1,2,New_Visitor,FALSE,FALSE 10,263.3583333,0,0,69,3416.279167,0.012004662,0.019056732,0,0,Nov,3,2,1,2,Returning_Visitor,FALSE,TRUE 0,0,0,0,13,1245.25,0,0.015384615,0,0,Dec,2,2,1,2,Returning_Visitor,TRUE,FALSE 8,440.4615385,1,0,89,3236.865632,0.012544803,0.027526998,7.329008963,0,Nov,1,1,1,10,Returning_Visitor,TRUE,FALSE 1,42,0,0,19,682.1333333,0,0.021666667,14.14050684,0,Dec,1,1,4,10,Returning_Visitor,FALSE,FALSE 3,80.625,1,54,11,314.25,0.037333333,0.042857143,0,0,Nov,3,2,2,13,Returning_Visitor,FALSE,FALSE 1,5,0,0,58,2492.22402,0.008939014,0.025864237,0,0,Nov,3,2,1,2,Returning_Visitor,FALSE,FALSE 6,93.5,0,0,35,1197.93254,0,0.02029036,0,0,Dec,2,2,7,2,Returning_Visitor,FALSE,FALSE 0,0,0,0,48,3972,0,0.020991254,0,0,Dec,2,2,1,2,Returning_Visitor,FALSE,FALSE 6,48.625,0,0,14,211.4702381,0.001190476,0.039520717,0,0,Nov,3,2,6,10,Returning_Visitor,FALSE,FALSE 9,866.4280303,1,691,36,1617.612554,0.010869565,0.025668172,0,0,Nov,1,1,1,2,Returning_Visitor,TRUE,FALSE 4,234.1666667,0,0,24,1589.583333,0,0.007692308,61.20406619,0,Nov,2,2,2,10,Returning_Visitor,FALSE,TRUE 8,459.3333333,0,0,25,2408.815476,0,0.008333333,81.19520286,0,Nov,3,2,7,8,Returning_Visitor,TRUE,TRUE 0,0,0,0,8,120.5,0.05,0.1125,0,0,Dec,3,2,1,2,Returning_Visitor,TRUE,FALSE 0,0,0,0,13,226,0,0.023076923,0,0,Dec,2,2,9,8,New_Visitor,FALSE,FALSE 0,0,1,9,48,1512.75,0.004081633,0.033469388,0,0,Nov,2,2,1,2,Returning_Visitor,FALSE,TRUE 0,0,0,0,21,591.9,0.03015873,0.057142857,0,0,Dec,1,2,1,1,Returning_Visitor,TRUE,FALSE 0,0,0,0,13,514.4166667,0,0.023992674,0,0,Nov,2,2,1,2,Returning_Visitor,FALSE,FALSE 0,0,0,0,19,771.75,0.021052632,0.061403509,0,0,Nov,3,2,3,1,Returning_Visitor,FALSE,FALSE 15,572.5833333,2,37.5,318,13717.40205,0.004176829,0.015642808,45.15513521,0,Nov,2,2,1,2,Returning_Visitor,FALSE,TRUE 0,0,2,13.5,38,531.0369048,0,0.027916667,0,0,Dec,4,2,1,3,Returning_Visitor,FALSE,FALSE 8,171.75,2,16,49,1494.083333,0.001257862,0.028773585,0,0,Nov,2,2,1,1,Returning_Visitor,FALSE,FALSE 3,46,0,0,59,3478.75,0,0.013559322,0,0,Nov,2,10,3,1,Returning_Visitor,TRUE,FALSE 0,0,0,0,4,18,0,0.075,0,0,Dec,3,2,2,3,New_Visitor,FALSE,FALSE 0,0,0,0,10,677.6666667,0,0.022222222,0,0,Dec,4,1,3,2,New_Visitor,FALSE,FALSE 3,63.5,0,0,94,4620.088889,0.002150538,0.010752688,0,0,Dec,2,2,1,2,Returning_Visitor,TRUE,FALSE 0,0,0,0,4,27.5,0.1,0.15,0,0,Dec,3,2,1,13,Returning_Visitor,FALSE,FALSE 7,53.75,5,199,77,2194.490404,0,0.010341365,44.1286184,0,Nov,2,2,2,2,Returning_Visitor,FALSE,TRUE 0,0,0,0,8,650.25,0,0.025,0,0,Nov,6,2,1,3,Returning_Visitor,FALSE,FALSE 0,0,0,0,3,54.5,0,0.066666667,0,0,Nov,2,5,2,3,Returning_Visitor,FALSE,FALSE 0,0,0,0,3,402,0,0.066666667,0,0,Nov,4,2,3,2,Returning_Visitor,FALSE,TRUE 0,0,0,0,6,44.33333333,0.025,0.094444444,0,0,Nov,1,1,1,2,Returning_Visitor,FALSE,FALSE 3,66.375,0,0,85,3344.958929,0.002325581,0.012754534,0,0,Dec,2,2,4,3,Returning_Visitor,FALSE,FALSE 0,0,0,0,6,306.25,0.053571429,0.052592593,0,0,Nov,3,2,1,13,Returning_Visitor,FALSE,FALSE 1,32.75,0,0,24,553.8333333,0,0.024,8.958652232,0,Dec,2,2,1,2,Returning_Visitor,FALSE,FALSE 0,0,0,0,10,273.75,0.02,0.053333333,0,0,Nov,1,1,2,8,Returning_Visitor,FALSE,TRUE 0,0,0,0,3,79.5,0,0.066666667,0,0,Dec,4,1,3,2,Returning_Visitor,TRUE,FALSE 3,96,0,0,12,175.175,0.02,0.031111111,0,0,Dec,3,2,8,2,New_Visitor,FALSE,FALSE 0,0,0,0,27,892.9679487,0.014814815,0.022751323,0,0,Dec,2,2,3,2,Returning_Visitor,TRUE,FALSE 8,152,0,0,4,72.5,0,0.00952381,0,0,Nov,2,2,1,8,New_Visitor,FALSE,FALSE 0,0,0,0,9,668,0,0.022222222,0,0,Nov,2,4,9,6,Returning_Visitor,FALSE,FALSE 0,0,0,0,6,269.25,0,0.008333333,0,0,Nov,3,2,1,11,New_Visitor,FALSE,FALSE 0,0,0,0,11,332.2916667,0,0.009090909,0,0,Dec,2,2,2,2,Returning_Visitor,FALSE,FALSE 4,178.1666667,4,162.6666667,4,214.8333333,0,0.018181818,0,0,Dec,2,2,2,1,Returning_Visitor,FALSE,FALSE 13,301.5616883,1,14,55,3328.903752,0.007936508,0.014212018,38.60204838,0,Nov,1,1,1,2,Returning_Visitor,FALSE,FALSE 0,0,0,0,5,89.5,0.04,0.053333333,0,0,Dec,1,1,3,2,Returning_Visitor,TRUE,FALSE 0,0,0,0,15,734.25,0,0.015384615,40.4014481,0,Dec,2,2,9,2,Returning_Visitor,FALSE,TRUE 3,57,0,0,42,3556.516667,0,0.00968254,26.98362279,0,Dec,2,4,3,1,Returning_Visitor,FALSE,FALSE 3,34.25,2,17,60,2335.125,0.0015625,0.01875,0,0,Dec,2,2,1,2,Returning_Visitor,TRUE,FALSE 2,7,0,0,20,454.9166667,0.009090909,0.025757576,0,0,Nov,2,2,1,10,Returning_Visitor,FALSE,FALSE 8,164.625,0,0,43,1110.579167,0.002083333,0.010671296,16.35594008,0,Nov,2,2,3,3,Returning_Visitor,FALSE,TRUE 6,331.25,3,365.25,6,267,0,0.021428571,0,0,Dec,1,2,7,3,Returning_Visitor,TRUE,FALSE 0,0,0,0,28,950.8166667,0.032183908,0.046360153,0,0,Dec,3,2,1,1,Returning_Visitor,FALSE,FALSE 11,286.9333333,0,0,119,7609.82065,0.0032,0.015231111,0,0,Dec,2,2,3,1,Returning_Visitor,FALSE,FALSE 1,13.75,0,0,32,855.5416667,0.000840336,0.027058824,22.95499447,0,Nov,2,2,2,6,Returning_Visitor,FALSE,TRUE 2,42.4,0,0,24,2095.819048,0.025,0.041319444,0,0,Dec,3,2,3,1,Returning_Visitor,FALSE,FALSE 1,46.5,2,418.5,88,2085.972565,0,0.006273946,0,0,Nov,2,2,1,2,Returning_Visitor,TRUE,FALSE 6,133.3333333,1,398,129,4653.190245,0.015662578,0.025219763,6.017563943,0,Nov,3,2,1,3,Returning_Visitor,FALSE,TRUE 0,0,0,0,37,1156.416667,0,0.011111111,0,0,Nov,1,1,9,2,Returning_Visitor,TRUE,FALSE 0,0,0,0,5,46.75,0,0.04,0,0,Dec,1,1,3,3,Returning_Visitor,FALSE,FALSE 7,198.2585034,4,492.9,81,2237.757205,0.007231405,0.018203638,3.0265717,0,Nov,3,2,3,6,Returning_Visitor,TRUE,FALSE 5,73.125,2,53.5,200,6671.290144,0.003458896,0.012152531,0,0,Nov,2,2,4,2,Returning_Visitor,FALSE,FALSE 1,76.75,0,0,33,1356.458333,0,0.020833333,0,0,Nov,2,2,1,10,Returning_Visitor,TRUE,FALSE 1,25.5,0,0,0,0,0,0.066666667,0,0,Nov,1,1,8,8,New_Visitor,FALSE,TRUE 0,0,0,0,2,0,0.2,0.2,0,0,Dec,3,7,3,1,Returning_Visitor,FALSE,FALSE 6,358.0833333,1,18,93,3422.525,0.020408163,0.039301175,0,0,Nov,2,4,2,1,Returning_Visitor,FALSE,FALSE 0,0,0,0,76,1777.520455,0.008,0.025492063,0,0,Nov,1,2,1,10,Returning_Visitor,TRUE,FALSE 0,0,0,0,9,191.5625,0,0.024691358,0,0,Nov,3,3,1,11,New_Visitor,TRUE,FALSE 0,0,0,0,13,221.5,0,0.020512821,0,0,Nov,2,2,1,2,New_Visitor,FALSE,FALSE 3,31.1,0,0,11,975.2893939,0,0.062414966,0,0,Nov,4,5,1,8,Returning_Visitor,FALSE,FALSE 1,27.33333333,0,0,3,20,0.028571429,0.085714286,0,0,Nov,1,1,4,8,Returning_Visitor,FALSE,FALSE 0,0,0,0,11,461,0.036363636,0.054545455,0,0,Dec,2,2,5,6,Returning_Visitor,FALSE,FALSE 0,0,0,0,12,201.3333333,0.016666667,0.035714286,0,0,Nov,3,2,1,13,Returning_Visitor,TRUE,FALSE 0,0,0,0,5,51,0,0.04,0,0,Nov,3,2,3,13,Returning_Visitor,FALSE,FALSE 9,186.6730769,0,0,73,1606.30825,0.010295359,0.019108884,0,0,Nov,3,2,8,2,Returning_Visitor,TRUE,FALSE 2,67.5,0,0,65,1828.733333,0.002985075,0.018017058,0,0,Dec,2,2,1,2,New_Visitor,FALSE,FALSE 1,5,1,17.75,93,3009.042857,0.003617021,0.018145897,6.992767815,0,Dec,1,2,4,3,Returning_Visitor,TRUE,FALSE 0,0,0,0,1,0,0.2,0.2,0,0,Nov,2,2,3,1,Returning_Visitor,FALSE,FALSE 3,42.5,0,0,54,5972.416667,0.003508772,0.010526316,70.33953705,0,Nov,2,6,1,3,Returning_Visitor,FALSE,TRUE 0,0,2,34,144,5485.017857,0.005517241,0.0165084,0,0,Nov,2,2,1,13,Returning_Visitor,FALSE,FALSE 0,0,0,0,16,1773.25,0.04375,0.072916667,0,0,Nov,2,2,3,13,Returning_Visitor,FALSE,FALSE 0,0,0,0,11,496,0.018181818,0.063636364,0,0,Dec,3,12,2,3,Returning_Visitor,FALSE,FALSE 0,0,1,0,11,185.6666667,0.05,0.083333333,0,0,Dec,3,2,1,1,Returning_Visitor,FALSE,FALSE 0,0,0,0,21,1461.833333,0.021052632,0.036842105,23.82324931,0,Nov,2,2,1,1,Returning_Visitor,TRUE,TRUE 0,0,0,0,3,0,0.2,0.2,0,0,Nov,1,8,9,1,Returning_Visitor,TRUE,FALSE 0,0,0,0,7,914.5,0,0.028571429,0,0,Nov,2,2,2,1,Returning_Visitor,FALSE,FALSE 0,0,0,0,5,128,0,0.04,0,0,Nov,2,2,2,1,Returning_Visitor,FALSE,FALSE 0,0,0,0,27,468.5416667,0.014814815,0.022222222,0,0,Dec,3,2,3,3,Returning_Visitor,TRUE,FALSE 1,4,0,0,30,1409.53355,0,0.028,0,0,Nov,3,2,3,2,Returning_Visitor,TRUE,FALSE 5,126.25,0,0,15,286.5166667,0.005263158,0.01754386,0,0,Dec,1,1,2,10,Returning_Visitor,FALSE,FALSE 0,0,0,0,17,842.75,0,0.04,0,0,Dec,2,5,3,1,Returning_Visitor,FALSE,FALSE 0,0,0,0,34,910.2333333,0,0.023529412,0,0,Dec,2,2,1,2,Returning_Visitor,FALSE,FALSE 11,298.0820225,4,138.5,397,11940.0165,0.006959671,0.009804129,9.221243019,0,Nov,1,1,3,3,Returning_Visitor,TRUE,TRUE 5,62,0,0,30,3815.166667,0,0.026200717,0,0,Nov,2,2,4,2,Returning_Visitor,FALSE,FALSE 8,222.875,4,285.625,31,1622.883333,0.005405405,0.017488397,15.26399679,0,Dec,3,2,1,2,Returning_Visitor,FALSE,FALSE 0,0,0,0,4,96.33333333,0.083333333,0.0875,0,0,Dec,1,1,2,3,Returning_Visitor,FALSE,FALSE 6,131.75,0,0,32,789.4166667,0,0.00745098,0,0,Dec,1,1,1,2,Returning_Visitor,TRUE,FALSE 0,0,0,0,8,221,0,0.025,0,0,Dec,3,2,1,1,Returning_Visitor,TRUE,FALSE 0,0,0,0,48,1916.873737,0.017391304,0.050362319,0,0,Dec,2,2,5,1,Returning_Visitor,FALSE,FALSE 3,182.6428571,3,119.375,64,2671.06994,0.000275482,0.017352092,37.59646822,0,Nov,2,5,6,2,Returning_Visitor,FALSE,TRUE 0,0,0,0,51,6505.333333,0.036,0.050333333,0,0,Dec,2,2,6,1,Returning_Visitor,TRUE,FALSE 4,571.25,1,0,11,470.9166667,0.015384615,0.061538462,0,0,Nov,1,1,1,2,Returning_Visitor,TRUE,FALSE 0,0,0,0,20,565.6666667,0.015,0.026666667,0,0,Nov,2,4,6,1,Returning_Visitor,FALSE,FALSE 2,67.5,0,0,11,294,0,0.009090909,0,0,Dec,1,1,3,8,Returning_Visitor,TRUE,FALSE 0,0,0,0,15,278.2916667,0,0.017777778,0,0,Dec,2,2,9,10,Returning_Visitor,FALSE,FALSE 3,103,2,58.5,94,2807.149729,0,0.002777778,0,0,Nov,2,2,7,2,New_Visitor,TRUE,FALSE 3,88,0,0,7,64,0,0.022222222,0,0,Dec,3,2,1,2,New_Visitor,FALSE,FALSE 9,509.8333333,2,23,27,1438.854167,0.020952381,0.043333333,0,0,Dec,1,1,3,2,Returning_Visitor,FALSE,FALSE 2,90,1,47.5,40,1186.141667,0,0.013821138,0,0,Nov,2,10,3,2,Returning_Visitor,FALSE,FALSE 0,0,0,0,3,70,0,0.066666667,0,0,Nov,1,1,2,2,New_Visitor,TRUE,FALSE 4,226,0,0,81,4836.486905,0.002380952,0.010714286,5.312300227,0,Nov,2,2,1,2,Returning_Visitor,FALSE,TRUE 9,283.25,0,0,38,1166.166667,0,0.004761905,18.60589711,0,Nov,1,1,9,20,New_Visitor,FALSE,TRUE 4,99.125,0,0,38,876.3,0,0.0175,0,0,Nov,4,1,7,3,Returning_Visitor,FALSE,FALSE 4,390.5,0,0,27,1285.583333,0,0.006896552,14.67222588,0,Dec,2,2,8,2,Returning_Visitor,FALSE,TRUE 0,0,0,0,11,357.3333333,0,0.022222222,0,0,Dec,1,1,3,2,New_Visitor,FALSE,FALSE 6,571.5,0,0,49,1196.25,0.007843137,0.018954248,0,0,Dec,2,2,3,13,Returning_Visitor,TRUE,FALSE 0,0,0,0,5,94.8,0.07,0.057777778,0,0,Nov,3,2,7,13,Returning_Visitor,FALSE,FALSE 2,133.5,0,0,4,454.3333333,0,0.025,0,0,Nov,3,2,1,3,New_Visitor,FALSE,FALSE 0,0,0,0,33,777.1166667,0,0.015104167,0,0,Nov,2,2,6,2,New_Visitor,TRUE,FALSE 0,0,0,0,2,84,0.15,0.166666667,0,0,Nov,3,2,6,3,Returning_Visitor,TRUE,FALSE 13,278.1519608,1,46.5,150,5575.614608,7.27E-05,0.017155355,1.529570511,0,Dec,2,2,2,1,Returning_Visitor,FALSE,TRUE 11,227.2272727,0,0,44,2038.052273,0.007719298,0.028421053,0,0,Nov,2,2,3,8,Returning_Visitor,TRUE,FALSE 3,33,0,0,17,386.4166667,0,0.01,0,0,Dec,2,2,1,2,New_Visitor,FALSE,TRUE 0,0,0,0,9,79.75,0,0.025,0,0,Dec,2,4,1,2,Returning_Visitor,TRUE,FALSE 0,0,0,0,4,18,0.05,0.1,0,0,Nov,2,2,1,13,Returning_Visitor,FALSE,FALSE 0,0,0,0,50,999.8333333,0,0.009,0,0,Dec,2,2,1,8,New_Visitor,TRUE,FALSE 3,112.75,6,287.5,27,1491.5,0.013968254,0.021326007,5.772982827,0,Nov,1,1,1,2,Returning_Visitor,FALSE,FALSE 0,0,0,0,2,35.5,0,0.1,0,0,Dec,2,6,1,11,Returning_Visitor,TRUE,FALSE 0,0,0,0,8,444.6333333,0,0.025,0,0,Nov,2,4,1,10,Returning_Visitor,FALSE,FALSE 0,0,0,0,5,93,0,0.04,0,0,Nov,2,2,1,3,Returning_Visitor,TRUE,FALSE 0,0,2,248.5,121,4218.074847,0.009985097,0.026514442,0.624510388,0,Dec,2,2,1,2,Returning_Visitor,FALSE,FALSE 1,10,0,0,9,700,0,0.011111111,29.62188989,0,Dec,2,2,9,10,Returning_Visitor,FALSE,TRUE 2,115,0,0,97,1940.293452,0.01717251,0.032369194,0,0,Nov,1,1,3,10,Returning_Visitor,TRUE,FALSE 4,55.25,0,0,27,705.5,0,0.007901235,0,0,Nov,1,1,9,3,Returning_Visitor,FALSE,FALSE 6,91.75,0,0,11,380.6666667,0,0.005128205,0,0,Dec,2,2,1,2,Returning_Visitor,TRUE,FALSE 15,2657.318056,13,1949.166667,343,29970.46597,0.005315857,0.02897116,0,0,Dec,2,2,1,2,Returning_Visitor,FALSE,FALSE 3,37.5,0,0,18,607.5,0,0.010526316,361.7637419,0,Nov,2,2,3,2,New_Visitor,FALSE,TRUE 0,0,0,0,9,53,0.066666667,0.111111111,0,0,Dec,2,5,3,1,Returning_Visitor,TRUE,FALSE 0,0,0,0,6,112.7083333,0.04,0.023333333,0,0,Nov,3,2,6,11,New_Visitor,TRUE,FALSE 10,274.25,5,247.75,61,2267.330952,0.002739726,0.005348989,48.6094789,0,Nov,2,2,1,2,Returning_Visitor,FALSE,TRUE 0,0,0,0,12,252.75,0,0.016666667,0,0,Dec,2,2,7,3,Returning_Visitor,FALSE,FALSE 0,0,0,0,27,857.1833333,0,0.017283951,0,0,Nov,2,2,9,10,Returning_Visitor,FALSE,FALSE 0,0,1,15,103,3626.003223,0.00751634,0.035131628,0,0,Nov,1,1,4,2,Returning_Visitor,TRUE,FALSE 0,0,0,0,7,512.25,0,0.014285714,0,0,Dec,3,2,1,11,New_Visitor,FALSE,FALSE 0,0,0,0,1,0,0.2,0.2,0,0,Nov,2,4,7,3,Returning_Visitor,FALSE,FALSE 4,196.5,3,72.5,245,7569.248612,0.007646236,0.016468484,4.770910365,0,Nov,3,2,1,13,Returning_Visitor,TRUE,FALSE 16,427.0666667,3,111,122,3492.316304,0.006547137,0.015792367,86.57772177,0,Nov,1,1,1,3,Returning_Visitor,FALSE,FALSE 12,214.05,0,0,33,536.3227273,0.007317073,0.015853659,0,0,Dec,1,1,1,2,Returning_Visitor,FALSE,FALSE 2,92,2,59.5,39,2207.757143,0.009756098,0.023577236,0,0,Dec,3,2,1,2,Returning_Visitor,FALSE,FALSE 6,86.58333333,2,160,41,1119.854167,0.004545455,0.012045455,27.03986205,0,Nov,2,2,9,8,New_Visitor,TRUE,FALSE 9,65.16666667,0,0,123,5969.573168,0.001538462,0.013923077,10.69237969,0,Nov,2,1,1,2,New_Visitor,FALSE,TRUE 6,218.6666667,0,0,24,1737,0.006896552,0.015172414,23.51599702,0,Nov,1,1,6,10,Returning_Visitor,TRUE,TRUE 0,0,0,0,113,5178.531731,0.003896104,0.030191572,0,0,Nov,2,2,3,2,Returning_Visitor,FALSE,TRUE 0,0,0,0,10,426.5,0,0.02,0,0,Nov,2,2,1,2,New_Visitor,FALSE,FALSE 4,56.5,2,8,9,198.25,0.018181818,0.036363636,0,0,Nov,1,8,3,6,Returning_Visitor,TRUE,FALSE 2,57.5,0,0,16,1226,0,0.0125,143.4766783,0,Dec,2,2,9,2,New_Visitor,TRUE,TRUE 0,0,0,0,10,108.9166667,0.02,0.04,0,0,Nov,2,4,1,10,Returning_Visitor,FALSE,FALSE 2,115.8888889,2,45.5,26,1198.024145,0.003888889,0.03,14.50274386,0,Nov,3,2,1,2,Returning_Visitor,FALSE,FALSE 0,0,0,0,60,2606.779545,0.006666667,0.021944444,0,0,Dec,2,2,3,2,Returning_Visitor,FALSE,FALSE 4,131,0,0,10,285.5,0.033333333,0.083333333,0,0,Dec,3,2,4,3,Returning_Visitor,FALSE,FALSE 12,202.25,0,0,175,4877.122575,0.006906077,0.014774057,0,0,Nov,2,2,4,2,Returning_Visitor,TRUE,TRUE 0,0,4,8,66,1514.83631,0.022887324,0.044913928,0,0,Dec,2,2,6,2,Returning_Visitor,FALSE,FALSE 0,0,0,0,3,896.5,0,0.033333333,0,0,Dec,2,2,3,3,Returning_Visitor,FALSE,FALSE 3,89.5,0,0,48,1572.2,0.017687075,0.0388854,20.11392747,0,Nov,4,2,1,3,Returning_Visitor,FALSE,TRUE 2,30,1,0,24,567.25,0,0.011538462,0,0,Dec,2,5,1,2,Returning_Visitor,FALSE,FALSE 0,0,0,0,159,5878.940603,0.008962264,0.018569755,0,0,Nov,3,2,1,2,Returning_Visitor,FALSE,TRUE 0,0,0,0,25,314.6964286,0,0.015666667,0,0,Dec,2,2,3,2,Returning_Visitor,TRUE,FALSE 0,0,0,0,31,445.625,0.019354839,0.030322581,0,0,Nov,2,2,2,3,Returning_Visitor,FALSE,FALSE 4,302.5,0,0,15,257.1666667,0.05,0.04875,0,0,Dec,3,2,7,3,Returning_Visitor,FALSE,FALSE 2,39.5,0,0,28,986.9333333,0.044166667,0.057,0,0,Dec,1,1,3,2,Returning_Visitor,FALSE,FALSE 5,140.25,4,282,4,213.5,0,0.009090909,0,0,Nov,3,2,1,2,New_Visitor,FALSE,TRUE 0,0,0,0,13,549.2,0.019230769,0.045421245,0,0,Nov,3,2,6,2,Returning_Visitor,FALSE,FALSE 3,38.5,0,0,15,130.05,0.023529412,0.050980392,0,0,Nov,1,1,3,1,Returning_Visitor,FALSE,FALSE 0,0,0,0,1,0,0.2,0.2,0,0,Nov,2,2,8,6,Returning_Visitor,FALSE,FALSE 3,92.75,2,7,23,1347.9625,0.008333333,0.020833333,32.4141704,0,Nov,2,2,6,2,Returning_Visitor,FALSE,FALSE 0,0,0,0,17,1040.1,0,0.013333333,128.8278691,0,Dec,2,2,5,2,New_Visitor,FALSE,TRUE 12,295.9619048,1,0,45,2033.845238,0,0.007154088,21.23244876,0,Nov,2,2,9,2,Returning_Visitor,FALSE,FALSE 3,115.5,0,0,55,1833.244643,0,0.008286252,18.61684302,0,Nov,2,2,6,2,Returning_Visitor,TRUE,TRUE 0,0,0,0,8,138.375,0,0.025,0,0,Dec,2,2,1,2,New_Visitor,FALSE,FALSE 0,0,0,0,24,445,0,0.033333333,0,0,Dec,2,2,3,2,New_Visitor,FALSE,FALSE 4,207,0,0,31,1592.233333,0,0.011574074,57.06546413,0,Dec,2,2,4,1,Returning_Visitor,FALSE,FALSE 0,0,1,10,88,2366.192105,0.01247191,0.030501264,0,0,Nov,2,4,7,1,Returning_Visitor,FALSE,FALSE 2,47.5,0,0,23,472.4166667,0,0.032985131,0,0,Dec,1,1,1,3,Returning_Visitor,FALSE,FALSE 0,0,0,0,30,1438,0,0.013793103,100.1655678,0,Nov,4,1,1,3,Returning_Visitor,FALSE,TRUE 0,0,0,0,9,125.5,0.007407407,0.022222222,0,0,Nov,3,2,4,2,Returning_Visitor,FALSE,FALSE 0,0,0,0,8,143.25,0,0.05,0,0,Dec,2,2,1,1,Returning_Visitor,FALSE,FALSE 0,0,0,0,2,145.5,0,0.05,0,0,Nov,1,1,3,2,New_Visitor,FALSE,FALSE 0,0,0,0,5,117.75,0,0.04,0,0,Dec,1,1,1,2,Returning_Visitor,FALSE,FALSE 8,269.0416667,3,64.5,171,6400.430101,0.006967985,0.017556729,4.213364204,0,Nov,1,1,1,1,Returning_Visitor,FALSE,TRUE 5,134.5,0,0,11,237.6666667,0,0.018181818,0,0,Dec,3,2,2,2,Returning_Visitor,FALSE,FALSE 4,126.0769231,2,43.33333333,34,1395.445862,0.038927739,0.042311136,48.66071756,0,Nov,3,2,1,10,Returning_Visitor,FALSE,TRUE 2,67.5,0,0,45,1660,0.022727273,0.0375,0,0,Nov,2,5,1,1,Returning_Visitor,FALSE,FALSE 0,0,0,0,1,0,0.2,0.2,0,0,Dec,3,2,3,1,Returning_Visitor,TRUE,FALSE 0,0,0,0,22,1368,0.004545455,0.013636364,0,0,Nov,2,5,7,3,Returning_Visitor,FALSE,FALSE 5,85.5,0,0,30,2188.258333,0,0.0078125,45.58268367,0,Nov,2,2,2,8,New_Visitor,TRUE,TRUE 15,241.8541667,1,27.5,202,10014.26935,0.004038462,0.021947073,1.606825723,0,Nov,2,2,1,2,Returning_Visitor,FALSE,TRUE 3,130.5,0,0,24,876.55,0.003076923,0.018034188,0,0,Nov,1,1,1,8,Returning_Visitor,FALSE,FALSE 0,0,0,0,116,3184.815705,0.00015674,0.022635468,0,0,Nov,2,2,1,10,Returning_Visitor,TRUE,FALSE 6,138.5,0,0,27,546.6071429,0.013095238,0.034702381,0,0,Dec,1,8,1,2,Returning_Visitor,FALSE,FALSE 0,0,0,0,4,186.25,0.05,0.066666667,0,0,Nov,3,2,3,2,Returning_Visitor,FALSE,FALSE 2,15,0,0,19,523.25,0.0375,0.046666667,28.18955966,0,Nov,3,2,1,8,Returning_Visitor,FALSE,TRUE 0,0,0,0,45,801.05,0.004444444,0.019444444,0,0,Dec,2,2,1,2,Returning_Visitor,FALSE,FALSE 0,0,0,0,8,92.25,0.041666667,0.066666667,0,0,Nov,3,2,4,13,Returning_Visitor,FALSE,FALSE 0,0,0,0,1,0,0.2,0.2,0,0,Dec,3,2,3,1,Returning_Visitor,TRUE,FALSE 10,303.9279762,0,0,82,1853.286538,0,0.015957265,11.01940759,0,Dec,2,2,3,2,Returning_Visitor,FALSE,FALSE 0,0,0,0,16,1558.25,0,0.013244048,0,0,Nov,3,2,3,2,New_Visitor,TRUE,FALSE 0,0,0,0,12,312.35,0,0.041666667,0,0,Nov,2,2,1,2,Returning_Visitor,FALSE,FALSE 0,0,0,0,22,573.75,0,0.018181818,0,0,Nov,3,2,8,2,New_Visitor,FALSE,FALSE 7,350.6,9,406.9166667,68,2435.74375,0.002564103,0.01555896,0,0,Dec,1,1,1,2,Returning_Visitor,FALSE,FALSE 0,0,0,0,16,241.4,0.029166667,0.045833333,0,0,Nov,3,2,6,13,Returning_Visitor,FALSE,FALSE 0,0,0,0,49,2259.047619,0.008163265,0.034693878,0,0,Nov,2,2,8,1,Returning_Visitor,FALSE,FALSE 7,251.75,0,0,5,50,0,0.05,0,0,Dec,1,1,3,2,Returning_Visitor,FALSE,FALSE 0,0,0,0,38,1338.778571,0.011111111,0.024532828,0,0,Nov,1,1,1,3,Returning_Visitor,TRUE,FALSE 0,0,1,98.66666667,94,3085.25,0,0.00609319,0,0,Nov,2,2,2,3,Returning_Visitor,FALSE,FALSE 0,0,0,0,107,3723.529739,0.001094276,0.01972313,0,0,Nov,2,2,1,2,Returning_Visitor,FALSE,FALSE 5,65.5,0,0,27,354.5833333,0.014285714,0.023809524,0,0,Nov,1,1,8,1,Returning_Visitor,FALSE,FALSE 1,14,0,0,50,1317.795833,0.01,0.021469506,0,0,Nov,3,2,4,1,Returning_Visitor,TRUE,FALSE 0,0,0,0,1,0,0.2,0.2,0,0,Nov,2,2,6,2,Returning_Visitor,FALSE,FALSE 0,0,0,0,53,661.4455285,0.000754717,0.006528045,0,0,Nov,3,2,1,11,Returning_Visitor,TRUE,FALSE 3,163.3333333,0,0,96,1949.647252,0.00742268,0.014405743,0,0,Dec,1,1,1,1,Returning_Visitor,FALSE,FALSE 1,65.5,0,0,51,1544.855159,0.012244898,0.027755102,0,0,Nov,3,2,1,1,Returning_Visitor,TRUE,FALSE 5,173.2632132,0,0,49,1465.555338,0.02384954,0.023755148,20.6672459,0,Nov,3,2,3,1,Returning_Visitor,FALSE,TRUE 0,0,0,0,1,70.5,0,0.1,0,0,Nov,2,10,1,3,Returning_Visitor,FALSE,FALSE 0,0,0,0,49,3247.583333,0.025531915,0.037163121,0,0,Dec,2,4,9,1,Returning_Visitor,TRUE,FALSE 1,12,0,0,71,1733.658333,0,0.002898551,0,0,Nov,8,2,1,3,Returning_Visitor,TRUE,FALSE 0,0,0,0,9,82.5,0,0.033333333,0,0,Dec,2,2,1,1,Returning_Visitor,FALSE,FALSE 2,13,0,0,13,477.5,0.0125,0.01875,0,0,Dec,2,5,1,1,Returning_Visitor,FALSE,FALSE 1,45.5,3,1021.666667,130,3376.027564,0.007518797,0.029169274,0,0,Dec,2,2,1,1,Returning_Visitor,FALSE,FALSE 0,0,0,0,33,629.8583333,0.006060606,0.03030303,0,0,Nov,2,2,3,1,Returning_Visitor,TRUE,FALSE 13,249.3916667,1,103,71,2582.699717,0,0.013853727,6.733744751,0,Nov,2,2,1,2,Returning_Visitor,FALSE,FALSE 4,41,0,0,50,2428.35,0.020512821,0.041868132,9.442927064,0,Nov,2,5,3,3,Returning_Visitor,TRUE,FALSE 0,0,3,34.3,137,4045.726634,0.009993099,0.021710569,0,0,Nov,1,2,1,2,Returning_Visitor,FALSE,FALSE 8,103,0,0,19,2081.5,0,0.01,24.54706296,0,Nov,2,2,3,2,New_Visitor,TRUE,TRUE 10,187.6944444,5,495.5,122,2743.213113,0.000199005,0.005114363,1.483210939,0,Nov,2,2,1,2,Returning_Visitor,FALSE,TRUE 0,0,0,0,11,203.5,0.018181818,0.042424242,0,0,Dec,4,5,3,3,Returning_Visitor,FALSE,FALSE 0,0,0,0,6,254.5,0,0.033333333,0,0,Nov,2,2,1,2,New_Visitor,FALSE,TRUE 3,34,0,0,17,161.55,0,0.018518519,0,0,Nov,2,2,5,2,Returning_Visitor,TRUE,FALSE 3,92,0,0,91,4155.620833,0.002553191,0.022150056,0,0,Nov,1,2,1,2,Returning_Visitor,TRUE,FALSE 2,12.625,3,95,29,1268.125,0.008333333,0.038,25.04441774,0,Nov,2,2,1,10,Returning_Visitor,FALSE,TRUE 0,0,0,0,2,155.5,0.1,0.15,0,0,Dec,3,2,3,1,Returning_Visitor,TRUE,FALSE 0,0,0,0,29,2374.5,0,0.002298851,0,0,Nov,2,5,1,2,Returning_Visitor,FALSE,FALSE 5,216.25,2,107,39,1056.511364,0.02,0.031666667,0,0,Dec,3,2,1,1,Returning_Visitor,TRUE,FALSE 0,0,0,0,1,0,0.2,0.2,0,0,Nov,1,1,1,1,New_Visitor,FALSE,FALSE 3,129,0,0,74,2378.263492,0.005333333,0.025042424,4.141458811,0,Nov,2,6,9,1,Returning_Visitor,FALSE,TRUE 3,21,0,0,109,2587.530411,0,0.01036036,0,0,Nov,2,2,1,3,Returning_Visitor,FALSE,TRUE 7,150.375,0,0,7,219.5833333,0,0.018181818,0,0,Dec,1,1,1,2,Returning_Visitor,TRUE,FALSE 0,0,0,0,40,1542.35,0,0.031666667,0,0,Nov,2,2,1,2,Returning_Visitor,TRUE,TRUE 1,55.5,1,18,19,444.75,0,0.010526316,21.2112655,0,Nov,2,5,1,10,New_Visitor,FALSE,TRUE 4,66,0,0,52,1423.790476,0.005454545,0.008484848,0,0,Dec,2,4,7,20,Returning_Visitor,FALSE,FALSE 0,0,0,0,6,184,0.066666667,0.116666667,0,0,Nov,2,10,4,3,Returning_Visitor,FALSE,FALSE 3,1528.25,2,91,60,1548.08631,0.006349206,0.025925926,22.92341151,0,Nov,1,1,1,2,Returning_Visitor,TRUE,FALSE 0,0,0,0,11,234.1,0,0.022222222,0,0,Dec,3,2,1,2,Returning_Visitor,FALSE,FALSE 4,88,1,1174,173,6014.398401,0.009530632,0.025130309,1.53931845,0,Nov,3,2,1,2,Returning_Visitor,TRUE,TRUE 0,0,0,0,4,251,0.05,0.1,0,0,Nov,2,2,4,3,Returning_Visitor,FALSE,FALSE 0,0,0,0,24,3181.5,0.002777778,0.027083333,0,0,Nov,2,2,1,13,Returning_Visitor,TRUE,FALSE 0,0,0,0,10,383,0,0.05,0,0,Nov,2,2,9,2,New_Visitor,TRUE,FALSE 0,0,0,0,16,530.625,0,0.0125,0,0,Nov,2,5,3,3,Returning_Visitor,TRUE,FALSE 1,47.5,0,0,10,1202.875,0,0.025454545,0,0,Dec,1,1,8,3,Returning_Visitor,FALSE,FALSE 3,35,0,0,7,117.0833333,0,0.044444444,0,0,Nov,2,2,1,2,Returning_Visitor,FALSE,FALSE 0,0,3,38.5,97,1835.556213,0.019984314,0.031314735,0,0,Nov,1,1,3,2,Returning_Visitor,TRUE,FALSE 0,0,0,0,27,867.7777778,0,0.004487179,0,0,Dec,2,2,3,1,New_Visitor,FALSE,FALSE 4,31.70833333,1,4,51,946.3934211,0.02254902,0.035630252,10.37175846,0,Nov,1,1,3,2,Returning_Visitor,FALSE,FALSE 0,0,0,0,1,0,0.2,0.2,0,0,Dec,2,13,9,20,Returning_Visitor,FALSE,FALSE 4,52.66666667,0,0,41,2303.183333,0.005714286,0.015873016,0,0,Dec,2,2,1,2,Returning_Visitor,TRUE,FALSE 2,54.5,0,0,5,66.5,0,0.028571429,0,0,Dec,8,13,9,20,Other,FALSE,FALSE 9,175.9345238,4,54.5,229,6606.783899,0.00373444,0.015020196,8.896836973,0,Nov,2,2,1,2,Returning_Visitor,FALSE,TRUE 0,0,0,0,2,15,0,0.1,0,0,Dec,2,2,9,1,Returning_Visitor,FALSE,FALSE 4,42,1,28.5,34,1367,0,0.001960784,0,0,Nov,2,2,3,2,Returning_Visitor,FALSE,FALSE 14,160.1115392,6,1079.833333,154,7405.360281,0.004924242,0.021452975,8.533928457,0,Nov,4,1,4,1,Returning_Visitor,FALSE,TRUE 6,63.08333333,0,0,174,3486.370682,0.009364162,0.019687687,0,0,Nov,1,1,1,2,Returning_Visitor,FALSE,TRUE 0,0,0,0,11,1267.25,0.02,0.03,0,0,Nov,1,8,2,11,New_Visitor,TRUE,FALSE 2,96,0,0,61,1260.524994,0,0.003225806,131.5040438,0,Nov,2,2,7,10,Returning_Visitor,TRUE,TRUE 0,0,0,0,42,2239.363889,0.02,0.035454545,0,0,Dec,3,2,7,2,Returning_Visitor,FALSE,FALSE 4,37,0,0,9,224.25,0,0.015384615,0,0,Nov,2,2,3,2,New_Visitor,FALSE,FALSE 0,0,3,16,35,1621.766667,0.007894737,0.040131579,0,0,Nov,2,2,1,2,Returning_Visitor,TRUE,FALSE 6,71.2,0,0,101,3800.089234,0.003883495,0.024032278,3.179063602,0,Nov,1,1,3,1,Returning_Visitor,FALSE,TRUE 0,0,0,0,11,925.8988095,0.02,0.02952381,0,0,Nov,3,2,2,2,Returning_Visitor,FALSE,FALSE 0,0,0,0,11,212.9166667,0,0.009090909,0,0,Nov,3,2,5,2,New_Visitor,FALSE,FALSE 2,47.5,1,17,40,1202.916667,0.015813953,0.048604651,0,0,Nov,2,6,1,3,Returning_Visitor,FALSE,FALSE 10,162.2291667,0,0,101,2681.998105,0.010185185,0.021374201,4.195335178,0,Nov,3,2,3,2,Returning_Visitor,FALSE,FALSE 5,80.25,0,0,63,2618.681061,0.005797101,0.011111111,17.60939304,0,Nov,3,2,1,8,Returning_Visitor,FALSE,TRUE 0,0,0,0,1,0,0.2,0.2,0,0,Nov,1,1,3,2,Returning_Visitor,FALSE,FALSE 0,0,0,0,9,740.1666667,0,0.014285714,0,0,Nov,3,2,1,1,Returning_Visitor,FALSE,FALSE 2,30.75,0,0,150,4760.778303,0.01212528,0.019068861,0,0,Nov,3,2,8,13,Returning_Visitor,FALSE,FALSE 0,0,0,0,30,4262.966667,0,0.013333333,65.29516039,0,Dec,2,10,7,1,Returning_Visitor,TRUE,TRUE 0,0,0,0,10,130.75,0.04,0.076666667,0,0,Nov,3,2,1,3,Returning_Visitor,FALSE,FALSE 0,0,3,45.5,117,3228.502137,0.005932203,0.018995891,0,0,Nov,1,2,1,2,Returning_Visitor,TRUE,FALSE 1,37.5,0,0,45,2018.3,0.005681818,0.030822511,0,0,Nov,2,2,2,2,Returning_Visitor,FALSE,FALSE 0,0,0,0,4,47,0,0.05,0,0,Nov,3,2,6,1,Returning_Visitor,FALSE,FALSE 1,0,0,0,21,658.25,0.035497835,0.057482993,0,0,Nov,3,2,5,13,Returning_Visitor,FALSE,FALSE 0,0,0,0,6,109.25,0,0.033333333,0,0,Dec,8,13,9,20,Other,FALSE,FALSE 17,195.4864719,1,60.5,94,2267.472565,0.002111614,0.008213489,4.511078764,0,Nov,3,2,3,2,Returning_Visitor,FALSE,TRUE 0,0,0,0,2,4,0,0.1,0,0,Nov,2,2,2,2,Returning_Visitor,FALSE,FALSE 0,0,0,0,4,206.5,0.05,0.05,0,0,Dec,1,1,1,8,Returning_Visitor,FALSE,FALSE 0,0,0,0,4,82,0,0.05,0,0,Nov,2,2,4,1,Returning_Visitor,FALSE,FALSE 3,22,0,0,152,4251.74125,0.003947368,0.009780702,0,0,Nov,2,2,1,10,Returning_Visitor,FALSE,TRUE 7,644.5,2,166.5,31,1629.641667,0,0.026063126,0,0,Dec,2,2,4,2,Returning_Visitor,FALSE,FALSE 10,212.7616279,4,109,222,9049.534024,0.008536232,0.018457454,10.66936985,0,Nov,1,2,3,2,Returning_Visitor,TRUE,FALSE 0,0,0,0,9,125.5,0,0.007407407,0,0,Nov,2,2,7,11,Returning_Visitor,FALSE,FALSE 4,76,0,0,186,5106.262991,0.003723404,0.023642242,0,0,Nov,2,2,3,2,Returning_Visitor,TRUE,FALSE 0,0,3,191.3333333,57,1315.455182,0,0.009501916,0,0,Nov,3,2,3,2,Returning_Visitor,TRUE,TRUE 0,0,0,0,1,0,0.2,0.2,0,0,Dec,2,2,1,3,Returning_Visitor,FALSE,FALSE 0,0,0,0,13,98.66666667,0.015384615,0.046153846,0,0,Dec,2,2,3,13,Returning_Visitor,FALSE,FALSE 0,0,0,0,1,0,0.2,0.2,0,0,Dec,2,2,4,1,Returning_Visitor,FALSE,FALSE 1,6.333333333,0,0,36,1378.166667,0.005555556,0.026587302,0,0,Nov,2,2,1,13,Returning_Visitor,TRUE,FALSE 0,0,0,0,32,348.25,0,0.010793651,0,0,Dec,2,2,1,6,Returning_Visitor,FALSE,FALSE 0,0,0,0,11,382.25,0.018181818,0.063636364,0,0,Dec,2,2,3,2,Returning_Visitor,FALSE,FALSE 0,0,0,0,5,256.5,0,0.04,0,0,Dec,2,2,3,3,Returning_Visitor,FALSE,FALSE 0,0,0,0,10,249,0.04,0.064,0,0,Nov,1,1,8,3,Returning_Visitor,FALSE,FALSE 4,98.91666667,0,0,110,4096.712716,0.002654867,0.011061947,0,0,Nov,1,2,6,2,Returning_Visitor,FALSE,FALSE 1,18,0,0,62,1288.130556,0.003278689,0.009071038,13.63692586,0,Nov,2,10,1,3,Returning_Visitor,FALSE,TRUE 0,0,0,0,10,594,0,0.031666667,0,0,Dec,2,2,1,2,Returning_Visitor,FALSE,FALSE 5,158.7,0,0,51,1364.266667,0.007692308,0.011987179,0,0,Nov,3,2,3,2,Returning_Visitor,TRUE,FALSE 0,0,0,0,1,0,0.2,0.2,0,0,Dec,1,1,5,1,Returning_Visitor,FALSE,FALSE 0,0,0,0,11,189.25,0.027272727,0.048484848,0,0,Dec,2,5,1,1,Returning_Visitor,FALSE,FALSE 4,98.5,0,0,9,281.75,0,0.030909091,0,0,Dec,1,1,3,2,Returning_Visitor,FALSE,FALSE 7,87.91666667,1,0,46,1621.025379,0.004081633,0.020544218,6.02352321,0,Dec,1,1,3,2,Returning_Visitor,FALSE,FALSE 0,0,0,0,37,2323.166667,0.010810811,0.018198198,0,0,Nov,1,2,4,2,Returning_Visitor,FALSE,FALSE 14,324,2,40.5,12,338.25,0,0.002380952,0,0,Dec,1,1,1,10,Returning_Visitor,TRUE,FALSE 3,38,0,0,2,15,0,0.04,0,0,Nov,2,2,1,2,New_Visitor,FALSE,FALSE 0,0,0,0,7,459.6666667,0.028571429,0.057142857,0,0,Nov,2,2,1,3,Returning_Visitor,FALSE,FALSE 3,30.5,0,0,79,2431.242995,0,0.005590278,0,0,Nov,2,2,2,6,Returning_Visitor,FALSE,FALSE 0,0,0,0,24,1151,0,0.0125,44.20382094,0,Nov,2,2,3,8,New_Visitor,TRUE,TRUE 0,0,0,0,12,155,0.066666667,0.091666667,0,0,Nov,4,2,1,1,Returning_Visitor,FALSE,FALSE 0,0,0,0,4,32,0,0.05,0,0,Dec,2,2,3,2,Returning_Visitor,FALSE,FALSE 0,0,0,0,37,1048.980263,0.005555556,0.024074074,0,0,Nov,2,2,7,2,Returning_Visitor,TRUE,FALSE 0,0,0,0,12,115.6666667,0.058333333,0.091666667,0,0,Nov,2,4,4,3,Returning_Visitor,FALSE,TRUE 1,0,0,0,4,94.75,0,0.053333333,0,0,Dec,3,2,1,20,Returning_Visitor,FALSE,FALSE 1,82,0,0,62,1243.17619,0.006451613,0.00983871,0,0,Dec,1,1,6,2,Returning_Visitor,FALSE,FALSE 1,120.5,1,260.5,22,1148.166667,0.009090909,0.036363636,7.518979759,0,Nov,2,2,1,1,Returning_Visitor,TRUE,TRUE 5,150.4444444,0,0,23,659.6361111,0.039463602,0.056586058,0,0,Nov,3,2,1,1,Returning_Visitor,FALSE,TRUE 0,0,0,0,4,42,0,0.05,0,0,Dec,2,10,1,2,Returning_Visitor,FALSE,FALSE 0,0,0,0,1,0,0.2,0.2,0,0,Dec,2,2,8,3,Returning_Visitor,FALSE,FALSE 1,5,0,0,43,1990.125,0.001162791,0.024031008,6.889766003,0,Dec,2,2,3,3,Returning_Visitor,FALSE,FALSE 2,33.5,0,0,23,398.4,0,0.017391304,0,0,Dec,2,2,3,6,Returning_Visitor,FALSE,FALSE 1,42.5,0,0,67,2413.754403,0.006153846,0.01030303,0,0,Dec,1,1,6,1,Returning_Visitor,FALSE,FALSE 0,0,0,0,14,422.1666667,0,0.023809524,0,0,Dec,1,1,1,2,Returning_Visitor,TRUE,FALSE 0,0,0,0,3,62.25,0,0.066666667,0,0,Dec,1,1,7,2,Returning_Visitor,FALSE,FALSE 0,0,1,35.5,130,3313.455754,0.007633588,0.028972728,0,0,Nov,2,2,1,2,Returning_Visitor,TRUE,FALSE 0,0,0,0,9,651,0,0.022222222,68.258708,0,Nov,2,2,2,2,Returning_Visitor,FALSE,TRUE 0,0,0,0,2,96,0,0.05,0,0,Nov,2,4,3,2,New_Visitor,FALSE,FALSE 0,0,0,0,8,126.25,0,0.05,0,0,Dec,2,2,3,2,New_Visitor,TRUE,FALSE 0,0,0,0,2,11.5,0,0.1,0,0,Dec,3,2,1,2,Returning_Visitor,TRUE,FALSE 1,42.5,2,54.5,47,3954.95,0.006,0.023714286,0,0,Nov,2,2,1,2,Returning_Visitor,FALSE,FALSE 0,0,0,0,1,0,0.2,0.2,0,0,Nov,2,4,1,10,Returning_Visitor,TRUE,FALSE 3,726,0,0,47,3993.833333,0.002040816,0.043171115,0,0,Nov,2,2,9,2,Returning_Visitor,FALSE,FALSE 0,0,0,0,14,1071.083333,0.014285714,0.029047619,0,0,Dec,2,2,7,2,Returning_Visitor,FALSE,FALSE 0,0,0,0,8,148.5,0,0.014285714,0,0,Dec,1,1,1,1,Returning_Visitor,TRUE,FALSE 0,0,0,0,17,315.3,0,0.027843137,0,0,Nov,1,1,2,3,Returning_Visitor,FALSE,FALSE 3,86,0,0,27,3188.483333,0.017241379,0.031034483,0,0,Dec,2,5,4,2,Returning_Visitor,FALSE,FALSE 0,0,0,0,19,1056.658333,0.005882353,0.041176471,0,0,Dec,1,1,1,8,Returning_Visitor,FALSE,FALSE 0,0,0,0,12,115,0.083333333,0.1,0,0,Dec,3,2,8,1,Returning_Visitor,FALSE,FALSE 2,22,3,41.5,67,3397.957955,0,0.015504136,0,0,Dec,2,2,2,2,Returning_Visitor,TRUE,FALSE 4,23.375,2,231.6,35,996.1480769,0.006081081,0.041741742,0,0,Nov,2,2,5,2,Returning_Visitor,TRUE,FALSE 7,201.5625,1,2,95,5696.165963,0.004081633,0.023369348,19.34978396,0,Nov,2,4,3,10,Returning_Visitor,FALSE,TRUE 5,74.75,3,86.75,51,2003.375779,0,0.008874272,44.33548922,0,Nov,2,2,7,2,Returning_Visitor,FALSE,FALSE 0,0,0,0,1,0,0.2,0.2,0,0,Nov,2,2,7,10,Returning_Visitor,TRUE,FALSE 5,235.625,1,0,14,629.2916667,0,0.0275,0,0,Nov,2,4,1,8,Returning_Visitor,FALSE,FALSE 0,0,0,0,29,819.5892857,0.003448276,0.025448823,0,0,Nov,3,2,1,3,Returning_Visitor,FALSE,FALSE 2,109,0,0,9,652.5,0,0.036363636,0,0,Dec,2,2,3,2,Returning_Visitor,FALSE,FALSE 0,0,0,0,40,974.0833333,0.006837607,0.028205128,0,0,Nov,2,2,1,3,Returning_Visitor,FALSE,FALSE 0,0,0,0,1,0,0.2,0.2,0,0,Dec,3,2,4,11,Returning_Visitor,TRUE,FALSE 8,133.4,0,0,38,2666.066667,0.002272727,0.016576479,17.06291074,0,Dec,1,1,4,2,Returning_Visitor,FALSE,FALSE 8,636,0,0,52,3374.016667,0,0.003703704,108.0893873,0,Nov,2,2,3,13,Returning_Visitor,FALSE,TRUE 5,237.25,1,181,63,3445.0125,0.011897436,0.028190306,0,0,Dec,3,2,1,2,Returning_Visitor,FALSE,FALSE 0,0,0,0,6,66.25,0,0.066666667,0,0,Nov,2,2,9,1,Returning_Visitor,FALSE,FALSE 0,0,0,0,12,348.75,0,0.027777778,0,0,Nov,3,2,4,3,Returning_Visitor,FALSE,FALSE 1,19,0,0,4,51.5,0.05,0.1,0,0,Nov,3,2,6,13,Returning_Visitor,FALSE,FALSE 0,0,0,0,12,748.25,0.016666667,0.022222222,0,0,Nov,1,2,1,2,Returning_Visitor,TRUE,FALSE 3,27,0,0,10,1579.375,0.0125,0.06,0,0,Nov,2,2,1,2,Returning_Visitor,FALSE,FALSE 0,0,0,0,2,11,0.088888889,0.188888889,0,0,Dec,2,2,2,2,Returning_Visitor,TRUE,FALSE 0,0,0,0,6,104.6666667,0,0.041666667,0,0,Dec,3,2,1,10,Returning_Visitor,FALSE,FALSE 3,74.5,1,88,113,3770.021698,0.008004926,0.019004516,14.85051676,0,Dec,3,2,1,1,Returning_Visitor,TRUE,FALSE 7,65.83333333,2,39.66666667,135,5520.175505,0.002962963,0.010258137,0,0,Dec,2,2,1,1,Returning_Visitor,FALSE,FALSE 0,0,0,0,2,171,0,0.1,0,0,Nov,3,2,1,13,Returning_Visitor,TRUE,FALSE 0,0,0,0,7,130,0.057142857,0.085714286,0,0,Nov,2,5,9,10,Returning_Visitor,TRUE,FALSE 0,0,0,0,2,22,0,0.1,0,0,Nov,3,2,3,3,Returning_Visitor,FALSE,FALSE 0,0,0,0,20,1078.583117,0.011111111,0.025925926,0,0,Dec,1,1,8,2,Returning_Visitor,FALSE,FALSE 6,162.7,6,205,52,2983.358135,0.010112994,0.017261259,17.4767153,0,Nov,3,2,4,2,Returning_Visitor,FALSE,FALSE 0,0,0,0,9,69.75,0.088888889,0.122222222,0,0,Dec,2,2,1,3,Returning_Visitor,FALSE,FALSE 7,722.2083333,9,151.5,238,10340.36916,0.002419355,0.016345046,3.851653215,0,Dec,2,2,4,1,Returning_Visitor,FALSE,FALSE 5,122.1,3,158.5,167,8701.851617,0.002401067,0.012477354,0,0,Nov,2,2,1,2,Returning_Visitor,FALSE,FALSE 1,250.5,0,0,1,0,0,0.1,0,0,Nov,2,2,9,13,Returning_Visitor,FALSE,FALSE 4,69.83333333,0,0,133,4885.049069,0.002962963,0.008689192,15.95538838,0,Nov,1,2,1,2,Returning_Visitor,TRUE,FALSE 5,158.5,2,15,34,2193.383333,0,0.024324324,0,0,Nov,1,1,1,2,Returning_Visitor,TRUE,TRUE 10,84.4375,3,147,35,1092.354167,0.005073996,0.034935835,0,0,Nov,2,5,9,8,Returning_Visitor,TRUE,FALSE 0,0,0,0,16,977.125,0,0.0125,0,0,Dec,2,2,1,2,Returning_Visitor,TRUE,FALSE 7,79.5,2,31.5,65,5410.956705,0.002898551,0.019031516,0,0,Nov,2,2,3,2,Returning_Visitor,FALSE,FALSE 2,268,2,84,46,1679.666667,0.012,0.026,0,0,Nov,3,2,1,13,Returning_Visitor,TRUE,FALSE 1,30.5,0,0,34,1333.166667,0,0.006060606,8.754045182,0,Dec,2,2,7,2,New_Visitor,FALSE,TRUE 1,25.5,0,0,43,914.2294372,0.013636364,0.025762987,0,0,Nov,1,1,1,1,Returning_Visitor,FALSE,FALSE 0,0,0,0,1,0,0.2,0.2,0,0,Dec,2,2,1,3,Returning_Visitor,FALSE,FALSE 0,0,0,0,12,349.5,0,0.008333333,0,0,Dec,1,1,6,1,Returning_Visitor,FALSE,FALSE 0,0,0,0,4,105,0,0.05,0,0,Nov,2,5,3,1,Returning_Visitor,FALSE,FALSE 0,0,0,0,17,315.0833333,0,0.014705882,0,0,Dec,2,2,3,1,Returning_Visitor,FALSE,TRUE 0,0,0,0,65,3873.02197,0.014393939,0.01991342,0,0,Nov,3,2,1,2,Returning_Visitor,TRUE,TRUE 4,353.5,0,0,24,583.0833333,0,0.007692308,0,0,Nov,1,1,1,2,Returning_Visitor,FALSE,FALSE 0,0,0,0,7,155.5,0.047619048,0.085714286,0,0,Nov,1,1,2,3,Returning_Visitor,FALSE,FALSE 1,0,0,0,55,2780.609524,0.019242424,0.045272727,9.137331307,0,Dec,2,2,8,3,Returning_Visitor,FALSE,FALSE 0,0,0,0,24,612.2083333,0.016666667,0.026388889,0,0,Dec,3,2,3,10,Returning_Visitor,FALSE,FALSE 7,266.875,0,0,32,2092.5,0.010526316,0.026315789,23.5676355,0,Dec,2,2,1,1,Returning_Visitor,TRUE,FALSE 0,0,0,0,2,0,0.2,0.2,0,0,Dec,3,2,3,6,Returning_Visitor,TRUE,FALSE 2,48.5,2,32,45,2336.319444,0.020833333,0.039236111,0,0,Dec,1,1,1,2,Returning_Visitor,FALSE,FALSE 0,0,0,0,45,747.0428571,0.017777778,0.031296296,0,0,Nov,3,2,1,13,Returning_Visitor,FALSE,FALSE 14,205.9924242,1,65,52,1090.385281,0.006479313,0.039161584,5.539900181,0,Nov,1,1,1,10,Returning_Visitor,FALSE,FALSE 0,0,0,0,44,1975.825,0,0.032142857,0,0,Dec,2,4,1,1,Returning_Visitor,FALSE,FALSE 12,225.25,1,0,129,3367.041281,0.001428571,0.018456829,1.03529974,0,Nov,2,4,1,10,Returning_Visitor,FALSE,TRUE 0,0,0,0,23,647.1428571,0.004347826,0.022222222,0,0,Nov,1,8,1,11,Returning_Visitor,TRUE,FALSE 5,98,0,0,6,60,0.05,0.0525,0,0,Dec,3,2,5,2,Returning_Visitor,FALSE,FALSE 0,0,0,0,13,138.5416667,0.023076923,0.028205128,0,0,Dec,1,1,8,1,Returning_Visitor,TRUE,FALSE 0,0,0,0,5,298,0.04,0.05,0,0,Nov,3,2,8,3,Returning_Visitor,FALSE,FALSE 0,0,1,1729,19,1401.083333,0.02,0.05,0,0,Dec,2,2,1,3,Returning_Visitor,FALSE,FALSE 0,0,0,0,27,393.7888889,0,0.032193939,0,0,Nov,3,2,1,11,New_Visitor,TRUE,FALSE 0,0,0,0,7,2478,0.028571429,0.057142857,0,0,Nov,2,2,1,11,Returning_Visitor,TRUE,TRUE 1,33.5,0,0,37,1702.916667,0,0.009047619,0,0,Nov,2,4,3,2,New_Visitor,FALSE,FALSE 0,0,0,0,63,1401.50873,0,0.003225806,88.95486906,0,Nov,1,1,1,7,Returning_Visitor,FALSE,TRUE 0,0,0,0,1,0,0.2,0.2,0,0,Dec,2,2,1,13,Returning_Visitor,FALSE,FALSE 3,57.425,3,55,11,281.425,0.004166667,0.053125,0,0,Nov,2,2,4,2,Returning_Visitor,FALSE,FALSE 0,0,0,0,34,1491.775,0.002020202,0.024793388,0,0,Nov,2,2,6,11,Returning_Visitor,TRUE,TRUE 0,0,0,0,91,2056.106981,0.008888889,0.023937057,0,0,Nov,2,2,1,2,Returning_Visitor,TRUE,FALSE 0,0,0,0,3,63,0,0.033333333,0,0,Nov,5,11,8,11,Returning_Visitor,FALSE,FALSE 3,36.25,1,10,35,2109.919048,0.018918919,0.027190827,23.8705054,0,Dec,3,2,3,1,Returning_Visitor,FALSE,FALSE 2,63.25,0,0,7,460.75,0.022222222,0.037037037,0,0,Dec,3,2,3,2,Returning_Visitor,FALSE,FALSE 3,103.0833333,2,39.25,39,1406.95,0.01533101,0.039562226,5.606382766,0,Nov,3,2,5,2,Returning_Visitor,FALSE,FALSE 2,4,5,161.5,64,3757.079762,0.012254902,0.022009804,18.88360212,0,Nov,1,2,6,2,Returning_Visitor,FALSE,FALSE 4,37.25,1,5,50,1295.008333,0.000892857,0.015595238,0,0,Nov,3,2,4,2,Returning_Visitor,TRUE,TRUE 0,0,0,0,69,1002.104167,0,0.013140097,0,0,Nov,2,2,4,2,Returning_Visitor,FALSE,FALSE 5,96.625,0,0,27,1883.725,0,0.019047619,0,0,Nov,2,2,9,13,Returning_Visitor,FALSE,FALSE 2,95,0,0,10,445,0.016666667,0.033333333,0,0,Dec,2,2,4,2,New_Visitor,TRUE,FALSE 0,0,0,0,69,2243.676587,0,0.002985075,97.9922066,0,Nov,2,2,2,2,Returning_Visitor,FALSE,TRUE 0,0,0,0,18,191,0,0.033333333,0,0,Nov,4,1,3,1,Returning_Visitor,FALSE,FALSE 0,0,0,0,13,193.3666667,0.015384615,0.023076923,0,0,Nov,3,2,1,13,New_Visitor,TRUE,FALSE 0,0,0,0,64,1690.778409,0.014285714,0.025219349,0,0,Dec,1,1,3,2,Returning_Visitor,FALSE,FALSE 3,48,1,58.5,35,1698,0.005714286,0.005714286,93.52994426,0,Nov,4,1,1,2,Returning_Visitor,TRUE,TRUE 1,7,4,103.75,99,3517.039683,0.007189542,0.020421866,0,0,Nov,2,2,1,8,Returning_Visitor,FALSE,TRUE 4,80.5,0,0,27,472.5,0,0.015384615,0,0,Dec,4,1,3,1,Returning_Visitor,TRUE,FALSE 5,106,0,0,111,8684.983333,0.002678571,0.019613095,3.696136038,0,Dec,2,2,3,1,Returning_Visitor,FALSE,FALSE 2,29.5,0,0,36,1653.395833,0.005555556,0.022839506,68.25041784,0,Dec,3,2,1,2,Returning_Visitor,FALSE,FALSE 1,24.91666667,1,7.666666667,98,3561.139286,0.008163265,0.018367347,78.19779587,0,Nov,2,2,1,13,Returning_Visitor,TRUE,TRUE 3,22.27777778,1,0,6,36.90277778,0,0.086666667,0,0,Nov,2,2,2,2,Returning_Visitor,FALSE,FALSE 8,522.25,0,0,53,1899.058333,0.004938272,0.007694004,14.16739222,0,Dec,1,1,1,2,Returning_Visitor,FALSE,FALSE 0,0,0,0,4,46.25,0,0.1,0,0,Nov,1,1,1,2,Returning_Visitor,FALSE,TRUE 3,224,2,42,132,5098.75498,0.010632603,0.014962157,2.209003373,0,Nov,1,2,1,2,Returning_Visitor,FALSE,TRUE 0,0,0,0,1,0,0.2,0.2,0,0,Nov,2,2,9,1,Returning_Visitor,FALSE,FALSE 0,0,0,0,4,90.5,0.1,0.166666667,0,0,Nov,3,2,1,10,Returning_Visitor,FALSE,FALSE 5,132,1,8.5,167,5080.233631,0.000238095,0.004785636,64.81173101,0,Nov,4,2,1,1,Returning_Visitor,TRUE,TRUE 5,161.35,0,0,18,251.9,0,0.03015873,0,0,Nov,1,1,1,2,Returning_Visitor,TRUE,FALSE 1,12,0,0,22,851.25,0.004347826,0.020289855,0,0,Nov,2,2,1,2,New_Visitor,FALSE,FALSE 0,0,0,0,1,0,0.2,0.2,0,0,Nov,3,2,4,3,Returning_Visitor,FALSE,FALSE 0,0,0,0,1,0,0.2,0.2,0,0,Dec,1,13,9,20,Returning_Visitor,FALSE,FALSE 1,3,0,0,13,543,0,0.008333333,0,0,Dec,2,2,1,2,New_Visitor,FALSE,FALSE 0,0,0,0,6,185.5,0.016666667,0.05,0,0,Dec,2,2,3,8,New_Visitor,FALSE,FALSE 1,18.83333333,0,0,2,23.83333333,0,0.05,0,0,Dec,3,2,1,10,Returning_Visitor,FALSE,FALSE 0,0,0,0,61,1964.847222,0.021311475,0.041065574,0,0,Nov,3,2,2,13,Returning_Visitor,FALSE,FALSE 0,0,0,0,5,0,0.2,0.2,0,0,Nov,2,2,2,1,Returning_Visitor,FALSE,FALSE 13,293.5785714,3,26,161,3457.628812,0.006140351,0.018923933,15.988034,0,Nov,2,5,7,2,Returning_Visitor,TRUE,TRUE 9,88.60416667,0,0,114,2997.00203,0,0.005989942,32.56725746,0,Nov,2,4,2,2,Returning_Visitor,FALSE,TRUE 0,0,0,0,39,4022,0,0.034188034,0,0,Nov,2,2,6,1,Returning_Visitor,FALSE,FALSE 0,0,0,0,1,41.5,0.133333333,0.15,0,0,Dec,3,2,3,1,Returning_Visitor,TRUE,FALSE 4,1172,0,0,19,1542.791667,0.00952381,0.042857143,0,0,Dec,2,2,3,1,Returning_Visitor,FALSE,FALSE 11,347.7916667,0,0,91,2434.144872,0.002020202,0.019021966,10.51901813,0,Dec,2,2,4,3,Returning_Visitor,FALSE,FALSE 3,283.375,4,590,110,5442.085417,0.005409357,0.02942233,9.294011981,0,Nov,2,2,4,2,Returning_Visitor,TRUE,TRUE 0,0,0,0,62,3299.525,0,0.009539422,5.1680996,0,Nov,2,2,1,2,Returning_Visitor,FALSE,TRUE 0,0,0,0,16,885.75,0,0.025,0,0,Dec,2,2,1,2,Returning_Visitor,TRUE,FALSE 0,0,0,0,16,219.55,0,0.014583333,0,0,Nov,4,1,1,20,Returning_Visitor,FALSE,FALSE 0,0,0,0,1,0,0.2,0.2,0,0,Dec,2,2,8,1,Returning_Visitor,FALSE,FALSE 0,0,0,0,3,47.5,0.1,0.166666667,0,0,Nov,3,3,3,1,Returning_Visitor,FALSE,FALSE 4,113.8333333,0,0,3,23.66666667,0,0.05625,0,0,Dec,2,2,6,2,Returning_Visitor,TRUE,FALSE 14,243.1666667,0,0,34,671.6428571,0,0.016216216,0,0,Dec,4,5,3,1,Returning_Visitor,FALSE,FALSE 9,145.7708333,8,408.5,139,6566.867562,0.003477444,0.015531015,67.39709637,0,Nov,2,2,3,2,Returning_Visitor,TRUE,FALSE 6,102,0,0,11,501.5,0,0.007142857,0,0,Nov,4,1,1,8,New_Visitor,FALSE,FALSE 0,0,0,0,12,770.25,0,0.033333333,0,0,Dec,2,4,1,8,New_Visitor,FALSE,FALSE 2,68.33333333,0,0,38,1370.038095,0,0.004444444,0,0,Nov,3,2,4,2,Returning_Visitor,TRUE,FALSE 7,743.5824176,4,909.25,337,11835.10389,0.005228377,0.015719446,0,0,Nov,1,2,1,2,Returning_Visitor,FALSE,FALSE 0,0,0,0,1,0,0.2,0.2,0,0,Dec,3,2,3,1,Returning_Visitor,FALSE,FALSE 7,218.8333333,0,0,33,916.4583333,0,0.013333333,0,0,Dec,2,2,1,2,Returning_Visitor,FALSE,FALSE 0,0,0,0,4,132.5,0.175,0.183333333,0,0,Nov,3,2,4,13,Returning_Visitor,TRUE,FALSE 3,30,1,0,3,14,0,0.028571429,0,0,Nov,2,4,4,2,Returning_Visitor,FALSE,FALSE 0,0,0,0,1,0,0.2,0.2,0,0,Nov,2,2,1,1,Returning_Visitor,FALSE,FALSE 5,170.25,0,0,55,3057.045833,0.011666667,0.018737892,0,0,Nov,2,2,4,2,Returning_Visitor,FALSE,FALSE 0,0,0,0,2,442.5,0,0.1,0,0,Dec,4,2,4,3,Returning_Visitor,FALSE,FALSE 9,151.5666667,0,0,54,2446.144571,0.004678363,0.009409888,1.943058508,0,Nov,3,2,1,3,Returning_Visitor,TRUE,TRUE 12,238.4833333,0,0,13,269.2333333,0,0.01,0,0,Dec,2,10,1,3,New_Visitor,FALSE,FALSE 2,110.5,1,48,5,156,0,0.028571429,0,0,Dec,2,2,1,2,New_Visitor,FALSE,FALSE 1,34.5,0,0,24,819.15,0,0.001449275,0,0,Dec,1,1,2,2,Returning_Visitor,FALSE,FALSE 0,0,0,0,23,1434.908333,0,0.030952381,0,0,Dec,1,1,1,2,Returning_Visitor,FALSE,FALSE 2,122,0,0,8,177.25,0,0.025,0,0,Dec,2,2,1,3,New_Visitor,FALSE,FALSE 0,0,0,0,81,1950.840873,0.004938272,0.024773663,0,0,Nov,2,2,1,2,Returning_Visitor,FALSE,TRUE 0,0,0,0,3,116.75,0,0.066666667,0,0,Nov,3,2,1,10,Returning_Visitor,TRUE,FALSE 6,75.41666667,1,105,21,752.5666667,0.015384615,0.022435897,7.072429969,0,Nov,1,1,4,10,Returning_Visitor,TRUE,FALSE 4,736,1,9,99,2171.547149,0.016281269,0.029507982,4.495585092,0,Nov,3,2,4,13,Returning_Visitor,TRUE,FALSE 8,463.75,4,337.5,157,6123.741494,0.007198364,0.019816374,0,0,Nov,2,2,7,2,Returning_Visitor,TRUE,TRUE 0,0,2,5.5,141,4310.775,0.001408451,0.013891039,0,0,Dec,2,2,1,2,Returning_Visitor,FALSE,FALSE 7,42,0,0,23,502.525,0.007142857,0.021428571,0,0,Nov,2,5,3,3,Returning_Visitor,TRUE,FALSE 6,171.5,3,178.5,26,1565.958333,0.005882353,0.017647059,8.045767732,0,Nov,2,2,4,2,Returning_Visitor,FALSE,TRUE 5,82,1,137.5,118,2595.095833,0.001680672,0.012371615,38.7483363,0,Nov,2,2,1,1,Returning_Visitor,FALSE,TRUE 2,41.5,0,0,22,1140.15,0.005555556,0.039814815,5.00765405,0,Nov,1,1,1,10,Returning_Visitor,FALSE,FALSE 0,0,0,0,1,0,0.2,0.2,0,0,Dec,1,2,3,1,Returning_Visitor,FALSE,FALSE 5,488.462963,0,0,56,3327.537566,0.006578947,0.022060248,0,0,Dec,2,2,3,1,Returning_Visitor,FALSE,FALSE 0,0,0,0,17,664.5,0,0.011764706,0,0,Nov,4,1,9,2,Returning_Visitor,FALSE,FALSE 9,140.875,0,0,95,2625.185014,0.003,0.027077656,2.815539375,0,Nov,2,2,4,1,Returning_Visitor,FALSE,FALSE 0,0,2,485.8333333,89,3747.814286,0.013636364,0.030211039,0,0,Nov,1,1,1,2,Returning_Visitor,TRUE,TRUE 1,8,2,61.5,31,1126.25,0,0.024242424,0,0,Nov,4,2,1,2,Returning_Visitor,FALSE,FALSE 2,36.75,2,9,20,393.5833333,0.008333333,0.033872603,0,0,Nov,1,1,3,6,Returning_Visitor,FALSE,FALSE 2,22.9,0,0,145,5962.732411,0.002857143,0.013265367,0.780277403,0,Nov,2,10,7,3,Returning_Visitor,TRUE,TRUE 4,99.5,0,0,3,21.25,0,0.05,0,0,Nov,1,1,2,8,New_Visitor,TRUE,TRUE 0,0,0,0,4,121.75,0,0.05,0,0,Dec,1,8,9,2,Returning_Visitor,FALSE,FALSE 2,65.5,0,0,11,263,0,0.016666667,46.308063,0,Nov,2,2,1,2,Returning_Visitor,FALSE,TRUE 2,78,0,0,22,325,0,0.007246377,19.10349568,0,Dec,4,1,3,1,Returning_Visitor,TRUE,TRUE 0,0,0,0,5,178.8333333,0,0.04,0,0,Nov,2,2,7,13,Returning_Visitor,TRUE,FALSE 0,0,0,0,9,165.5833333,0.007407407,0.06,0,0,Dec,3,2,1,3,Returning_Visitor,FALSE,FALSE 1,63.5,0,0,7,58.9,0,0.025,0,0,Dec,1,1,2,2,New_Visitor,FALSE,FALSE 1,0,3,447.5,27,1381.75,0.006896552,0.034482759,0,0,Nov,2,2,1,2,Returning_Visitor,TRUE,FALSE 5,408.4083333,2,47.5,54,1547.477273,0,0.017333333,0,0,Nov,2,2,3,2,Returning_Visitor,TRUE,TRUE 0,0,0,0,19,465.6333333,0,0.026315789,0,0,Nov,1,1,1,2,New_Visitor,TRUE,FALSE 0,0,0,0,10,328.5,0,0.041666667,0,0,Nov,2,2,7,6,Returning_Visitor,FALSE,FALSE 3,70.5,0,0,44,737.1819347,0.005581395,0.023137121,0,0,Nov,1,1,4,1,Returning_Visitor,TRUE,FALSE 0,0,0,0,5,55.5,0,0.04,0,0,Dec,3,2,3,6,Returning_Visitor,FALSE,FALSE 0,0,0,0,11,145,0.018181818,0.036363636,0,0,Nov,1,1,1,10,Returning_Visitor,FALSE,FALSE 0,0,2,478.9166667,5,204,0.057142857,0.104081633,0,0,Nov,3,2,1,13,Returning_Visitor,FALSE,FALSE 1,17,0,0,17,1340.583333,0.047058824,0.071568627,0,0,Nov,3,2,1,2,Returning_Visitor,FALSE,TRUE 0,0,0,0,51,742.9603175,0.003921569,0.012121212,0,0,Nov,1,1,3,2,Returning_Visitor,TRUE,FALSE 0,0,0,0,17,2186.961538,0.01875,0.02422619,0,0,Dec,1,1,1,10,Returning_Visitor,FALSE,FALSE 2,21.75,0,0,6,84.25,0.033333333,0.044444444,0,0,Dec,3,2,4,20,Returning_Visitor,FALSE,FALSE 0,0,0,0,64,4492.311111,0.004761905,0.022705971,0,0,Nov,2,2,3,2,Returning_Visitor,FALSE,TRUE 2,49.8,0,0,41,857.2666667,0.012195122,0.0242741,5.850665427,0,Nov,2,2,1,1,Returning_Visitor,FALSE,FALSE 1,75,0,0,19,1773.375,0.011764706,0.030392157,28.11635484,0,Nov,2,2,1,13,Returning_Visitor,FALSE,TRUE 5,46.14285714,0,0,51,1346.630231,0.007126437,0.029719534,0,0,Nov,2,2,3,2,Returning_Visitor,TRUE,FALSE 0,0,0,0,1,0,0.2,0.2,0,0,Nov,3,2,7,20,Returning_Visitor,FALSE,FALSE 0,0,0,0,30,805.8333333,0,0.003333333,0,0,Nov,1,1,3,8,New_Visitor,FALSE,FALSE 2,1668.5,4,35,18,1372.975,0,0.017391304,41.64625944,0,Dec,2,2,1,6,Returning_Visitor,FALSE,TRUE 0,0,1,14.75,30,2445.066667,0.033333333,0.026888889,0,0,Dec,3,2,3,3,Returning_Visitor,FALSE,FALSE 5,426.7,1,200,72,4140.333766,0.001315789,0.024597953,0,0,Nov,3,2,4,2,Returning_Visitor,FALSE,FALSE 0,0,0,0,23,348.3833333,0.008695652,0.004347826,0,0,Nov,1,1,3,2,New_Visitor,FALSE,FALSE 0,0,0,0,15,325.5,0.02,0.058666667,0,0,Dec,2,2,1,1,Returning_Visitor,FALSE,FALSE 0,0,0,0,64,1911.610665,0.0125,0.026739194,0,0,Nov,1,2,2,10,Returning_Visitor,TRUE,FALSE 8,388.7916667,1,944.75,17,1464.416667,0.019047619,0.034920635,0,0,Nov,1,1,4,3,Returning_Visitor,FALSE,FALSE 2,40.5,1,4,19,472.5416667,0,0.005,0,0,Nov,1,1,4,2,Returning_Visitor,FALSE,FALSE 0,0,0,0,38,1593.583333,0,0.010526316,0,0,Nov,2,2,2,1,Returning_Visitor,FALSE,FALSE 5,87.75,0,0,6,135.0833333,0,0.009090909,0,0,Nov,1,1,2,2,New_Visitor,TRUE,FALSE 1,0,0,0,77,4350.647321,0.012727273,0.023842532,14.38546841,0,Nov,2,2,1,2,Returning_Visitor,FALSE,FALSE 2,56,0,0,33,1399.15,0,0.011290323,12.45078371,0,Nov,1,1,2,8,New_Visitor,TRUE,TRUE 1,3.5,0,0,49,663.5277778,0,0.005319149,0,0,Nov,2,2,3,2,Returning_Visitor,FALSE,FALSE 7,146.7,5,195,42,1717.666667,0.001960784,0.029981326,0,0,Dec,2,2,1,2,Returning_Visitor,FALSE,FALSE 0,0,0,0,34,740.3071429,0,0.014052288,0,0,Nov,2,2,1,2,Returning_Visitor,TRUE,FALSE 0,0,0,0,7,105,0,0.033333333,0,0,Dec,2,2,1,8,New_Visitor,FALSE,FALSE 5,173.5416667,5,109.75,38,1859.382937,0.004545455,0.009090909,0,0,Dec,3,2,8,10,Returning_Visitor,TRUE,FALSE 0,0,0,0,29,874.952381,0.004597701,0.044367816,0,0,Dec,1,1,1,2,Returning_Visitor,FALSE,FALSE 6,264.4166667,0,0,22,1275.092424,0,0.013512907,65.74605279,0,Dec,3,2,5,2,Returning_Visitor,FALSE,FALSE 5,97,0,0,53,3372.748214,0.003773585,0.002515723,0,0,Dec,2,2,7,2,Returning_Visitor,FALSE,FALSE 4,190,0,0,53,1328.626894,0.017857143,0.022238793,0,0,Dec,1,1,1,10,Returning_Visitor,TRUE,FALSE 10,175.4666667,1,7,82,2378.377381,0.004318937,0.017004257,13.9750483,0,Nov,3,2,1,13,Returning_Visitor,FALSE,TRUE 0,0,0,0,2,67.5,0,0.1,0,0,Dec,2,2,7,13,Returning_Visitor,FALSE,FALSE 6,276.75,0,0,12,342.125,0,0.007142857,0,0,Nov,1,2,3,2,New_Visitor,FALSE,FALSE 0,0,0,0,27,1009.375,0.007407407,0.040740741,0,0,Nov,2,2,1,2,New_Visitor,TRUE,TRUE 0,0,2,52.5,18,502,0,0.01,0,0,Dec,8,2,9,2,New_Visitor,TRUE,FALSE 0,0,0,0,6,530,0.016666667,0.041666667,0,0,Nov,3,2,3,2,Returning_Visitor,TRUE,FALSE 2,146,2,52.25,1,0,0,0.04,0,0,Dec,1,1,1,2,Returning_Visitor,FALSE,FALSE 0,0,0,0,7,340.5,0,0.028571429,0,0,Nov,2,4,4,2,New_Visitor,TRUE,FALSE 4,1946,0,0,31,731,0,0.005882353,0,0,Dec,2,2,4,2,New_Visitor,TRUE,FALSE 0,0,0,0,25,948.3428571,0,0.009,0,0,Nov,2,4,1,3,Returning_Visitor,FALSE,FALSE 1,75,2,34.5,100,4436.376299,0,0.007150715,0,0,Nov,2,2,1,2,Returning_Visitor,FALSE,TRUE 1,9,0,0,10,219.5,0,0.027272727,0,0,Dec,2,2,1,11,Returning_Visitor,FALSE,FALSE 0,0,2,0,3,56.75,0.1,0.125,0,0,Nov,1,1,1,2,Returning_Visitor,FALSE,FALSE 3,40.5,0,0,20,758.75,0,0.02,38.26667925,0,Nov,2,2,8,3,Returning_Visitor,FALSE,TRUE 0,0,0,0,4,72.5,0,0.05,0,0,Dec,1,1,8,10,Returning_Visitor,FALSE,FALSE 5,64.58333333,0,0,36,1334.083333,0,0.005405405,0,0,Nov,2,2,3,2,Returning_Visitor,FALSE,FALSE 6,152,0,0,17,450.75,0.028571429,0.052380952,14.10739957,0,Nov,1,8,3,11,Returning_Visitor,FALSE,TRUE 0,0,0,0,3,1952.5,0.066666667,0.122222222,0,0,Nov,1,1,2,1,Returning_Visitor,FALSE,FALSE 0,0,0,0,3,121.5,0,0.007407407,0,0,Nov,1,1,1,2,New_Visitor,TRUE,FALSE 0,0,0,0,2,0,0.2,0.2,0,0,Nov,1,1,9,1,Returning_Visitor,FALSE,FALSE 11,320,0,0,36,776.1666667,0,0.014880952,0,0,Dec,3,2,1,13,Returning_Visitor,FALSE,FALSE 0,0,0,0,83,3091.146033,0.004938272,0.019075614,0,0,Nov,2,2,1,1,Returning_Visitor,FALSE,FALSE 1,51.5,0,0,27,818.3166667,0.007407407,0.017354497,0,0,Nov,3,2,1,13,Returning_Visitor,FALSE,FALSE 0,0,0,0,14,684.75,0,0.014285714,0,0,Nov,2,2,9,2,Returning_Visitor,FALSE,FALSE 0,0,0,0,27,1224.541667,0,0.024691358,0,0,Dec,4,2,1,2,New_Visitor,TRUE,FALSE 0,0,0,0,12,459.0833333,0.004545455,0.084545455,0,0,Dec,2,2,1,2,Returning_Visitor,FALSE,FALSE 0,0,0,0,6,474.3333333,0,0.01,0,0,Dec,1,1,3,3,Returning_Visitor,FALSE,FALSE 0,0,0,0,11,315,0,0.036363636,0,0,Dec,3,2,6,2,New_Visitor,TRUE,FALSE 6,93,2,23,37,783.875,0.003409091,0.031709957,0,0,Nov,2,2,1,11,Returning_Visitor,TRUE,FALSE 6,167,0,0,71,1445.458333,0.004054054,0.013963964,12.30811748,0,Nov,3,2,1,10,Returning_Visitor,FALSE,TRUE 0,0,0,0,19,517,0.010526316,0.026315789,0,0,Dec,4,1,7,1,Returning_Visitor,TRUE,FALSE 0,0,0,0,7,444,0,0.028571429,59.75418351,0,Dec,1,1,2,8,New_Visitor,TRUE,TRUE 0,0,0,0,87,1197.700198,0,0.002352941,38.00063931,0,Nov,2,2,1,2,New_Visitor,FALSE,TRUE 0,0,0,0,3,16,0.066666667,0.133333333,0,0,Dec,2,4,7,3,Returning_Visitor,FALSE,FALSE 6,185.9904762,5,111.1666667,179,10415.78364,0.002551834,0.020876741,18.08361086,0,Nov,2,2,3,2,Returning_Visitor,TRUE,FALSE 6,114.2591575,9,352.0833333,119,6245.704152,0.003125,0.012179841,32.32493691,0,Dec,3,2,1,2,Returning_Visitor,TRUE,TRUE 7,399.25,1,298,85,2409.875,0.006818182,0.017267562,10.1319644,0,Dec,3,2,1,2,Returning_Visitor,FALSE,FALSE 0,0,0,0,52,2004.708333,0,0.019337607,0,0,Dec,2,2,3,1,Returning_Visitor,FALSE,FALSE 1,113.75,0,0,26,1905.078571,0.022666667,0.0346,11.70614611,0,Nov,3,2,2,1,Returning_Visitor,FALSE,TRUE 0,0,0,0,22,903.4305556,0.00952381,0.021428571,0,0,Nov,2,2,7,8,Returning_Visitor,FALSE,FALSE 0,0,0,0,1,0,0.2,0.2,0,0,Nov,8,2,9,3,Other,FALSE,FALSE 5,943,2,16,7,935.6666667,0,0.0375,0,0,Nov,2,2,3,6,Returning_Visitor,TRUE,TRUE 0,0,2,22,41,979.375,0.004651163,0.009302326,0,0,Nov,2,2,3,2,Returning_Visitor,TRUE,FALSE 2,126,0,0,6,127.1666667,0.05,0.04375,0,0,Dec,3,2,5,3,Returning_Visitor,TRUE,FALSE 1,73,0,0,23,1280.45,0,0.008695652,36.54680355,0,Dec,2,4,1,2,New_Visitor,FALSE,TRUE 0,0,0,0,9,427,0,0.025,13.1168283,0,Dec,1,1,1,2,New_Visitor,TRUE,TRUE 0,0,0,0,2,93,0,0.05,0,0,Dec,3,2,5,2,Returning_Visitor,FALSE,FALSE 1,8,0,0,27,787.5357143,0,0.005142857,0,0,Nov,3,2,1,20,Returning_Visitor,FALSE,FALSE 0,0,0,0,8,210.4,0,0.05,0,0,Nov,1,1,3,8,New_Visitor,TRUE,FALSE 0,0,0,0,11,257.75,0,0.024242424,0,0,Dec,3,2,2,2,New_Visitor,FALSE,FALSE 16,180.6666667,1,649.25,113,8041.58887,0.007936508,0.026808161,0,0,Dec,2,6,7,1,Returning_Visitor,FALSE,FALSE 6,634.5,3,312.75,172,7017.192635,0.001396648,0.019865374,0,0,Nov,2,4,1,2,Returning_Visitor,FALSE,FALSE 0,0,0,0,38,1244.35,0.039473684,0.053909774,0,0,Dec,1,1,1,2,Returning_Visitor,FALSE,FALSE 7,253.1,0,0,40,1591.888095,0,0.012186396,48.66217061,0,Dec,2,2,3,2,Returning_Visitor,TRUE,TRUE 5,121.2666667,0,0,8,114.6,0,0.01,0,0,Nov,1,1,1,2,New_Visitor,TRUE,FALSE 12,176.9638554,9,266.875,351,8650.048771,0.012452132,0.027372411,1.984555788,0,Nov,3,2,1,3,Returning_Visitor,FALSE,FALSE 0,0,0,0,8,123,0.025,0.029166667,0,0,Nov,3,2,3,11,Returning_Visitor,TRUE,FALSE 0,0,0,0,1,0,0.2,0.2,0,0,Dec,1,1,4,1,Returning_Visitor,FALSE,FALSE 2,88,10,719,54,1986.553118,0.005128205,0.019939049,25.82676446,0,Dec,1,1,2,2,Returning_Visitor,FALSE,FALSE 8,381.45,2,64.5,31,1363.7,0.011111111,0.027830688,13.89178731,0,Nov,1,1,1,2,Returning_Visitor,FALSE,FALSE 2,30,2,33,162,3753.19449,0,0.003477932,77.10821943,0,Nov,2,2,1,8,New_Visitor,FALSE,TRUE 0,0,0,0,2,0,0.2,0.2,0,0,Nov,1,1,1,1,Returning_Visitor,FALSE,FALSE 5,180.5,0,0,28,1070.625,0,0.022427035,0,0,Dec,3,2,6,3,Returning_Visitor,FALSE,FALSE 2,20,0,0,15,1839.25,0,0.004444444,0,0,Nov,2,2,9,2,Returning_Visitor,FALSE,FALSE 0,0,0,0,1,0,0.2,0.2,0,0,Nov,3,2,7,13,Returning_Visitor,FALSE,FALSE 5,255.75,0,0,70,1787.479365,0.004285714,0.019809524,0,0,Dec,2,4,1,3,Returning_Visitor,FALSE,FALSE 17,323.3636364,0,0,45,1534.825143,0.002678571,0.020699598,7.180348301,0,Nov,1,1,3,10,Returning_Visitor,FALSE,TRUE 2,58.5,0,0,11,1568.5,0,0.036363636,0,0,Nov,2,2,7,2,Returning_Visitor,FALSE,FALSE 5,60.5,0,0,9,111.5,0.026666667,0.04,0,0,Nov,2,2,9,1,Returning_Visitor,FALSE,FALSE 3,131.5,0,0,94,2332.109524,0.001030928,0.009450172,0,0,Nov,2,2,9,10,Returning_Visitor,FALSE,FALSE 0,0,0,0,13,598.5,0,0.016666667,0,0,Nov,2,10,7,2,New_Visitor,FALSE,FALSE 3,163.375,0,0,4,24,0,0.028571429,0,0,Nov,2,2,1,3,Returning_Visitor,TRUE,FALSE 1,18,2,13,20,1287.27381,0.015873016,0.038095238,0,0,Nov,3,2,4,2,Returning_Visitor,FALSE,FALSE 13,216.3333333,1,9,59,985.3151515,0,0.004545455,167.2308336,0,Nov,2,2,4,10,Returning_Visitor,TRUE,TRUE 0,0,0,0,23,2420.65,0.013043478,0.047826087,0,0,Nov,4,1,9,3,Returning_Visitor,FALSE,FALSE 1,0,0,0,9,300.3333333,0.041666667,0.06875,0,0,Dec,3,2,9,1,Returning_Visitor,FALSE,FALSE 3,114.125,1,13,64,1602.94026,0.013432836,0.028731343,29.88386066,0,Nov,3,2,1,3,Returning_Visitor,TRUE,FALSE 10,1455.35,0,0,66,1646.612195,0.007142857,0.029065112,0,0,Nov,2,2,1,2,Returning_Visitor,TRUE,FALSE 6,198.5,0,0,41,1824.083333,0,0.01,0,0,Dec,3,2,3,11,New_Visitor,FALSE,FALSE 13,493.75,0,0,22,1525,0.008,0.018666667,25.00423187,0,Dec,3,2,1,1,Returning_Visitor,TRUE,TRUE 0,0,0,0,6,114.4166667,0,0.016666667,0,0,Nov,3,2,1,6,New_Visitor,TRUE,FALSE 0,0,0,0,53,1768.657051,0,0.003921569,0,0,Nov,2,4,3,2,Returning_Visitor,FALSE,FALSE 8,2086.75,1,46.5,81,5546,0.002325581,0.014562569,15.56462164,0,Nov,2,2,1,2,Returning_Visitor,FALSE,TRUE 9,177.3333333,0,0,70,2346.220238,0.002898551,0.003457557,0,0,Nov,1,1,5,3,Returning_Visitor,FALSE,FALSE 0,0,0,0,19,2177.333333,0,0.010526316,0,0,Dec,2,4,1,2,New_Visitor,FALSE,FALSE 17,239.9288462,2,60.5,49,1929.185664,0.017486339,0.025063352,2.580028334,0,Dec,3,2,1,13,Returning_Visitor,FALSE,FALSE 2,136.5,0,0,8,1261.5,0,0.01,0,0,Dec,8,1,3,2,Returning_Visitor,TRUE,FALSE 0,0,0,0,41,898.15,0,0.011282051,19.51010101,0,Dec,2,5,3,1,Returning_Visitor,FALSE,TRUE 5,111.3,0,0,138,4172.11572,0,0.008337205,17.63434621,0,Nov,2,2,9,2,Returning_Visitor,FALSE,FALSE 0,0,0,0,7,143,0,0.028571429,0,0,Nov,2,5,2,3,Returning_Visitor,FALSE,FALSE 4,85,0,0,3,43.5,0,0.05,0,0,Dec,1,1,1,8,New_Visitor,TRUE,FALSE 4,24.5,0,0,29,749.125,0.005882353,0.02745098,0,0,Nov,1,1,3,2,Returning_Visitor,FALSE,TRUE 5,448.5,4,52,81,2537.807082,0.013793103,0.028674678,0,0,Nov,3,2,3,2,Returning_Visitor,TRUE,FALSE 0,0,0,0,11,580.25,0.022222222,0.044444444,0,0,Nov,3,2,1,2,Returning_Visitor,TRUE,FALSE 5,111,1,11,55,3494.06746,0,0.029188713,4.007065498,0,Nov,2,2,3,1,Returning_Visitor,FALSE,FALSE 3,16,6,137.8333333,15,1605.583333,0.011594203,0.037267081,0,0,Nov,2,10,1,2,Returning_Visitor,FALSE,TRUE 5,45,0,0,59,1100.475,0,0.008391881,24.7526533,0,Nov,2,5,4,2,Returning_Visitor,TRUE,TRUE 4,112.875,0,0,6,134.675,0,0.037037037,0,0,Nov,1,2,1,2,Returning_Visitor,FALSE,FALSE 0,0,0,0,9,296.3928571,0,0.025,0,0,Dec,1,1,1,3,Returning_Visitor,FALSE,FALSE 0,0,0,0,20,691.75,0,0.003333333,0,0,Dec,3,2,4,2,New_Visitor,FALSE,FALSE 0,0,0,0,9,444,0,0.044444444,0,0,Nov,2,2,6,13,Returning_Visitor,FALSE,FALSE 13,311.8300935,4,201.5,166,6850.499335,0,0.017541022,4.115853586,0,Dec,2,2,1,3,Returning_Visitor,FALSE,FALSE 5,152.5,0,0,15,291.1666667,0,0.02,0,0,Dec,4,1,1,2,New_Visitor,TRUE,FALSE 0,0,0,0,8,232.5,0,0.05,0,0,Nov,4,2,1,2,New_Visitor,FALSE,FALSE 0,0,0,0,40,2046.832418,0.004166667,0.033880952,0,0,Nov,1,8,1,11,Returning_Visitor,FALSE,TRUE 3,83,4,220.25,106,2974.734799,0.005165165,0.02044187,19.03391341,0,Nov,2,2,7,2,Returning_Visitor,FALSE,TRUE 0,0,0,0,13,277,0,0.015384615,0,0,Nov,4,1,1,8,Returning_Visitor,FALSE,FALSE 4,92.75,0,0,9,108.25,0,0.021428571,0,0,Nov,2,2,3,2,New_Visitor,FALSE,FALSE 2,64.5,0,0,24,1151.136905,0,0.0186,15.27655756,0,Dec,2,2,3,2,Returning_Visitor,FALSE,TRUE 0,0,3,584,66,2060.508333,0.005797101,0.022567288,0,0,Nov,2,2,1,1,Returning_Visitor,FALSE,FALSE 9,201.3666667,1,204,116,2764.290278,0.000655738,0.005854801,42.7752197,0,Nov,3,2,2,2,Returning_Visitor,FALSE,TRUE 1,11,0,0,15,175.75,0,0.025,0,0,Nov,1,1,2,2,New_Visitor,FALSE,FALSE 1,466,0,0,6,609.5416667,0.033333333,0.116666667,0,0,Nov,2,2,1,2,Returning_Visitor,FALSE,FALSE 0,0,0,0,64,2264.091667,0.00625,0.008854167,0,0,Nov,1,1,3,10,Returning_Visitor,FALSE,FALSE 2,75,0,0,9,349,0,0.01,0,0,Dec,2,4,3,8,Returning_Visitor,FALSE,FALSE 0,0,0,0,1,0,0.2,0.2,0,0,Dec,2,2,2,1,Returning_Visitor,FALSE,FALSE 0,0,0,0,11,546.75,0.036363636,0.054545455,0,0,Nov,2,2,5,1,Returning_Visitor,FALSE,FALSE 10,317.9583333,4,821,20,781.425,0,0.002666667,0,0,Dec,2,2,7,2,Returning_Visitor,FALSE,FALSE 0,0,0,0,8,463.6666667,0,0.0375,0,0,Nov,1,1,1,3,Returning_Visitor,FALSE,FALSE 2,20,0,0,66,3391.68588,0.028955224,0.038335618,0,0,Dec,3,2,8,1,Returning_Visitor,FALSE,FALSE 2,63,0,0,4,43.75,0.016666667,0.016666667,0,0,Nov,1,2,3,2,New_Visitor,FALSE,FALSE 0,0,0,0,41,1423.833333,0.007317073,0.030243902,0,0,Dec,2,2,1,1,Returning_Visitor,FALSE,FALSE 0,0,0,0,1,0,0.2,0.2,0,0,Dec,3,2,4,13,Returning_Visitor,FALSE,FALSE 4,157,0,0,20,834.075,0,0.013333333,4.84736534,0,Dec,2,2,1,2,Returning_Visitor,FALSE,TRUE 0,0,0,0,21,706.7,0.052380952,0.074716553,0,0,Nov,3,2,3,2,Returning_Visitor,FALSE,FALSE 0,0,0,0,35,806.125,0.012121212,0.024444444,0,0,Nov,1,1,4,2,Returning_Visitor,FALSE,FALSE 13,295.9285714,5,17.9,154,6759.311928,0.005767417,0.02883646,0,0,Nov,2,2,3,10,Returning_Visitor,FALSE,FALSE 0,0,0,0,1,0,0.2,0.2,0,0,Dec,3,2,1,1,Returning_Visitor,TRUE,FALSE 0,0,0,0,12,440.75,0,0.041666667,0,0,Dec,2,2,6,1,Returning_Visitor,TRUE,FALSE 1,20,1,115,50,1404.459091,0.013,0.044,0,0,Dec,2,2,2,2,Returning_Visitor,TRUE,FALSE 8,288.0875,0,0,20,326.9277778,0.033333333,0.059464286,0,0,Dec,3,2,1,1,Returning_Visitor,TRUE,FALSE 5,92.5625,0,0,2,99.5,0,0.022222222,0,0,Nov,2,2,1,8,New_Visitor,TRUE,TRUE 0,0,0,0,6,137,0.066666667,0.1,0,0,Nov,1,1,1,1,Returning_Visitor,FALSE,FALSE 0,0,0,0,2,64.5,0,0.05,0,0,Nov,3,2,8,2,Returning_Visitor,FALSE,FALSE 5,83,0,0,69,2517.216667,0.000342466,0.021765601,0,0,Nov,2,2,9,2,Returning_Visitor,FALSE,FALSE 1,7.5,0,0,63,3060.055556,0.024623656,0.03604734,0,0,Nov,3,2,1,3,Returning_Visitor,FALSE,FALSE 10,322.8333333,1,12,28,866.2166667,0.006451613,0.012903226,0,0,Dec,2,4,7,1,Returning_Visitor,FALSE,FALSE 0,0,0,0,8,188,0.05,0.0875,0,0,Dec,1,1,2,1,Returning_Visitor,FALSE,FALSE 0,0,0,0,49,2110.124603,0,0.004081633,62.93549178,0,Nov,2,2,1,10,Returning_Visitor,FALSE,TRUE 1,65.5,1,0,82,5316.459524,0.005,0.01125,77.25951671,0,Nov,1,1,9,3,Returning_Visitor,TRUE,TRUE 0,0,0,0,15,176.7666667,0.0125,0.051041667,0,0,Nov,2,2,1,2,Returning_Visitor,FALSE,FALSE 0,0,0,0,9,591.25,0,0.022222222,0,0,Dec,2,2,6,13,Returning_Visitor,FALSE,FALSE 2,197.5,0,0,17,820.5,0.011111111,0.011111111,0,0,Dec,1,1,1,8,Returning_Visitor,FALSE,FALSE 0,0,0,0,5,368,0,0.016666667,0,0,Dec,4,1,2,1,Returning_Visitor,TRUE,FALSE 0,0,0,0,3,158,0.033333333,0.033333333,0,0,Dec,1,1,1,3,New_Visitor,FALSE,FALSE 0,0,0,0,5,72,0,0.05,0,0,Dec,3,2,1,2,Returning_Visitor,FALSE,FALSE 0,0,0,0,49,2990.3125,0.012244898,0.038231293,0,0,Nov,3,2,7,13,Returning_Visitor,FALSE,FALSE 0,0,0,0,37,2152.588889,0.001081081,0.017747748,0,0,Dec,3,3,3,3,Returning_Visitor,FALSE,FALSE 8,1592.916667,2,146.6666667,14,1722.583333,0,0.031578947,0,0,Nov,4,1,3,8,New_Visitor,FALSE,TRUE 2,44.5,0,0,137,7012.266796,0.008321168,0.01964546,0,0,Nov,1,1,1,10,Returning_Visitor,TRUE,TRUE 1,60.5,0,0,21,320.9833333,0,0.01,0,0,Nov,2,5,1,2,Returning_Visitor,FALSE,FALSE 0,0,0,0,1,0,0.2,0.2,0,0,Dec,8,13,9,20,Other,FALSE,FALSE 8,183.0833333,1,40.5,349,6879.801826,0.000568182,0.004306345,1.625051033,0,Nov,4,2,9,10,Returning_Visitor,FALSE,FALSE 1,49,0,0,112,2237.603885,0.003933137,0.019128308,0,0,Dec,1,1,4,2,Returning_Visitor,FALSE,FALSE 3,284,0,0,33,2270.75,0,0.010840336,0,0,Dec,2,2,7,2,Returning_Visitor,FALSE,FALSE 2,50,0,0,24,602.25,0,0.003846154,0,0,Nov,2,2,1,2,Returning_Visitor,TRUE,FALSE 12,158.5952381,2,167,48,1256.291667,0.003846154,0.016570513,29.76290794,0,Nov,3,2,1,2,Returning_Visitor,FALSE,FALSE 8,47,0,0,18,1796.583333,0,0.02173913,0,0,Nov,2,2,2,1,Returning_Visitor,FALSE,FALSE 0,0,0,0,9,269.7,0,0.033333333,0,0,Dec,3,2,3,10,New_Visitor,FALSE,FALSE 2,33.75,0,0,6,96,0,0.025,0,0,Dec,2,2,4,2,New_Visitor,FALSE,FALSE 4,53.75,0,0,41,1130.80754,0.019047619,0.018783069,0,0,Dec,3,2,1,2,Returning_Visitor,FALSE,FALSE 0,0,0,0,42,1333.914286,0.01,0.01094246,0,0,Nov,1,2,2,2,Returning_Visitor,FALSE,FALSE 2,34,0,0,35,1907.135417,0.006944444,0.021993464,64.25350176,0,Dec,2,2,1,10,Returning_Visitor,TRUE,FALSE 0,0,0,0,40,1085.333333,0.0075,0.045,0,0,Dec,2,2,6,1,Returning_Visitor,FALSE,FALSE 9,272.0416667,6,502.75,33,3239.418207,0.02,0.025011905,17.02873771,0,Dec,1,1,1,2,Returning_Visitor,FALSE,FALSE 0,0,0,0,1,0,0.2,0.2,0,0,Dec,1,1,6,2,Returning_Visitor,TRUE,FALSE 3,109,0,0,70,2002.25,0,0.004166667,0,0,Nov,2,2,1,2,New_Visitor,FALSE,FALSE 0,0,1,77,58,3757.75,0.015819209,0.038418079,0,0,Nov,2,5,1,1,Returning_Visitor,FALSE,TRUE 0,0,0,0,11,115.25,0.027272727,0.045454545,0,0,Nov,3,2,9,10,New_Visitor,TRUE,FALSE 0,0,0,0,8,204.75,0,0.033333333,0,0,Dec,1,1,1,1,Returning_Visitor,TRUE,FALSE 2,33.5,0,0,8,556,0,0.015151515,0,0,Dec,2,2,1,2,New_Visitor,TRUE,FALSE 0,0,3,50.5,6,238.55,0,0.018518519,0,0,Nov,3,2,1,2,Returning_Visitor,FALSE,FALSE 0,0,0,0,16,423.1833333,0.0125,0.016071429,0,0,Nov,3,2,7,10,Returning_Visitor,FALSE,FALSE 0,0,0,0,22,1664,0.03030303,0.032424242,0,0,Nov,1,1,1,2,Returning_Visitor,FALSE,TRUE 0,0,0,0,2,46.5,0.1,0.1,0,0,Nov,2,2,3,1,Returning_Visitor,FALSE,FALSE 3,159.1666667,2,8,69,2766.942172,0.002816901,0.028665325,5.931332852,0,Dec,2,2,1,2,Returning_Visitor,FALSE,FALSE 0,0,0,0,1,0,0.2,0.2,0,0,Nov,1,1,2,2,Returning_Visitor,FALSE,FALSE 2,64.5,1,0,97,2696.10873,0.003436426,0.021408935,5.770973155,0,Nov,2,2,1,13,Returning_Visitor,FALSE,TRUE 5,34.5,0,0,8,154.5,0.022222222,0.014814815,0,0,Dec,3,2,4,2,Returning_Visitor,FALSE,FALSE 1,139.5,0,0,100,1831.149084,0,0.000505051,0,0,Dec,2,2,3,2,New_Visitor,FALSE,FALSE 0,0,0,0,1,0,0.2,0.2,0,0,Nov,4,1,1,3,New_Visitor,FALSE,FALSE 4,45,0,0,7,246.5,0.03,0.03,0,0,Dec,2,2,1,2,Returning_Visitor,FALSE,FALSE 1,6,0,0,14,1626,0.051282051,0.092307692,0,0,Nov,1,1,1,1,Returning_Visitor,FALSE,FALSE 4,43.92857143,0,0,182,9951.359644,0.003850267,0.021183788,8.665280918,0,Nov,2,2,1,2,Returning_Visitor,FALSE,TRUE 2,569,1,58.5,23,941.6994048,0,0.008,9.331744184,0,Dec,2,2,4,2,New_Visitor,FALSE,TRUE 1,5,2,66.5,64,1716.583333,0.012121212,0.019444444,0,0,Nov,4,2,1,2,Returning_Visitor,TRUE,FALSE 0,0,0,0,20,597.25,0,0.02,0,0,Dec,2,2,1,13,Returning_Visitor,FALSE,FALSE 0,0,0,0,7,43,0.085714286,0.121428571,0,0,Dec,3,2,1,1,Returning_Visitor,FALSE,FALSE 3,37.58333333,0,0,67,1982.523611,0.004411765,0.010971792,0,0,Dec,2,2,3,1,Returning_Visitor,FALSE,FALSE 1,2,0,0,61,1335.737854,0,0.002,20.15710234,0,Nov,4,4,1,2,Returning_Visitor,TRUE,TRUE 0,0,0,0,28,690.9583333,0,0.022857143,0,0,Nov,2,2,6,2,Returning_Visitor,TRUE,FALSE 13,331.9833333,5,128,202,8217.678753,0.001904762,0.013668745,3.651726351,0,Nov,2,2,3,10,Returning_Visitor,TRUE,TRUE 0,0,2,157.5,35,5999.808333,0.005405405,0.027207207,0,0,Dec,2,2,4,1,Returning_Visitor,TRUE,FALSE 4,56.25,0,0,10,182.25,0,0.025,0,0,Nov,3,2,6,2,New_Visitor,FALSE,FALSE 6,224.875,0,0,21,845.75,0,0.008695652,0,0,Dec,8,13,9,20,Other,FALSE,FALSE 6,279,2,69.5,41,1430.25,0,0.009929078,3.836079931,0,Dec,2,2,2,7,Returning_Visitor,FALSE,TRUE 0,0,0,0,4,179.25,0,0.05,0,0,Dec,2,2,6,2,Returning_Visitor,FALSE,FALSE 8,197.7083333,6,130.5166667,35,1359.771429,0.004761905,0.018611111,34.98992634,0,Nov,3,2,1,11,Returning_Visitor,TRUE,FALSE 1,3,2,34,41,1021.519444,0.009302326,0.017829457,0,0,Dec,1,2,3,2,New_Visitor,TRUE,FALSE 2,34.5,0,0,17,491.8333333,0,0.014705882,0,0,Dec,1,1,3,2,New_Visitor,FALSE,FALSE 0,0,0,0,38,907.943254,0,0.005405405,23.6645871,0,Dec,2,2,1,2,Returning_Visitor,FALSE,TRUE 0,0,1,78,108,8402.715986,0.008873457,0.019558305,0,0,Nov,3,2,2,13,Returning_Visitor,FALSE,FALSE 0,0,0,0,4,184.3333333,0.1,0.0875,0,0,Dec,3,2,3,2,Returning_Visitor,FALSE,FALSE 2,49.7,1,0,64,1991.822567,0.014830918,0.022398206,21.74053053,0,Nov,1,1,1,2,Returning_Visitor,TRUE,FALSE 4,61.25,0,0,21,595.75,0.038095238,0.076190476,0,0,Dec,2,2,4,1,Returning_Visitor,FALSE,FALSE 0,0,3,588,153,5315.211905,0.006174089,0.020586081,0,0,Nov,2,2,1,3,Returning_Visitor,FALSE,FALSE 5,110,6,1188.5,230,9648.177094,0.004444444,0.015486639,3.359435653,0,Nov,2,2,1,2,Returning_Visitor,FALSE,FALSE 0,0,0,0,5,73,0.12,0.16,0,0,Nov,2,2,2,3,Returning_Visitor,FALSE,FALSE 2,47,1,0,20,738.9107143,0.017391304,0.023671498,0,0,Nov,3,2,1,2,Returning_Visitor,FALSE,FALSE 4,20.5,1,59,5,83.5,0,0.014814815,0,0,Dec,2,2,9,3,Returning_Visitor,FALSE,FALSE 0,0,0,0,1,0,0.2,0.2,0,0,Dec,2,2,3,1,Other,TRUE,FALSE 1,4,0,0,22,1749,0,0.015,0,0,Dec,2,2,3,10,Returning_Visitor,FALSE,FALSE 0,0,0,0,7,140.4583333,0.028571429,0.034285714,0,0,Nov,3,3,9,2,Returning_Visitor,FALSE,FALSE 0,0,0,0,37,698.5416667,0.003809524,0.015238095,0,0,Nov,2,2,6,11,Returning_Visitor,FALSE,FALSE 2,76.5,0,0,10,123.2291667,0.022857143,0.059444444,0,0,Dec,1,2,1,10,Returning_Visitor,FALSE,FALSE 1,576,0,0,13,587.75,0.030769231,0.061538462,0,0,Nov,1,1,4,1,Returning_Visitor,FALSE,FALSE 0,0,0,0,2,17,0,0.1,0,0,Dec,8,2,7,2,Returning_Visitor,FALSE,FALSE 0,0,2,21,0,0,0,0.05,0,0,Dec,1,1,4,10,New_Visitor,FALSE,FALSE 7,121,0,0,16,589.875,0,0.00952381,0,0,Dec,2,6,1,2,Returning_Visitor,TRUE,FALSE 9,904.5833333,4,83.5,132,4096.15859,0.012738095,0.022973065,8.960747931,0,Nov,3,2,3,2,Returning_Visitor,TRUE,TRUE 0,0,0,0,7,723,0,0.028571429,32.90611766,0,Dec,2,2,4,1,Returning_Visitor,TRUE,TRUE 2,45.25,1,0,42,4126.448611,0.000775194,0.013436693,0,0,Dec,2,2,7,2,Returning_Visitor,FALSE,FALSE 0,0,0,0,53,5504.695238,0.011538462,0.031446886,12.32661404,0,Nov,2,2,1,1,Returning_Visitor,FALSE,TRUE 2,17,2,1231,8,27.375,0.038888889,0.088888889,0,0,Nov,2,2,1,11,Returning_Visitor,FALSE,FALSE 0,0,0,0,1,0,0.2,0.2,0,0,Dec,2,2,6,13,Returning_Visitor,FALSE,FALSE 1,16,0,0,44,2939.625,0,0.002222222,32.81205711,0,Nov,2,2,8,2,Returning_Visitor,FALSE,TRUE 4,102.5555556,2,79.5,133,3713.580105,0.003381995,0.015889736,28.03795985,0,Nov,3,2,1,6,Returning_Visitor,TRUE,FALSE 0,0,4,84.25,141,2596.204762,0.006103286,0.020121639,0,0,Nov,2,2,7,2,Returning_Visitor,FALSE,FALSE 1,36.5,0,0,30,1668.116667,0,0.023809524,0,0,Dec,1,1,8,2,Returning_Visitor,FALSE,FALSE 5,66.4,0,0,39,742.6875,0,0.015217391,71.38981221,0,Dec,2,2,4,3,Returning_Visitor,FALSE,TRUE 2,18,0,0,17,1218.75,0,0.050980392,0,0,Nov,2,2,1,1,Returning_Visitor,FALSE,FALSE 2,77.7,1,2,24,551.1583333,0.004166667,0.054444444,7.945368291,0,Dec,2,2,9,2,Returning_Visitor,FALSE,FALSE 4,72,0,0,4,46.5,0.04,0.06,0,0,Nov,3,2,2,3,Returning_Visitor,FALSE,FALSE 7,237.5,1,0,37,1302.518056,0.005952381,0.023185941,0,0,Dec,3,2,2,2,Returning_Visitor,FALSE,TRUE 0,0,0,0,25,929.5833333,0.016,0.0296,0,0,Dec,3,2,4,13,Returning_Visitor,FALSE,FALSE 0,0,0,0,59,1309.658333,0.017241379,0.030879354,0,0,Dec,3,2,3,1,Returning_Visitor,FALSE,FALSE 0,0,0,0,100,2852.013141,0.000444444,0.024301656,0,0,Dec,2,2,1,2,Returning_Visitor,FALSE,FALSE 0,0,0,0,14,272.1666667,0,0.025,0,0,Dec,3,2,3,2,Returning_Visitor,FALSE,FALSE 3,424,0,0,3,61.5,0,0.05,0,0,Dec,2,4,4,2,New_Visitor,FALSE,FALSE 0,0,0,0,7,295,0.028571429,0.057142857,0,0,Nov,2,4,1,2,Returning_Visitor,FALSE,TRUE 4,68.75,0,0,137,3770.916071,0.003165468,0.014745252,0,0,Dec,2,2,3,1,Returning_Visitor,FALSE,FALSE 1,43,0,0,26,834.3611111,0.016,0.019666667,25.21523293,0,Dec,1,1,1,10,Returning_Visitor,TRUE,FALSE 0,0,0,0,3,0,0.2,0.2,0,0,Nov,1,1,1,3,Returning_Visitor,FALSE,FALSE 0,0,1,0,26,668.5,0,0.007407407,0,0,Nov,2,2,3,1,Returning_Visitor,FALSE,FALSE 0,0,0,0,19,521.925,0.010526316,0.029824561,0,0,Dec,3,2,6,2,Returning_Visitor,TRUE,FALSE 0,0,0,0,23,1544.595238,0.058550725,0.092944664,0,0,Nov,3,2,8,13,Returning_Visitor,TRUE,FALSE 0,0,0,0,2,12,0,0.1,0,0,Dec,3,2,3,13,Returning_Visitor,FALSE,FALSE 0,0,6,77.95833333,423,17086.23424,0.006293706,0.02159902,0.25272174,0,Nov,2,2,7,1,Returning_Visitor,TRUE,FALSE 2,14.25,0,0,29,1082.75,0,0.006896552,84.87572674,0,Nov,2,5,3,2,New_Visitor,FALSE,TRUE 0,0,0,0,7,1803.666667,0.082142857,0.08,0,0,Dec,2,5,1,8,Returning_Visitor,TRUE,FALSE 4,58,2,28.5,82,4729.065909,0,0.014544651,9.055465887,0,Nov,2,2,1,2,Returning_Visitor,FALSE,TRUE 4,52.5,0,0,11,463.5,0,0.007692308,0,0,Nov,3,2,6,11,New_Visitor,TRUE,FALSE 0,0,0,0,38,1355.366667,0,0.009259259,0,0,Nov,1,2,6,2,Returning_Visitor,FALSE,FALSE 0,0,0,0,1,0,0.2,0.2,0,0,Dec,2,2,1,13,Returning_Visitor,FALSE,FALSE 1,21,0,0,23,529.75,0,0.009090909,57.3158913,0,Nov,1,2,1,8,New_Visitor,FALSE,TRUE 0,0,0,0,23,836.5,0,0.013043478,0,0,Nov,3,2,3,2,Returning_Visitor,FALSE,FALSE 0,0,0,0,8,719.2,0.058333333,0.067708333,0,0,Nov,3,2,3,13,Returning_Visitor,FALSE,FALSE 7,66,0,0,6,146.5,0,0.018181818,0,0,Nov,2,4,1,2,New_Visitor,FALSE,FALSE 0,0,0,0,32,2632.7,0.00625,0.013541667,0,0,Nov,1,1,1,3,Returning_Visitor,TRUE,FALSE 6,198.6666667,2,89,148,8178.226923,0.008732719,0.021205728,6.194287817,0,Nov,2,2,1,1,Returning_Visitor,FALSE,TRUE 8,395.7083333,4,182.8,46,1461.725,0.003773585,0.014375562,13.90010917,0,Nov,1,1,3,3,Returning_Visitor,TRUE,TRUE 8,403,0,0,52,1616.54816,0,0.003685897,0,0,Nov,1,1,1,3,Returning_Visitor,FALSE,FALSE 1,38.5,0,0,6,197.75,0,0.04,0,0,Dec,1,1,2,8,New_Visitor,TRUE,FALSE 0,0,0,0,2,11,0,0.1,0,0,Nov,3,2,1,10,Returning_Visitor,FALSE,FALSE 1,20,2,39.5,156,8613.257312,0.006289308,0.015117319,0,0,Nov,1,1,1,2,Returning_Visitor,TRUE,FALSE 11,494.5333333,0,0,33,2070.616667,0,0.020720721,9.581162201,0,Nov,2,5,1,3,Returning_Visitor,FALSE,TRUE 10,225.45,0,0,60,1449.608333,0.004545455,0.011320346,0,0,Dec,2,2,4,1,Returning_Visitor,FALSE,FALSE 2,28,0,0,9,163.375,0,0.018181818,0,0,Dec,1,2,3,2,New_Visitor,FALSE,FALSE 4,87,5,242.75,61,4081.041667,0,0.019375,0,0,Nov,2,5,6,2,Returning_Visitor,TRUE,FALSE 1,57.5,0,0,22,353.1527778,0,0.009090909,0,0,Nov,1,2,1,2,New_Visitor,FALSE,FALSE 0,0,0,0,32,791.9166667,0.0125,0.025520833,0,0,Nov,3,2,4,2,Returning_Visitor,FALSE,FALSE 0,0,0,0,21,1088.866667,0,0.010526316,16.1585582,0,Nov,3,2,1,2,Returning_Visitor,TRUE,TRUE 3,265.75,0,0,8,168.75,0,0.03,0,0,Nov,3,2,1,7,New_Visitor,FALSE,FALSE 0,0,0,0,8,181,0.025,0.070833333,0,0,Nov,2,2,4,13,Returning_Visitor,FALSE,FALSE 0,0,0,0,1,0,0.2,0.2,0,0,Nov,2,2,6,13,Returning_Visitor,TRUE,FALSE 0,0,0,0,10,198.75,0,0.04,0,0,Nov,2,2,3,20,Returning_Visitor,FALSE,FALSE 10,396.960452,6,168.8333333,280,9247.383869,0.003424658,0.016184197,7.983438638,0,Nov,2,2,7,2,Returning_Visitor,TRUE,FALSE 0,0,0,0,18,1659.25,0.0375,0.062202381,0,0,Nov,2,2,4,10,Returning_Visitor,FALSE,FALSE 0,0,0,0,2,39.5,0,0.1,0,0,Dec,2,2,8,1,Returning_Visitor,FALSE,FALSE 10,175.3333333,0,0,88,2638.244429,0.004395604,0.022464739,8.27583441,0,Nov,1,1,8,10,Returning_Visitor,TRUE,FALSE 1,235,0,0,3,127.6,0,0.025,0,0,Dec,2,2,1,2,New_Visitor,FALSE,FALSE 2,90,1,48.5,40,3789.583333,0,0.023170732,0,0,Dec,2,2,4,2,Returning_Visitor,TRUE,FALSE 0,0,0,0,9,122,0.088888889,0.108333333,0,0,Nov,1,1,5,1,Returning_Visitor,FALSE,FALSE 0,0,0,0,1,0,0.2,0.2,0,0,Dec,3,3,1,8,New_Visitor,FALSE,FALSE 0,0,0,0,5,375,0,0.04,0,0,Nov,1,1,3,10,Returning_Visitor,TRUE,FALSE 14,352.1071429,2,759,64,3660.342875,0.014155251,0.023835616,9.641067457,0,Nov,3,2,3,2,Returning_Visitor,TRUE,TRUE 4,63,0,0,10,565.5,0,0.015384615,0,0,Dec,8,13,9,20,Other,FALSE,FALSE 0,0,0,0,58,2021.320833,0.005172414,0.031609195,0,0,Nov,2,2,1,2,Returning_Visitor,FALSE,FALSE 0,0,0,0,3,20,0,0.066666667,0,0,Dec,2,4,3,2,Returning_Visitor,FALSE,FALSE 0,0,0,0,1,0,0.2,0.2,0,0,Nov,1,1,1,20,Returning_Visitor,FALSE,FALSE 0,0,0,0,4,97.5,0.1,0.125,0,0,Nov,3,2,6,13,Returning_Visitor,FALSE,FALSE 0,0,0,0,68,2614.209722,0,0.006942149,0,0,Nov,2,2,1,3,Returning_Visitor,TRUE,FALSE 0,0,0,0,17,308.25,0.023529412,0.064705882,0,0,Nov,2,2,1,13,Returning_Visitor,FALSE,FALSE 0,0,0,0,39,1020.013889,0.005405405,0.014226044,0,0,Dec,3,2,7,1,Returning_Visitor,TRUE,FALSE 0,0,0,0,3,0,0.2,0.2,0,0,Nov,2,7,2,1,Returning_Visitor,FALSE,FALSE 0,0,0,0,1,0,0.2,0.2,0,0,Nov,2,6,1,1,Returning_Visitor,FALSE,FALSE 0,0,0,0,6,93,0.066666667,0.083333333,0,0,Nov,4,2,3,13,Returning_Visitor,FALSE,FALSE 3,74,0,0,258,5154.777093,0.002316602,0.011675429,0,0,Dec,2,2,2,1,Returning_Visitor,FALSE,FALSE 1,64.5,0,0,32,1443.641667,0,0.010784314,111.2629939,0,Nov,2,2,1,2,Returning_Visitor,TRUE,TRUE 0,0,0,0,8,480,0,0.028571429,14.1374752,0,Nov,1,1,2,3,New_Visitor,TRUE,TRUE 0,0,0,0,26,548.2678571,0.024,0.046666667,0,0,Nov,1,2,6,2,Returning_Visitor,FALSE,FALSE 0,0,0,0,31,828.25,0.012903226,0.030107527,0,0,Nov,2,2,9,2,New_Visitor,TRUE,FALSE 10,132.7222222,1,104,103,3608.346752,0,0.007905605,14.69960115,0,Nov,2,10,7,2,Returning_Visitor,FALSE,FALSE 1,19.83333333,0,0,17,545.0833333,0,0.00625,9.836358025,0,Nov,3,2,5,2,New_Visitor,FALSE,TRUE 16,301.1369048,4,886.5,78,5097.41833,0.014132104,0.030390424,7.296889074,0,Nov,3,2,2,2,Returning_Visitor,TRUE,TRUE 17,446.9333333,8,597.5833333,62,2350.667857,0.007452575,0.033986814,79.48403933,0,Dec,1,1,7,10,Returning_Visitor,FALSE,FALSE 0,0,0,0,13,129.5,0.015384615,0.053846154,0,0,Nov,2,2,3,1,Returning_Visitor,FALSE,FALSE 1,0,2,10,69,1310.636905,0.010869565,0.032793149,0,0,Nov,2,3,1,3,Returning_Visitor,TRUE,FALSE 4,184.4583333,1,8.166666667,44,1398.390775,0.017391304,0.026402848,10.57361945,0,Nov,1,2,3,1,Returning_Visitor,TRUE,FALSE 7,74.08333333,1,3,197,2915.571429,0,0.005025126,1.573423226,0,Nov,2,2,3,3,Returning_Visitor,TRUE,FALSE 1,61.5,0,0,13,949.75,0,0.007692308,51.75775772,0,Dec,1,1,1,2,New_Visitor,FALSE,TRUE 0,0,0,0,35,2381.097222,0.005714286,0.021959184,0,0,Nov,2,2,1,2,Returning_Visitor,FALSE,FALSE 3,32.25,0,0,27,1273.25,0,0.008888889,43.89691103,0,Nov,2,2,1,2,New_Visitor,FALSE,TRUE 0,0,0,0,50,3002.902778,0.002083333,0.013611111,0,0,Nov,2,2,1,2,Returning_Visitor,TRUE,FALSE 3,54.5,0,0,6,148.75,0.022222222,0.033333333,0,0,Nov,3,2,1,3,Returning_Visitor,FALSE,FALSE 9,335.1666667,5,219,107,6235.06603,0.005309735,0.016223404,0,0,Nov,1,1,1,2,Returning_Visitor,FALSE,FALSE 0,0,1,0,0,0,0.2,0.2,0,0,Nov,1,1,4,15,Returning_Visitor,FALSE,FALSE 18,1103.330357,8,696.8333333,359,12959.91515,0.000795756,0.008975428,7.231074783,0,Nov,2,2,1,2,Returning_Visitor,FALSE,TRUE 0,0,0,0,63,1361.203175,0,0.014761905,0,0,Nov,2,2,7,2,Returning_Visitor,FALSE,FALSE 0,0,2,28.75,82,2375.114286,0.007058824,0.031764706,0,0,Nov,2,2,1,1,Returning_Visitor,TRUE,FALSE 0,0,0,0,14,1404.75,0,0.021428571,0,0,Dec,2,2,1,2,New_Visitor,FALSE,FALSE 3,875,0,0,34,780.7916667,0,0.015675676,0,0,Dec,4,2,3,2,Returning_Visitor,TRUE,FALSE 11,250.25,0,0,169,8276.292565,0.002406417,0.016051967,8.174728678,0,Nov,4,1,1,8,Returning_Visitor,TRUE,FALSE 6,104.5,0,0,11,1206.75,0,0.013333333,0,0,Dec,4,1,4,8,New_Visitor,FALSE,FALSE 6,166.375,1,0,39,1858.833333,0.001515152,0.011818182,50.17693633,0,Dec,1,1,1,2,Returning_Visitor,FALSE,FALSE 1,11.83333333,2,27,31,424.75,0.019047619,0.039292517,30.81857956,0,Nov,2,2,3,1,Returning_Visitor,FALSE,TRUE 0,0,0,0,1,0,0.2,0.2,0,0,Nov,4,2,1,1,Returning_Visitor,TRUE,FALSE 18,261.1619048,0,0,58,1992.717308,0.008955224,0.015829187,0,0,Nov,3,2,6,2,Returning_Visitor,FALSE,FALSE 4,364.3333333,2,19,85,3642.066026,0.007777778,0.029549858,5.052145956,0,Nov,2,2,1,1,Returning_Visitor,FALSE,FALSE 0,0,0,0,23,480.9166667,0.017391304,0.032608696,0,0,Dec,4,2,1,2,Returning_Visitor,FALSE,FALSE 0,0,0,0,5,77.5,0.1,0.08,0,0,Nov,3,2,4,11,Returning_Visitor,TRUE,FALSE 2,64.5,0,0,52,2120.630952,0,0.011965812,0,0,Nov,2,2,1,2,Returning_Visitor,FALSE,FALSE 3,84.25,0,0,52,1725.966667,0,0.031188811,0,0,Nov,2,2,1,2,New_Visitor,FALSE,FALSE 1,0,0,0,58,1200.5,0.002298851,0.017816092,17.98756852,0,Dec,2,10,3,6,Returning_Visitor,FALSE,FALSE 0,0,0,0,13,1314.083333,0,0.008333333,103.0610827,0,Dec,8,13,9,20,Other,FALSE,TRUE 0,0,0,0,23,2225.288889,0.004347826,0.021516165,0,0,Nov,1,1,4,1,Returning_Visitor,FALSE,FALSE 0,0,0,0,12,528.5833333,0,0.02,0,0,Dec,8,13,9,20,Other,FALSE,FALSE 0,0,0,0,6,42,0.033333333,0.05,0,0,Nov,2,2,9,3,Returning_Visitor,FALSE,FALSE 10,147.4583333,4,216.5,68,1587.765598,0,0.010045837,0,0,Dec,2,2,3,2,Returning_Visitor,FALSE,FALSE 0,0,0,0,2,0,0.2,0.2,0,0,Dec,2,5,2,1,Returning_Visitor,FALSE,FALSE 1,8,2,134.5,21,507.2916667,0.008333333,0.031944444,0,0,Nov,2,10,1,1,Returning_Visitor,FALSE,TRUE 0,0,2,6,7,162.625,0,0.022222222,0,0,Nov,2,2,1,2,Returning_Visitor,TRUE,FALSE 0,0,0,0,12,442.5,0,0.008333333,0,0,Nov,2,2,1,2,New_Visitor,FALSE,FALSE 0,0,0,0,4,185,0.05,0.1,0,0,Nov,2,2,1,20,Returning_Visitor,FALSE,FALSE 12,252.8125,4,78.5,100,4597.124242,0.00925181,0.028496768,2.638074656,0,Nov,1,1,1,10,Returning_Visitor,TRUE,TRUE 4,142.5,0,0,56,1885.33744,0.010714286,0.038102106,5.04016763,0,Nov,2,5,3,11,Returning_Visitor,FALSE,TRUE 9,965.5,0,0,30,1895.133333,0.005405405,0.011186186,0,0,Dec,3,2,3,2,Returning_Visitor,TRUE,FALSE 1,22,3,39,80,1903.081639,0.007228916,0.032248996,0,0,Dec,3,2,1,2,Returning_Visitor,FALSE,FALSE 0,0,0,0,42,2872.125,0.0025,0.025777778,0,0,Nov,2,2,1,13,Returning_Visitor,TRUE,FALSE 9,196.25,1,62.5,6,148.75,0,0.02,0,0,Dec,4,1,9,2,Returning_Visitor,FALSE,FALSE 1,55.5,0,0,53,4048.583333,0.007843137,0.015490196,0,0,Dec,4,1,1,1,Returning_Visitor,FALSE,FALSE 2,140.5,0,0,10,431.5,0,0.006666667,0,0,Dec,2,2,2,1,Returning_Visitor,FALSE,FALSE 0,0,0,0,3,60.5,0.1,0.1,0,0,Nov,3,2,6,6,Returning_Visitor,FALSE,FALSE 0,0,0,0,4,105.5,0,0.05,0,0,Dec,2,2,2,2,Returning_Visitor,FALSE,FALSE 0,0,0,0,16,1902.25,0.00625,0.016666667,0,0,Nov,2,4,6,2,New_Visitor,TRUE,FALSE 0,0,0,0,1,0,0.2,0.2,0,0,Nov,2,2,6,10,Returning_Visitor,FALSE,FALSE 3,106.5,2,49.5,29,1433.634921,0.008064516,0.018932412,0,0,Nov,3,2,4,2,Returning_Visitor,TRUE,FALSE 0,0,0,0,23,709.75,0.029166667,0.043055556,0,0,Nov,2,2,1,2,Returning_Visitor,TRUE,FALSE 7,218.6666667,0,0,5,145.6666667,0,0.014285714,0,0,Nov,2,2,1,3,Returning_Visitor,FALSE,FALSE 6,115.5833333,0,0,30,1076.958333,0,0.022857143,10.3889438,0,Nov,2,2,4,3,Returning_Visitor,TRUE,TRUE 2,242.5,0,0,59,5004.229167,0.006779661,0.034387947,0,0,Dec,2,5,3,2,Returning_Visitor,TRUE,FALSE 0,0,0,0,6,1038,0.033333333,0.066666667,0,0,Dec,2,4,1,2,Returning_Visitor,FALSE,FALSE 0,0,0,0,12,337,0,0.006666667,19.37005896,0,Dec,2,5,4,1,Returning_Visitor,TRUE,TRUE 0,0,0,0,3,98,0,0.066666667,0,0,Nov,4,1,6,2,Returning_Visitor,FALSE,FALSE 0,0,0,0,30,1029.583333,0.003571429,0.009166667,0,0,Dec,3,2,1,2,Returning_Visitor,FALSE,FALSE 0,0,0,0,12,1437.75,0.016666667,0.066666667,0,0,Nov,2,2,3,1,Returning_Visitor,FALSE,FALSE 1,0,0,0,8,534.875,0.008888889,0.074074074,0,0,Nov,2,2,1,10,Returning_Visitor,FALSE,FALSE 3,89.75,2,16,36,1751.847727,0.005263158,0.023684211,0,0,Nov,2,2,7,1,Returning_Visitor,FALSE,FALSE 0,0,0,0,26,1435.375,0,0.020833333,20.79483821,0,Dec,2,2,4,2,Returning_Visitor,FALSE,FALSE 0,0,0,0,37,638.25,0.005555556,0.016666667,0,0,Nov,2,2,4,1,Returning_Visitor,FALSE,TRUE 5,97.83333333,0,0,15,489,0,0.00625,81.334441,0,Dec,8,13,9,20,Other,FALSE,TRUE 0,0,0,0,25,954.0833333,0.034722222,0.061527778,120.587914,0,Nov,3,2,1,13,Returning_Visitor,FALSE,FALSE 5,48.97619048,1,65,40,1159.5671,0,0.016324111,12.48912129,0,Dec,1,1,4,2,Returning_Visitor,TRUE,FALSE 0,0,0,0,10,80.5,0.083333333,0.092222222,0,0,Dec,2,2,1,3,Returning_Visitor,FALSE,FALSE 0,0,0,0,2,27.5,0,0.1,0,0,Nov,2,2,1,1,Returning_Visitor,TRUE,FALSE 0,0,0,0,9,258.2916667,0,0.022222222,0,0,Nov,2,5,3,2,Returning_Visitor,FALSE,FALSE 0,0,0,0,8,101.8333333,0,0.058333333,0,0,Nov,2,2,3,2,Returning_Visitor,FALSE,FALSE 8,247.0833333,0,0,163,4600.540873,0.008383234,0.021052657,5.28608849,0,Nov,2,4,3,2,Returning_Visitor,FALSE,TRUE 1,25.75,5,452.0833333,34,953.5892857,0,0.011071429,8.838027292,0,Dec,2,2,3,2,Returning_Visitor,FALSE,FALSE 8,222.75,3,15.75,61,1749.842262,0.015196078,0.02894958,0,0,Nov,1,1,1,3,Returning_Visitor,TRUE,FALSE 9,230.6666667,0,0,66,2178.85,0.003555556,0.010977778,11.73221807,0,Nov,2,2,3,2,Returning_Visitor,TRUE,TRUE 11,167.4061355,0,0,49,1512.574405,0,0.007188359,0,0,Nov,1,1,3,2,New_Visitor,FALSE,FALSE 4,19.66666667,3,254.9166667,147,4254.666667,0.008445946,0.01925038,0,0,Nov,2,2,1,1,Returning_Visitor,FALSE,FALSE 0,0,0,0,1,0,0.2,0.2,0,0,Dec,3,2,6,1,Returning_Visitor,FALSE,FALSE 0,0,0,0,24,506.452381,0,0.009090909,76.7910465,0,Nov,2,4,1,3,New_Visitor,FALSE,TRUE 0,0,2,36.5,41,1383.661111,0.002790698,0.011436389,0,0,Dec,1,1,1,2,Returning_Visitor,FALSE,FALSE 3,99,0,0,58,1261.658333,0,0.003452381,0,0,Nov,1,2,9,2,New_Visitor,FALSE,TRUE 0,0,0,0,12,466.125,0,0.022222222,0,0,Nov,3,2,1,3,Returning_Visitor,FALSE,FALSE 5,96,0,0,81,2833.814103,0.00021645,0.025137085,0,0,Nov,4,1,7,2,Returning_Visitor,FALSE,FALSE 0,0,0,0,25,711.1666667,0,0.016,0,0,Dec,2,2,2,3,Returning_Visitor,FALSE,FALSE 0,0,2,207.25,114,3788.8,0.003478261,0.010434783,1.795451001,0,Dec,2,2,3,2,Returning_Visitor,TRUE,FALSE 0,0,0,0,4,213.5,0,0.1,0,0,Dec,2,10,9,1,Returning_Visitor,FALSE,FALSE 2,94,1,4,264,8584.025049,0.006289308,0.023315309,0,0,Nov,2,2,1,2,Returning_Visitor,FALSE,TRUE 0,0,0,0,8,1004.5,0,0.05,27.77725875,0,Dec,1,1,4,1,Returning_Visitor,FALSE,TRUE 2,46.5,0,0,90,1322.004437,0,0.00547491,16.87878625,0,Dec,2,2,7,10,Returning_Visitor,FALSE,FALSE 0,0,0,0,53,2051.466667,0.004528302,0.017735849,0,0,Nov,2,2,1,2,Returning_Visitor,FALSE,TRUE 3,60.25,0,0,8,131.5833333,0.02,0.013333333,0,0,Nov,3,2,1,2,New_Visitor,FALSE,FALSE 2,69.5,0,0,21,308.4583333,0.027272727,0.043939394,0,0,Dec,2,2,1,13,Returning_Visitor,FALSE,FALSE 2,15,0,0,1,8,0,0.066666667,0,0,Nov,2,4,1,2,Returning_Visitor,FALSE,FALSE 1,70.5,0,0,12,541.5357143,0,0.008333333,115.7819471,0,Nov,2,5,3,2,New_Visitor,TRUE,TRUE 0,0,0,0,22,282.0833333,0.045454545,0.063636364,0,0,Nov,2,2,4,1,Returning_Visitor,FALSE,FALSE 5,191.8333333,1,17,10,209.3333333,0,0.018181818,0,0,Dec,1,1,8,2,Returning_Visitor,FALSE,FALSE 0,0,3,121.25,89,3296.602091,0.00326087,0.017287492,0,0,Dec,1,1,4,2,Returning_Visitor,FALSE,FALSE 3,136,0,0,39,1426.583333,0,0.020731707,3.941736143,0,Nov,2,2,4,2,Returning_Visitor,FALSE,FALSE 0,0,2,288,132,6725.66369,0.003731343,0.01286425,0,0,Nov,2,2,2,2,Returning_Visitor,FALSE,FALSE 5,110.75,0,0,11,146.75,0,0.02,0,0,Nov,1,1,1,15,Returning_Visitor,FALSE,FALSE 0,0,0,0,1,0,0.2,0.2,0,0,Dec,2,4,1,1,Returning_Visitor,TRUE,FALSE 0,0,0,0,4,10,0.1,0.15,0,0,Nov,2,2,1,1,Returning_Visitor,FALSE,FALSE 0,0,0,0,21,864.4583333,0.003174603,0.01031746,0,0,Dec,3,2,1,2,New_Visitor,FALSE,FALSE 3,22,0,0,15,259.75,0,0.010416667,88.80448992,0,Dec,2,2,1,2,Returning_Visitor,TRUE,TRUE 0,0,0,0,23,1215.666667,0,0.004761905,0,0,Dec,2,2,5,2,New_Visitor,FALSE,FALSE 0,0,0,0,4,66.5,0,0.025,0,0,Dec,2,2,1,10,Returning_Visitor,FALSE,FALSE 7,164.5833333,1,28.5,115,3427.665476,0,0.016290123,4.970219813,0,Nov,2,2,4,10,Returning_Visitor,FALSE,TRUE 0,0,3,62.75,24,680.2559524,0.015384615,0.038461538,72.06822266,0,Nov,1,1,8,10,Returning_Visitor,FALSE,FALSE 1,4,0,0,8,492.25,0,0.044444444,0,0,Dec,2,2,1,2,New_Visitor,FALSE,FALSE 0,0,0,0,27,799.375,0.007407407,0.027601411,0,0,Dec,3,2,2,1,Returning_Visitor,TRUE,FALSE 1,140.5,0,0,19,1475.166667,0.02037037,0.043333333,16.9796231,0,Nov,3,2,2,10,Returning_Visitor,FALSE,FALSE 1,31.5,0,0,3,32.83333333,0,0.05,0,0,Dec,2,2,9,2,New_Visitor,TRUE,FALSE 0,0,2,82,134,4772.919574,0.00344942,0.01682332,5.667450916,0,Nov,1,2,1,2,Returning_Visitor,FALSE,TRUE 8,105,1,24.5,70,5778.966667,0.005921856,0.023453364,0,0,Nov,2,2,1,8,Returning_Visitor,TRUE,FALSE 0,0,0,0,2,20,0,0.1,0,0,Dec,2,2,1,1,Returning_Visitor,FALSE,FALSE 1,139.5,0,0,18,816.8333333,0,0.005263158,0,0,Nov,2,2,3,2,New_Visitor,FALSE,FALSE 4,59.5,2,662,31,1586.154762,0.016666667,0.037334126,0,0,Nov,1,1,1,2,Returning_Visitor,FALSE,FALSE 4,208.8888889,4,134,37,1335.809127,0.024242424,0.038460744,6.062330164,0,Dec,1,1,6,1,Returning_Visitor,FALSE,FALSE 0,0,0,0,24,743.75,0,0.016666667,0,0,Dec,2,2,2,2,New_Visitor,TRUE,FALSE 0,0,1,17.75,65,6203.690476,0.007395833,0.027280668,0,0,Dec,2,1,1,2,Returning_Visitor,FALSE,FALSE 2,41.5,1,57.5,19,2689.625,0,0.024242424,0,0,Dec,2,2,3,2,Returning_Visitor,FALSE,FALSE 4,63.75,0,0,13,774.7288136,0,0.012150434,0,0,Nov,2,2,6,2,Returning_Visitor,FALSE,TRUE 2,9,1,0,106,6617.791228,0.009047619,0.026412307,1.777492546,0,Nov,3,2,1,1,Returning_Visitor,TRUE,TRUE 0,0,2,65.5,4,71.5,0,0.033333333,0,0,Nov,2,2,1,11,Returning_Visitor,TRUE,FALSE 2,67.5,0,0,7,393.5,0,0.025,0,0,Dec,2,2,1,2,New_Visitor,FALSE,FALSE 4,37.5,2,82,4,96.625,0,0.0175,0,0,Nov,2,2,9,2,New_Visitor,FALSE,FALSE 0,0,0,0,3,0,0.2,0.2,0,0,Nov,3,2,4,3,Returning_Visitor,TRUE,FALSE 9,181.275,2,65,120,3283.715359,0.004651163,0.024543027,2.442153455,0,Nov,1,2,1,2,Returning_Visitor,FALSE,FALSE 0,0,0,0,5,62.75,0,0.04,0,0,Dec,2,2,6,2,Returning_Visitor,TRUE,FALSE 6,149.7619048,3,267.25,120,3934.201024,0.002459016,0.023646,8.788976395,0,Nov,2,2,4,2,Returning_Visitor,TRUE,FALSE 2,104,1,136.5,12,977.5333333,0,0.015384615,0,0,Dec,2,2,1,2,Returning_Visitor,FALSE,FALSE 0,0,0,0,41,4373.847619,0.00232288,0.033787661,0,0,Dec,2,5,1,3,Returning_Visitor,TRUE,FALSE 0,0,0,0,7,162.25,0.071428571,0.09047619,0,0,Dec,2,2,3,3,Returning_Visitor,FALSE,FALSE 0,0,0,0,1,0,0.2,0.2,0,0,Nov,3,2,4,1,Other,FALSE,FALSE 0,0,0,0,35,584.3083333,0,0.008571429,0,0,Dec,3,2,3,1,Returning_Visitor,FALSE,FALSE 0,0,0,0,18,559,0.047058824,0.098039216,0,0,Nov,1,1,4,1,Returning_Visitor,TRUE,FALSE 0,0,0,0,10,305,0,0.02,0,0,Nov,2,2,1,8,Returning_Visitor,FALSE,FALSE 1,27.5,0,0,20,922.7083333,0,0.00952381,103.1402899,0,Dec,2,2,6,2,New_Visitor,FALSE,TRUE 0,0,0,0,6,97.5,0,0.016666667,0,0,Dec,1,1,3,2,Returning_Visitor,FALSE,FALSE 1,88,0,0,22,516.8333333,0,0.016666667,0,0,Dec,1,1,3,8,Returning_Visitor,FALSE,FALSE 2,29.75,0,0,33,689.202381,0.001010101,0.027020202,0,0,Dec,2,2,2,2,Returning_Visitor,FALSE,FALSE 9,497.1666667,0,0,20,1170.166667,0.012121212,0.023484848,0,0,Nov,3,2,3,2,Returning_Visitor,FALSE,FALSE 7,135.2,5,430.3333333,68,3974.534501,0.013513514,0.019008442,0,0,Nov,3,2,1,13,Returning_Visitor,TRUE,FALSE 1,27.5,3,17,1,10,0,0.04,0,0,Nov,1,1,1,8,New_Visitor,FALSE,TRUE 0,0,1,14,19,1666.75,0,0.021052632,0,0,Nov,8,13,9,20,Other,FALSE,FALSE 17,311.25,4,428.547619,139,3891.214286,0.001973684,0.011184211,86.79082569,0,Dec,2,2,1,6,Returning_Visitor,FALSE,FALSE 4,93.75,0,0,70,2161.388889,0.008767123,0.017491169,0,0,Nov,1,1,1,2,Returning_Visitor,FALSE,TRUE 1,43.5,0,0,79,2006.834416,0.002531646,0.006439185,0,0,Nov,3,3,1,3,Returning_Visitor,TRUE,FALSE 0,0,1,29.5,7,103.5208333,0,0.002777778,0,0,Nov,3,2,8,1,Returning_Visitor,TRUE,FALSE 0,0,0,0,6,123.75,0,0.011111111,0,0,Dec,2,2,7,6,Returning_Visitor,FALSE,FALSE 4,140.475,1,0,48,1252.561414,0.014038462,0.024738632,0,0,Nov,3,2,1,2,Returning_Visitor,TRUE,FALSE 0,0,0,0,4,478.0333333,0.05,0.047619048,0,0,Nov,1,1,3,2,Returning_Visitor,TRUE,FALSE 0,0,0,0,2,22,0,0.1,0,0,Dec,2,2,3,1,Returning_Visitor,FALSE,FALSE 5,140.8,4,254.75,61,1740.052778,0.007179487,0.023796204,14.62413644,0,Nov,2,2,2,2,Returning_Visitor,TRUE,FALSE 8,162.5,0,0,9,156.5,0.018181818,0.027272727,0,0,Dec,4,1,4,8,New_Visitor,FALSE,FALSE 0,0,0,0,1,0,0.2,0.2,0,0,Nov,2,4,3,3,Returning_Visitor,FALSE,FALSE 0,0,0,0,10,242.0833333,0,0.046666667,0,0,Nov,2,4,7,6,Returning_Visitor,TRUE,FALSE 5,200.5,2,390,32,904.5666667,0.009009009,0.025405405,0,0,Dec,1,2,1,2,Returning_Visitor,TRUE,FALSE 3,179,0,0,15,432.75,0,0.011111111,0,0,Dec,2,2,1,2,New_Visitor,FALSE,FALSE 8,52.5,0,0,33,2714.958333,0,0.02,18.16617631,0,Dec,2,2,7,1,Returning_Visitor,FALSE,FALSE 3,72,0,0,12,871.5,0.015384615,0.030769231,0,0,Nov,2,2,9,3,Returning_Visitor,FALSE,FALSE 0,0,2,27,40,735.5151515,0,0.015547416,0,0,Dec,3,2,3,2,Returning_Visitor,FALSE,FALSE 3,68.28571429,0,0,26,741.0555556,0.014285714,0.039707071,13.24667166,0,Nov,3,2,4,10,Returning_Visitor,FALSE,TRUE 2,58.25,3,47.75,24,833.1666667,0.028571429,0.053401361,0,0,Dec,3,2,1,1,Returning_Visitor,FALSE,FALSE 0,0,0,0,2,87,0,0.033333333,0,0,Nov,3,2,4,11,Returning_Visitor,FALSE,FALSE 0,0,0,0,3,136.5,0,0.066666667,0,0,Dec,1,1,4,3,Other,FALSE,FALSE 0,0,0,0,17,212.25,0,0.023529412,0,0,Dec,2,2,1,20,Returning_Visitor,FALSE,FALSE 2,47.5,0,0,50,1870.791667,0.002641509,0.024025157,0,0,Dec,2,2,6,1,Returning_Visitor,FALSE,FALSE 1,3,0,0,10,577.1666667,0,0.022222222,0,0,Dec,1,1,3,1,Returning_Visitor,FALSE,FALSE 7,51.5,0,0,59,1954.754167,0,0.003225806,0,0,Dec,2,2,3,1,Returning_Visitor,FALSE,FALSE 4,36,0,0,23,2359.375,0,0.010666667,58.20233822,0,Dec,4,2,1,1,New_Visitor,FALSE,TRUE 7,109.0416667,0,0,35,1136.083333,0,0.013157895,46.21605051,0,Nov,3,2,3,3,Returning_Visitor,FALSE,TRUE 0,0,0,0,25,686.5,0.016,0.032,0,0,Dec,2,2,7,1,Returning_Visitor,TRUE,FALSE 6,184.2727273,0,0,32,1427.689394,0.010810811,0.01966967,13.49031898,0,Nov,2,2,1,13,Returning_Visitor,FALSE,TRUE 21,1079.184314,4,176.5,67,2162.840879,0.007407407,0.018259847,0.548811351,0,Nov,3,2,1,11,Returning_Visitor,TRUE,FALSE 0,0,2,174,105,5254.158333,0.001869159,0.017589264,0,0,Nov,2,2,3,2,Returning_Visitor,FALSE,FALSE 2,154.5,0,0,7,108.4166667,0,0.044444444,0,0,Nov,1,5,9,2,New_Visitor,FALSE,FALSE 11,570.25,0,0,39,1728.25,0.006666667,0.019777778,22.42576795,0,Nov,2,2,1,2,Returning_Visitor,FALSE,TRUE 0,0,0,0,4,156.5,0,0.05,0,0,Dec,1,8,1,6,New_Visitor,FALSE,FALSE 9,116.5416667,4,208.6666667,57,1813.250382,0.003030303,0.011897547,30.60955642,0,Nov,1,2,1,2,Returning_Visitor,FALSE,TRUE 1,21,0,0,31,1998.25,0.01010101,0.037373737,0,0,Nov,2,2,1,2,Returning_Visitor,TRUE,FALSE 0,0,0,0,9,396.1666667,0,0.022222222,0,0,Nov,3,2,8,13,Other,FALSE,FALSE 0,0,0,0,16,576.3,0.016666667,0.011458333,0,0,Nov,3,2,1,2,Returning_Visitor,FALSE,FALSE 0,0,0,0,33,1035.083333,0.012121212,0.032323232,0,0,Nov,2,10,1,3,Returning_Visitor,FALSE,FALSE 6,81.54166667,0,0,23,808.0416667,0.000833333,0.044166667,0,0,Dec,2,2,7,8,Returning_Visitor,TRUE,FALSE 1,63.5,0,0,10,552.3333333,0,0.016363636,0,0,Dec,1,1,4,10,Returning_Visitor,FALSE,FALSE 0,0,0,0,1,0,0.2,0.2,0,0,Dec,8,13,9,20,Other,FALSE,FALSE 0,0,0,0,18,491.75,0,0.027777778,0,0,Dec,2,2,6,11,New_Visitor,FALSE,FALSE 0,0,0,0,23,901.2666667,0,0.017391304,0,0,Dec,1,1,1,2,New_Visitor,TRUE,FALSE 0,0,0,0,10,158.75,0,0.02,0,0,Nov,2,2,1,2,Returning_Visitor,FALSE,FALSE 0,0,0,0,58,1599.5,0,0.007142857,0,0,Nov,4,2,1,3,Returning_Visitor,FALSE,FALSE 1,7,0,0,5,78.5,0,0.028571429,0,0,Dec,2,2,7,1,Returning_Visitor,FALSE,FALSE 1,13,4,37.5,32,675.6666667,0.005714286,0.028571429,0,0,Dec,2,4,1,10,Returning_Visitor,FALSE,FALSE 0,0,0,0,108,4590.431277,0.005555556,0.022751323,0,0,Nov,2,2,2,1,Returning_Visitor,FALSE,FALSE 1,55.5,0,0,8,158.25,0,0.022222222,0,0,Nov,2,2,3,10,Returning_Visitor,FALSE,FALSE 2,53.5,0,0,13,287.3333333,0,0.013333333,0,0,Nov,2,2,1,6,New_Visitor,TRUE,FALSE 0,0,0,0,6,253.5833333,0.05,0.056666667,0,0,Dec,3,3,1,3,Returning_Visitor,FALSE,FALSE 0,0,0,0,49,1768.980303,0,0.004166667,9.0847678,0,Nov,3,2,4,11,Returning_Visitor,TRUE,TRUE 4,119.3333333,0,0,174,5602.593506,0,0.009057596,0,0,Dec,2,2,4,1,Returning_Visitor,FALSE,FALSE 1,13.83333333,1,0,200,8336.039583,0.004207921,0.017597513,3.730318306,0,Nov,2,5,4,1,Returning_Visitor,FALSE,TRUE 4,302,0,0,29,675.375,0,0.001333333,0,0,Dec,2,2,1,6,Returning_Visitor,TRUE,FALSE 1,0,0,0,56,2627.756494,0.021052632,0.025044663,0,0,Nov,3,2,1,2,Returning_Visitor,TRUE,TRUE 0,0,0,0,14,244.75,0,0.014285714,0,0,Dec,2,2,1,3,Returning_Visitor,FALSE,FALSE 10,790.9,0,0,54,2702.390385,0.003076923,0.018518854,0,0,Nov,2,2,1,10,Returning_Visitor,FALSE,TRUE 3,52.75,0,0,31,3069.077778,0.006451613,0.020537634,5.793026792,0,Nov,1,1,3,2,Returning_Visitor,FALSE,TRUE 0,0,0,0,1,0,0.2,0.2,0,0,Nov,2,4,1,3,Returning_Visitor,FALSE,FALSE 0,0,0,0,1,0,0.2,0.2,0,0,Dec,1,1,2,3,Returning_Visitor,FALSE,FALSE 2,36.5,0,0,47,1073.31002,0.004787234,0.029749907,0,0,Nov,1,1,1,3,Returning_Visitor,TRUE,FALSE 8,125.6309524,6,75,312,13379.36658,0.002769231,0.014969192,8.055833998,0,Dec,2,2,4,2,Returning_Visitor,FALSE,FALSE 2,105.75,0,0,19,1124.888889,0.010526316,0.028229665,0,0,Nov,3,2,9,2,Returning_Visitor,TRUE,FALSE 0,0,2,26.5,149,6001.171475,0.004,0.022010916,0,0,Nov,2,2,1,2,Returning_Visitor,FALSE,TRUE 1,56.5,1,41.5,87,2504.870761,0.019555912,0.036429937,0,0,Nov,1,2,1,11,Returning_Visitor,TRUE,FALSE 1,8,3,181.25,124,4722.568651,0.015892388,0.02243106,24.28901413,0,Nov,1,1,1,3,Returning_Visitor,FALSE,TRUE 3,63,2,204.25,20,457.25,0.009090909,0.013636364,0,0,Dec,3,2,7,2,New_Visitor,FALSE,TRUE 1,54,0,0,23,816.7761905,0,0.012318841,0,0,Dec,2,2,5,10,Returning_Visitor,FALSE,FALSE 0,0,0,0,108,3279.238428,0.012037037,0.030709877,0,0,Nov,2,2,7,1,Returning_Visitor,FALSE,FALSE 16,305,0,0,147,4123.956674,0.003797468,0.010521398,0,0,Nov,2,2,1,8,Returning_Visitor,FALSE,TRUE 1,0,0,0,60,1995.416667,0,0.003389831,61.58171224,0,Nov,2,4,1,7,New_Visitor,FALSE,TRUE 0,0,0,0,24,1242,0.02173913,0.062318841,0,0,Nov,2,2,7,1,Returning_Visitor,FALSE,FALSE 3,54.5,0,0,67,2891.744571,0.013235294,0.023901833,0,0,Nov,3,2,1,13,Returning_Visitor,TRUE,FALSE 4,934.5,1,92,18,937.7166667,0,0.005555556,0,0,Dec,1,1,6,2,New_Visitor,FALSE,FALSE 1,4,0,0,39,983.1388889,0.015384615,0.017599068,0,0,Nov,3,2,6,3,Returning_Visitor,FALSE,FALSE 0,0,2,1368,26,650.8083333,0,0.014285714,0,0,Nov,1,1,1,2,Returning_Visitor,TRUE,TRUE 1,65.5,0,0,2,99,0.033333333,0.066666667,0,0,Nov,4,2,1,8,New_Visitor,TRUE,FALSE 5,142.75,0,0,76,1523.57803,0.012987013,0.021101928,0,0,Nov,2,2,1,1,Returning_Visitor,FALSE,FALSE 0,0,0,0,15,729,0,0.033333333,0,0,Nov,3,2,1,8,New_Visitor,TRUE,FALSE 9,241.8416667,0,0,25,673.0333333,0.002150538,0.005222734,27.97341787,0,Nov,1,1,3,2,New_Visitor,FALSE,FALSE 0,0,2,21,17,190.7083333,0.022222222,0.039259259,0,0,Nov,3,2,1,13,Returning_Visitor,TRUE,FALSE 1,34.5,0,0,10,277.0357143,0,0.002777778,0,0,Dec,1,1,8,8,New_Visitor,TRUE,FALSE 5,61.0125,0,0,51,1141.066667,0.003571429,0.011093074,0,0,Dec,2,2,6,2,New_Visitor,FALSE,FALSE 0,0,0,0,15,1506.916667,0.004444444,0.033015873,0,0,Nov,1,8,2,11,Returning_Visitor,FALSE,FALSE 0,0,0,0,39,1346.966667,0,0.005128205,67.02771302,0,Dec,1,1,4,2,New_Visitor,FALSE,TRUE 0,0,0,0,4,60.5,0.1,0.15,0,0,Nov,1,1,1,1,Returning_Visitor,FALSE,FALSE 2,51.5,2,99.5,15,771.875,0.01,0.026666667,15.39534668,0,Dec,2,2,3,2,Returning_Visitor,FALSE,FALSE 9,178.5,0,0,10,558,0.011764706,0.023529412,0,0,Nov,2,5,1,11,Returning_Visitor,FALSE,FALSE 12,152.75,1,0,219,7044.064508,0.003312325,0.015735853,11.65798375,0,Nov,2,2,1,1,Returning_Visitor,FALSE,TRUE 1,8,0,0,34,709.3333333,0,0.014761905,0,0,Nov,2,2,1,1,Returning_Visitor,FALSE,FALSE 10,770.7388889,2,138.6666667,35,726.0388889,0,0.005434783,10.82357287,0,Nov,2,2,7,2,Returning_Visitor,FALSE,TRUE 7,172.375,2,52.5,226,12900.7676,0.004361055,0.017955889,44.69642794,0,Nov,2,2,1,2,Returning_Visitor,FALSE,TRUE 0,0,4,82.5,22,1087.986111,0.007692308,0.020244755,0,0,Nov,3,2,3,2,Returning_Visitor,FALSE,FALSE 0,0,0,0,11,188.4583333,0,0.009090909,0,0,Nov,1,1,1,8,New_Visitor,FALSE,FALSE 0,0,0,0,79,4218.916667,0.012820513,0.028846154,0,0,Dec,2,4,7,1,Returning_Visitor,FALSE,FALSE 5,316.5,4,185.5,144,5757.853961,0.002968037,0.014030074,0,0,Nov,1,1,1,2,Returning_Visitor,FALSE,FALSE 6,315.1666667,1,85,10,402.8055556,0.014285714,0.014285714,0,0,Dec,3,2,2,2,New_Visitor,FALSE,TRUE 0,0,0,0,32,2787.75,0.00625,0.003125,0,0,Nov,1,1,1,2,New_Visitor,FALSE,FALSE 2,40.5,0,0,133,4815.701606,0.003731343,0.011738458,8.969447973,0,Nov,2,2,3,1,Returning_Visitor,FALSE,TRUE 0,0,0,0,91,4766.021032,0,0.009213483,0,0,Dec,2,2,1,2,New_Visitor,FALSE,FALSE 0,0,1,59.5,37,925.6666667,0,0.007894737,0,0,Nov,3,2,4,2,Returning_Visitor,TRUE,FALSE 17,856.8333333,0,0,78,4716.572619,0.011494253,0.026155811,0,0,Dec,3,2,1,1,Returning_Visitor,TRUE,FALSE 0,0,0,0,2,15,0,0.1,0,0,Nov,2,2,1,1,Returning_Visitor,FALSE,FALSE 0,0,0,0,5,184,0,0.04,0,0,Nov,4,1,4,2,Returning_Visitor,FALSE,FALSE 0,0,0,0,20,1245.57619,0,0.005263158,0,0,Nov,3,2,1,3,Returning_Visitor,FALSE,FALSE 11,504.2833333,1,0,58,2451.058466,0.012365591,0.022769886,0,0,Dec,3,2,1,1,Returning_Visitor,FALSE,FALSE 0,0,0,0,12,292.875,0,0.028333333,0,0,Nov,4,2,3,2,Returning_Visitor,TRUE,FALSE 0,0,0,0,37,2296.25,0,0.014864865,0,0,Nov,2,2,1,13,Returning_Visitor,FALSE,FALSE 4,59.2,0,0,5,38.35833333,0,0.043915344,0,0,Dec,3,2,8,2,Returning_Visitor,FALSE,FALSE 0,0,0,0,21,476.8666667,0.00952381,0.034126984,0,0,Nov,3,2,1,2,Returning_Visitor,TRUE,FALSE 0,0,0,0,11,435.3333333,0.063636364,0.104242424,0,0,Nov,1,1,1,3,Returning_Visitor,FALSE,FALSE 0,0,0,0,13,597.625,0.007692308,0.034615385,0,0,Nov,3,2,1,8,Returning_Visitor,FALSE,TRUE 1,120.5,0,0,75,1677.805952,0.006578947,0.023128655,0,0,Dec,2,2,1,1,Returning_Visitor,FALSE,FALSE 2,170,1,22,39,1177.291667,0.002564103,0.01965812,0,0,Nov,2,5,7,2,Returning_Visitor,FALSE,FALSE 9,387.75,0,0,64,980.5021994,0.000866739,0.012798697,9.581561053,0,Nov,3,2,3,1,Returning_Visitor,FALSE,TRUE 0,0,0,0,2,0,0.2,0.2,0,0,Nov,1,1,1,1,Returning_Visitor,FALSE,FALSE 2,44.5,0,0,35,1330.666667,0.005882353,0.014705882,0,0,Nov,2,2,6,3,Returning_Visitor,FALSE,FALSE 0,0,0,0,24,1050.5,0.022727273,0.062121212,0,0,Dec,2,10,4,1,Returning_Visitor,FALSE,FALSE 4,101.3333333,7,693.5,171,9119.885413,0.00441989,0.021627971,0.686565227,0,Dec,2,2,3,10,Returning_Visitor,FALSE,FALSE 4,55,0,0,2,13,0,0.028571429,0,0,Dec,2,10,4,2,New_Visitor,FALSE,TRUE 0,0,0,0,48,1053.952381,0.005434783,0.009815547,0,0,Nov,1,1,1,2,Returning_Visitor,FALSE,FALSE 4,192.5714286,8,1193.125,146,8150.556716,0.001290323,0.01127957,1.842913617,0,Nov,2,4,4,2,Returning_Visitor,TRUE,FALSE 0,0,0,0,40,1385.666667,0.005,0.0225,0,0,Nov,2,2,1,1,Returning_Visitor,FALSE,FALSE 5,39,0,0,13,137,0,0.011764706,0,0,Nov,2,4,3,2,New_Visitor,FALSE,TRUE 0,0,0,0,7,88,0.085714286,0.142857143,0,0,Nov,2,4,3,1,Returning_Visitor,FALSE,FALSE 1,3,0,0,11,1046.666667,0.005555556,0.025,0,0,Nov,3,2,1,10,Returning_Visitor,TRUE,FALSE 1,10,0,0,27,548.2833333,0.003703704,0.025308642,0,0,Dec,2,4,9,2,Returning_Visitor,FALSE,FALSE 0,0,0,0,20,526.5,0.02,0.035,0,0,Nov,3,2,2,13,Returning_Visitor,FALSE,FALSE 6,190.9231928,0,0,7,603.2565261,0.021862348,0.034024526,0,0,Nov,1,1,3,2,Returning_Visitor,FALSE,FALSE 0,0,0,0,4,67.75,0,0.05,0,0,Nov,2,2,1,1,Returning_Visitor,FALSE,FALSE 0,0,0,0,17,559.75,0.011764706,0.019607843,0,0,Nov,1,1,3,3,Returning_Visitor,FALSE,FALSE 1,30.5,0,0,324,14213.63652,0.002063983,0.010072239,4.485584737,0,Nov,2,2,9,1,Returning_Visitor,TRUE,TRUE 0,0,0,0,2,8,0.15,0.15,0,0,Nov,1,1,1,3,Returning_Visitor,FALSE,FALSE 0,0,1,0,44,2807.164836,0.025,0.027424242,0,0,Nov,1,1,4,2,Returning_Visitor,FALSE,TRUE 3,86.5,0,0,30,2623.916667,0.006666667,0.007619048,23.00194502,0,Dec,2,5,1,2,New_Visitor,TRUE,TRUE 0,0,0,0,85,2112.101515,0.003529412,0.014196078,0,0,Nov,2,2,1,3,Returning_Visitor,FALSE,FALSE 0,0,0,0,2,34.5,0,0.1,0,0,Nov,2,2,3,3,Returning_Visitor,TRUE,FALSE 5,526.85,4,143.25,266,9770.812093,0.007348856,0.024720163,0.245152903,0,Nov,2,4,4,2,Returning_Visitor,TRUE,FALSE 12,209.375,0,0,43,2754.117424,0.0046875,0.033875153,13.68108942,0,Dec,3,2,3,2,Returning_Visitor,FALSE,FALSE 0,0,0,0,45,1682.25,0.026666667,0.057777778,0,0,Nov,2,2,4,1,Returning_Visitor,FALSE,FALSE 5,64.9,0,0,21,1922.15,0,0.009333333,60.62885042,0,Nov,3,2,1,2,New_Visitor,FALSE,TRUE 0,0,0,0,17,1293.083333,0,0.013333333,0,0,Dec,2,5,7,2,New_Visitor,FALSE,FALSE 0,0,0,0,3,34,0,0.066666667,0,0,Dec,2,2,1,2,Returning_Visitor,TRUE,FALSE 0,0,0,0,1,0,0.2,0.2,0,0,Dec,2,5,1,1,Returning_Visitor,FALSE,FALSE 3,238.05,5,1488,104,3275.87381,0.003030303,0.010822511,1.560184165,0,Nov,1,1,3,10,Returning_Visitor,FALSE,TRUE 0,0,0,0,38,935,0,0.007769424,0,0,Nov,2,2,3,11,New_Visitor,FALSE,FALSE 0,0,0,0,14,208.9,0,0.014285714,0,0,Nov,1,8,4,11,Returning_Visitor,TRUE,FALSE 1,79,2,255,137,2668.725046,0.002138405,0.020907736,0,0,Nov,2,2,1,2,Returning_Visitor,FALSE,FALSE 2,55.5,0,0,2,26,0,0.06,0,0,Dec,1,2,9,3,Returning_Visitor,TRUE,TRUE 4,73.33333333,1,0,52,1543.75,0.004166667,0.023639456,0,0,Nov,2,2,9,13,Returning_Visitor,FALSE,FALSE 0,0,0,0,11,352.5,0,0.03,0,0,Dec,2,2,1,1,Returning_Visitor,FALSE,FALSE 2,56.5,0,0,12,544.8333333,0,0.007142857,31.38886179,0,Dec,1,2,1,2,Other,TRUE,FALSE 7,102.5,0,0,9,64.75,0,0.027272727,0,0,Nov,2,2,5,2,New_Visitor,FALSE,FALSE 0,0,0,0,21,289.5,0.00952381,0.038095238,0,0,Dec,2,2,6,10,Returning_Visitor,FALSE,FALSE 0,0,0,0,1,0,0.2,0.2,0,0,Nov,2,2,7,8,New_Visitor,FALSE,FALSE 0,0,0,0,6,285.5,0,0.04,18.1695357,0,Nov,1,1,1,8,New_Visitor,FALSE,TRUE 2,49.5,0,0,260,8555.765985,0.005401961,0.017330846,0,0,Nov,1,2,1,2,Returning_Visitor,FALSE,TRUE 0,0,0,0,4,76.75,0,0.016666667,0,0,Nov,1,1,8,2,New_Visitor,FALSE,TRUE 0,0,2,84.5,42,2762.408333,0.004545455,0.025151515,0,0,Nov,2,2,1,2,New_Visitor,FALSE,FALSE 5,199.125,0,0,9,430.6071429,0,0.008333333,0,0,Nov,3,2,1,11,Returning_Visitor,FALSE,FALSE 0,0,0,0,10,851.1666667,0,0.022222222,0,0,Dec,2,2,2,1,Returning_Visitor,FALSE,FALSE 0,0,0,0,11,205.7,0,0.022222222,18.85086521,0,Dec,1,1,1,8,New_Visitor,FALSE,TRUE 0,0,0,0,1,0,0.2,0.2,0,0,Nov,1,1,3,2,Returning_Visitor,FALSE,FALSE 0,0,0,0,16,1092.333333,0.025,0.066666667,0,0,Dec,2,2,5,1,Returning_Visitor,FALSE,FALSE 1,21,3,19,39,754.3194444,0.004651163,0.036124031,0,0,Nov,2,2,1,10,Returning_Visitor,FALSE,FALSE 0,0,0,0,68,1806.166667,0.004411765,0.027941176,0,0,Nov,2,2,2,13,Returning_Visitor,TRUE,FALSE 0,0,0,0,25,674.9583333,0,0.016666667,0,0,Nov,2,2,4,8,Returning_Visitor,FALSE,FALSE 6,446.2380952,3,94,23,866.7071429,0.007142857,0.020522573,6.786958937,0,Nov,3,2,1,2,Returning_Visitor,TRUE,FALSE 0,0,0,0,7,72.25,0.114285714,0.142857143,0,0,Dec,1,1,4,3,Returning_Visitor,TRUE,FALSE 0,0,0,0,13,737.1666667,0.038461538,0.084615385,0,0,Nov,3,2,3,13,Returning_Visitor,FALSE,FALSE 2,13,0,0,19,637.4285714,0.00952381,0.042857143,13.1307834,0,Nov,2,2,1,3,Returning_Visitor,FALSE,FALSE 0,0,0,0,35,586.375,0.018285714,0.053714286,0,0,Nov,2,4,1,3,Returning_Visitor,FALSE,FALSE 5,123,1,12.25,26,934.15,0.014814815,0.034567901,0,0,Dec,1,1,3,3,Returning_Visitor,FALSE,FALSE 2,167.8888889,2,825.5,117,5256.472341,0.004030055,0.014604028,3.187495058,0,Nov,3,2,5,8,Returning_Visitor,FALSE,TRUE 4,111.1666667,2,25.75,16,411.9375,0.00952381,0.00952381,0,0,Nov,3,2,8,2,New_Visitor,TRUE,FALSE 0,0,0,0,1,318.5,0.166666667,0.171428571,0,0,Dec,3,2,2,1,Returning_Visitor,FALSE,FALSE 0,0,0,0,1,0,0.2,0.2,0,0,Dec,2,4,7,1,Returning_Visitor,FALSE,FALSE 1,15,2,4,108,6740.557143,0.013761468,0.032553517,24.80248887,0,Nov,2,2,1,1,Returning_Visitor,FALSE,TRUE 1,10,0,0,32,1277.75,0,0.006060606,78.12165404,0,Dec,8,13,9,20,Other,FALSE,TRUE 0,0,0,0,21,304.5416667,0.00952381,0.023809524,0,0,Dec,2,2,3,10,Returning_Visitor,FALSE,FALSE 1,0,0,0,14,1125.875,0,0.042857143,0,0,Nov,2,2,1,2,Returning_Visitor,FALSE,FALSE 3,371.7,5,106,85,2128.730927,0.003914141,0.007300316,43.71856886,0,Nov,1,1,1,2,Returning_Visitor,FALSE,TRUE 5,44,0,0,54,1391.733333,0.005357143,0.022619048,0,0,Dec,2,2,4,1,Returning_Visitor,FALSE,FALSE 0,0,2,324.5,4,64.25,0,0.033333333,0,0,Dec,3,3,2,8,New_Visitor,FALSE,FALSE 0,0,0,0,7,113.25,0.00952381,0.080952381,0,0,Dec,2,2,4,10,Returning_Visitor,FALSE,FALSE 0,0,0,0,44,691.252381,0.013953488,0.029457364,0,0,Nov,2,6,5,1,Returning_Visitor,FALSE,FALSE 3,37.75,1,10,24,274.5,0,0.006666667,0,0,Dec,2,2,1,2,New_Visitor,FALSE,FALSE 11,202.4254386,5,1767.666667,338,13265.35595,0.007520348,0.018855633,1.114150182,0,Nov,2,2,3,2,Returning_Visitor,FALSE,TRUE 0,0,0,0,20,2846.833333,0.006666667,0.0475,0,0,Nov,2,2,3,1,Returning_Visitor,FALSE,FALSE 7,51.70833333,2,280.5,149,7569.182143,8.01E-05,0.016297591,11.37726052,0,Nov,2,2,2,2,Returning_Visitor,FALSE,TRUE 3,47,0,0,89,3436.975,0,0.004387373,0,0,Nov,2,2,3,2,New_Visitor,FALSE,TRUE 4,900.5,0,0,14,1957.5,0,0.014117647,105.260972,0,Dec,1,1,3,10,Returning_Visitor,FALSE,TRUE 1,19.75,2,0,29,942.3958333,0.048181818,0.075942002,0,0,Dec,3,2,1,2,Returning_Visitor,FALSE,FALSE 0,0,4,758.5,23,1133.333333,0.008,0.020266667,12.36126667,0,Dec,3,2,6,2,Returning_Visitor,TRUE,FALSE 3,30.5,5,789.125,272,7434.743073,0.00931255,0.022108499,0,0,Nov,1,1,1,2,Returning_Visitor,FALSE,FALSE 4,367,5,2256.916667,74,8981.580128,0.002988954,0.043301833,0,0,Dec,2,2,1,8,Returning_Visitor,FALSE,FALSE 4,46.45833333,0,0,82,2875.615909,0,0.007267442,27.75787044,0,Nov,2,4,3,1,Returning_Visitor,FALSE,TRUE 2,33.5,0,0,108,2270.908333,0.005504587,0.029663609,0,0,Nov,2,2,1,3,Returning_Visitor,FALSE,FALSE 1,33.5,0,0,15,710.25,0,0.011111111,0,0,Dec,2,10,1,8,New_Visitor,FALSE,FALSE 4,43.225,0,0,13,293.725,0.022222222,0.064259259,0,0,Nov,2,2,3,2,Returning_Visitor,FALSE,FALSE 4,77.5,0,0,17,678.25,0,0.031578947,0,0,Nov,1,2,1,3,Returning_Visitor,FALSE,FALSE 0,0,0,0,61,1636.466667,0,0.009289617,0,0,Nov,2,2,1,2,New_Visitor,FALSE,FALSE 0,0,0,0,7,99.75,0,0.028571429,0,0,Dec,8,2,1,2,New_Visitor,TRUE,FALSE 3,76.25,0,0,16,301.6666667,0.014285714,0.031746032,0,0,Nov,1,1,4,8,Returning_Visitor,FALSE,FALSE 0,0,0,0,56,2354.958333,0,0.018571429,0,0,Nov,2,2,3,2,New_Visitor,TRUE,FALSE 4,296.75,0,0,10,183.7083333,0.015384615,0.026666667,0,0,Dec,1,1,3,2,Returning_Visitor,FALSE,FALSE 12,1449.666667,4,559.0833333,127,4876.007712,0.005755396,0.029203072,5.751219035,0,Nov,2,2,1,2,Returning_Visitor,FALSE,FALSE 0,0,0,0,21,1598.25,0.022222222,0.047301587,0,0,Nov,4,1,4,1,Returning_Visitor,FALSE,FALSE 0,0,0,0,10,385,0,0.022222222,0,0,Dec,2,2,3,2,New_Visitor,TRUE,FALSE 0,0,0,0,2,180.1470588,0.033333333,0.055555556,0,0,Nov,6,2,3,8,Returning_Visitor,FALSE,FALSE 0,0,1,5,89,2744.541071,0.003370787,0.015636704,0,0,Nov,3,2,6,10,Returning_Visitor,TRUE,FALSE 9,444.2847222,0,0,534,18504.12621,0.010856514,0.023309001,2.54795624,0,Nov,2,2,3,2,Returning_Visitor,TRUE,TRUE 8,171.75,0,0,32,486.6666667,0,0.005555556,0,0,Dec,4,2,1,2,New_Visitor,FALSE,FALSE 0,0,0,0,2,54,0,0.033333333,0,0,Nov,1,1,5,10,Returning_Visitor,TRUE,FALSE 2,55.375,1,566,29,1978.14881,0.001904762,0.016222222,0,0,Nov,3,2,1,10,Returning_Visitor,FALSE,FALSE 0,0,0,0,19,421,0,0.010526316,0,0,Dec,2,2,6,3,Returning_Visitor,FALSE,FALSE 5,218.8333333,3,54,28,1430.854167,0.018823529,0.022957516,9.509492485,0,Dec,3,2,1,2,Returning_Visitor,TRUE,FALSE 2,100,6,131,63,2611.651923,0,0.002985075,0,0,Dec,1,1,6,2,Returning_Visitor,FALSE,FALSE 0,0,0,0,6,161.6666667,0,0.05,0,0,Dec,2,2,9,3,New_Visitor,FALSE,FALSE 0,0,0,0,10,357.5555556,0,0.022222222,0,0,Dec,1,8,3,11,New_Visitor,FALSE,FALSE 4,793.5,0,0,61,2440.473611,0.00625,0.014867424,12.58179402,0,Nov,3,2,9,2,Returning_Visitor,TRUE,FALSE 0,0,0,0,22,348.4333333,0.028571429,0.041269841,0,0,Nov,1,8,6,11,Returning_Visitor,FALSE,TRUE 9,303.8666667,0,0,72,3752.66697,0.007042254,0.007280349,0,0,Nov,1,1,1,3,Returning_Visitor,FALSE,FALSE 0,0,0,0,5,41.66666667,0,0.04,0,0,Dec,2,2,4,2,New_Visitor,FALSE,FALSE 1,11,3,47.75,25,1925.979167,0.023076923,0.056025641,0,0,Dec,2,2,7,2,Returning_Visitor,FALSE,FALSE 0,0,0,0,15,263.25,0.013333333,0.04,0,0,Nov,1,1,3,10,Returning_Visitor,TRUE,FALSE 12,394.1,0,0,30,917.25,0,0.005405405,0,0,Nov,2,2,1,2,New_Visitor,TRUE,TRUE 8,342.75,0,0,12,653.125,0.01,0.03,0,0,Nov,2,2,7,2,Returning_Visitor,FALSE,FALSE 0,0,0,0,102,3284.5,0.003921569,0.010359477,0,0,Dec,2,2,1,3,Returning_Visitor,FALSE,FALSE 0,0,0,0,3,408,0,0.033333333,0,0,Nov,2,2,4,3,Returning_Visitor,FALSE,FALSE 0,0,0,0,4,0,0.2,0.2,0,0,Nov,3,2,1,3,Returning_Visitor,FALSE,FALSE 1,0,0,0,37,1731.5,0,0.005263158,17.22281788,0,Nov,2,2,1,2,New_Visitor,FALSE,TRUE 0,0,0,0,171,6793.383281,0.007766272,0.023198706,2.381971158,0,Nov,2,2,1,1,Returning_Visitor,FALSE,TRUE 3,36,2,16,74,4377.708157,0.02,0.029308735,0,0,Nov,1,2,3,2,Returning_Visitor,FALSE,TRUE 0,0,0,0,1,0,0.2,0.2,0,0,Dec,1,1,4,1,Returning_Visitor,TRUE,FALSE 0,0,0,0,14,289.625,0,0.028571429,0,0,Nov,2,2,1,2,New_Visitor,TRUE,FALSE 0,0,0,0,13,135.875,0.030769231,0.076923077,0,0,Nov,1,2,1,1,Returning_Visitor,TRUE,FALSE 0,0,0,0,10,1678.333333,0,0.02,0,0,Nov,2,2,1,2,Returning_Visitor,FALSE,FALSE 0,0,0,0,17,418.3333333,0,0.025,0,0,Nov,2,6,1,2,New_Visitor,FALSE,FALSE 4,49,0,0,17,283.7916667,0,0.030952381,0,0,Dec,2,2,1,13,Returning_Visitor,TRUE,FALSE 0,0,0,0,55,4156.9,0.018787879,0.05352381,0,0,Nov,2,2,1,1,Returning_Visitor,TRUE,FALSE 7,91,0,0,23,297.125,0,0.012068966,0,0,Nov,2,2,6,2,New_Visitor,FALSE,FALSE 0,0,0,0,13,562.25,0,0.015384615,34.97250838,0,Nov,1,1,1,2,New_Visitor,FALSE,TRUE 5,613.5,0,0,470,14129.8808,0,0.00362107,13.38149414,0,Nov,4,2,9,2,Returning_Visitor,TRUE,TRUE 2,87.83333333,1,113,67,4868,0,0.003174603,48.45370723,0,Dec,2,2,5,2,Returning_Visitor,FALSE,TRUE 5,68,2,9,32,1195.583333,0.01981982,0.036486486,32.0201901,0,Nov,2,5,3,2,Returning_Visitor,FALSE,TRUE 0,0,0,0,5,118.75,0.04,0.08,0,0,Dec,1,1,1,8,New_Visitor,FALSE,FALSE 3,62.5,0,0,46,1048.107143,0.004,0.013,20.88414313,0,Dec,2,2,3,2,Returning_Visitor,FALSE,TRUE 0,0,0,0,17,349.375,0.0125,0.01875,0,0,Nov,3,2,9,20,Returning_Visitor,FALSE,FALSE 0,0,0,0,10,190,0,0.04,0,0,Dec,2,6,7,20,Returning_Visitor,FALSE,FALSE 6,214.6666667,1,112,68,2822.172118,0.003105023,0.023152976,0,0,Nov,2,2,1,10,Returning_Visitor,FALSE,FALSE 3,335.5,1,15,4,96,0,0.025,0,0,Nov,1,1,8,15,Returning_Visitor,FALSE,FALSE 0,0,0,0,3,112,0,0.033333333,0,0,Dec,1,1,2,2,Returning_Visitor,FALSE,FALSE 10,506.3883929,1,5,58,2528.287431,0.001318681,0.005582418,5.552517289,0,Nov,3,2,3,10,Returning_Visitor,FALSE,FALSE 5,139.9833333,0,0,27,677.8833333,0,0.006060606,0,0,Dec,1,2,1,2,New_Visitor,TRUE,FALSE 9,395.8785714,0,0,124,3959.145344,0.004871324,0.014283416,3.464444066,0,Nov,2,2,1,2,Returning_Visitor,FALSE,TRUE 4,83.25,0,0,152,4307.409559,0.002631579,0.005971036,0.305312057,0,Dec,2,2,1,2,Returning_Visitor,FALSE,TRUE 2,131.5,0,0,26,1960.475,0.007407407,0.024074074,33.69233226,0,Nov,1,1,1,2,Returning_Visitor,FALSE,TRUE 3,474.6666667,1,30.5,86,1789.954762,0.002298851,0.012426564,5.523782302,0,Dec,2,6,1,2,Returning_Visitor,FALSE,FALSE 0,0,1,59.5,44,2284.488095,0,0.003359173,0,0,Dec,3,3,2,8,Returning_Visitor,FALSE,FALSE 6,27,1,0,141,6481.483333,0,0.019984285,1.257438906,0,Nov,2,2,1,2,Returning_Visitor,FALSE,FALSE 0,0,0,0,3,74.5,0.066666667,0.1,0,0,Dec,2,2,2,3,New_Visitor,FALSE,FALSE 0,0,0,0,21,1051.166667,0,0.00952381,14.14084367,0,Nov,1,8,2,6,New_Visitor,TRUE,TRUE 0,0,3,96.5,76,1966.092332,0.007876231,0.014700317,0,0,Dec,3,2,1,2,Returning_Visitor,FALSE,TRUE 0,0,0,0,5,127.5,0.04,0.06,0,0,Nov,3,2,6,3,Returning_Visitor,FALSE,FALSE 8,1040.25,0,0,207,5160.451362,0.002296651,0.013407291,0,0,Nov,2,2,1,2,Returning_Visitor,FALSE,FALSE 1,11,0,0,26,651.0666667,0,0.016666667,0,0,Nov,4,2,1,3,Returning_Visitor,FALSE,FALSE 1,12,0,0,16,565.4166667,0.028571429,0.027380952,0,0,Nov,3,2,1,10,Returning_Visitor,FALSE,FALSE 0,0,0,0,6,77,0.083333333,0.116666667,0,0,Dec,2,2,3,13,Returning_Visitor,FALSE,FALSE 0,0,0,0,28,1250.125,0,0.022857143,0,0,Nov,2,2,1,3,Returning_Visitor,FALSE,FALSE 5,75.25,0,0,35,572.9761905,0.005405405,0.014414414,0,0,Dec,3,2,1,3,Returning_Visitor,FALSE,FALSE 1,98,0,0,39,2555.994444,0.005263158,0.005668016,0,0,Dec,2,2,1,2,Returning_Visitor,FALSE,FALSE 0,0,0,0,8,113.6666667,0,0.025,0,0,Nov,2,2,4,13,Returning_Visitor,FALSE,FALSE 0,0,0,0,18,706.05,0.006666667,0.044938272,0,0,Nov,1,8,1,11,Returning_Visitor,TRUE,FALSE 3,75.5,1,0,130,4737.087716,0.006060606,0.011475964,43.77424082,0,Nov,2,2,1,2,Returning_Visitor,FALSE,TRUE 1,78,0,0,2,25.75,0,0.066666667,0,0,Nov,4,2,6,8,New_Visitor,FALSE,FALSE 3,53,5,9.5,218,8205.68794,0.02357037,0.039663918,1.18073661,0,Nov,1,1,3,3,Returning_Visitor,FALSE,TRUE 9,137.3222222,1,418.5,41,1933.244444,0.006079027,0.021222361,0,0,Dec,2,2,1,2,Returning_Visitor,FALSE,FALSE 7,154,3,248.1666667,34,886.4791667,0,0.005128205,77.801588,0,Dec,2,2,1,2,New_Visitor,FALSE,TRUE 3,77.5,0,0,35,1000.8,0,0.013888889,0,0,Dec,4,2,1,6,Returning_Visitor,FALSE,FALSE 7,235.75,4,66.5,37,729.7916667,0.004545455,0.018181818,60.42298474,0,Nov,2,2,1,2,Returning_Visitor,FALSE,TRUE 0,0,0,0,12,335,0.018181818,0.045454545,0,0,Dec,1,1,8,10,Returning_Visitor,FALSE,FALSE 0,0,0,0,8,96,0.025,0.05,0,0,Nov,2,4,9,20,Returning_Visitor,FALSE,FALSE 3,118,0,0,2,96.25,0,0.025,0,0,Dec,8,13,9,20,Other,FALSE,FALSE 0,0,0,0,24,386.75,0.016666667,0.051388889,0,0,Nov,2,4,1,3,Returning_Visitor,FALSE,FALSE 9,320.2708333,2,298.3333333,51,1788.370821,0.016969697,0.014709835,8.21617792,0,Nov,3,2,3,2,Returning_Visitor,TRUE,TRUE 0,0,0,0,11,251.2916667,0,0.011111111,0,0,Nov,3,2,2,11,Returning_Visitor,TRUE,FALSE 2,7,0,0,56,656.7681818,0,0.006941639,0,0,Nov,3,2,1,1,Returning_Visitor,TRUE,FALSE 2,21.25,0,0,33,968.6444444,0.019191919,0.023480257,0,0,Nov,3,2,1,8,Returning_Visitor,TRUE,FALSE 0,0,0,0,3,159,0,0.033333333,0,0,Dec,1,1,1,1,Returning_Visitor,FALSE,FALSE 0,0,0,0,47,2455.147619,0.007407407,0.023703704,0,0,Nov,3,2,1,3,Returning_Visitor,FALSE,FALSE 0,0,0,0,6,203,0,0.033333333,0,0,Nov,4,1,2,3,Returning_Visitor,FALSE,FALSE 1,131.5,1,19,62,1524.962997,0,0.003266621,83.96813664,0,Nov,2,2,1,2,New_Visitor,FALSE,TRUE 2,64,0,0,10,341.25,0.016666667,0.030555556,0,0,Nov,3,2,3,10,Returning_Visitor,FALSE,FALSE 4,68.10769231,6,1022.25,104,8130.964914,0.003787879,0.01763475,5.907475939,0,Nov,1,1,4,2,Returning_Visitor,FALSE,TRUE 1,5,2,17,126,5276.227193,0.011548556,0.033933258,0,0,Dec,2,2,9,1,Returning_Visitor,TRUE,FALSE 0,0,0,0,24,381.3583333,0.008333333,0.0225,0,0,Dec,2,2,1,1,Returning_Visitor,FALSE,FALSE 3,54.5,2,7,3,23,0.066666667,0.066666667,0,0,Dec,1,1,1,8,Returning_Visitor,FALSE,FALSE 9,243.25,3,1475.25,79,3497.214286,0.003448276,0.012643678,32.59864309,0,Dec,2,2,2,2,Returning_Visitor,FALSE,FALSE 3,150.25,0,0,71,2065.838165,0.006103286,0.015796946,0,0,Nov,2,2,1,2,Returning_Visitor,FALSE,FALSE 2,14,0,0,81,1441.910588,0.002469136,0.013932981,2.76959892,0,Nov,2,2,3,2,Returning_Visitor,FALSE,FALSE 1,7,0,0,50,2330.727381,0.023809524,0.031399417,0,0,Nov,3,2,3,3,Returning_Visitor,FALSE,FALSE 4,118.5,1,8.5,62,2910.632251,0.005833333,0.017587302,25.05062136,0,Nov,3,2,1,2,Returning_Visitor,FALSE,TRUE 0,0,0,0,9,1626,0.022222222,0.077777778,0,0,Nov,2,2,3,2,Returning_Visitor,FALSE,FALSE 0,0,0,0,74,2719.734091,0.008108108,0.024324324,0,0,Nov,2,2,5,1,Returning_Visitor,FALSE,FALSE 7,86.27777778,2,21,80,2170.286706,0.003571429,0.021527778,25.76598645,0,Nov,3,2,1,10,Returning_Visitor,TRUE,TRUE 0,0,0,0,20,837.25,0.015,0.041666667,0,0,Dec,6,5,1,1,Returning_Visitor,FALSE,FALSE 3,155.75,1,0,62,2038.772727,0.007070707,0.0254662,17.60269465,0,Nov,2,4,1,10,Returning_Visitor,TRUE,FALSE 6,31,0,0,17,1494.25,0.020289855,0.031449275,0,0,Nov,1,1,3,7,Returning_Visitor,FALSE,FALSE 0,0,0,0,8,249,0,0.020833333,0,0,Nov,2,2,2,2,Returning_Visitor,FALSE,FALSE 0,0,0,0,6,316.25,0,0.033333333,0,0,Dec,3,13,9,20,Returning_Visitor,FALSE,FALSE 2,25,0,0,25,243.1666667,0.016666667,0.057283951,0,0,Nov,2,2,3,6,Returning_Visitor,FALSE,FALSE 3,1115.5,0,0,21,1469.666667,0.008695652,0.014492754,0,0,Nov,1,1,1,8,Returning_Visitor,TRUE,FALSE 10,143.5833333,1,92,245,7793.158685,0.001587302,0.004610177,59.42458922,0,Nov,3,2,1,8,Returning_Visitor,FALSE,TRUE 2,144.5,0,0,7,416.5,0.044444444,0.031111111,0,0,Nov,3,2,3,8,Returning_Visitor,TRUE,FALSE 0,0,0,0,41,1130.92619,0,0.015447154,0,0,Dec,2,2,8,2,New_Visitor,FALSE,FALSE 0,0,0,0,10,371.5,0,0.04,0,0,Nov,2,2,4,10,Returning_Visitor,FALSE,FALSE 2,28,0,0,51,2587.766667,0,0.007434641,21.47448516,0,Nov,4,2,3,10,Returning_Visitor,FALSE,TRUE 1,6,0,0,6,125,0,0.057142857,0,0,Dec,2,2,6,1,Returning_Visitor,FALSE,FALSE 0,0,0,0,2,39.33333333,0,0.025,0,0,Dec,3,2,1,3,Returning_Visitor,FALSE,FALSE 1,5,0,0,63,1405.327056,0.003174603,0.006349206,8.44235219,0,Nov,2,2,1,1,Returning_Visitor,FALSE,TRUE 0,0,0,0,15,612,0,0.013333333,0,0,Nov,1,2,8,2,Returning_Visitor,TRUE,FALSE 0,0,0,0,8,183,0,0.00625,0,0,Dec,2,4,4,1,Returning_Visitor,FALSE,FALSE 7,222.8166667,1,0,22,1032.75,0,0.019230769,16.98117776,0,Nov,2,2,2,10,Returning_Visitor,TRUE,FALSE 8,130.7,0,0,3,87.5,0,0.022222222,0,0,Nov,2,2,1,3,New_Visitor,TRUE,FALSE 2,75,0,0,29,1279.089286,0.003333333,0.014166667,35.60912359,0,Nov,2,2,3,11,Returning_Visitor,FALSE,TRUE 1,22.75,0,0,1,0,0,0.066666667,0,0,Dec,1,1,1,8,New_Visitor,FALSE,TRUE 3,84.5,0,0,6,53.375,0.05,0.035,0,0,Nov,1,1,3,10,Returning_Visitor,TRUE,FALSE 4,195.5,0,0,31,1028.533333,0.018181818,0.031313131,77.66478213,0,Nov,3,2,1,13,Returning_Visitor,FALSE,FALSE 2,11,0,0,11,976,0.024358974,0.044688645,0,0,Dec,1,1,1,1,Returning_Visitor,FALSE,FALSE 0,0,2,59.5,43,760.875,0.018412698,0.036790123,0,0,Dec,4,2,1,2,Returning_Visitor,TRUE,FALSE 6,218.5714286,0,0,35,1760.613095,0,0.01375,16.81772593,0,Nov,2,2,3,2,Returning_Visitor,FALSE,TRUE 0,0,0,0,7,284.5,0,0.028571429,128.4686939,0,Dec,2,2,4,10,Returning_Visitor,FALSE,TRUE 9,198.4333333,7,636.75,113,3312.879422,0.004032258,0.016482015,0,0,Nov,2,2,2,2,Returning_Visitor,FALSE,TRUE 0,0,0,0,10,348.25,0.02,0.024,0,0,Nov,2,2,4,13,Returning_Visitor,FALSE,FALSE 2,90,0,0,19,921.6,0.02,0.03,55.46963196,0,Nov,3,2,3,8,Returning_Visitor,FALSE,TRUE 0,0,0,0,8,83.5,0.075,0.1,0,0,Dec,2,2,5,2,Returning_Visitor,FALSE,FALSE 0,0,0,0,7,244.25,0,0.028571429,0,0,Dec,2,2,1,13,Returning_Visitor,FALSE,FALSE 0,0,0,0,7,151.4166667,0,0.011111111,0,0,Dec,8,13,9,20,Other,FALSE,FALSE 2,279,0,0,14,553.3333333,0,0.0125,0,0,Nov,2,4,3,2,Returning_Visitor,FALSE,FALSE 0,0,0,0,6,612.5,0,0.04,0,0,Nov,2,2,1,2,Returning_Visitor,FALSE,FALSE 0,0,0,0,11,109.5,0,0.022222222,0,0,Dec,4,1,1,8,New_Visitor,FALSE,FALSE 0,0,0,0,116,5389.883333,0.009565217,0.020786749,0,0,Nov,3,2,1,1,Returning_Visitor,FALSE,FALSE 3,30,0,0,23,625.3,0.016666667,0.026388889,0,0,Dec,2,2,3,3,Returning_Visitor,FALSE,FALSE 7,86.75,1,0,101,3094.366667,0.000269542,0.014951401,56.72227169,0,Nov,2,2,1,2,Returning_Visitor,FALSE,TRUE 0,0,0,0,27,630.75,0.016,0.02,0,0,Nov,2,2,3,1,Returning_Visitor,TRUE,FALSE 0,0,0,0,7,276.1666667,0.025,0.05,55.31956658,0,Nov,2,2,1,8,Returning_Visitor,FALSE,FALSE 7,193.75,0,0,60,1316.434982,0.003125,0.014393939,0,0,Nov,1,2,3,2,Returning_Visitor,FALSE,FALSE 6,122.25,1,33.5,60,2719.654762,0.006557377,0.012080406,0,0,Dec,1,2,1,2,Returning_Visitor,FALSE,FALSE 9,65.54166667,0,0,6,744.1666667,0,0.004,0,0,Dec,2,4,1,2,Returning_Visitor,FALSE,FALSE 0,0,0,0,6,163,0,0.033333333,0,0,Dec,2,4,6,1,Returning_Visitor,FALSE,FALSE 0,0,0,0,5,200.9333333,0,0.08,0,0,Dec,3,2,1,6,Returning_Visitor,FALSE,FALSE 4,507.3333333,2,286.6666667,114,3616.008121,0.004093567,0.013535432,0,0,Dec,3,2,9,1,Returning_Visitor,FALSE,FALSE 2,324.5,1,0,21,696.225,0.008333333,0.018055556,0,0,Nov,1,1,4,2,Returning_Visitor,FALSE,FALSE 0,0,0,0,9,275.1666667,0.022222222,0.033333333,0,0,Nov,3,2,1,13,Returning_Visitor,FALSE,FALSE 0,0,2,4,45,2984.130952,0.004255319,0.02141844,0,0,Nov,2,2,1,1,Returning_Visitor,FALSE,FALSE 2,11,0,0,132,4424.916667,0.00610687,0.020093774,14.99601118,0,Nov,2,2,4,1,Returning_Visitor,FALSE,TRUE 0,0,0,0,5,168.9,0,0.011428571,0,0,Dec,3,2,1,2,New_Visitor,FALSE,FALSE 7,122.875,0,0,22,250.0357143,0,0.03974359,0,0,Dec,2,2,1,2,New_Visitor,FALSE,FALSE 2,87.5,0,0,29,1352.133333,0,0.025287356,34.60160214,0,Nov,3,2,6,2,Returning_Visitor,TRUE,TRUE 0,0,0,0,1,0,0.2,0.2,0,0,Dec,3,2,2,10,Other,FALSE,FALSE 0,0,0,0,11,169.375,0,0.018181818,0,0,Dec,3,2,3,1,Returning_Visitor,FALSE,FALSE 5,176.25,0,0,13,1183.05,0,0.0375,0,0,Dec,2,2,1,2,Returning_Visitor,FALSE,FALSE 0,0,0,0,8,158.25,0,0.003571429,0,0,Nov,1,8,4,11,Returning_Visitor,TRUE,FALSE 0,0,0,0,20,513.3333333,0,0.015357143,0,0,Dec,2,2,3,2,Returning_Visitor,FALSE,FALSE 12,509.53125,2,11,33,2032.599702,0,0.020476926,5.730539288,0,Nov,2,2,2,1,Returning_Visitor,FALSE,FALSE 0,0,0,0,2,0,0.2,0.2,0,0,Nov,1,1,8,1,Returning_Visitor,FALSE,FALSE 0,0,0,0,22,464.6583333,0.018181818,0.034090909,0,0,Nov,1,1,4,2,Returning_Visitor,TRUE,FALSE 0,0,0,0,69,1238.586029,0.018571429,0.045269841,0,0,Nov,1,8,7,1,Returning_Visitor,FALSE,FALSE 0,0,0,0,1,0,0.2,0.2,0,0,Nov,1,8,3,1,Returning_Visitor,FALSE,FALSE 0,0,0,0,69,3621.066667,0.001492537,0.02032147,0,0,Nov,2,1,1,2,Returning_Visitor,FALSE,TRUE 1,6,0,0,57,1836.775,0,0.004093567,0,0,Nov,2,2,7,2,Returning_Visitor,FALSE,FALSE 0,0,0,0,3,23,0,0.066666667,0,0,Nov,2,2,4,3,Returning_Visitor,FALSE,FALSE 0,0,0,0,16,898.7583333,0.00625,0.014583333,0,0,Nov,1,1,1,1,Returning_Visitor,FALSE,TRUE 4,80,0,0,326,11945.62954,0.001849665,0.017527555,0,0,Nov,2,2,1,2,Returning_Visitor,FALSE,TRUE 0,0,0,0,16,824.2291667,0,0.045138889,0,0,Dec,2,2,3,2,Returning_Visitor,FALSE,TRUE 0,0,0,0,41,451.7142857,0.007692308,0.018376068,0,0,Nov,2,2,6,11,Returning_Visitor,FALSE,FALSE 1,8,0,0,33,636.5833333,0,0.00625,0,0,Dec,3,2,1,2,New_Visitor,FALSE,FALSE 6,214,0,0,27,830.1041667,0,0.021428571,0,0,Dec,1,1,2,2,Returning_Visitor,FALSE,FALSE 0,0,0,0,10,249.5,0,0.02,0,0,Dec,2,2,3,8,Returning_Visitor,FALSE,FALSE 1,51.5,0,0,57,2689.291667,0.003571429,0.0275,0,0,Nov,2,2,9,2,Returning_Visitor,FALSE,FALSE 0,0,0,0,57,2167.126924,0.008181818,0.017137357,12.360043,0,Nov,3,2,3,1,Returning_Visitor,TRUE,TRUE 0,0,0,0,8,943.5,0.025,0.045833333,0,0,Dec,3,2,3,10,Returning_Visitor,FALSE,FALSE 0,0,0,0,5,62.5,0,0.04,0,0,Nov,2,2,1,8,New_Visitor,FALSE,FALSE 6,82.2,0,0,176,5505.401209,0,0.005809793,10.59423174,0,Nov,2,2,1,3,Returning_Visitor,FALSE,TRUE 9,268.5,0,0,8,407.5,0,0.0275,0,0,Dec,3,2,3,8,Returning_Visitor,FALSE,FALSE 0,0,0,0,28,363.25,0,0.007142857,0,0,Nov,2,2,1,3,Returning_Visitor,FALSE,FALSE 0,0,0,0,27,610.1916667,0.015384615,0.03974359,0,0,Dec,2,2,2,2,Returning_Visitor,FALSE,FALSE 7,133.4666667,4,46.5,26,1357.833333,0,0.018487395,18.26940096,0,Nov,3,2,9,2,New_Visitor,TRUE,TRUE 0,0,0,0,8,97,0,0.025,0,0,Nov,2,2,3,2,Returning_Visitor,FALSE,TRUE 0,0,0,0,4,133.5,0,0.025,0,0,Dec,4,2,1,2,New_Visitor,FALSE,FALSE 1,19,0,0,17,336.5833333,0,0.03,0,0,Nov,3,2,1,1,Returning_Visitor,FALSE,FALSE 4,379.5,0,0,188,8618.031872,0.003454411,0.019765654,0,0,Nov,2,2,1,2,Returning_Visitor,FALSE,FALSE 0,0,0,0,15,281.7380952,0,0.001923077,57.82473775,0,Nov,2,4,1,2,New_Visitor,FALSE,TRUE 0,0,0,0,1,0,0.2,0.2,0,0,Nov,2,5,1,1,Returning_Visitor,TRUE,FALSE 3,150.75,1,14,13,308.5,0,0.0125,0,0,Dec,2,2,5,2,New_Visitor,TRUE,FALSE 0,0,0,0,5,1289,0.066666667,0.084,0,0,Nov,3,2,1,13,Returning_Visitor,FALSE,FALSE 2,217,0,0,34,409.5,0,0.005555556,0,0,Dec,2,2,4,2,Returning_Visitor,FALSE,FALSE 5,39.83333333,0,0,34,2348.8125,0.02,0.055166667,0,0,Dec,2,2,3,1,Returning_Visitor,FALSE,FALSE 0,0,1,0,0,0,0.2,0.2,0,0,Nov,1,1,3,15,Returning_Visitor,FALSE,FALSE 4,189.6666667,0,0,27,687.5,0,0.005069124,0,0,Dec,2,2,3,2,New_Visitor,FALSE,FALSE 0,0,0,0,1,0,0.2,0.2,0,0,Nov,3,2,3,2,Returning_Visitor,FALSE,FALSE 5,501.75,0,0,18,628.5833333,0.004761905,0.012698413,37.02263046,0,Nov,2,2,3,3,Returning_Visitor,FALSE,TRUE 12,217.0833333,5,480.125,90,1996.565909,0.013684211,0.027807876,0,0,Nov,3,2,3,1,Returning_Visitor,FALSE,FALSE 1,2,1,0,77,2476.154789,0.005128205,0.02648199,0,0,Dec,2,2,1,2,Returning_Visitor,FALSE,TRUE 0,0,0,0,18,211.4166667,0.033333333,0.061111111,0,0,Nov,2,2,5,3,Returning_Visitor,TRUE,FALSE 1,21,0,0,81,2930.722222,0.003658537,0.018943089,0,0,Nov,2,2,3,1,Returning_Visitor,TRUE,TRUE 2,67.5,0,0,10,268,0,0.018181818,59.51850423,0,Dec,2,2,1,2,New_Visitor,FALSE,TRUE 3,334,0,0,32,4316.833333,0,0.003030303,42.70505393,0,Dec,8,13,9,20,Other,FALSE,TRUE 0,0,1,0,82,2710.381731,0.004938272,0.021957672,0,0,Nov,3,2,3,2,Returning_Visitor,FALSE,TRUE 9,183.3,2,34,45,2904.922294,0,0.011870748,0,0,Dec,1,1,1,8,Returning_Visitor,TRUE,FALSE 0,0,0,0,8,427.75,0,0.0125,0,0,Dec,4,2,1,2,New_Visitor,FALSE,FALSE 1,14,0,0,14,988.0625,0.014285714,0.028571429,68.84905671,0,Dec,2,5,9,1,Returning_Visitor,FALSE,TRUE 0,0,0,0,3,428.5,0,0.066666667,0,0,Nov,1,1,3,8,Returning_Visitor,FALSE,TRUE 2,157.25,0,0,29,2999.716667,0,0.005952381,0,0,Dec,3,2,2,6,Returning_Visitor,FALSE,FALSE 5,369.375,2,59,19,1328.5,0.027272727,0.031818182,14.78422174,0,Nov,3,2,1,13,Returning_Visitor,FALSE,FALSE 0,0,0,0,48,691.3572797,0.034873188,0.082209406,0,0,Nov,3,2,3,3,Returning_Visitor,FALSE,FALSE 0,0,0,0,28,912.4348485,0,0.026839827,0,0,Nov,2,2,7,2,Returning_Visitor,TRUE,FALSE 8,108.8333333,0,0,21,780.5833333,0.008695652,0.017391304,25.03946022,0,Nov,2,5,1,3,New_Visitor,FALSE,TRUE 0,0,0,0,65,1931.512121,0,0.025128205,0,0,Dec,2,2,3,2,Returning_Visitor,FALSE,FALSE 0,0,0,0,18,2334.916667,0.011111111,0.017592593,0,0,Dec,3,2,1,1,Returning_Visitor,FALSE,FALSE 0,0,0,0,24,754.3,0,0.0125,0,0,Nov,2,2,3,10,Returning_Visitor,FALSE,FALSE 0,0,0,0,8,167.8333333,0,0.05,0,0,Nov,2,2,1,2,Returning_Visitor,FALSE,FALSE 8,194.2916667,5,759.5,23,1291.35119,0.013131313,0.020454545,41.67776195,0,Dec,3,3,1,2,Returning_Visitor,TRUE,FALSE 0,0,0,0,8,335.25,0.1,0.108333333,0,0,Nov,3,12,1,1,Returning_Visitor,FALSE,TRUE 0,0,0,0,24,783.375,0,0.004166667,15.75636267,0,Nov,1,1,4,3,Returning_Visitor,FALSE,TRUE 0,0,1,5,27,1095.658333,0.017857143,0.01547619,60.73155753,0,Nov,1,1,1,10,Returning_Visitor,TRUE,TRUE 0,0,0,0,3,71.5,0.066666667,0.133333333,0,0,Nov,2,2,3,3,Returning_Visitor,FALSE,FALSE 5,91.5,0,0,14,187.9166667,0,0.02745098,0,0,Dec,1,1,3,3,Returning_Visitor,FALSE,FALSE 1,145.5,0,0,26,1258.791667,0.019230769,0.018589744,0,0,Nov,1,1,9,2,Returning_Visitor,FALSE,FALSE 0,0,0,0,2,71.5,0,0.05,0,0,Dec,3,2,1,10,Returning_Visitor,FALSE,FALSE 16,230.3263889,1,0,86,2351.834722,0.002061856,0.020836197,0.335232374,0,Dec,2,2,4,10,Returning_Visitor,TRUE,FALSE 2,52.5,0,0,13,605.5,0,0.006666667,0,0,Nov,2,2,3,2,Returning_Visitor,FALSE,FALSE 1,4,0,0,164,5767.465043,0.003636364,0.009348691,52.38420388,0,Nov,2,2,1,1,Returning_Visitor,FALSE,FALSE 0,0,0,0,30,1726.75,0,0.015555556,0,0,Nov,2,4,3,2,New_Visitor,FALSE,FALSE 10,138.75,0,0,69,2464.294444,0,0.000544218,0,0,Dec,2,2,3,2,New_Visitor,FALSE,FALSE 0,0,1,22,40,1284.569048,0.00625,0.019791667,0,0,Nov,1,1,9,2,Returning_Visitor,TRUE,TRUE 0,0,0,0,38,1787.395833,0.004824561,0.031871345,0,0,Nov,2,2,4,2,New_Visitor,FALSE,FALSE 3,55.75,0,0,55,1139.638889,0.003508772,0.03245614,6.190582078,0,Dec,2,2,3,10,Returning_Visitor,FALSE,FALSE 1,0,2,9,44,814.1547619,0,0.023188406,0,0,Nov,4,2,1,10,Returning_Visitor,FALSE,TRUE 2,143.25,0,0,38,2508.413853,0,0.00745614,54.16451768,0,Nov,2,2,7,2,Returning_Visitor,FALSE,TRUE 3,17,0,0,71,2086.666667,0.005555556,0.019907407,0,0,Dec,2,2,7,3,Returning_Visitor,FALSE,FALSE 3,29,0,0,2,7,0,0.04,0,0,Dec,2,2,3,2,New_Visitor,FALSE,FALSE 4,88.625,3,170.5833333,5,124.9583333,0,0.005555556,0,0,Nov,2,4,7,2,New_Visitor,FALSE,FALSE 0,0,1,16,25,902.0833333,0,0.007692308,0,0,Dec,3,3,3,2,New_Visitor,FALSE,TRUE 4,144.5,2,52.5,39,1299.5,0.004878049,0.021138211,0,0,Nov,2,2,1,1,Returning_Visitor,FALSE,FALSE 3,197.5,0,0,15,343.25,0.011764706,0.029411765,0,0,Dec,2,4,4,2,Returning_Visitor,FALSE,FALSE 1,13,1,10,18,445,0,0.003508772,0,0,Nov,1,1,4,3,Returning_Visitor,FALSE,FALSE 1,131.5,5,464.875,19,660.8039683,0.002173913,0.008038828,0,0,Nov,3,2,2,3,Returning_Visitor,TRUE,FALSE 6,123.5416667,0,0,12,189,0,0.014285714,0,0,Dec,3,12,3,1,New_Visitor,FALSE,FALSE 0,0,0,0,4,46.25,0.05,0.066666667,0,0,Nov,3,2,6,13,Returning_Visitor,FALSE,FALSE 8,131.9642857,7,1039.166667,61,1407.446429,0.000533333,0.019203175,0,0,Dec,2,2,1,2,Returning_Visitor,FALSE,FALSE 0,0,0,0,15,428,0,0.014285714,89.13481061,0,Nov,1,8,3,6,Returning_Visitor,TRUE,TRUE 1,19,0,0,8,208,0.04,0.1,0,0,Nov,2,2,1,1,Returning_Visitor,FALSE,FALSE 2,190.75,0,0,36,1604.791667,0,0.012719298,0,0,Dec,3,2,1,2,Returning_Visitor,TRUE,FALSE 2,32.83333333,0,0,16,877.6666667,0.004166667,0.020208333,0,0,Nov,3,2,6,6,Returning_Visitor,TRUE,FALSE 0,0,0,0,21,1030,0.005,0.025,0,0,Dec,2,2,7,1,Returning_Visitor,FALSE,FALSE 0,0,0,0,11,2076.75,0.036363636,0.081818182,0,0,Nov,2,2,4,3,Returning_Visitor,FALSE,FALSE 0,0,0,0,3,17,0,0.066666667,0,0,Nov,2,2,9,3,Returning_Visitor,FALSE,FALSE 1,9,0,0,8,602,0,0.044444444,0,0,Nov,4,1,7,3,Returning_Visitor,FALSE,FALSE 3,223.3333333,2,19.75,37,1118.987027,0.003252033,0.018958927,0,0,Nov,2,2,1,2,Returning_Visitor,TRUE,FALSE 4,22.91666667,0,0,22,221.25,0.014814815,0.035185185,0,0,Nov,2,2,1,2,Returning_Visitor,FALSE,FALSE 0,0,0,0,1,0,0.2,0.2,0,0,Dec,2,2,1,1,Returning_Visitor,FALSE,FALSE 4,50.33333333,0,0,3,28.33333333,0,0.016666667,0,0,Dec,2,2,9,10,Returning_Visitor,FALSE,FALSE 3,205.25,0,0,190,3679.128882,0,0.005951258,8.421921428,0,Dec,2,2,7,3,Returning_Visitor,FALSE,FALSE 0,0,0,0,1,0,0.2,0.2,0,0,Nov,2,2,3,1,Returning_Visitor,FALSE,FALSE 0,0,0,0,7,242.75,0.014285714,0.047619048,0,0,Nov,3,2,3,11,Returning_Visitor,TRUE,FALSE 0,0,0,0,3,26,0,0.066666667,0,0,Dec,2,2,1,2,New_Visitor,FALSE,FALSE 0,0,0,0,30,1643.958333,0.02,0.075555556,0,0,Dec,2,2,1,1,Returning_Visitor,FALSE,FALSE 1,236.5,5,1373.75,141,2834.697619,0.009684685,0.020551802,0,0,Nov,2,2,3,2,Returning_Visitor,FALSE,FALSE 1,0,0,0,0,0,0.2,0.2,0,0,Nov,1,1,3,8,Returning_Visitor,FALSE,FALSE 0,0,0,0,55,946.3636364,0.010909091,0.025454545,0,0,Nov,2,2,6,1,Returning_Visitor,TRUE,FALSE 3,32,2,9,16,1760.6,0,0.033333333,0,0,Nov,2,2,3,2,Returning_Visitor,FALSE,TRUE 0,0,0,0,3,0,0.2,0.2,0,0,Nov,3,2,1,13,Returning_Visitor,FALSE,FALSE 0,0,0,0,21,677,0.005,0.026666667,0,0,Dec,2,2,7,1,Returning_Visitor,FALSE,FALSE 3,168.5,0,0,5,84,0,0.025,0,0,Dec,3,2,7,10,New_Visitor,FALSE,FALSE 1,13,0,0,52,1380.166667,0.007692308,0.023076923,21.10579025,0,Nov,2,2,3,3,Returning_Visitor,TRUE,FALSE 0,0,0,0,16,381.2142857,0.0125,0.022395833,0,0,Nov,3,2,1,2,Returning_Visitor,FALSE,FALSE 0,0,0,0,4,58.5,0,0.05,0,0,Nov,2,2,7,1,Returning_Visitor,FALSE,FALSE 0,0,0,0,50,1784.516667,0.0016,0.020593939,0,0,Nov,1,1,4,2,Returning_Visitor,FALSE,FALSE 5,59.52777778,3,124.25,156,5189.960659,0.001234568,0.008928986,20.50963783,0,Nov,2,2,7,1,Returning_Visitor,FALSE,TRUE 1,17,0,0,15,247,0,0.014285714,0,0,Nov,4,1,1,3,New_Visitor,FALSE,FALSE 0,0,0,0,17,532.75,0.013333333,0.046666667,0,0,Dec,1,1,3,1,Returning_Visitor,TRUE,FALSE 0,0,0,0,10,228,0,0.06,0,0,Nov,2,10,1,1,Returning_Visitor,TRUE,FALSE 0,0,0,0,28,728.9791667,0.029761905,0.032959184,0,0,Nov,1,1,3,2,Returning_Visitor,FALSE,FALSE 3,65.5,3,60,30,1383.375,0.014141414,0.039393939,43.20756866,0,Nov,2,2,1,1,Returning_Visitor,FALSE,FALSE 3,107.2083333,0,0,17,513.25,0,0.012030075,17.54619116,0,Nov,2,2,1,10,Returning_Visitor,FALSE,TRUE 0,0,0,0,82,3247.433333,0.002439024,0.012804878,0,0,Nov,2,2,1,2,Returning_Visitor,FALSE,FALSE 0,0,0,0,20,1017.5,0.005,0.033333333,0,0,Nov,2,2,1,1,Returning_Visitor,FALSE,FALSE 0,0,0,0,26,2553.625,0.015384615,0.029487179,0,0,Nov,2,2,3,8,Returning_Visitor,FALSE,FALSE 0,0,0,0,7,108.25,0,0.028571429,0,0,Dec,2,2,7,2,New_Visitor,TRUE,FALSE 0,0,0,0,3,144.5,0,0.1,0,0,Nov,3,2,2,2,New_Visitor,FALSE,FALSE 0,0,0,0,12,142.75,0.016666667,0.043055556,0,0,Nov,1,1,6,2,Returning_Visitor,FALSE,FALSE 0,0,0,0,9,214.75,0,0.022222222,0,0,Nov,1,1,3,6,Returning_Visitor,FALSE,FALSE 6,61,0,0,21,585.3916667,0,0.007692308,0,0,Dec,2,2,4,1,New_Visitor,FALSE,TRUE 1,142.5,1,48.5,9,733.25,0,0.018181818,0,0,Dec,1,1,8,2,New_Visitor,TRUE,FALSE 0,0,0,0,4,121.5,0,0.05,0,0,Nov,1,1,4,8,Returning_Visitor,FALSE,FALSE 0,0,3,83,68,2162.657393,0.006527627,0.02055132,0,0,Dec,1,2,1,2,Returning_Visitor,FALSE,FALSE 0,0,0,0,3,20,0.1,0.166666667,0,0,Nov,3,2,3,13,Returning_Visitor,FALSE,FALSE 11,216.2285714,6,704.75,304,11250.48672,0.006697236,0.018717242,1.37270775,0,Nov,2,2,1,2,Returning_Visitor,FALSE,TRUE 0,0,0,0,3,159.5,0,0.066666667,0,0,Nov,2,4,1,3,Returning_Visitor,FALSE,FALSE 0,0,2,86,5,86,0,0.057142857,0,0,Nov,1,1,1,3,Returning_Visitor,FALSE,FALSE 0,0,0,0,14,862.7916667,0.014285714,0.02,0,0,Nov,1,1,1,10,Returning_Visitor,FALSE,FALSE 1,5,0,0,29,3441.439286,0.007142857,0.005654762,0,0,Dec,3,2,8,10,Returning_Visitor,FALSE,FALSE 0,0,0,0,84,4894.851299,0.002682927,0.028446662,0,0,Nov,2,2,3,1,Returning_Visitor,TRUE,TRUE 9,341,0,0,37,1538.950794,0.005128205,0.014848485,0,0,Dec,2,2,3,1,Returning_Visitor,FALSE,FALSE 0,0,0,0,15,242.9166667,0.006666667,0.006666667,0,0,Nov,3,2,1,11,Returning_Visitor,TRUE,FALSE 0,0,0,0,7,202.25,0,0.028571429,0,0,Dec,1,1,2,2,Returning_Visitor,FALSE,FALSE 0,0,0,0,3,71.16666667,0.066666667,0.083333333,0,0,Nov,3,2,1,3,Returning_Visitor,FALSE,TRUE 5,44.75,3,51.5,107,3074.852778,0.015454545,0.026239965,0,0,Nov,2,2,3,1,Returning_Visitor,TRUE,FALSE 0,0,0,0,14,263.175,0,0.021428571,0,0,Nov,5,11,1,8,Returning_Visitor,FALSE,FALSE 0,0,0,0,15,618.25,0,0.026666667,0,0,Dec,2,2,7,8,New_Visitor,TRUE,FALSE 0,0,3,137.5,71,2487.061777,0.001013514,0.015591693,0,0,Nov,1,2,1,11,Returning_Visitor,FALSE,FALSE 12,251.6190476,1,44,69,2269.732143,0,0.007,0.093546949,0,Dec,2,2,3,2,Returning_Visitor,FALSE,TRUE 14,321.2666667,4,230,39,2310.083333,0.005769231,0.02702758,27.03298037,0,Nov,1,1,1,2,Returning_Visitor,FALSE,FALSE 1,30.5,0,0,16,374.5,0.029411765,0.03627451,0,0,Nov,3,2,3,1,Returning_Visitor,TRUE,FALSE 9,296.1166667,1,26.5,79,5057.175,0.002298851,0.022687466,17.78922266,0,Nov,2,2,1,10,Returning_Visitor,FALSE,TRUE 0,0,0,0,21,604.5,0,0.019047619,0,0,Nov,2,2,1,13,New_Visitor,FALSE,FALSE 0,0,0,0,53,2634.476245,0.000943396,0.016429608,0,0,Nov,2,2,1,2,Returning_Visitor,FALSE,FALSE 4,207.5,0,0,2,56.5,0,0.02,0,0,Dec,3,2,2,2,New_Visitor,TRUE,FALSE 0,0,0,0,7,58.5,0,0.057142857,0,0,Nov,2,6,9,2,Returning_Visitor,FALSE,FALSE 7,440.5833333,0,0,48,1777.702381,0,0.021538462,14.74449646,0,Dec,2,2,1,2,Returning_Visitor,FALSE,FALSE 0,0,0,0,64,1558.361111,0.0046875,0.021875,0,0,Nov,2,4,3,2,Returning_Visitor,FALSE,TRUE 0,0,2,21,43,1825.557143,0,0.018115942,0,0,Nov,2,6,1,2,Returning_Visitor,TRUE,FALSE 0,0,0,0,1,0,0.2,0.2,0,0,Nov,2,2,1,1,Returning_Visitor,FALSE,FALSE 6,90.41666667,0,0,5,100.4166667,0,0.02,0,0,Dec,1,2,6,8,New_Visitor,FALSE,FALSE 0,0,0,0,3,95,0.066666667,0.133333333,0,0,Nov,1,1,9,3,Returning_Visitor,FALSE,FALSE 0,0,0,0,4,28,0.05,0.075,0,0,Nov,2,2,3,3,Returning_Visitor,FALSE,FALSE 1,0,2,360,82,6858.672122,0.002409639,0.013380361,0,0,Nov,1,1,3,2,Returning_Visitor,FALSE,FALSE 4,27,0,0,9,79.25,0,0.046153846,0,0,Nov,2,2,3,1,Returning_Visitor,FALSE,FALSE 6,81.83333333,0,0,7,166.5,0,0.015384615,0,0,Nov,2,2,3,3,New_Visitor,FALSE,FALSE 12,152.75,6,1024.25,127,3247.644636,0.008844998,0.017025608,33.85339122,0,Nov,3,1,1,2,Returning_Visitor,TRUE,TRUE 0,0,0,0,5,41,0.04,0.08,0,0,Dec,2,5,1,1,Returning_Visitor,FALSE,FALSE 22,1153.682251,3,108,205,4295.305066,0.001746725,0.008801049,177.5288252,0,Nov,2,5,3,3,Returning_Visitor,TRUE,FALSE 12,521.075,0,0,83,5124.691667,0,0.011196593,35.25420124,0,Dec,2,2,7,6,Returning_Visitor,FALSE,FALSE 3,150.125,4,26.5,26,1332.695346,0.026523297,0.04656682,5.473874968,0,Dec,3,2,3,2,Returning_Visitor,FALSE,FALSE 3,20.16666667,0,0,87,2467.256349,0.000189394,0.019568765,11.6803149,0,Dec,4,2,1,2,Returning_Visitor,FALSE,FALSE 5,194.2857143,4,162.75,98,5325.234886,0.001941748,0.012604407,56.1773806,0,Nov,2,2,1,10,Returning_Visitor,TRUE,TRUE 0,0,0,0,9,123.1666667,0.022222222,0.077777778,0,0,Nov,2,2,9,3,Returning_Visitor,FALSE,FALSE 0,0,0,0,39,902.6527778,0,0.008852259,0,0,Nov,2,2,1,2,New_Visitor,FALSE,FALSE 1,24.5,0,0,47,1330.375,0.004347826,0.015450311,0,0,Dec,2,2,7,8,New_Visitor,FALSE,FALSE 8,52.85,2,33.5,71,1714.778571,0.018333333,0.035661897,0,0,Nov,2,2,1,1,Returning_Visitor,FALSE,FALSE 0,0,0,0,9,205.0833333,0,0.044444444,0,0,Dec,2,2,7,2,Returning_Visitor,FALSE,FALSE 0,0,0,0,23,517.0357143,0,0.00952381,23.30000662,0,Dec,4,2,8,2,New_Visitor,FALSE,TRUE 2,27.5,0,0,44,1026.450758,0.004651163,0.037364341,0,0,Dec,2,2,9,3,Returning_Visitor,FALSE,FALSE 0,0,0,0,4,57.66666667,0,0.025,0,0,Nov,3,2,1,2,Returning_Visitor,FALSE,FALSE 0,0,0,0,3,25.5,0.066666667,0.133333333,0,0,Nov,1,1,3,2,Returning_Visitor,TRUE,FALSE 0,0,0,0,53,3575,0,0.003846154,119.8943327,0,Nov,2,5,6,2,Returning_Visitor,FALSE,TRUE 0,0,0,0,57,3696.225342,0.007017544,0.037615417,0,0,Nov,3,2,1,2,Returning_Visitor,TRUE,FALSE 0,0,0,0,13,1600,0.015384615,0.020512821,0,0,Dec,2,2,1,20,Returning_Visitor,FALSE,FALSE 5,417.5,3,151.25,45,1739.25,0.014666667,0.0178,0,0,Dec,3,2,1,1,Returning_Visitor,FALSE,FALSE 1,61.25,3,1083,14,2230.5,0,0.011764706,0,0,Dec,2,2,1,2,Returning_Visitor,FALSE,FALSE 0,0,0,0,12,153.625,0,0.008333333,0,0,Nov,2,2,3,2,New_Visitor,FALSE,TRUE 1,16,3,585.625,40,765.0416667,0.010731707,0.023436807,0,0,Dec,3,2,4,2,Returning_Visitor,FALSE,FALSE 3,165.25,0,0,49,1256.383333,0.025133333,0.046907359,0,0,Nov,3,2,1,13,Returning_Visitor,FALSE,FALSE 0,0,2,19.25,4,95,0,0.033333333,0,0,Nov,2,2,4,2,Returning_Visitor,TRUE,FALSE 11,424.475,5,185.75,76,2642.458333,0.011481481,0.026290598,0,0,Nov,2,2,1,2,Returning_Visitor,TRUE,TRUE 4,356.5,0,0,15,1992.916667,0,0.014285714,17.85338057,0,Dec,2,4,1,1,Returning_Visitor,FALSE,TRUE 10,779.45,0,0,39,1002.491667,0.01875,0.033796296,0,0,Nov,3,2,1,3,Returning_Visitor,FALSE,FALSE 5,88.83333333,0,0,27,494.6440476,0,0.006781609,0,0,Nov,1,1,1,2,Returning_Visitor,FALSE,FALSE 0,0,0,0,31,835.2380952,0,0.009811828,0,0,Dec,2,2,1,10,Returning_Visitor,FALSE,FALSE 2,166.9166667,7,115,233,7952.491839,0.001834947,0.019045831,8.920900783,0,Nov,2,2,1,2,Returning_Visitor,FALSE,TRUE 0,0,0,0,27,1433.71256,0.021937322,0.062113617,0,0,Nov,2,4,3,2,Returning_Visitor,FALSE,FALSE 0,0,0,0,16,277.3,0.0125,0.041666667,0,0,Nov,2,2,1,2,Returning_Visitor,FALSE,FALSE 0,0,0,0,37,907.5,0.005405405,0.021621622,0,0,Nov,2,2,1,8,New_Visitor,FALSE,FALSE 9,73.25,0,0,26,2923.25,0.020689655,0.037931034,0,0,Dec,1,1,3,1,Returning_Visitor,FALSE,FALSE 7,98.17261905,0,0,47,1889.953409,0.014814815,0.028251029,0,0,Nov,2,2,1,1,Returning_Visitor,FALSE,FALSE 10,244.7083333,0,0,213,8125.852288,0.002790698,0.017631698,0,0,Dec,4,1,1,1,Returning_Visitor,TRUE,FALSE 0,0,0,0,2,0,0.2,0.2,0,0,Nov,3,2,9,1,Returning_Visitor,FALSE,FALSE 0,0,0,0,10,199.6666667,0.01,0.045,0,0,Nov,3,2,4,3,Returning_Visitor,FALSE,FALSE 0,0,0,0,3,48.5,0,0.066666667,0,0,Dec,4,1,1,2,Returning_Visitor,FALSE,FALSE 8,108.3844538,0,0,65,2008.900974,0.001449275,0.02037037,17.43149651,0,Dec,2,2,3,2,Returning_Visitor,FALSE,FALSE 6,215.25,0,0,29,672.125,0,0.011764706,22.82159308,0,Nov,2,2,1,8,New_Visitor,TRUE,TRUE 0,0,0,0,8,140.1666667,0,0.0375,0,0,Nov,2,2,1,1,Returning_Visitor,TRUE,FALSE 0,0,0,0,1,0,0.2,0.2,0,0,Dec,2,2,3,13,Returning_Visitor,FALSE,FALSE 15,404.7666667,4,240.5,120,4173.131061,0.006716418,0.027927974,9.269827963,0,Nov,2,2,3,2,Returning_Visitor,TRUE,FALSE 9,227.375,4,102.25,48,2849.482143,0,0.014853801,4.741626063,0,Nov,2,2,4,2,Returning_Visitor,FALSE,FALSE 0,0,0,0,10,444.5,0,0.02,13.9394091,0,Nov,2,2,1,1,Returning_Visitor,FALSE,TRUE 0,0,0,0,57,3079.083333,0.005847953,0.012907268,0,0,Dec,2,2,3,10,Returning_Visitor,FALSE,FALSE 3,37.25,0,0,19,656.25,0,0.023529412,0,0,Dec,2,5,3,6,Returning_Visitor,FALSE,FALSE 0,0,0,0,11,205.75,0.018181818,0.036363636,0,0,Dec,4,1,8,2,Returning_Visitor,FALSE,FALSE 0,0,0,0,9,470,0.0375,0.066666667,0,0,Dec,2,4,1,1,Returning_Visitor,FALSE,FALSE 11,225.5,7,258.25,40,1352.833333,0.002,0.015733333,0,0,Dec,3,2,1,2,Returning_Visitor,TRUE,FALSE 0,0,0,0,8,497.25,0.016666667,0.025,0,0,Dec,1,8,3,1,Returning_Visitor,TRUE,FALSE 0,0,0,0,5,23,0.12,0.16,0,0,Dec,1,1,1,3,Returning_Visitor,FALSE,FALSE 11,192.847619,0,0,186,6975.887249,0.003163265,0.00929719,14.45829487,0,Nov,1,1,3,2,Returning_Visitor,FALSE,TRUE 0,0,0,0,5,125.75,0.066666667,0.14,0,0,Nov,3,2,1,10,Returning_Visitor,TRUE,FALSE 0,0,0,0,80,3734.958333,0.0025,0.026547619,0,0,Nov,2,2,1,2,Returning_Visitor,FALSE,FALSE 0,0,0,0,4,0,0.2,0.2,0,0,Dec,1,1,2,1,Returning_Visitor,FALSE,FALSE 4,82.5,0,0,16,299.5,0,0.005555556,0,0,Dec,1,1,4,2,Returning_Visitor,TRUE,FALSE 0,0,0,0,5,75,0.04,0.08,0,0,Dec,3,2,7,1,Returning_Visitor,FALSE,FALSE 0,0,0,0,16,318.75,0,0.004166667,0,0,Nov,2,2,7,1,Returning_Visitor,FALSE,TRUE 0,0,0,0,42,1766.833333,0.023015873,0.062641723,0,0,Nov,2,2,1,13,Returning_Visitor,FALSE,FALSE 0,0,0,0,9,608.75,0,0.025,42.422531,0,Dec,2,2,9,2,Returning_Visitor,FALSE,TRUE 0,0,0,0,8,186,0,0.044444444,0,0,Dec,2,2,1,10,Returning_Visitor,FALSE,FALSE 1,41.5,0,0,12,237.5,0.015384615,0.046153846,0,0,Dec,1,8,2,1,Returning_Visitor,FALSE,FALSE 3,38,0,0,12,279.0833333,0,0.038461538,0,0,Nov,1,1,8,2,Returning_Visitor,FALSE,FALSE 0,0,0,0,6,1098.5,0.066666667,0.1,0,0,Dec,2,2,7,1,Returning_Visitor,FALSE,FALSE 7,98,3,61,83,2355.682738,0.002873563,0.022487296,0,0,Dec,1,1,1,2,Returning_Visitor,FALSE,FALSE 0,0,0,0,3,39.5,0,0.033333333,0,0,Dec,1,1,9,8,New_Visitor,FALSE,FALSE 0,0,2,686,11,288.125,0.030769231,0.066666667,0,0,Dec,4,1,3,3,Returning_Visitor,FALSE,FALSE 1,94,0,0,54,1210.921324,0.005454545,0.008671329,0,0,Dec,2,2,3,1,Returning_Visitor,FALSE,FALSE 13,340.5916667,0,0,43,863.3468254,0,0.006730769,40.01028435,0,Nov,2,2,4,1,Returning_Visitor,FALSE,TRUE 0,0,0,0,12,412.875,0,0.02,0,0,Dec,3,2,4,1,Returning_Visitor,TRUE,FALSE 1,5,0,0,26,850.6666667,0.007407407,0.061728395,0,0,Dec,2,2,1,3,Returning_Visitor,FALSE,FALSE 5,85.33333333,3,8,213,8591.591742,0.003993856,0.01587654,8.024865728,0,Nov,2,2,1,10,Returning_Visitor,FALSE,TRUE 0,0,0,0,77,1903.601743,0.003151515,0.029314358,0,0,Dec,1,1,3,1,Returning_Visitor,FALSE,FALSE 10,60.7,1,110.25,75,1585.60873,0.005063291,0.021603376,0,0,Dec,2,2,4,1,Returning_Visitor,FALSE,FALSE 12,1098.5,1,0,115,6367.506345,0.016,0.030930013,0,0,Nov,1,1,1,3,Returning_Visitor,FALSE,TRUE 0,0,0,0,3,79,0,0.033333333,0,0,Nov,1,2,1,1,Returning_Visitor,FALSE,FALSE 7,167.0083333,0,0,40,978.0916667,0.001136364,0.016060606,0,0,Dec,3,2,2,2,Returning_Visitor,FALSE,FALSE 0,0,0,0,6,118.3333333,0.033333333,0.044444444,0,0,Nov,3,2,1,3,Returning_Visitor,TRUE,FALSE 0,0,0,0,48,4293.833333,0.008333333,0.034166667,0,0,Nov,2,1,1,2,New_Visitor,TRUE,FALSE 5,114.25,0,0,107,9126.982468,0.005612245,0.017992979,0,0,Nov,2,2,1,2,Returning_Visitor,FALSE,TRUE 0,0,5,53.83333333,2,31.16666667,0,0.033333333,0,0,Nov,2,2,4,2,Returning_Visitor,FALSE,FALSE 7,288.5208333,0,0,12,402.3958333,0,0.002857143,0,0,Dec,1,1,8,2,New_Visitor,TRUE,FALSE 2,48.5,0,0,5,396.1666667,0,0.008333333,0,0,Dec,3,2,5,10,Returning_Visitor,FALSE,FALSE 0,0,0,0,22,1632.533333,0.009090909,0.025,0,0,Dec,3,2,1,2,New_Visitor,TRUE,FALSE 0,0,0,0,17,399.5,0.017647059,0.047058824,0,0,Dec,2,2,1,2,Returning_Visitor,FALSE,FALSE 0,0,0,0,2,59.5,0,0.1,0,0,Dec,1,1,7,2,Returning_Visitor,TRUE,FALSE 0,0,0,0,20,624.0083333,0.005,0.015178571,0,0,Dec,1,2,3,2,Returning_Visitor,FALSE,FALSE 0,0,0,0,11,116.5,0.036363636,0.075757576,0,0,Dec,2,2,1,1,Returning_Visitor,FALSE,FALSE 0,0,0,0,18,391.5833333,0,0.025,0,0,Dec,2,2,7,2,New_Visitor,FALSE,FALSE 0,0,0,0,26,664.85,0.026923077,0.066666667,0,0,Dec,2,10,7,1,Returning_Visitor,TRUE,FALSE 8,225.4166667,0,0,44,1254.112179,0.007092199,0.030129179,0,0,Dec,3,2,3,1,Returning_Visitor,FALSE,FALSE 2,31,0,0,88,5321.441032,0.016853933,0.019314057,25.74700712,0,Nov,3,2,1,2,Returning_Visitor,FALSE,TRUE 0,0,0,0,13,751.5416667,0.033846154,0.06025641,0,0,Dec,1,1,7,2,Returning_Visitor,FALSE,FALSE 3,99.75,0,0,89,2520.056936,0.002173913,0.012934783,40.51760545,0,Nov,2,2,1,3,Returning_Visitor,FALSE,TRUE 2,14,0,0,5,45.25,0,0.038095238,0,0,Dec,2,2,1,2,Returning_Visitor,FALSE,FALSE 4,48.5,0,0,7,31.5,0,0.026666667,0,0,Dec,2,2,1,2,Returning_Visitor,FALSE,FALSE 0,0,0,0,18,1456.916667,0.005882353,0.030392157,0,0,Dec,3,3,3,2,Returning_Visitor,FALSE,FALSE 0,0,1,51.5,31,618.75,0.003225806,0.027956989,0,0,Nov,2,2,1,13,Returning_Visitor,FALSE,FALSE 0,0,0,0,40,1699.583333,0.023076923,0.033799534,0,0,Nov,2,2,3,1,Returning_Visitor,TRUE,FALSE 0,0,0,0,39,935.0972222,0.005128205,0.015042735,0,0,Dec,2,2,8,2,Returning_Visitor,FALSE,FALSE 0,0,0,0,13,541.9166667,0,0.015384615,26.09995422,0,Dec,2,4,1,2,New_Visitor,FALSE,TRUE 0,0,0,0,11,559.25,0.036363636,0.042424242,0,0,Dec,2,2,1,10,Returning_Visitor,FALSE,FALSE 0,0,2,63.5,14,376,0.0375,0.040625,0,0,Nov,2,2,2,11,Returning_Visitor,TRUE,TRUE 0,0,0,0,6,93.5,0.016666667,0.066666667,0,0,Dec,1,1,3,1,Returning_Visitor,TRUE,FALSE 2,31.5,0,0,27,811.8630952,0,0.015873016,0,0,Nov,1,1,4,8,Returning_Visitor,TRUE,FALSE 3,7,1,25.16666667,21,446,0.019047619,0.031746032,32.38852793,0,Nov,2,2,1,1,Returning_Visitor,FALSE,FALSE 0,0,0,0,27,1166.683333,0.014074074,0.026492375,0,0,Nov,3,2,7,3,Returning_Visitor,FALSE,FALSE 5,80.66666667,2,278.25,4,233.375,0.022222222,0.02,0,0,Nov,1,1,1,2,Returning_Visitor,FALSE,FALSE 14,200.4166667,0,0,7,72.41666667,0,0.004761905,0,0,Dec,8,13,9,20,Other,FALSE,FALSE 0,0,0,0,20,301,0.005555556,0.066666667,0,0,Dec,1,1,1,2,Returning_Visitor,FALSE,FALSE 0,0,0,0,1,0,0.2,0.2,0,0,Dec,1,1,1,1,Returning_Visitor,TRUE,FALSE 4,240.1666667,0,0,73,2371.371545,0.013333333,0.033805229,0,0,Nov,3,2,1,2,Returning_Visitor,TRUE,FALSE 2,20,3,402,85,3881.79359,0.00453159,0.039115224,0,0,Nov,2,2,1,2,Returning_Visitor,FALSE,FALSE 4,52.83333333,0,0,57,1761.285714,0.006896552,0.022220854,0,0,Nov,1,1,1,3,Returning_Visitor,TRUE,FALSE 0,0,0,0,20,333.3333333,0.01,0.04,0,0,Nov,2,2,7,1,Returning_Visitor,FALSE,FALSE 3,171.375,0,0,29,1343.946429,0.035714286,0.034983766,0,0,Nov,3,2,8,13,Returning_Visitor,TRUE,FALSE 0,0,0,0,9,87.91666667,0,0.003174603,0,0,Nov,8,2,5,1,Returning_Visitor,TRUE,FALSE 7,152.6,2,50.5,30,1089.285714,0,0.012952381,23.59811637,0,Nov,2,2,4,2,Returning_Visitor,FALSE,FALSE 0,0,0,0,4,143.5,0.033333333,0.058333333,0,0,Dec,1,1,1,2,Returning_Visitor,FALSE,FALSE 3,365,0,0,6,737.75,0,0.008333333,0,0,Nov,1,1,6,2,Returning_Visitor,FALSE,FALSE 0,0,0,0,29,1102.85,0.009195402,0.028275862,0,0,Nov,1,1,1,3,Returning_Visitor,FALSE,FALSE 4,96.25,0,0,67,3763.000758,0,0.003076923,15.84839963,0,Nov,2,4,9,3,Other,FALSE,TRUE 0,0,0,0,1,0,0.2,0.2,0,0,Nov,3,2,2,11,Returning_Visitor,TRUE,FALSE 5,107.125,0,0,34,916,0,0.002857143,0,0,Dec,2,2,2,1,Returning_Visitor,FALSE,FALSE 0,0,0,0,7,491.5833333,0,0.008333333,45.9675105,0,Dec,3,2,4,2,New_Visitor,TRUE,TRUE 3,224.5,0,0,1,26.5,0,0.033333333,0,0,Dec,3,2,2,6,New_Visitor,TRUE,FALSE 5,46.95833333,0,0,30,585.8,0,0.002083333,0,0,Nov,2,2,1,10,Returning_Visitor,TRUE,FALSE 0,0,0,0,28,630.0833333,0.013580247,0.032839506,0,0,Nov,1,1,1,10,Returning_Visitor,TRUE,FALSE 2,23,0,0,18,657.25,0,0.0125,0,0,Dec,2,2,9,2,New_Visitor,FALSE,FALSE 0,0,0,0,20,366.5,0,0.01,0,0,Nov,2,2,1,2,Returning_Visitor,FALSE,FALSE 0,0,0,0,45,1478.07381,0.020454545,0.038636364,0,0,Nov,2,2,7,13,Returning_Visitor,FALSE,FALSE 7,52.875,0,0,9,152.4166667,0,0.017777778,0,0,Dec,2,2,4,2,New_Visitor,FALSE,FALSE 1,15,1,4,49,711.3630952,0,0.002040816,0,0,Dec,1,1,1,2,New_Visitor,FALSE,FALSE 8,146,5,125.5,99,3881.938907,0.001851852,0.026820988,10.12959599,0,Nov,2,2,9,3,Returning_Visitor,FALSE,FALSE 3,173.9444444,0,0,14,890.5277778,0,0.028571429,52.74177636,0,Dec,4,5,5,10,Returning_Visitor,FALSE,TRUE 1,105,1,0,59,1125.904762,0,0.014043584,0,0,Dec,3,2,3,2,Returning_Visitor,FALSE,FALSE 5,111.6666667,1,123.5,42,1610.783197,0.009090909,0.017794258,4.646503212,0,Dec,3,2,3,2,Returning_Visitor,FALSE,FALSE 5,100.9166667,0,0,114,3488.070803,8.14E-05,0.022685492,5.698711043,0,Nov,2,2,3,2,Returning_Visitor,FALSE,FALSE 1,27.33333333,1,0,4,78.83333333,0,0.044444444,0,0,Nov,1,2,8,2,Returning_Visitor,FALSE,FALSE 2,36.5,0,0,80,2762.770833,0,0.006410256,10.0986778,0,Nov,4,2,3,2,Returning_Visitor,FALSE,TRUE 0,0,0,0,1,0,0.2,0.2,0,0,Nov,3,2,1,1,Other,TRUE,FALSE 0,0,0,0,11,460.8333333,0.027272727,0.025757576,0,0,Dec,3,2,6,2,Returning_Visitor,TRUE,FALSE 0,0,0,0,1,0,0.2,0.2,0,0,Dec,3,2,7,3,Returning_Visitor,FALSE,FALSE 0,0,0,0,10,211.8333333,0,0.04,0,0,Dec,2,2,3,13,Returning_Visitor,FALSE,FALSE 0,0,0,0,3,13.25,0.066666667,0.133333333,0,0,Nov,1,1,1,1,Returning_Visitor,FALSE,FALSE 0,0,0,0,6,166,0,0.033333333,0,0,Dec,2,2,1,3,Returning_Visitor,FALSE,FALSE 0,0,0,0,110,5244.471512,0.002201835,0.013960708,0,0,Nov,2,2,1,2,Returning_Visitor,FALSE,TRUE 2,131,2,9,14,237.75,0,0.022222222,0,0,Dec,2,10,4,2,New_Visitor,FALSE,FALSE 1,0,0,0,1,0,0.2,0.2,0,0,Nov,3,2,3,8,Returning_Visitor,FALSE,TRUE 0,0,0,0,24,671.0886525,0.026666667,0.036566293,0,0,Nov,1,1,3,2,Returning_Visitor,FALSE,FALSE 0,0,0,0,8,107.1666667,0,0.025,0,0,Nov,2,2,9,2,New_Visitor,FALSE,FALSE 0,0,0,0,1,0,0.2,0.2,0,0,Nov,4,1,4,3,Returning_Visitor,FALSE,FALSE 0,0,0,0,10,443.75,0.06,0.07,38.19240444,0,Nov,1,8,8,1,Returning_Visitor,FALSE,TRUE 0,0,0,0,1,0,0.2,0.2,0,0,Dec,3,2,4,1,Returning_Visitor,FALSE,FALSE 0,0,0,0,1,0,0.2,0.2,0,0,Nov,1,1,4,1,Returning_Visitor,FALSE,FALSE 0,0,0,0,5,99,0,0.04,0,0,Nov,2,4,1,3,Returning_Visitor,FALSE,FALSE 0,0,0,0,21,517.5416667,0,0.010526316,16.1585582,0,Nov,1,1,3,8,Returning_Visitor,FALSE,TRUE 1,3,0,0,34,1767.4,0.011515152,0.039004329,0,0,Nov,3,2,4,3,Returning_Visitor,FALSE,FALSE 0,0,0,0,23,338.5833333,0,0.008695652,0,0,Nov,3,2,1,10,Returning_Visitor,FALSE,FALSE 0,0,3,27,45,1218.030952,0.023333333,0.047679739,0,0,Dec,2,2,7,1,Returning_Visitor,TRUE,FALSE 12,271.3,3,627.75,40,1358.874152,0.016571429,0.019758242,10.0674357,0,Dec,1,2,2,2,Returning_Visitor,FALSE,FALSE 1,29.5,0,0,50,1831.903509,0,0.010333333,52.03463312,0,Dec,2,4,3,8,Returning_Visitor,FALSE,TRUE 8,340.9333333,0,0,26,1076.861905,0,0.014942529,11.9331881,0,Dec,1,1,1,8,New_Visitor,TRUE,FALSE 3,24,0,0,35,529.2916667,0,0.021621622,0,0,Nov,2,2,1,1,Returning_Visitor,FALSE,FALSE 0,0,0,0,11,194.5,0.036363636,0.045454545,0,0,Nov,2,10,3,1,Returning_Visitor,FALSE,FALSE 0,0,0,0,31,1297.464286,0,0.018010753,0,0,Nov,2,1,1,2,Returning_Visitor,TRUE,FALSE 9,274.6666667,0,0,40,720.4257937,0,0.015259259,0,0,Dec,2,2,2,1,Returning_Visitor,FALSE,FALSE 0,0,1,8,38,2370.729167,0.015384615,0.030433925,0,0,Nov,2,10,2,2,Returning_Visitor,FALSE,FALSE 0,0,0,0,8,391.25,0,0.0125,0,0,Nov,3,2,4,10,Returning_Visitor,TRUE,FALSE 3,31.5,0,0,2,48.5,0,0.05,0,0,Nov,1,1,1,2,New_Visitor,FALSE,FALSE 6,121,0,0,7,65.5,0.0125,0.0625,0,0,Nov,1,1,6,1,Returning_Visitor,FALSE,FALSE 1,30.25,0,0,13,392.1875,0,0.005128205,60.26309658,0,Dec,1,8,1,20,New_Visitor,FALSE,TRUE 0,0,0,0,2,8,0,0.1,0,0,Dec,1,1,1,2,New_Visitor,TRUE,FALSE 0,0,0,0,6,33,0.033333333,0.1,0,0,Nov,2,5,3,20,Returning_Visitor,FALSE,FALSE 5,41.7,1,114,401,14568.16201,0.006633907,0.020148947,3.546315545,0,Nov,2,2,4,1,Returning_Visitor,FALSE,TRUE 6,86.32857143,4,24,131,5619.384897,0.01294547,0.035488294,3.61382908,0,Nov,3,2,1,2,Returning_Visitor,FALSE,FALSE 2,15.41666667,6,160.8333333,8,143.3333333,0.013333333,0.045238095,0,0,Nov,1,1,4,2,Returning_Visitor,FALSE,FALSE 0,0,0,0,51,1655.358333,0,0.013071895,0,0,Nov,2,2,1,2,Returning_Visitor,TRUE,FALSE 0,0,0,0,91,3511.833059,0.002247191,0.007151775,0,0,Dec,3,2,1,1,Returning_Visitor,FALSE,FALSE 0,0,0,0,12,405.1,0.05,0.055555556,0,0,Nov,3,2,2,13,Returning_Visitor,FALSE,FALSE 2,18.75,4,355.5,57,1182.590278,0.015384615,0.027094017,14.15437174,0,Nov,1,1,1,10,Returning_Visitor,FALSE,TRUE 0,0,0,0,4,103.5,0,0.05,0,0,Nov,2,2,1,6,Returning_Visitor,FALSE,FALSE 1,5,0,0,108,2367.4,0,0.003773585,0,0,Dec,2,2,1,2,New_Visitor,TRUE,FALSE 2,25.625,0,0,162,8596.669979,0.005075446,0.012349829,1.469693253,0,Dec,2,2,5,2,Returning_Visitor,FALSE,FALSE 3,40.25,3,9,26,683.0292208,0.006451613,0.032319508,0,0,Nov,1,1,1,2,Returning_Visitor,FALSE,TRUE 0,0,1,0,36,671.425,0.005405405,0.016216216,0,0,Dec,2,4,1,2,Returning_Visitor,FALSE,FALSE 7,167.375,6,135,155,4263.977734,0.005373737,0.012004543,112.6661365,0,Nov,1,2,1,2,Returning_Visitor,FALSE,TRUE 0,0,0,0,4,87.875,0,0.026666667,0,0,Nov,1,1,4,10,Returning_Visitor,TRUE,TRUE 0,0,0,0,27,427.8166667,0,0.017283951,0,0,Nov,2,2,1,1,Returning_Visitor,FALSE,FALSE 0,0,0,0,6,192.0833333,0.044444444,0.035555556,0,0,Nov,3,2,1,2,Returning_Visitor,FALSE,FALSE 0,0,0,0,2,0,0.2,0.2,0,0,Dec,2,2,1,20,Returning_Visitor,FALSE,FALSE 0,0,1,0,46,2183.8875,0,0.010638298,0,0,Nov,2,2,9,8,Returning_Visitor,FALSE,TRUE 13,448.0621212,2,504.5,171,5341.784253,0.009401851,0.016398799,12.09622118,0,Nov,3,2,1,13,Returning_Visitor,FALSE,TRUE 0,0,0,0,47,3650.316667,0,0.003333333,97.19657367,0,Nov,2,2,1,10,Returning_Visitor,FALSE,TRUE 5,58.28571429,1,685.25,177,4041.734915,0.002247191,0.010759765,9.419346491,0,Nov,2,2,1,2,Returning_Visitor,FALSE,FALSE 0,0,0,0,3,120.5,0,0.066666667,0,0,Nov,2,2,3,1,Other,TRUE,FALSE 0,0,0,0,98,3986.789646,0.006122449,0.027110844,0,0,Dec,2,2,1,2,Returning_Visitor,FALSE,FALSE 10,130.625,0,0,20,707.1666667,0,0.007222222,0,0,Nov,4,1,1,3,Returning_Visitor,FALSE,FALSE 3,63.5,0,0,8,253.25,0,0.02,0,0,Dec,2,2,7,2,New_Visitor,FALSE,FALSE 0,0,0,0,3,119,0,0.066666667,0,0,Nov,1,1,2,10,Returning_Visitor,FALSE,FALSE 0,0,0,0,1,0,0.2,0.2,0,0,Nov,2,2,3,1,Returning_Visitor,FALSE,FALSE 0,0,0,0,1,0,0.2,0.2,0,0,Nov,3,2,2,1,Returning_Visitor,FALSE,FALSE 0,0,0,0,7,259,0.028571429,0.057142857,0,0,Nov,2,2,8,2,Returning_Visitor,FALSE,FALSE 0,0,0,0,48,1900.675,0.008510638,0.009574468,0,0,Nov,1,1,3,10,Returning_Visitor,TRUE,TRUE 0,0,0,0,7,47,0,0.028571429,0,0,Nov,2,2,4,8,Returning_Visitor,FALSE,FALSE 0,0,0,0,14,549.95,0.014285714,0.028571429,0,0,Dec,3,2,3,2,Returning_Visitor,TRUE,FALSE 0,0,3,34,18,271.1666667,0,0.00952381,0,0,Nov,2,4,9,2,New_Visitor,FALSE,FALSE 0,0,0,0,2,138.5,0,0.1,0,0,Nov,3,2,4,11,Returning_Visitor,TRUE,FALSE 0,0,0,0,26,556.9166667,0.01025641,0.022435897,0,0,Dec,4,2,1,8,Returning_Visitor,FALSE,FALSE 0,0,0,0,9,125.25,0,0.033333333,0,0,Nov,2,10,4,3,Returning_Visitor,TRUE,FALSE 0,0,0,0,13,263,0.015384615,0.044615385,0,0,Nov,2,2,9,13,Returning_Visitor,TRUE,FALSE 2,86.38888889,0,0,29,897.0902778,0.013978495,0.04659824,7.053335001,0,Nov,2,2,6,3,Returning_Visitor,FALSE,FALSE 0,0,0,0,12,366.5,0,0.016666667,287.9537928,0,Dec,2,2,1,2,New_Visitor,FALSE,TRUE 0,0,0,0,15,535.25,0,0.02,0,0,Nov,3,2,1,2,Returning_Visitor,TRUE,FALSE 0,0,0,0,57,1091.005876,0.007017544,0.027875244,0,0,Nov,2,2,6,2,Returning_Visitor,FALSE,FALSE 3,34,3,66,20,374.4166667,0.033333333,0.076086957,0,0,Dec,3,2,1,13,Returning_Visitor,FALSE,FALSE 2,16,0,0,42,1743.517857,0,0.012804878,0,0,Nov,2,2,1,2,Returning_Visitor,FALSE,FALSE 3,90.5,2,16,47,2986.916667,0.022278912,0.044557823,0,0,Dec,2,2,8,1,Returning_Visitor,FALSE,FALSE 0,0,0,0,17,1731.625,0,0.035294118,0,0,Dec,2,2,1,3,Returning_Visitor,FALSE,FALSE 0,0,0,0,1,104,0,0.1,0,0,Dec,2,6,4,1,Returning_Visitor,FALSE,FALSE 0,0,0,0,46,797.8333333,0,0.02384058,0,0,Nov,2,2,3,1,Returning_Visitor,FALSE,FALSE 2,68.54545455,3,60,91,2345.58914,0.002749141,0.012841597,51.27258615,0,Dec,2,5,7,2,Returning_Visitor,FALSE,FALSE 5,248.9214286,0,0,131,6225.988835,0.003562341,0.025012117,7.963976424,0,Nov,2,2,2,2,Returning_Visitor,TRUE,TRUE 0,0,0,0,24,810.5,0,0.017207792,0,0,Nov,1,1,1,3,Returning_Visitor,TRUE,FALSE 0,0,1,0,6,504.5,0,0.057142857,0,0,Nov,2,2,3,8,Returning_Visitor,TRUE,FALSE 5,100,0,0,7,137.25,0.02,0.05,0,0,Dec,1,2,3,3,New_Visitor,FALSE,FALSE 0,0,1,3,39,1174.666667,0.01,0.040833333,0,0,Nov,2,2,1,2,Returning_Visitor,FALSE,FALSE 0,0,0,0,1,0,0.2,0.2,0,0,Dec,2,4,3,1,Returning_Visitor,FALSE,FALSE 0,0,4,122.5,330,11208.35506,0.008042825,0.022913324,0,0,Nov,2,2,1,2,Returning_Visitor,FALSE,TRUE 0,0,0,0,16,107.9291667,0,0.008531746,0,0,Dec,2,2,7,2,Returning_Visitor,FALSE,FALSE 0,0,0,0,19,661.5,0.031578947,0.052631579,0,0,Nov,2,2,1,1,Returning_Visitor,FALSE,FALSE 0,0,0,0,15,803.3333333,0,0.038888889,0,0,Dec,4,1,1,2,Returning_Visitor,FALSE,FALSE 7,104.45,0,0,63,1242.661699,0.011911765,0.02691802,0,0,Nov,1,1,8,2,Returning_Visitor,FALSE,FALSE 0,0,0,0,10,267.25,0,0.011111111,0,0,Dec,2,4,7,2,New_Visitor,TRUE,FALSE 15,176.489011,1,32.75,95,3879.83176,0.001960784,0.017745098,0,0,Nov,3,2,3,10,Returning_Visitor,TRUE,TRUE 0,0,0,0,20,1055.333333,0.01,0.009285714,0,0,Nov,3,2,4,13,Returning_Visitor,FALSE,FALSE 8,212.625,3,124.5,52,968.1964286,0,0.005172414,214.3066627,0,Nov,2,2,1,2,Returning_Visitor,FALSE,TRUE 5,98.91666667,0,0,10,177.5833333,0,0.018181818,0,0,Nov,4,2,7,2,Returning_Visitor,FALSE,FALSE 0,0,0,0,12,183.625,0.016666667,0.027777778,0,0,Nov,3,2,1,2,Returning_Visitor,FALSE,FALSE 1,14,0,0,5,92.33333333,0,0.016666667,0,0,Dec,2,4,1,1,New_Visitor,FALSE,FALSE 7,69.875,0,0,33,1051.125,0,0.010526316,25.12339065,0,Nov,2,2,9,11,Returning_Visitor,TRUE,TRUE 5,248.9166667,0,0,12,1581.5,0.003703704,0.026851852,63.1498595,0,Dec,2,2,1,8,Returning_Visitor,FALSE,FALSE 1,104,0,0,5,152.5,0.028571429,0.057142857,64.95645099,0,Dec,2,13,9,20,Returning_Visitor,FALSE,TRUE 4,92,0,0,21,755,0,0.004347826,0,0,Nov,3,2,1,2,New_Visitor,FALSE,FALSE 3,105,1,0,286,9952.349817,0.004181185,0.015495489,0,0,Nov,2,2,1,2,Returning_Visitor,FALSE,TRUE 1,19,0,0,21,702.125,0.01,0.018333333,0,0,Nov,2,4,1,1,Returning_Visitor,FALSE,FALSE 15,444.4444444,0,0,32,641.6944444,0.024738676,0.04411104,0,0,Nov,3,2,1,13,Returning_Visitor,FALSE,FALSE 3,60,0,0,34,783.6363636,0,0.000505051,0,0,Dec,2,2,1,1,Returning_Visitor,TRUE,FALSE 3,555.5,0,0,58,2034.261905,0.00862069,0.035172414,0,0,Nov,2,2,1,1,Returning_Visitor,FALSE,TRUE 0,0,0,0,48,1361.63442,0.010204082,0.01728863,14.5218625,0,Dec,1,1,4,3,Returning_Visitor,FALSE,FALSE 1,4,0,0,11,724,0,0.036363636,0,0,Nov,2,2,1,1,Returning_Visitor,FALSE,FALSE 2,52.5,0,0,10,753.1,0,0.02,0,0,Nov,2,2,1,2,Returning_Visitor,FALSE,FALSE 0,0,0,0,26,2107.258333,0.005128205,0.035897436,0,0,Dec,2,5,6,2,Returning_Visitor,TRUE,FALSE 5,58.83333333,0,0,128,6483.0804,0.003307888,0.012517824,3.83397413,0,Nov,2,2,2,1,Returning_Visitor,TRUE,FALSE 4,37.66666667,0,0,43,686.8789683,0.004761905,0.012581699,0,0,Nov,3,2,1,3,Returning_Visitor,FALSE,FALSE 2,18,0,0,49,1550.916667,0.016,0.041333333,0,0,Nov,2,2,3,1,Returning_Visitor,FALSE,FALSE 0,0,3,60.5,74,3105.365207,0.015789474,0.022261905,0,0,Nov,1,2,5,2,Returning_Visitor,FALSE,FALSE 5,43.5,0,0,6,110.3333333,0,0.02,0,0,Dec,2,2,8,8,New_Visitor,TRUE,FALSE 0,0,0,0,18,327.65,0.011111111,0.044444444,0,0,Nov,1,1,1,1,Returning_Visitor,FALSE,FALSE 1,3.5,0,0,20,309.1666667,0,0.001428571,0,0,Nov,2,2,2,2,Returning_Visitor,FALSE,FALSE 0,0,0,0,10,383.5,0,0.022222222,0,0,Nov,1,1,1,2,New_Visitor,FALSE,FALSE 9,124,1,61.5,25,432.1666667,0,0.007692308,0,0,Dec,2,2,8,2,New_Visitor,FALSE,FALSE 0,0,0,0,8,140.25,0.121428571,0.14375,0,0,Dec,3,2,2,13,Returning_Visitor,FALSE,FALSE 6,186.5,0,0,16,476.25,0.011111111,0.038888889,16.04403014,0,Dec,2,2,3,1,Returning_Visitor,FALSE,TRUE 0,0,0,0,14,419.375,0.042857143,0.083333333,0,0,Nov,2,2,8,1,Returning_Visitor,FALSE,FALSE 0,0,0,0,22,348.5,0.004545455,0.022727273,0,0,Nov,2,2,1,1,Returning_Visitor,TRUE,TRUE 0,0,0,0,3,57.75,0,0.033333333,0,0,Nov,1,1,6,2,Returning_Visitor,FALSE,FALSE 10,392,2,193.1666667,106,3711.605357,0.001754386,0.012573099,0.850509525,0,Nov,2,2,1,1,Returning_Visitor,FALSE,TRUE 5,47.33333333,0,0,65,1267.502814,0.004545455,0.021632395,0,0,Nov,3,2,1,10,Returning_Visitor,FALSE,FALSE 0,0,0,0,41,2997.083333,0.004878049,0.021138211,36.8670177,0,Dec,2,2,1,2,Returning_Visitor,TRUE,FALSE 1,0,0,0,32,1493.958333,0,0.020967742,0,0,Nov,2,2,2,1,Returning_Visitor,TRUE,FALSE 18,257.5625,3,70.25,247,10415.32566,0.011229304,0.029681426,0,0,Nov,2,2,6,1,Returning_Visitor,FALSE,FALSE 4,99.5,0,0,9,139.5,0,0.017222222,0,0,Dec,2,2,3,8,New_Visitor,FALSE,FALSE 0,0,0,0,2,697.5,0,0.1,0,0,Dec,4,1,3,1,Returning_Visitor,FALSE,FALSE 2,26.17857143,6,665.5,14,484.8452381,0.000606061,0.043661654,0,0,Nov,3,2,3,10,Returning_Visitor,TRUE,FALSE 6,623.5,0,0,122,3068.062904,0.01312,0.016953816,0,0,Nov,1,1,1,2,Returning_Visitor,FALSE,FALSE 0,0,0,0,1,0,0.2,0.2,0,0,Nov,1,1,3,8,Returning_Visitor,FALSE,FALSE 0,0,0,0,5,47,0,0.04,0,0,Nov,2,2,3,3,Returning_Visitor,FALSE,FALSE 1,3,0,0,161,3763.447588,0.001666667,0.012346041,0,0,Nov,1,1,1,2,Returning_Visitor,FALSE,TRUE 0,0,1,16,65,2863.400427,0.003125,0.011346726,0,0,Nov,2,2,1,2,Returning_Visitor,FALSE,FALSE 0,0,0,0,14,1277.741667,0,0.035714286,0,0,Nov,2,2,5,1,Returning_Visitor,FALSE,FALSE 2,48.5,0,0,77,1069.97381,0.002531646,0.012466437,0,0,Dec,2,2,3,2,Returning_Visitor,FALSE,FALSE 0,0,0,0,3,75,0.066666667,0.133333333,0,0,Dec,3,2,1,20,Returning_Visitor,FALSE,FALSE 0,0,7,840.5833333,162,5918.308915,0.004790419,0.019256933,0,0,Nov,2,2,1,1,Returning_Visitor,FALSE,FALSE 0,0,0,0,2,29.5,0,0.1,0,0,Nov,1,1,3,2,Returning_Visitor,FALSE,FALSE 6,36,0,0,13,125.125,0.047058824,0.053333333,0,0,Nov,3,2,3,1,Returning_Visitor,TRUE,FALSE 7,397.6309524,1,88.5,107,6449.457197,0.010353866,0.031510604,5.391767492,0,Nov,2,2,9,1,Returning_Visitor,FALSE,TRUE 1,28.5,0,0,79,1608.214286,0.002597403,0.007748918,0,0,Nov,1,1,3,3,Returning_Visitor,FALSE,FALSE 0,0,0,0,2,299.5,0,0.05,0,0,Nov,2,2,3,2,New_Visitor,FALSE,FALSE 9,210.625,3,143.5,167,8249.106771,0.006940472,0.021347061,2.907879344,0,Nov,1,1,1,10,Returning_Visitor,FALSE,TRUE 3,107.4,0,0,14,382.0333333,0,0.021428571,0,0,Dec,2,2,1,1,Returning_Visitor,FALSE,FALSE 0,0,0,0,6,90.16666667,0.033333333,0.066666667,0,0,Nov,3,2,4,1,Returning_Visitor,TRUE,FALSE 2,23,0,0,50,2473.196429,0,0.013301282,0,0,Nov,2,2,3,8,New_Visitor,FALSE,FALSE 5,211.25,0,0,22,1344.75,0.034782609,0.048447205,0,0,Nov,3,2,2,8,Returning_Visitor,FALSE,FALSE 5,71,0,0,57,1422.909324,0.005263158,0.018891527,13.55041394,0,Nov,2,4,4,2,Returning_Visitor,FALSE,TRUE 1,34.5,0,0,40,1473.658333,0,0.031153846,0,0,Nov,1,1,8,10,Returning_Visitor,TRUE,FALSE 3,922.75,0,0,56,4681.420833,0.007118644,0.022668819,0,0,Nov,2,2,1,13,Returning_Visitor,FALSE,FALSE 2,40.5,0,0,89,1938.996646,0.003863636,0.01808048,0,0,Dec,1,1,1,3,Returning_Visitor,FALSE,FALSE 1,10,1,342.5,55,3165.958333,0.005660377,0.032620545,0,0,Dec,2,2,3,1,Returning_Visitor,TRUE,FALSE 0,0,0,0,1,0,0.2,0.2,0,0,Nov,8,5,3,3,Returning_Visitor,TRUE,FALSE 16,485.9065934,2,19,42,753.6373626,0,0.011568627,31.63684138,0,Nov,2,2,1,8,Returning_Visitor,FALSE,TRUE 1,5,0,0,31,1263.9,0,0.009111111,0,0,Dec,2,2,7,2,Returning_Visitor,FALSE,FALSE 0,0,0,0,4,98.25,0,0.025,0,0,Nov,2,4,3,10,Returning_Visitor,FALSE,FALSE 3,32.83333333,1,13.25,58,1470.629167,0.011111111,0.015396825,0,0,Nov,3,2,1,1,Returning_Visitor,TRUE,FALSE 3,44,0,0,2,28,0,0.04,0,0,Nov,1,1,1,2,New_Visitor,FALSE,FALSE 0,0,0,0,11,782.5,0,0.01,0,0,Dec,1,1,3,2,New_Visitor,FALSE,FALSE 3,63,0,0,31,770.5833333,0.005714286,0.023809524,0,0,Dec,2,2,1,8,Returning_Visitor,FALSE,FALSE 5,242.125,3,176,10,225.8333333,0,0.024509804,0,0,Nov,2,2,7,2,Returning_Visitor,FALSE,FALSE 2,29.75,0,0,44,1410.428571,0.008695652,0.026086957,0,0,Nov,2,2,1,8,Returning_Visitor,TRUE,FALSE 0,0,0,0,5,124,0,0.06,0,0,Nov,1,1,3,11,Returning_Visitor,FALSE,FALSE 0,0,0,0,2,0,0.2,0.2,0,0,Nov,3,2,1,13,Returning_Visitor,TRUE,FALSE 0,0,3,56,0,0,0,0.033333333,0,0,Nov,3,2,1,6,Returning_Visitor,FALSE,FALSE 0,0,0,0,1,0,0.2,0.2,0,0,Dec,3,2,3,2,Returning_Visitor,FALSE,FALSE 7,122,3,40,111,3154.970833,0.003448276,0.014655172,2.613062787,0,Dec,2,2,1,1,Returning_Visitor,TRUE,TRUE 4,396.5,0,0,6,415.5,0,0.0125,0,0,Dec,1,1,3,8,New_Visitor,FALSE,FALSE 15,238.5833333,3,119.5,147,2933.695013,0.003797468,0.016656739,18.1270044,0,Nov,2,2,1,1,Returning_Visitor,FALSE,TRUE 0,0,0,0,10,411.9166667,0,0.036190476,0,0,Dec,3,2,1,2,Returning_Visitor,FALSE,TRUE 0,0,1,0,13,268.75,0.007692308,0.038461538,13.43133991,0,Dec,1,1,1,1,Returning_Visitor,FALSE,FALSE 0,0,0,0,1,0,0.2,0.2,0,0,Nov,3,2,1,8,New_Visitor,TRUE,FALSE 0,0,0,0,12,363,0.016666667,0.047222222,0,0,Nov,4,1,4,2,Returning_Visitor,FALSE,FALSE 0,0,0,0,5,126.0833333,0.04,0.053333333,0,0,Nov,3,2,7,13,Returning_Visitor,FALSE,FALSE 0,0,0,0,15,489.7083333,0,0.003846154,0,0,Dec,1,1,1,2,New_Visitor,FALSE,FALSE 0,0,0,0,3,0,0.2,0.2,0,0,Dec,1,1,1,3,Returning_Visitor,FALSE,FALSE 0,0,1,112,43,1866.107143,0,0.025,0,0,Nov,2,2,1,2,Returning_Visitor,TRUE,FALSE 0,0,0,0,4,54,0,0.066666667,0,0,Dec,1,1,2,2,Returning_Visitor,TRUE,FALSE 1,29.33333333,0,0,6,330.4166667,0,0.023809524,0,0,Dec,3,3,4,8,New_Visitor,FALSE,FALSE 2,44.3125,2,7,39,2447.548611,0.028354978,0.057999382,5.291989149,0,Nov,1,1,9,1,Returning_Visitor,TRUE,FALSE 1,9,1,26.5,18,2269.336111,0,0.017160494,0,0,Nov,2,2,7,2,Returning_Visitor,FALSE,FALSE 1,28.5,2,44.5,12,445.5,0.00625,0.022916667,0,0,Nov,3,2,1,8,New_Visitor,FALSE,FALSE 3,56,0,0,61,1669.988095,0.008064516,0.025778523,0,0,Nov,1,1,7,2,Returning_Visitor,FALSE,FALSE 0,0,0,0,17,569.75,0.029411765,0.054621849,0,0,Dec,2,2,1,2,Returning_Visitor,FALSE,FALSE 1,81,0,0,93,3359.625,0,0.015104167,0,0,Nov,2,2,3,1,Returning_Visitor,FALSE,TRUE 0,0,1,39.5,357,8864.036841,0.001685393,0.007310889,9.964255807,0,Nov,2,2,1,2,Returning_Visitor,FALSE,TRUE 0,0,0,0,8,93,0,0.05,0,0,Dec,2,5,3,1,Returning_Visitor,FALSE,FALSE 1,0,0,0,23,440.5833333,0.025,0.036666667,0,0,Dec,1,1,8,2,Returning_Visitor,TRUE,FALSE 0,0,0,0,3,19.25,0,0.066666667,0,0,Nov,1,1,8,2,Returning_Visitor,TRUE,FALSE 8,974,0,0,32,1003.416667,0.005263158,0.010526316,16.61144026,0,Dec,2,10,7,2,New_Visitor,TRUE,TRUE 9,185.75,0,0,40,2128.416667,0.004761905,0.019047619,0,0,Nov,2,2,3,3,Returning_Visitor,TRUE,FALSE 0,0,6,129.125,47,1117.642857,0.00754717,0.019339623,0,0,Dec,1,1,1,2,Returning_Visitor,FALSE,FALSE 0,0,0,0,50,1241.75,0,0.008163265,47.83758971,0,Nov,2,10,1,2,New_Visitor,FALSE,TRUE 0,0,0,0,3,423.5,0.095454545,0.121153846,0,0,Dec,3,2,1,1,Returning_Visitor,FALSE,FALSE 5,265.2916667,4,191.75,29,991.8270202,0.008823529,0.008921569,0,0,Nov,3,2,7,6,Returning_Visitor,TRUE,FALSE 0,0,0,0,51,2786.916667,0.003921569,0.011764706,0,0,Nov,2,2,1,3,Returning_Visitor,FALSE,FALSE 1,54.5,2,132.5,14,516.5,0.017647059,0.043137255,18.71641694,0,Dec,2,2,3,1,Returning_Visitor,FALSE,FALSE 5,339,4,188,5,376,0,0.019047619,0,0,Dec,2,2,9,8,New_Visitor,FALSE,FALSE 0,0,3,67.5,45,2028.172619,0.004347826,0.020652174,0,0,Dec,4,1,6,2,Returning_Visitor,FALSE,FALSE 10,204.0952381,1,170,138,4042.949908,0.004026846,0.01446207,18.21579469,0,Dec,2,2,1,6,Returning_Visitor,FALSE,TRUE 0,0,0,0,1,0,0.2,0.2,0,0,Nov,2,4,3,3,Returning_Visitor,FALSE,FALSE 0,0,2,38.25,51,2767.97619,0.003846154,0.030128205,0,0,Dec,2,5,1,2,Returning_Visitor,FALSE,FALSE 0,0,0,0,74,3237.114166,0.002702703,0.009113578,0,0,Nov,4,2,3,3,Returning_Visitor,FALSE,FALSE 1,165.5,0,0,47,1708.625,0.008695652,0.01884058,5.909295033,0,Dec,2,2,7,2,Returning_Visitor,FALSE,FALSE 2,30,0,0,77,2531.342297,0.003037975,0.015696203,12.99938379,0,Nov,1,1,6,8,Returning_Visitor,FALSE,TRUE 4,108,0,0,9,212.5,0,0.018181818,0,0,Dec,1,1,1,1,Returning_Visitor,TRUE,FALSE 1,12,2,39.5,23,488.75,0,0.015277778,0,0,Dec,2,2,1,1,Returning_Visitor,TRUE,FALSE 9,354.2916667,0,0,79,4409.3,0.007228916,0.011546722,12.07905093,0,Nov,3,2,2,3,Returning_Visitor,FALSE,TRUE 1,18,0,0,22,495,0,0.005,0,0,Nov,2,5,6,2,New_Visitor,FALSE,FALSE 1,6,0,0,7,212,0,0.025,0,0,Nov,1,1,1,3,Returning_Visitor,FALSE,FALSE 0,0,0,0,41,1066.339286,0.012195122,0.043292683,0,0,Nov,3,2,3,1,Returning_Visitor,FALSE,FALSE 5,59.5,0,0,116,3363.182143,0.000420168,0.014048347,0,0,Nov,2,2,1,2,Returning_Visitor,FALSE,FALSE 1,8,5,358.75,36,991.5,0.014285714,0.031904762,0,0,Dec,1,1,8,2,Returning_Visitor,FALSE,FALSE 3,107.4285714,0,0,59,1843.187696,0.014385965,0.041993382,5.71562257,0,Dec,2,2,7,7,Returning_Visitor,TRUE,FALSE 0,0,0,0,35,2352.166667,0,0.006060606,0,0,Nov,2,2,6,2,New_Visitor,FALSE,FALSE 3,57.5,0,0,37,1269.645833,0.014197531,0.012037037,0,0,Nov,1,1,4,1,Returning_Visitor,TRUE,FALSE 8,160.5,2,5,56,1167.321429,0.004761905,0.018915344,0,0,Dec,2,2,3,1,Returning_Visitor,FALSE,FALSE 0,0,0,0,5,72.75,0,0.04,0,0,Dec,3,2,1,2,New_Visitor,TRUE,FALSE 1,19,0,0,43,1457.113095,0.023809524,0.04015873,0,0,Nov,3,6,1,2,Returning_Visitor,TRUE,FALSE 0,0,0,0,39,1262.651515,0.005128205,0.007264957,0,0,Nov,3,2,2,10,Returning_Visitor,FALSE,TRUE 2,43.5,0,0,26,971.6666667,0,0.009259259,15.1510483,0,Dec,2,2,3,2,Returning_Visitor,FALSE,TRUE 0,0,0,0,29,4279.966667,0.044827586,0.072413793,0,0,Dec,2,2,1,1,Returning_Visitor,FALSE,FALSE 0,0,0,0,40,694.0178571,0.005,0.0175,0,0,Dec,2,2,3,2,New_Visitor,FALSE,FALSE 0,0,0,0,56,1531.920136,0.011507937,0.017881096,0,0,Nov,3,2,1,2,Returning_Visitor,TRUE,FALSE 0,0,0,0,50,2147.983333,0.012285714,0.024768116,0,0,Nov,3,2,1,2,Returning_Visitor,TRUE,FALSE 2,80,6,141.75,47,3573.472222,0.003773585,0.012006861,34.85660705,0,Dec,2,2,3,1,Returning_Visitor,FALSE,FALSE 3,21.5,2,70.875,28,1338.225,0.007070707,0.032525253,0,0,Dec,1,1,2,3,Returning_Visitor,FALSE,FALSE 0,0,0,0,3,181.5,0,0.033333333,0,0,Dec,4,1,1,6,Returning_Visitor,TRUE,FALSE 0,0,0,0,72,3559.375,0.008571429,0.023797619,0,0,Nov,2,2,1,2,Returning_Visitor,FALSE,FALSE 0,0,0,0,8,1231.791667,0.0825,0.080555556,0,0,Dec,3,2,1,3,Returning_Visitor,FALSE,FALSE 0,0,0,0,11,146.5,0.018181818,0.018181818,0,0,Nov,1,1,1,2,New_Visitor,FALSE,FALSE 0,0,0,0,5,115.625,0.04,0.088,0,0,Nov,2,2,3,3,Returning_Visitor,FALSE,FALSE 0,0,0,0,3,13,0.066666667,0.133333333,0,0,Dec,3,2,3,1,Returning_Visitor,TRUE,FALSE 0,0,0,0,11,357,0.018181818,0.036363636,0,0,Dec,2,2,7,2,Returning_Visitor,TRUE,FALSE 0,0,0,0,1,0,0.2,0.2,0,0,Dec,2,2,2,3,Returning_Visitor,FALSE,FALSE 1,2,0,0,23,1016.160714,0.024347826,0.02402746,0,0,Nov,3,2,2,13,Returning_Visitor,FALSE,FALSE 7,190.2628205,4,195,124,3197.290064,0.010320513,0.01989345,7.059929851,0,Nov,3,2,6,2,Returning_Visitor,TRUE,FALSE 3,215,0,0,53,1481.429167,0.001851852,0.012345679,56.31760103,0,Nov,1,8,1,11,Returning_Visitor,TRUE,TRUE 8,164.5833333,1,880.25,62,2587.150595,0.008333333,0.013161376,0,0,Dec,1,2,7,2,Returning_Visitor,FALSE,FALSE 0,0,0,0,17,390.7738095,0,0.025,0,0,Nov,1,1,3,3,New_Visitor,TRUE,FALSE 0,0,0,0,20,402.9583333,0.02,0.036666667,0,0,Dec,3,2,3,2,Returning_Visitor,FALSE,FALSE 4,47.83333333,0,0,69,2499.990079,0,0.007608395,0,0,Nov,2,2,8,2,New_Visitor,FALSE,FALSE 0,0,0,0,37,1212,0.010810811,0.017117117,0,0,Nov,2,2,6,2,Returning_Visitor,FALSE,TRUE 2,84.5,0,0,21,710.5416667,0.012121212,0.028787879,0,0,Nov,2,4,4,2,Returning_Visitor,FALSE,FALSE 0,0,0,0,15,313.25,0.013333333,0.033333333,0,0,Nov,2,2,3,10,Returning_Visitor,FALSE,FALSE 3,199,0,0,34,1425.566667,0,0.008823529,0,0,Nov,2,2,9,2,New_Visitor,FALSE,TRUE 10,236.0795455,2,5,107,2589.881537,0.010601564,0.019627539,0,0,Dec,3,2,4,2,Returning_Visitor,FALSE,FALSE 0,0,0,0,2,17,0,0.1,0,0,Nov,4,1,4,10,Returning_Visitor,TRUE,FALSE 3,33,1,10,45,1142.195833,0,0.024444444,0,0,Dec,2,2,1,2,Returning_Visitor,FALSE,FALSE 0,0,0,0,36,777.3333333,0,0.005882353,89.2954867,0,Dec,3,2,4,8,New_Visitor,TRUE,TRUE 0,0,0,0,23,736.2333333,0.004545455,0.013636364,140.6621366,0,Dec,1,8,1,11,New_Visitor,FALSE,TRUE 0,0,0,0,1,0,0.2,0.2,0,0,Nov,3,2,9,13,Returning_Visitor,TRUE,FALSE 0,0,1,0,10,210.25,0,0.018181818,0,0,Nov,1,1,1,2,New_Visitor,FALSE,FALSE 0,0,1,0,77,1266.094048,0.002597403,0.023632241,0,0,Dec,2,2,1,2,Returning_Visitor,FALSE,FALSE 5,75.5,0,0,24,1009.95,0.007407407,0.018518519,0,0,Nov,2,4,6,3,Returning_Visitor,FALSE,FALSE 0,0,0,0,1,0,0.2,0.2,0,0,Dec,3,2,6,1,Returning_Visitor,FALSE,FALSE 0,0,0,0,82,1661.133333,0.02,0.034479167,18.5845777,0,Dec,4,1,1,2,Returning_Visitor,TRUE,FALSE 0,0,0,0,43,907.5083333,0,0.002439024,0,0,Nov,1,1,1,2,New_Visitor,TRUE,FALSE 0,0,0,0,10,105.6166667,0.022222222,0.048148148,0,0,Nov,1,1,3,2,Returning_Visitor,FALSE,FALSE 0,0,0,0,1,0,0.2,0.2,0,0,Nov,3,2,1,3,Returning_Visitor,FALSE,FALSE 0,0,0,0,9,521,0,0.02962963,0,0,Dec,3,2,3,11,New_Visitor,FALSE,FALSE 0,0,0,0,15,411.0833333,0.013333333,0.033333333,0,0,Nov,2,2,3,3,Returning_Visitor,FALSE,FALSE 0,0,5,118.5,17,558.3988095,0.022727273,0.046212121,0,0,Dec,1,8,5,11,Returning_Visitor,FALSE,FALSE 0,0,0,0,3,104,0,0.066666667,0,0,Nov,2,4,3,1,Returning_Visitor,FALSE,FALSE 0,0,0,0,50,3421.575758,0,0.012333333,0,0,Nov,2,2,1,2,New_Visitor,TRUE,FALSE 0,0,0,0,24,806.125,0,0.016666667,0,0,Nov,1,1,6,3,Returning_Visitor,FALSE,FALSE 0,0,0,0,46,2158.5,0.019565217,0.032246377,0,0,Nov,1,2,7,2,Returning_Visitor,FALSE,FALSE 7,261.5,0,0,117,3484.269118,0.0025,0.014375,0,0,Dec,2,2,6,1,Returning_Visitor,FALSE,FALSE 1,28.5,1,0,315,11232.65691,0.003164557,0.019252583,0,0,Nov,2,2,1,2,Returning_Visitor,FALSE,FALSE 2,82,0,0,9,587.25,0.01,0.041666667,0,0,Dec,3,2,1,8,Returning_Visitor,TRUE,FALSE 7,330.0555556,5,251.5,116,2971.435101,0.001858304,0.012303523,3.701618891,0,Dec,2,2,2,2,Returning_Visitor,FALSE,FALSE 1,12,2,11,18,974.1666667,0.005454545,0.032251082,0,0,Nov,3,2,1,2,New_Visitor,FALSE,FALSE 4,102.0833333,0,0,21,863.1666667,0,0.015833333,0,0,Dec,1,1,1,8,Returning_Visitor,FALSE,FALSE 0,0,0,0,5,103.5,0,0.04,0,0,Dec,1,1,4,2,Returning_Visitor,TRUE,FALSE 1,23,1,284,78,2448.536054,0.011898734,0.02841508,0,0,Dec,2,2,3,10,Returning_Visitor,FALSE,FALSE 0,0,0,0,1,0,0.2,0.2,0,0,Nov,2,2,7,13,Returning_Visitor,FALSE,FALSE 1,50.5,0,0,3,192.5,0,0.05,0,0,Nov,2,2,8,2,New_Visitor,TRUE,FALSE 1,8.214285714,3,62.5,166,9666.857845,0.002380952,0.012980504,29.34770837,0,Nov,2,2,1,2,Returning_Visitor,FALSE,TRUE 0,0,1,0,48,783.6944444,0.012244898,0.01877551,0,0,Dec,2,2,3,2,Returning_Visitor,FALSE,FALSE 4,34.83333333,2,33.75,32,929.0833333,0.005714286,0.016190476,34.29431289,0,Dec,2,2,1,2,New_Visitor,FALSE,TRUE 13,296.0416667,3,46.25,52,1249.481746,0.036885246,0.061460507,0,0,Dec,1,1,3,1,Returning_Visitor,FALSE,FALSE 5,222.75,0,0,31,4374.416667,0.011764706,0.025098039,0,0,Dec,1,1,2,1,Returning_Visitor,FALSE,FALSE 1,11.5,0,0,19,887.8,0.008823529,0.032679739,0,0,Nov,3,2,1,3,Returning_Visitor,FALSE,FALSE 9,559.1666667,0,0,126,4558.372222,0,0.00620155,0,0,Nov,2,2,3,2,Returning_Visitor,FALSE,FALSE 0,0,0,0,50,2549.534524,0.012244898,0.036530612,0,0,Nov,1,2,2,2,Returning_Visitor,FALSE,FALSE 0,0,0,0,5,168,0.04,0.08,0,0,Nov,3,2,1,2,Returning_Visitor,FALSE,FALSE 0,0,0,0,22,849.8333333,0,0.021212121,0,0,Nov,2,2,1,3,Returning_Visitor,FALSE,FALSE 0,0,0,0,19,621.4166667,0,0.015789474,0,0,Dec,4,1,1,2,Returning_Visitor,TRUE,FALSE 0,0,1,273,22,2386,0,0.020289855,0,0,Dec,2,2,6,2,New_Visitor,FALSE,TRUE 2,63.79166667,1,74,18,385.2916667,0.017042607,0.03754386,12.00869742,0,Nov,3,2,3,2,Returning_Visitor,FALSE,FALSE 1,5,0,0,65,2498.833658,0.003174603,0.01005291,0,0,Dec,3,2,3,6,Returning_Visitor,TRUE,FALSE 0,0,0,0,2,0,0.2,0.2,0,0,Nov,2,2,1,1,Returning_Visitor,FALSE,FALSE 0,0,0,0,1,0,0.2,0.2,0,0,Nov,1,1,5,3,Returning_Visitor,FALSE,FALSE 0,0,0,0,24,550.1666667,0.008333333,0.050833333,0,0,Nov,2,2,1,2,Returning_Visitor,FALSE,FALSE 0,0,0,0,1,0,0.2,0.2,0,0,Nov,6,2,9,2,Returning_Visitor,FALSE,FALSE 1,11,0,0,20,1166.333333,0,0.002631579,14.1273698,0,Nov,2,2,2,2,Returning_Visitor,TRUE,TRUE 0,0,0,0,15,814,0.030769231,0.053846154,0,0,Nov,1,8,7,1,Returning_Visitor,FALSE,FALSE 7,194.0833333,1,71.25,15,446.25,0,0.015686275,0,0,Dec,2,2,1,2,Returning_Visitor,FALSE,FALSE 0,0,0,0,8,143.2,0,0.029166667,0,0,Nov,3,2,1,2,Returning_Visitor,FALSE,FALSE 6,141,1,16,143,4081.248718,0.002721088,0.01562134,5.376740585,0,Nov,2,2,1,2,Returning_Visitor,FALSE,FALSE 0,0,0,0,48,1607.191667,0,0.004609929,5.157803677,0,Dec,1,1,1,2,Returning_Visitor,TRUE,TRUE 11,316.8229167,1,10,29,1233.307765,0.009142857,0.03247619,0,0,Dec,2,2,1,2,Returning_Visitor,FALSE,FALSE 5,142,2,234.5,5,60.5,0.033333333,0.048148148,0,0,Dec,3,5,3,8,Returning_Visitor,FALSE,FALSE 0,0,0,0,4,47.5,0,0.05,0,0,Nov,2,5,1,2,Returning_Visitor,FALSE,FALSE 0,0,0,0,12,676.25,0,0.025,0,0,Nov,2,4,1,6,Returning_Visitor,FALSE,FALSE 7,294,0,0,7,639,0,0.018181818,0,0,Dec,2,4,9,10,Returning_Visitor,FALSE,FALSE 0,0,0,0,79,4709.992857,0,0.005574489,0,0,Nov,2,2,6,2,New_Visitor,FALSE,TRUE 0,0,0,0,5,168.5,0,0.04,0,0,Nov,2,4,1,2,Returning_Visitor,FALSE,FALSE 0,0,0,0,231,11246.13379,0.001746725,0.010029112,0,0,Nov,2,2,3,2,Returning_Visitor,TRUE,FALSE 3,29.25,0,0,32,1729.75,0,0.005714286,16.7293697,0,Dec,2,4,3,2,New_Visitor,FALSE,TRUE 3,45.5,0,0,13,661.9166667,0,0.025,0,0,Nov,2,2,1,2,New_Visitor,FALSE,FALSE 11,392.5,0,0,206,8437.099188,0,0.006257147,0,0,Nov,2,4,1,2,Returning_Visitor,FALSE,FALSE 0,0,0,0,12,1145.416667,0.009090909,0.048484848,0,0,Nov,3,2,8,10,Returning_Visitor,TRUE,FALSE 0,0,0,0,10,174.0833333,0.06,0.07,0,0,Nov,2,2,9,1,Returning_Visitor,TRUE,FALSE 4,229.25,0,0,8,372.25,0.022222222,0.00952381,0,0,Dec,1,2,1,8,Returning_Visitor,FALSE,FALSE 11,353.125,6,230.25,85,3725.670725,0.007446809,0.014960902,2.527217484,0,Dec,3,2,6,1,Returning_Visitor,FALSE,TRUE 4,39.58333333,0,0,115,2547.717986,0.003478261,0.007207729,13.26221446,0,Nov,2,2,4,10,Returning_Visitor,FALSE,TRUE 4,140.75,2,8,16,2073.383333,0,0.016666667,0,0,Nov,3,2,3,2,Returning_Visitor,FALSE,FALSE 0,0,0,0,73,2784.8,0.002739726,0.015317559,6.135982448,0,Nov,2,2,5,2,Returning_Visitor,TRUE,FALSE 1,20,1,35.5,35,946.5952381,0,0.013333333,0,0,Nov,2,2,1,2,Returning_Visitor,FALSE,FALSE 8,305.6666667,1,0,35,796.25,0.005555556,0.011574074,0,0,Dec,1,1,3,10,Returning_Visitor,FALSE,FALSE 2,25,0,0,7,181.75,0,0.022222222,0,0,Dec,2,2,1,2,Returning_Visitor,FALSE,FALSE 0,0,0,0,1,0,0.2,0.2,0,0,Dec,1,1,1,3,Returning_Visitor,FALSE,FALSE 1,12,0,0,43,2209.554876,0.01031746,0.022317022,0,0,Dec,1,1,1,2,Returning_Visitor,FALSE,FALSE 0,0,4,14,8,284.7,0.016666667,0.047222222,0,0,Nov,3,2,7,2,Returning_Visitor,FALSE,TRUE 3,39.875,1,414.5,88,4937.639583,0.002173913,0.005857488,25.57242933,0,Nov,2,1,1,2,Returning_Visitor,FALSE,TRUE 1,37.5,0,0,25,593.2916667,0.008333333,0.00375,0,0,Dec,1,1,1,1,Returning_Visitor,TRUE,FALSE 0,0,0,0,23,752.7619048,0.013043478,0.015217391,0,0,Dec,1,1,6,3,Returning_Visitor,FALSE,FALSE 7,170.5,3,161.5,114,3494.831225,0.003601695,0.018762198,0,0,Dec,2,10,1,2,Returning_Visitor,FALSE,FALSE 0,0,0,0,7,248,0.057142857,0.080952381,0,0,Nov,3,2,3,13,Returning_Visitor,FALSE,FALSE 2,992.7142857,5,809.5,28,1793.214286,0.005714286,0.016428571,9.69917706,0,Nov,2,2,1,10,Returning_Visitor,FALSE,TRUE 7,228.25,0,0,39,968.0924451,0.01547619,0.023015873,7.497155058,0,Nov,3,2,1,1,Returning_Visitor,FALSE,FALSE 5,145,2,349,33,2161.133333,0.012820513,0.044786325,16.58165201,0,Nov,2,2,7,1,Returning_Visitor,FALSE,TRUE 0,0,0,0,1,0,0.2,0.2,0,0,Nov,2,2,1,13,Returning_Visitor,FALSE,FALSE 8,164.4,0,0,33,865.8071429,0,0.012280702,61.61833364,0,Dec,2,2,5,2,New_Visitor,FALSE,TRUE 0,0,0,0,23,1202.75,0,0.004545455,0,0,Dec,8,13,9,20,Other,TRUE,FALSE 8,318.8012821,3,164,107,4447.110508,0.001169591,0.013146819,0,0,Nov,2,2,1,3,Returning_Visitor,FALSE,TRUE 1,0,0,0,59,1669.940186,0.004137931,0.011611098,0,0,Dec,2,2,3,7,Returning_Visitor,TRUE,FALSE 0,0,0,0,15,290.1,0,0.026666667,0,0,Dec,2,5,6,3,Returning_Visitor,FALSE,FALSE 6,145,0,0,85,1704.580556,0.002930403,0.02539857,0,0,Nov,1,2,3,2,Returning_Visitor,FALSE,TRUE 5,77.08333333,2,96.5,15,2106.833333,0.014285714,0.037301587,0,0,Nov,1,1,4,2,Returning_Visitor,TRUE,TRUE 0,0,0,0,11,398.6944444,0.014814815,0.059259259,0,0,Nov,1,8,3,11,Returning_Visitor,FALSE,FALSE 5,39.5,3,62.25,339,16138.2908,0.004778942,0.020193811,0,0,Nov,2,2,3,2,Returning_Visitor,FALSE,FALSE 0,0,0,0,11,2123,0.018181818,0.036363636,0,0,Dec,2,2,1,6,Returning_Visitor,FALSE,FALSE 0,0,0,0,7,133.5,0,0.025,0,0,Nov,2,2,6,6,Other,FALSE,FALSE 1,12,0,0,43,851.8857143,0,0.009883721,11.65996434,0,Nov,2,2,1,2,Returning_Visitor,FALSE,TRUE 2,113,2,501,14,381.75,0,0.035294118,0,0,Dec,2,10,2,2,Returning_Visitor,FALSE,FALSE 2,45.5,0,0,22,535,0,0.025,9.650250036,0,Nov,2,2,3,11,Returning_Visitor,TRUE,TRUE 0,0,0,0,2,0,0.2,0.2,0,0,Nov,1,1,1,8,New_Visitor,FALSE,FALSE 9,250.2222222,5,343.875,82,5076.380556,0,0.010982424,28.41708136,0,Dec,3,2,1,13,Returning_Visitor,FALSE,TRUE 2,42.5,2,10,43,1543.5,0,0.004444444,8.671344071,0,Dec,8,13,9,20,Other,FALSE,TRUE 0,0,0,0,1,0,0.2,0.2,0,0,Nov,3,2,1,13,Returning_Visitor,FALSE,FALSE 0,0,0,0,6,60,0.15,0.138888889,0,0,Nov,3,2,5,1,Returning_Visitor,FALSE,FALSE 0,0,0,0,40,3675.5,0,0.016666667,0,0,Nov,2,2,8,10,Returning_Visitor,FALSE,FALSE 0,0,0,0,2,41.5,0.1,0.1,0,0,Dec,1,1,3,1,Returning_Visitor,TRUE,FALSE 1,8,1,47.5,46,689.0606061,0.002272727,0.012062937,1.125146064,0,Nov,2,2,2,11,Returning_Visitor,TRUE,TRUE 0,0,0,0,18,323.6833333,0,0.005208333,0,0,Dec,3,2,2,1,Returning_Visitor,FALSE,FALSE 0,0,0,0,17,935.25,0,0.009803922,0,0,Nov,2,2,1,2,New_Visitor,FALSE,FALSE 4,50,4,486.6666667,6,555.6666667,0,0.008333333,0,0,Dec,3,2,1,2,Returning_Visitor,FALSE,FALSE 0,0,0,0,1,0,0.2,0.2,0,0,Nov,3,2,1,20,Returning_Visitor,FALSE,FALSE 5,224.75,0,0,10,235.4166667,0.015384615,0.030769231,0,0,Dec,1,1,8,2,New_Visitor,FALSE,FALSE 0,0,0,0,13,524,0,0.016666667,45.4642609,0,Nov,2,4,9,8,New_Visitor,TRUE,TRUE 0,0,0,0,25,445.5833333,0.004,0.024,0,0,Dec,2,10,1,1,Returning_Visitor,FALSE,FALSE 7,112.375,0,0,24,916.5,0.010967742,0.036129032,0,0,Nov,3,2,3,3,Returning_Visitor,FALSE,FALSE 11,162.75,3,67.5,80,2453.177381,0.004022989,0.017778905,4.289678861,0,Nov,2,2,3,2,Returning_Visitor,FALSE,TRUE 3,119.5,0,0,17,673.625,0.02,0.02,0,0,Dec,3,2,1,10,Returning_Visitor,TRUE,FALSE 0,0,0,0,2,0,0.1,0.15,0,0,Nov,2,2,1,13,Returning_Visitor,FALSE,FALSE 0,0,0,0,15,384,0,0.014285714,0,0,Dec,3,2,1,2,New_Visitor,FALSE,FALSE 1,10,0,0,12,220.2666667,0,0.02,60.622383,0,Nov,1,1,1,8,New_Visitor,TRUE,TRUE 3,93.5,0,0,8,170.25,0,0.02,0,0,Nov,2,2,3,2,Returning_Visitor,FALSE,FALSE 0,0,0,0,9,242.75,0,0.074074074,0,0,Nov,2,5,7,2,Returning_Visitor,FALSE,FALSE 1,11,1,0,12,91.75,0,0.038095238,0,0,Nov,1,1,1,3,Returning_Visitor,FALSE,FALSE 0,0,0,0,25,316.5416667,0,0.02,0,0,Dec,2,2,9,1,Returning_Visitor,FALSE,FALSE 7,198.75,0,0,20,818.5833333,0,0.002777778,0,0,Dec,3,2,4,8,New_Visitor,FALSE,FALSE 2,15,0,0,7,620.5,0.028571429,0.085714286,0,0,Nov,2,2,1,3,Returning_Visitor,FALSE,FALSE 0,0,0,0,7,275.5,0,0.028571429,0,0,Nov,3,2,2,1,Returning_Visitor,FALSE,FALSE 16,237.35,0,0,46,1143.833333,0.009803922,0.014488017,0,0,Nov,3,2,4,1,Returning_Visitor,TRUE,FALSE 2,45.5,0,0,39,2011.041667,0,0.0125,53.73655336,0,Nov,2,2,1,8,Returning_Visitor,FALSE,TRUE 0,0,0,0,1,0,0.2,0.2,0,0,Dec,3,2,1,2,Returning_Visitor,FALSE,FALSE 0,0,0,0,2,0,0.1,0.2,0,0,Nov,2,2,1,1,Returning_Visitor,FALSE,FALSE 7,1463.333333,0,0,30,1410.238889,0.005263158,0.010557237,0,0,Nov,1,1,4,2,New_Visitor,FALSE,FALSE 6,87.7,2,60.5,24,448.1945887,0,0.005747126,0,0,Nov,2,5,1,8,New_Visitor,FALSE,FALSE 0,0,0,0,30,953.6666667,0.026666667,0.071111111,0,0,Dec,2,2,2,1,Returning_Visitor,FALSE,FALSE 8,268.25,0,0,71,2426.791667,0.002702703,0.010810811,33.86849093,0,Nov,2,2,1,8,Returning_Visitor,FALSE,TRUE 2,14,2,189,134,6732.124414,0.008018648,0.025030224,0,0,Nov,1,8,3,11,Returning_Visitor,FALSE,FALSE 2,38,0,0,3,33.33333333,0,0.030555556,0,0,Nov,2,2,8,10,Returning_Visitor,FALSE,TRUE 4,44.75,0,0,9,404.5,0,0.03,0,0,Nov,2,2,1,2,Returning_Visitor,FALSE,FALSE 0,0,0,0,6,81.66666667,0,0.066666667,0,0,Dec,2,2,7,2,Returning_Visitor,TRUE,FALSE 0,0,0,0,1,48.5,0,0.1,0,0,Nov,1,1,1,3,Other,FALSE,FALSE 0,0,0,0,12,544,0,0.016666667,0,0,Nov,2,2,2,1,Returning_Visitor,FALSE,FALSE 0,0,0,0,3,31,0,0.033333333,0,0,Dec,2,2,1,2,New_Visitor,FALSE,FALSE 1,9,0,0,13,858.8333333,0.014285714,0.051190476,0,0,Nov,1,1,6,11,Returning_Visitor,FALSE,TRUE 3,49.125,0,0,19,344.3452381,0.010526316,0.015789474,0,0,Nov,3,2,1,13,Returning_Visitor,FALSE,FALSE 0,0,2,48.5,59,2195.283333,0.009836066,0.030601093,0,0,Nov,2,2,7,2,Returning_Visitor,FALSE,TRUE 7,105.2083333,2,139.5,98,3452.2625,0.003543956,0.02462704,0,0,Nov,4,1,1,3,Returning_Visitor,FALSE,FALSE 0,0,0,0,16,865.5833333,0,0.002083333,93.77761706,0,Dec,1,1,1,2,New_Visitor,TRUE,TRUE 0,0,0,0,89,3614.105147,0.006439394,0.020569813,0,0,Nov,1,1,1,2,Returning_Visitor,TRUE,FALSE 0,0,0,0,58,1893.444444,0.002380952,0.019047619,0,0,Nov,1,1,6,10,Returning_Visitor,TRUE,TRUE 4,305,0,0,16,1090.7,0,0.041666667,0,0,Nov,2,2,1,2,Returning_Visitor,FALSE,FALSE 2,21,0,0,33,682.9583333,0.008823529,0.025653595,0,0,Dec,3,2,9,2,New_Visitor,FALSE,FALSE 2,19,0,0,28,1183.65,0,0.006896552,21.2112655,0,Dec,3,12,9,2,Returning_Visitor,FALSE,TRUE 5,59,0,0,152,7114.798341,0.002597403,0.010709751,0,0,Nov,2,2,1,1,Returning_Visitor,FALSE,FALSE 1,33.5,0,0,33,880.6333333,0,0.034117647,0,0,Dec,2,2,4,3,Returning_Visitor,FALSE,FALSE 0,0,0,0,11,191,0.036363636,0.036363636,0,0,Dec,1,1,8,2,Returning_Visitor,FALSE,FALSE 7,214.25,0,0,118,2652.552904,0.001652893,0.010020661,0,0,Nov,2,2,4,10,Returning_Visitor,FALSE,FALSE 5,55.5,2,132.5,61,2190.429367,0.013846154,0.038778999,0,0,Nov,3,2,3,13,Returning_Visitor,FALSE,FALSE 3,72.5,2,523.75,8,178.75,0.023076923,0.069230769,0,0,Nov,1,1,1,3,Returning_Visitor,FALSE,FALSE 0,0,0,0,17,356.0833333,0,0.013333333,151.5650523,0,Nov,2,5,5,8,New_Visitor,FALSE,TRUE 2,229.5,2,29.75,8,212.75,0,0.025,0,0,Dec,3,2,3,2,New_Visitor,FALSE,FALSE 0,0,0,0,7,357.75,0.014285714,0.077142857,0,0,Nov,4,1,4,10,Returning_Visitor,TRUE,FALSE 1,96.66666667,0,0,23,1605.875,0.031818182,0.05030303,54.10230816,0,Dec,2,2,2,10,Returning_Visitor,FALSE,TRUE 1,52.5,0,0,162,3608.432937,0.001242236,0.004347826,54.0486956,0,Nov,2,2,7,2,New_Visitor,FALSE,TRUE 1,20,0,0,283,9354.484111,0.003043735,0.023041703,0,0,Nov,2,2,1,3,Returning_Visitor,FALSE,FALSE 7,70,4,35,131,4442.394444,0.011678832,0.014283502,0,0,Nov,4,1,1,2,Returning_Visitor,TRUE,FALSE 0,0,0,0,1,343,0,0.066666667,0,0,Nov,2,2,4,3,Other,FALSE,FALSE 0,0,0,0,31,2767.625,0,0.006896552,52.67778465,0,Nov,8,13,9,20,Other,FALSE,TRUE 9,319.8333333,1,18,10,332.3333333,0,0.015384615,0,0,Dec,1,1,5,2,Returning_Visitor,FALSE,FALSE 0,0,0,0,1,0,0.2,0.2,0,0,Dec,8,13,9,20,Other,FALSE,FALSE 1,0,0,0,106,4568.338675,0.003647799,0.013529215,0,0,Nov,1,1,2,10,Returning_Visitor,FALSE,TRUE 15,270.0166667,3,122,66,2780.6,0,0.016533333,0,0,Nov,2,2,3,10,Returning_Visitor,FALSE,FALSE 4,31,4,106,27,825.1401515,0.006896552,0.025517241,0,0,Nov,2,2,3,2,Returning_Visitor,TRUE,FALSE 0,0,0,0,6,172.2083333,0.033333333,0.044444444,0,0,Dec,3,2,6,2,New_Visitor,FALSE,TRUE 0,0,0,0,14,416.6666667,0,0.019047619,0,0,Dec,2,2,2,3,Returning_Visitor,FALSE,FALSE 0,0,0,0,6,226.6666667,0,0.033333333,0,0,Dec,4,1,9,1,Returning_Visitor,TRUE,FALSE 0,0,0,0,4,95,0,0.05,0,0,Dec,8,13,9,20,Other,FALSE,FALSE 0,0,0,0,4,66,0,0.05,0,0,Dec,1,1,8,2,Returning_Visitor,FALSE,FALSE 3,34,1,1530,97,3989.485553,0.002,0.012201868,0,0,Nov,2,2,1,3,Returning_Visitor,TRUE,FALSE 2,17.33333333,0,0,89,3401.3,0.002222222,0.014835979,5.133911853,0,Nov,2,2,1,2,Returning_Visitor,TRUE,FALSE 4,48.66666667,0,0,66,2436.106169,0.006036217,0.034560698,12.02904527,0,Nov,2,2,3,6,Returning_Visitor,FALSE,FALSE 0,0,0,0,15,314.5,0,0.013333333,0,0,Dec,8,13,9,20,Other,FALSE,FALSE 12,198.7727273,4,11,76,4514.739394,0.010978836,0.024656643,0,0,Nov,2,2,3,1,Returning_Visitor,FALSE,TRUE 14,184.1556741,7,360.1666667,127,3418.696975,0.003900709,0.013700977,2.136325585,0,Dec,3,2,4,2,Returning_Visitor,TRUE,FALSE 3,28.5,0,0,8,181.6666667,0.022222222,0.044444444,0,0,Dec,1,1,3,10,Returning_Visitor,FALSE,FALSE 0,0,0,0,11,689.9916667,0,0.026666667,0,0,Nov,3,2,3,6,Returning_Visitor,FALSE,FALSE 0,0,0,0,1,0,0.2,0.2,0,0,Nov,8,2,6,13,Returning_Visitor,FALSE,FALSE 0,0,0,0,15,755.25,0.026666667,0.033333333,0,0,Nov,2,2,6,1,Returning_Visitor,TRUE,FALSE 1,2,0,0,6,106.75,0.033333333,0.044444444,0,0,Nov,3,2,3,3,Returning_Visitor,FALSE,FALSE 0,0,0,0,23,180.5,0.009090909,0.036363636,0,0,Nov,2,2,1,7,Returning_Visitor,TRUE,FALSE 3,40,0,0,24,3830,0,0.025333333,0,0,Nov,1,1,7,8,New_Visitor,FALSE,TRUE 0,0,1,97,143,8060.963948,0.002923977,0.020798504,0,0,Nov,2,2,7,2,Returning_Visitor,FALSE,FALSE 8,175.125,0,0,7,96.125,0,0.026666667,0,0,Nov,1,1,2,8,New_Visitor,FALSE,FALSE 6,96,6,251.6666667,16,261,0.012820513,0.029871795,0,0,Nov,3,2,7,1,Returning_Visitor,TRUE,FALSE 0,0,0,0,1,0,0.2,0.2,0,0,Dec,2,2,1,1,Returning_Visitor,TRUE,FALSE 0,0,0,0,7,1544.75,0.014285714,0.033333333,0,0,Dec,1,1,3,1,Returning_Visitor,FALSE,FALSE 12,1276.264286,0,0,118,3215.173074,0,0.005446194,22.45841786,0,Nov,3,2,8,3,Returning_Visitor,FALSE,TRUE 1,17,1,23.75,62,3688.846861,0.016612903,0.027150538,3.663777878,0,Dec,3,2,3,1,Returning_Visitor,TRUE,FALSE 0,0,0,0,15,827.1666667,0.013333333,0.046666667,0,0,Dec,1,1,8,20,Returning_Visitor,FALSE,FALSE 1,11,0,0,59,4165.13869,0.008333333,0.017458333,0,0,Nov,3,2,3,10,Returning_Visitor,FALSE,FALSE 0,0,2,246.5,52,1838.85,0.003703704,0.014506173,0,0,Dec,2,7,3,2,New_Visitor,FALSE,FALSE 2,12,0,0,85,3887.222321,0.000595238,0.018506494,0,0,Dec,3,2,7,3,Returning_Visitor,FALSE,FALSE 4,82.25,6,144,82,3174.642793,0.004494382,0.012081072,0,0,Nov,1,1,1,2,Returning_Visitor,TRUE,FALSE 0,0,0,0,40,700.1277778,0.011,0.027788462,0,0,Nov,1,1,1,10,Returning_Visitor,TRUE,FALSE 0,0,0,0,1,0,0.2,0.2,0,0,Nov,3,2,3,13,Returning_Visitor,FALSE,FALSE 0,0,0,0,27,1316.035714,0,0.022222222,0,0,Nov,2,2,1,1,Returning_Visitor,FALSE,FALSE 6,49.08333333,1,619.5,58,2572.429487,0.00625,0.020729167,0,0,Nov,1,2,1,2,Returning_Visitor,FALSE,TRUE 0,0,0,0,36,1287.958333,0.034259259,0.073333333,0,0,Dec,2,2,1,3,Returning_Visitor,FALSE,FALSE 1,53.5,0,0,53,2019.828571,0.007692308,0.015734266,0,0,Nov,2,2,3,2,Returning_Visitor,FALSE,FALSE 2,93,2,221,41,716.7916667,0.008888889,0.041481481,0,0,Nov,2,2,3,2,Returning_Visitor,FALSE,FALSE 0,0,2,1494.5,109,3339.07598,0.008181818,0.014582448,0,0,Nov,2,2,4,8,Returning_Visitor,TRUE,FALSE 1,60.75,0,0,13,355.1666667,0.015384615,0.056410256,0,0,Nov,3,2,3,3,Returning_Visitor,FALSE,FALSE 1,151.5,0,0,10,304.75,0,0.007407407,0,0,Dec,3,2,2,2,Returning_Visitor,FALSE,FALSE 5,36,0,0,237,6817.942235,0.00167364,0.01380616,3.960306296,0,Nov,2,2,1,3,Returning_Visitor,FALSE,TRUE 4,712,0,0,77,3785.5625,0.002564103,0.00982906,0,0,Nov,2,2,2,8,New_Visitor,FALSE,TRUE 0,0,0,0,8,361,0,0.025,0,0,Dec,2,2,3,3,Returning_Visitor,FALSE,FALSE 4,406,0,0,55,2570.42619,0.003508772,0.017278044,0,0,Nov,3,2,4,2,Returning_Visitor,TRUE,FALSE 1,108,0,0,37,1209.558333,0.016666667,0.033386243,0,0,Dec,2,2,1,1,Returning_Visitor,FALSE,FALSE 12,393.2666667,0,0,15,366.5833333,0.00952381,0.013809524,0,0,Dec,2,4,6,1,New_Visitor,FALSE,FALSE 7,367.6666667,3,9,58,4191.031746,0.014583333,0.033093378,1.385791839,0,Nov,1,1,7,15,Returning_Visitor,TRUE,FALSE 13,344.4922161,5,271.25,182,5402.78526,0.000968661,0.006829256,16.27511574,0,Dec,3,2,1,2,Returning_Visitor,FALSE,FALSE 2,270,3,228.5,13,2451,0,0.021428571,42.22150549,0,Nov,2,2,4,2,Returning_Visitor,FALSE,TRUE 14,367.25,3,400.375,195,7980.363439,7.50E-05,0.005865824,3.86863332,0,Nov,2,2,2,1,Returning_Visitor,TRUE,TRUE 4,33,0,0,3,20,0,0.04,0,0,Nov,2,2,6,2,Returning_Visitor,FALSE,FALSE 0,0,0,0,27,910,0.007407407,0.011111111,0,0,Nov,4,2,1,3,New_Visitor,TRUE,FALSE 0,0,0,0,8,866,0.05,0.058333333,0,0,Dec,2,2,2,6,Returning_Visitor,FALSE,FALSE 2,58.5,0,0,28,968.6666667,0.013793103,0.024712644,14.02863873,0,Nov,2,5,1,3,Returning_Visitor,FALSE,TRUE 0,0,0,0,15,1390.583333,0.025,0.05,39.48337116,0,Dec,2,2,2,1,Returning_Visitor,FALSE,FALSE 5,400,0,0,20,991.5,0,0.013043478,22.13419743,0,Dec,2,2,3,2,Returning_Visitor,FALSE,TRUE 0,0,0,0,2,0,0.2,0.2,0,0,Nov,3,2,4,13,Returning_Visitor,FALSE,FALSE 2,55.5,0,0,25,2466.533333,0.007407407,0.028888889,49.01323885,0,Dec,2,2,1,2,Returning_Visitor,FALSE,FALSE 0,0,0,0,12,592.5,0,0.022222222,0,0,Dec,2,2,1,2,Returning_Visitor,FALSE,FALSE 0,0,0,0,55,1933.423611,0.001818182,0.017355372,0,0,Nov,2,2,1,2,Returning_Visitor,FALSE,FALSE 5,166.3333333,1,0,38,1869.45119,0.004545455,0.010037879,0,0,Dec,2,4,8,2,New_Visitor,FALSE,TRUE 8,177.6666667,1,23,56,2581.355128,0.003278689,0.011373326,0,0,Nov,3,2,3,2,Returning_Visitor,TRUE,FALSE 2,32.5,0,0,3,39,0.04,0.026666667,0,0,Dec,1,1,1,2,Returning_Visitor,TRUE,FALSE 0,0,0,0,3,170.6666667,0.033333333,0.026666667,0,0,Dec,3,3,1,11,New_Visitor,FALSE,FALSE 0,0,0,0,26,875.1559524,0,0.001333333,40.42327574,0,Nov,2,2,7,2,Returning_Visitor,FALSE,TRUE 11,208.25,2,109,54,2871.104167,0,0.021468927,0,0,Nov,2,2,1,2,Returning_Visitor,FALSE,FALSE 0,0,0,0,5,185,0,0.04,0,0,Nov,3,2,7,6,New_Visitor,TRUE,FALSE 4,72,0,0,32,2258.175,0,0.025714286,38.19067209,0,Nov,2,2,9,2,Returning_Visitor,FALSE,FALSE 6,57,0,0,80,1982.884524,0.007594937,0.033499444,0,0,Nov,2,6,6,1,Returning_Visitor,FALSE,FALSE 4,91,2,9.5,104,2856.9936,0.016929293,0.041369348,0,0,Dec,2,2,2,10,Returning_Visitor,FALSE,FALSE 0,0,0,0,38,2232.833333,0,0.026315789,0,0,Nov,2,2,2,2,Returning_Visitor,TRUE,TRUE 4,364.5,0,0,27,955.525,0.013333333,0.022222222,0,0,Nov,3,2,3,1,Returning_Visitor,TRUE,FALSE 1,39.5,0,0,52,3329.1,0.012231559,0.042469796,0,0,Nov,2,2,3,10,Returning_Visitor,FALSE,FALSE 0,0,0,0,28,1283.75,0.014285714,0.023809524,0,0,Dec,2,2,1,2,Returning_Visitor,TRUE,FALSE 3,16,0,0,29,423.1666667,0.010344828,0.029885057,0,0,Dec,3,2,6,1,Returning_Visitor,TRUE,FALSE 6,123.4642857,0,0,34,1570.469048,0.001052632,0.010431717,19.66912605,0,Dec,2,2,2,7,Returning_Visitor,TRUE,TRUE 0,0,1,117,8,404,0.025,0.0375,0,0,Dec,2,2,8,2,Returning_Visitor,FALSE,FALSE 0,0,0,0,1,0,0.2,0.2,0,0,Dec,1,1,7,3,Returning_Visitor,FALSE,FALSE 1,17,0,0,21,602.9166667,0.026666667,0.0425,0,0,Nov,3,2,1,13,Returning_Visitor,FALSE,FALSE 0,0,0,0,9,205.5,0,0.004444444,0,0,Nov,8,1,1,3,Returning_Visitor,FALSE,FALSE 3,793.75,1,0,65,3186.958333,0.011594203,0.019935588,0,0,Nov,1,1,4,2,Returning_Visitor,FALSE,FALSE 1,59.5,0,0,20,646.1666667,0,0.025333333,0,0,Dec,2,2,9,3,Returning_Visitor,FALSE,FALSE 3,28,0,0,20,633.75,0,0.019047619,32.43717827,0,Dec,2,10,1,1,Returning_Visitor,TRUE,TRUE 0,0,0,0,4,100.25,0,0.066666667,0,0,Dec,3,2,3,20,Returning_Visitor,TRUE,FALSE 6,283.0166667,1,235.75,21,1338.933333,0,0.008695652,0,0,Dec,2,2,7,1,Returning_Visitor,FALSE,FALSE 0,0,0,0,18,542.9333333,0,0.009259259,0,0,Nov,3,2,1,11,New_Visitor,FALSE,FALSE 6,116.6973684,0,0,171,9405.836721,0.008313228,0.028256171,0,0,Dec,3,2,3,2,Returning_Visitor,FALSE,TRUE 0,0,0,0,1,0,0.2,0.2,0,0,Nov,8,1,2,8,New_Visitor,FALSE,FALSE 0,0,0,0,10,249.3333333,0,0.02,0,0,Nov,1,2,6,8,New_Visitor,FALSE,FALSE 3,14.5,5,1007.5,26,672.875,0,0.032352941,0,0,Nov,2,2,3,1,Returning_Visitor,FALSE,FALSE 4,99,3,112.25,46,930.0141026,0.001960784,0.016470588,0,0,Nov,1,1,3,10,Returning_Visitor,FALSE,FALSE 0,0,0,0,88,5093.3,0.004924242,0.023393841,0,0,Nov,2,2,1,2,Returning_Visitor,FALSE,FALSE 9,164.5833333,1,0,76,3580.333333,0.004819277,0.020522088,12.20780052,0,Nov,4,1,1,1,Returning_Visitor,FALSE,FALSE 2,134.5,0,0,18,362.5,0,0.013333333,0,0,Dec,3,2,1,2,New_Visitor,FALSE,FALSE 0,0,0,0,39,2034.466667,0.005128205,0.015555556,0,0,Nov,2,2,1,2,Returning_Visitor,TRUE,TRUE 0,0,0,0,8,101.75,0.025,0.075,0,0,Nov,2,2,6,2,Returning_Visitor,FALSE,FALSE 0,0,4,620.25,23,997.2878788,0.012,0.031897436,0,0,Dec,1,1,3,2,Returning_Visitor,TRUE,FALSE 1,7,2,12,5,412.25,0,0.0375,0,0,Dec,2,2,1,2,Returning_Visitor,FALSE,FALSE 0,0,0,0,12,680.5,0,0.018181818,42.422531,0,Dec,2,2,6,10,Returning_Visitor,FALSE,TRUE 6,723.5,2,410.5,5,109,0,0.015384615,0,0,Nov,2,5,2,2,Other,FALSE,TRUE 0,0,0,0,47,1093.833333,0.004444444,0.018518519,0,0,Dec,2,2,2,1,Returning_Visitor,FALSE,FALSE 0,0,0,0,7,343.25,0,0.028571429,49.36350729,0,Nov,2,2,2,7,Returning_Visitor,TRUE,TRUE 8,205.25,0,0,155,4502.511111,0.000176367,0.010885984,3.801288988,0,Nov,2,2,1,6,Returning_Visitor,FALSE,TRUE 1,21.25,0,0,92,2716.519048,0.006737589,0.037885297,23.7389113,0,Nov,2,2,3,2,Returning_Visitor,TRUE,FALSE 9,576.6208333,0,0,57,1878.195833,0.003472222,0.010446429,5.002669777,0,Nov,2,2,4,8,New_Visitor,FALSE,TRUE 8,264.8095238,0,0,35,1039.887446,0.009756098,0.035656214,0,0,Dec,2,2,3,2,Returning_Visitor,FALSE,FALSE 4,76.25,0,0,141,3274.521154,0,0.008391608,6.425757643,0,Dec,2,2,1,1,Returning_Visitor,FALSE,FALSE 16,244.2666667,1,0,101,4484.669445,0.008408408,0.024055759,3.50497054,0,Dec,3,2,2,10,Returning_Visitor,FALSE,FALSE 2,59.5,0,0,3,67.66666667,0,0.02,0,0,Nov,3,2,8,2,New_Visitor,FALSE,FALSE 0,0,0,0,37,5208.65,0,0.01829932,0,0,Nov,2,2,1,6,Returning_Visitor,FALSE,FALSE 5,106,0,0,44,646.825,0.007407407,0.028851852,0,0,Dec,3,2,9,13,Returning_Visitor,FALSE,FALSE 2,29,0,0,61,1617.519048,0.013978495,0.030295699,0,0,Nov,3,2,8,2,Returning_Visitor,TRUE,FALSE 6,91.25,0,0,56,955.5432151,0.007494145,0.010650662,0,0,Nov,3,2,4,2,Returning_Visitor,TRUE,TRUE 1,25.5,0,0,10,361.5,0,0.02,60.01343064,0,Dec,2,2,1,1,Returning_Visitor,FALSE,TRUE 1,49.5,0,0,9,350.25,0,0.035714286,0,0,Dec,2,2,1,7,New_Visitor,TRUE,FALSE 1,46.61111111,0,0,45,1873.589297,0,0.007242234,42.26791822,0,Dec,1,1,3,2,Returning_Visitor,FALSE,FALSE 0,0,0,0,11,1712.416667,0.018181818,0.036363636,0,0,Nov,3,2,1,10,Returning_Visitor,TRUE,FALSE 0,0,0,0,2,26.5,0,0.05,0,0,Dec,3,2,1,1,Returning_Visitor,TRUE,FALSE 0,0,0,0,19,696.875,0.010526316,0.035087719,0,0,Dec,1,2,4,2,Returning_Visitor,FALSE,FALSE 0,0,3,321,31,916.625,0.006617647,0.034935897,0,0,Dec,2,2,1,1,Returning_Visitor,FALSE,FALSE 0,0,0,0,3,56.5,0.066666667,0.133333333,0,0,Nov,2,2,3,1,Returning_Visitor,FALSE,FALSE 0,0,0,0,10,83.41666667,0,0.022222222,0,0,Nov,1,1,1,2,Returning_Visitor,TRUE,FALSE 0,0,0,0,3,255,0,0.066666667,0,0,Nov,2,5,8,3,Returning_Visitor,TRUE,FALSE 1,7.125,6,1043.928571,73,3585.177579,0.008175105,0.024451608,0,0,Nov,3,2,5,2,Returning_Visitor,FALSE,FALSE 2,26.75,3,49.5,62,2964.650595,0.010769231,0.026444444,0,0,Dec,3,2,1,2,Returning_Visitor,FALSE,FALSE 6,88.75,0,0,22,757.375,0.001481481,0.042222222,27.97632918,0,Nov,2,2,1,2,Returning_Visitor,FALSE,FALSE 6,190.4166667,0,0,78,3913.98703,0.005424955,0.006561181,0,0,Nov,3,2,5,1,Returning_Visitor,TRUE,FALSE 0,0,0,0,30,685.75,0,0.036507937,0,0,Dec,2,2,2,3,Returning_Visitor,FALSE,FALSE 0,0,0,0,11,572,0.036363636,0.072727273,0,0,Nov,1,1,4,3,Returning_Visitor,FALSE,FALSE 0,0,0,0,7,112,0.028571429,0.085714286,0,0,Nov,2,2,1,2,Returning_Visitor,FALSE,FALSE 1,12,0,0,11,213,0.05,0.066666667,0,0,Nov,2,2,4,13,Returning_Visitor,TRUE,FALSE 2,18,0,0,12,180.75,0,0.014285714,0,0,Nov,2,2,1,2,New_Visitor,FALSE,FALSE 0,0,0,0,34,933.85,0.021875,0.031505682,0,0,Nov,1,1,1,2,Returning_Visitor,TRUE,FALSE 6,137.9166667,0,0,92,3163.14329,0.001075269,0.0095145,32.22246226,0,Nov,2,2,1,1,Returning_Visitor,FALSE,TRUE 8,204.1071429,0,0,15,347.7321429,0,0.012280702,18.08071442,0,Nov,1,1,1,2,New_Visitor,FALSE,FALSE 1,585.5,0,0,80,3634.141512,0.004320988,0.007864356,26.41113767,0,Nov,3,2,2,2,Returning_Visitor,FALSE,TRUE 0,0,2,889.5,30,784.0833333,0.025,0.046875,0,0,Dec,2,2,6,13,Returning_Visitor,FALSE,FALSE 7,234.9285714,10,902.5,131,6050.883117,0.002857143,0.012557561,0,0,Dec,2,2,1,2,Returning_Visitor,FALSE,FALSE 8,335.0636364,3,738.0833333,36,2228.707287,0.005853659,0.010086083,27.79149135,0,Dec,1,1,3,2,New_Visitor,FALSE,FALSE 4,33,0,0,35,702.9166667,0,0.005428571,0,0,Dec,4,1,1,2,New_Visitor,FALSE,FALSE 5,72.5,0,0,55,936.1333333,0.003571429,0.020238095,23.27552205,0,Nov,2,2,1,2,Returning_Visitor,FALSE,TRUE 0,0,0,0,65,2706.595238,0,0.022692308,0,0,Nov,2,2,1,2,New_Visitor,FALSE,FALSE 8,335.875,2,75.25,18,1494.125,0,0.022,12.60913229,0,Nov,2,2,9,10,Returning_Visitor,FALSE,TRUE 0,0,0,0,3,60,0,0.066666667,0,0,Dec,1,1,7,2,Other,FALSE,FALSE 0,0,0,0,4,249,0,0.025,0,0,Dec,2,4,1,1,Returning_Visitor,FALSE,FALSE 0,0,1,0,18,648,0,0.021052632,0,0,Nov,2,4,1,2,New_Visitor,FALSE,FALSE 0,0,0,0,9,1178.25,0,0.025,0,0,Nov,3,2,1,10,Returning_Visitor,FALSE,FALSE 2,307.3,0,0,29,2447.3,0,0.013888889,26.25948159,0,Dec,2,2,2,10,Returning_Visitor,FALSE,FALSE 0,0,0,0,1,487,0,0.1,0,0,Nov,1,1,4,10,Returning_Visitor,TRUE,FALSE 3,98.5,1,21.25,46,1438.1,0.004255319,0.017801418,27.86366003,0,Dec,3,2,1,2,Returning_Visitor,TRUE,FALSE 0,0,0,0,23,533.7,0,0.021532091,0,0,Dec,1,1,3,2,New_Visitor,TRUE,FALSE 1,28.25,1,0,10,128.5,0.030555556,0.085119048,0,0,Nov,3,2,3,6,Returning_Visitor,TRUE,FALSE 0,0,0,0,10,316.6,0,0.022222222,0,0,Nov,2,2,1,10,Returning_Visitor,FALSE,FALSE 0,0,0,0,2,0,0,0.2,0,0,Nov,1,1,1,1,Returning_Visitor,FALSE,FALSE 0,0,0,0,32,2180.933333,0.00625,0.032291667,0,0,Dec,4,2,3,1,Returning_Visitor,FALSE,FALSE 12,297.2083333,0,0,38,778.675,0.004347826,0.020434783,13.28070081,0,Dec,1,1,9,3,Returning_Visitor,FALSE,FALSE 0,0,0,0,24,724.7666667,0.030555556,0.037797619,0,0,Nov,3,2,1,3,Returning_Visitor,TRUE,FALSE 0,0,1,0,34,1189.053571,0.000840336,0.027777778,0,0,Nov,4,1,6,8,Returning_Visitor,FALSE,FALSE 8,213.75,2,45.5,30,2054.841667,0,0.001041667,21.9646242,0,Dec,1,1,6,2,New_Visitor,FALSE,TRUE 0,0,0,0,10,192.75,0.033333333,0.028888889,0,0,Dec,1,2,3,1,Returning_Visitor,TRUE,FALSE 0,0,0,0,2,9,0,0.05,0,0,Dec,3,2,3,10,New_Visitor,FALSE,FALSE 1,20,0,0,18,375.5547619,0,0.0125,32.3171163,0,Nov,2,2,5,3,Returning_Visitor,FALSE,TRUE 10,198.2430556,3,276.25,151,4344.919986,0.002531646,0.01196589,0,0,Dec,4,2,4,2,Returning_Visitor,FALSE,FALSE 0,0,0,0,2,63.5,0,0.1,0,0,Nov,2,4,1,1,Returning_Visitor,FALSE,FALSE 7,280.2666667,0,0,11,329.6833333,0,0.023076923,0,0,Dec,2,2,7,2,Returning_Visitor,FALSE,FALSE 0,0,0,0,12,338.75,0,0.033333333,0,0,Nov,2,4,1,2,Returning_Visitor,FALSE,FALSE 4,137,2,5,186,8425.870475,0.003403141,0.015721509,0,0,Nov,2,2,1,2,Returning_Visitor,TRUE,FALSE 0,0,0,0,10,109.1666667,0,0.016666667,0,0,Nov,3,2,1,3,Returning_Visitor,FALSE,FALSE 0,0,0,0,33,1996.816667,0,0.00625,44.06491359,0,Nov,4,2,6,2,Returning_Visitor,FALSE,TRUE 0,0,0,0,1,0,0.2,0.2,0,0,Dec,2,2,2,2,Other,FALSE,FALSE 1,26.5,0,0,13,470.5,0,0.016666667,41.422095,0,Nov,1,1,4,2,New_Visitor,FALSE,TRUE 0,0,0,0,9,267.75,0,0.022222222,0,0,Dec,3,2,8,2,New_Visitor,TRUE,FALSE 0,0,0,0,10,244.6666667,0.005,0.065,0,0,Dec,1,1,1,1,Returning_Visitor,FALSE,FALSE 3,19,0,0,120,3013.116917,0.001680672,0.007407748,14.18391563,0,Nov,2,5,1,2,Returning_Visitor,FALSE,TRUE 4,87.25,0,0,37,1336.208333,0.009756098,0.03902439,0,0,Nov,2,2,1,1,Returning_Visitor,FALSE,FALSE 2,34.91666667,1,33.5,21,496.4821429,0.008695652,0.025120773,0,0,Dec,3,2,1,2,Returning_Visitor,FALSE,FALSE 6,106,0,0,42,1384.197436,0,0.004651163,136.937958,0,Dec,8,13,9,20,Other,FALSE,TRUE 0,0,0,0,14,404.3392857,0,0.033333333,0,0,Dec,1,8,3,20,Returning_Visitor,TRUE,FALSE 1,7,0,0,39,1118.833333,0.005405405,0.019099099,0,0,Nov,2,2,6,13,Returning_Visitor,FALSE,FALSE 1,46.5,0,0,22,578.8106061,0.00952381,0.04021164,0,0,Dec,1,1,2,2,Returning_Visitor,FALSE,FALSE 0,0,0,0,7,129.25,0,0.033333333,0,0,Dec,1,1,4,2,New_Visitor,TRUE,FALSE 0,0,0,0,26,586.85,0.015384615,0.031868132,0,0,Dec,3,2,1,10,Returning_Visitor,FALSE,FALSE 2,75.5,0,0,30,1661.166667,0,0.00625,0,0,Dec,2,2,2,10,New_Visitor,FALSE,FALSE 1,19,0,0,100,3457.285317,0.006122449,0.028231293,0,0,Nov,2,2,1,2,Returning_Visitor,FALSE,FALSE 3,208,0,0,16,1037.819444,0.020833333,0.028125,0,0,Nov,1,1,1,8,Returning_Visitor,TRUE,FALSE 7,87.25,4,199.5,87,4984.542857,0.00326087,0.02113009,0,0,Nov,2,2,3,2,Returning_Visitor,FALSE,FALSE 0,0,0,0,6,80.5,0.033333333,0.066666667,0,0,Nov,2,2,3,1,Returning_Visitor,FALSE,FALSE 0,0,0,0,9,1591,0.066666667,0.133333333,0,0,Dec,2,2,3,13,Returning_Visitor,FALSE,FALSE 0,0,0,0,6,196.5,0,0.016666667,0,0,Nov,1,1,7,2,Returning_Visitor,FALSE,FALSE 0,0,0,0,12,315.7797619,0.016666667,0.025,0,0,Nov,1,1,9,2,Returning_Visitor,TRUE,FALSE 4,54.5,0,0,74,1599.994589,0,0.002631579,67.26616125,0,Dec,2,4,6,2,Returning_Visitor,FALSE,TRUE 1,10.5,3,264.75,195,10044.75756,0.005025126,0.023180221,0,0,Dec,2,2,1,2,Returning_Visitor,FALSE,FALSE 3,32.5,0,0,24,603.25,0,0.014666667,14.57483754,0,Nov,4,2,1,3,Returning_Visitor,FALSE,FALSE 0,0,0,0,15,549,0.08,0.126666667,0,0,Nov,2,10,1,3,Returning_Visitor,FALSE,FALSE 0,0,0,0,19,686.5833333,0,0.010526316,0,0,Dec,1,1,5,2,Returning_Visitor,TRUE,FALSE 0,0,0,0,1,0,0.2,0.2,0,0,Nov,3,2,1,13,Returning_Visitor,FALSE,FALSE 2,389.5,0,0,8,117.625,0,0.013,0,0,Nov,1,1,8,2,Returning_Visitor,FALSE,FALSE 7,243.7469512,1,1073,33,4931.198912,0.016666667,0.028235129,25.10043261,0,Nov,3,2,2,2,Returning_Visitor,FALSE,FALSE 6,60.13636364,2,522.1666667,83,3708.383949,0.002420052,0.012547833,0,0,Nov,2,2,7,2,Returning_Visitor,TRUE,FALSE 10,261.5,1,55.5,122,3411.955853,0.01056,0.029876364,0,0,Nov,2,2,1,13,Returning_Visitor,FALSE,TRUE 0,0,3,82.5,9,143.375,0.016666667,0.055555556,0,0,Dec,3,2,6,2,Returning_Visitor,FALSE,FALSE 0,0,0,0,19,194.75,0.005263158,0.031578947,0,0,Dec,3,2,4,1,Returning_Visitor,FALSE,FALSE 9,125.25,0,0,3,119.25,0,0.025,0,0,Dec,8,13,9,20,Other,FALSE,FALSE 1,16.25,0,0,34,456.525,0.006060606,0.03030303,0,0,Nov,1,1,4,1,Returning_Visitor,TRUE,FALSE 0,0,0,0,12,262.9166667,0.008333333,0.055555556,0,0,Dec,1,6,1,2,Returning_Visitor,FALSE,FALSE 0,0,0,0,12,250.25,0.05,0.083333333,0,0,Dec,1,1,1,3,Returning_Visitor,TRUE,FALSE 3,49.5,0,0,68,2853.020833,0.008823529,0.012826797,0,0,Nov,1,1,1,3,Returning_Visitor,FALSE,FALSE 4,26.75,1,0,20,1029.25,0.021153846,0.03974359,0,0,Nov,1,1,1,2,Returning_Visitor,FALSE,FALSE 15,1535.355556,0,0,43,3606.088889,0.009770115,0.043951451,0,0,Dec,2,2,1,1,Returning_Visitor,FALSE,FALSE 0,0,0,0,2,24.5,0,0.1,0,0,Dec,3,2,2,11,Other,FALSE,FALSE 0,0,0,0,1,0,0.2,0.2,0,0,Dec,3,2,1,1,Other,FALSE,FALSE 0,0,2,19,22,2267.833333,0,0.041666667,0,0,Nov,2,2,1,1,Returning_Visitor,FALSE,FALSE 3,14,0,0,3,18,0,0.04,0,0,Dec,2,4,3,8,Other,FALSE,FALSE 2,113.5,0,0,37,1344.340476,0,0.005405405,11.77908986,0,Dec,2,2,3,2,New_Visitor,FALSE,TRUE 0,0,0,0,55,2415.416667,0,0.019811321,0,0,Nov,2,2,3,11,Returning_Visitor,TRUE,FALSE 1,70.5,0,0,28,1398.9375,0.014285714,0.016269841,17.46396119,0,Nov,3,2,3,1,Returning_Visitor,FALSE,FALSE 9,279.5,0,0,14,313.5833333,0,0.010526316,0,0,Nov,1,1,1,2,New_Visitor,FALSE,FALSE 2,26.75,0,0,103,2546.856685,0.014920635,0.025223356,0,0,Nov,3,2,1,1,Returning_Visitor,FALSE,FALSE 0,0,0,0,4,163.5,0,0.025,0,0,Nov,3,6,2,2,Returning_Visitor,FALSE,FALSE 2,62.5,0,0,43,1253.176407,0,0.01468254,41.53706414,0,Nov,3,2,9,2,Returning_Visitor,FALSE,FALSE 0,0,0,0,26,938.5833333,0.007692308,0.021794872,0,0,Dec,2,2,6,1,Returning_Visitor,FALSE,FALSE 1,57.5,0,0,23,914.3333333,0,0.008695652,23.18305163,0,Dec,4,5,1,8,New_Visitor,TRUE,TRUE 2,33.5,0,0,23,833.6695513,0.009090909,0.012947658,29.38287135,0,Dec,2,2,1,2,Returning_Visitor,FALSE,TRUE 4,32.75,0,0,13,704,0,0.024285714,76.79037287,0,Dec,2,2,1,2,Returning_Visitor,FALSE,TRUE 0,0,1,22,81,2253.799603,0.011463415,0.015875182,0,0,Nov,3,2,1,2,Returning_Visitor,TRUE,TRUE 8,46.875,3,10,111,5358.375439,0,0.011957672,10.21771151,0,Nov,2,2,1,11,Returning_Visitor,FALSE,TRUE 7,120.375,0,0,17,446.3833333,0,0.008333333,0,0,Dec,2,2,7,2,New_Visitor,FALSE,FALSE 7,119.5666667,0,0,25,499.6083333,0.004166667,0.033333333,0,0,Nov,1,1,1,8,Returning_Visitor,FALSE,FALSE 3,37,0,0,21,1231.5,0,0.016,0,0,Nov,2,4,9,8,New_Visitor,TRUE,TRUE 0,0,0,0,1,0,0.2,0.2,0,0,Nov,8,6,1,6,Returning_Visitor,FALSE,FALSE 3,258,2,34.625,25,1364.8125,0.029761905,0.035714286,3.18693501,0,Dec,1,1,3,10,Returning_Visitor,TRUE,FALSE 1,6,0,0,18,339.75,0,0.00625,0,0,Nov,1,1,5,2,Returning_Visitor,FALSE,FALSE 0,0,0,0,8,362.75,0.025,0.075,0,0,Nov,2,5,3,8,Returning_Visitor,FALSE,FALSE 4,190.5,3,575,30,2375.083333,0,0.011111111,34.79013586,0,Dec,2,2,3,2,New_Visitor,FALSE,TRUE 0,0,0,0,19,577.5416667,0,0.011764706,90.9285217,0,Nov,2,2,1,2,Returning_Visitor,FALSE,TRUE 0,0,0,0,22,1483.8,0,0.01446281,0,0,Dec,4,1,1,2,Returning_Visitor,FALSE,FALSE 0,0,0,0,23,943.25,0.008695652,0.046376812,0,0,Nov,1,1,3,3,Returning_Visitor,FALSE,FALSE 11,281.3333333,0,0,41,1167.95,0,0.011347518,7.907379545,0,Nov,1,1,3,2,New_Visitor,TRUE,TRUE 4,94.25,3,79.5,77,3719.643711,0.002469136,0.010684624,14.96948769,0,Nov,3,2,3,3,Returning_Visitor,FALSE,TRUE 3,81.33333333,1,32.5,17,733.5,0,0.002631579,0,0,Dec,2,7,9,10,Returning_Visitor,TRUE,FALSE 0,0,0,0,2,63.5,0,0.1,0,0,Nov,1,1,1,3,Returning_Visitor,FALSE,FALSE 4,252,1,56.5,42,947.357702,0.004347826,0.017294686,0,0,Nov,1,1,7,8,Returning_Visitor,TRUE,FALSE 6,71.375,0,0,73,2335.633333,0,0.0125,2.51591586,0,Nov,2,2,7,11,Returning_Visitor,FALSE,TRUE 4,26,0,0,22,1671.375,0.018333333,0.040555556,0,0,Nov,2,2,3,8,Returning_Visitor,FALSE,FALSE 0,0,0,0,32,1109.083333,0.00125,0.019375,0,0,Dec,2,2,1,2,Returning_Visitor,TRUE,FALSE 0,0,0,0,11,225.5,0.018181818,0.054545455,0,0,Nov,2,5,4,11,Returning_Visitor,FALSE,FALSE 0,0,0,0,23,662.5,0,0.013043478,0,0,Nov,2,2,6,2,New_Visitor,TRUE,TRUE 3,64.5,0,0,17,269.6833333,0,0.003703704,0,0,Nov,2,2,1,2,Returning_Visitor,FALSE,FALSE 1,6,0,0,15,293,0.021428571,0.026190476,0,0,Nov,2,2,7,1,Returning_Visitor,FALSE,FALSE 2,40.25,0,0,23,766.25,0.008333333,0.025,24.07951908,0,Dec,1,1,2,8,Returning_Visitor,TRUE,TRUE 1,11,1,10,74,3487.12471,0.002739726,0.009519461,0,0,Nov,3,2,4,3,Returning_Visitor,TRUE,FALSE 4,70.4,1,645.5,82,11257.1083,0.005574913,0.019401736,0,0,Nov,2,5,1,2,Returning_Visitor,TRUE,FALSE 0,0,0,0,9,89,0,0.055555556,0,0,Nov,2,2,9,1,Returning_Visitor,TRUE,FALSE 0,0,0,0,48,755.175,0.000472813,0.026312057,13.18501,0,Dec,2,2,4,10,Returning_Visitor,FALSE,TRUE 0,0,0,0,2,100,0,0.1,0,0,Nov,2,2,3,3,Returning_Visitor,TRUE,FALSE 0,0,0,0,1,0,0.2,0.2,0,0,Nov,1,2,7,10,Returning_Visitor,FALSE,FALSE 0,0,0,0,42,1265.892857,0.028253968,0.02856387,0,0,Dec,3,2,2,13,Returning_Visitor,TRUE,FALSE 0,0,0,0,3,81.75,0,0.033333333,0,0,Dec,3,2,3,6,Returning_Visitor,FALSE,FALSE 1,14,0,0,7,170.75,0,0.025,0,0,Nov,1,1,1,2,New_Visitor,FALSE,FALSE 10,247.75,0,0,27,1182.416667,0.017741935,0.038556068,7.994904291,0,Dec,2,2,4,6,Returning_Visitor,FALSE,TRUE 4,68.75,0,0,23,585.65,0,0.008695652,0,0,Dec,8,1,1,2,New_Visitor,TRUE,FALSE 8,145.1,6,2166.5,14,1805.425,0,0.031138344,10.90023366,0,Dec,4,5,1,2,Returning_Visitor,FALSE,FALSE 5,148.5,1,25.5,36,2590.016667,0,0.020954545,19.29866138,0,Dec,1,1,1,1,Returning_Visitor,FALSE,FALSE 8,102,0,0,9,374,0.015384615,0.035897436,0,0,Dec,3,2,3,2,Returning_Visitor,TRUE,FALSE 0,0,0,0,46,3346.041667,0,0.01884058,0,0,Nov,2,2,3,2,Returning_Visitor,TRUE,TRUE 0,0,0,0,68,1212.441667,0.002898551,0.014381271,64.4161607,0,Nov,4,1,1,1,Returning_Visitor,FALSE,FALSE 0,0,0,0,12,429,0,0.016666667,0,0,Nov,1,1,3,8,Returning_Visitor,FALSE,FALSE 11,1284.3375,3,303.625,96,2478.070833,0.007355769,0.018144023,0,0,Dec,1,1,3,2,Returning_Visitor,FALSE,FALSE 7,360.75,0,0,264,10497.15443,0.00295026,0.012825542,8.833510027,0,Nov,2,2,3,2,Returning_Visitor,FALSE,FALSE 2,149.5,0,0,6,326,0,0.014285714,0,0,Dec,1,1,1,2,New_Visitor,FALSE,TRUE 7,177.25,0,0,13,297.8333333,0,0.021428571,0,0,Nov,1,2,3,2,New_Visitor,TRUE,FALSE 2,26,0,0,38,1689.708333,0.0125,0.0295,0,0,Nov,2,2,1,13,Returning_Visitor,FALSE,FALSE 6,65.25,0,0,21,829.3333333,0,0.008333333,0,0,Nov,2,5,4,1,Returning_Visitor,TRUE,FALSE 1,29.5,0,0,26,1659.866667,0.008,0.016,21.81920724,0,Nov,2,5,2,1,Returning_Visitor,FALSE,TRUE 1,59.5,0,0,39,650.8333333,0,0.003070175,0,0,Nov,1,2,1,11,Returning_Visitor,TRUE,TRUE 12,244.25,0,0,110,4006.359314,0.01,0.030496867,11.10333103,0,Nov,2,7,1,1,Returning_Visitor,FALSE,FALSE 0,0,0,0,30,1285.166667,0.002758621,0.022660099,0,0,Nov,3,3,1,8,Returning_Visitor,TRUE,FALSE 2,56.5,1,28.75,42,1753.083333,0,0.007301587,4.59675105,0,Nov,4,1,9,8,Returning_Visitor,TRUE,TRUE 0,0,0,0,12,1952.75,0.033333333,0.068333333,0,0,Nov,2,4,8,1,Returning_Visitor,FALSE,FALSE 0,0,0,0,6,83.5,0,0.066666667,0,0,Nov,1,1,3,10,Returning_Visitor,FALSE,FALSE 0,0,0,0,20,1194.75,0,0.026315789,0,0,Nov,2,2,1,8,Returning_Visitor,TRUE,TRUE 0,0,0,0,10,235.5,0,0.013333333,0,0,Nov,3,2,6,1,Returning_Visitor,FALSE,FALSE 0,0,0,0,91,2631.473214,0.002747253,0.023321123,0,0,Nov,2,2,1,2,Returning_Visitor,FALSE,FALSE 3,77.83333333,0,0,26,812.1047619,0.021428571,0.038095238,0,0,Nov,1,2,3,2,Returning_Visitor,FALSE,FALSE 0,0,0,0,10,422.5,0,0.02,0,0,Nov,3,2,5,10,Returning_Visitor,FALSE,FALSE 1,68.5,0,0,11,372.8166667,0,0.036363636,54.76629511,0,Dec,1,1,1,2,Returning_Visitor,FALSE,TRUE 5,229.3333333,4,115.25,28,1474.530303,0,0.005429293,0,0,Nov,1,1,1,2,Returning_Visitor,FALSE,FALSE 0,0,0,0,48,832.0833333,0.038478261,0.064782609,0,0,Dec,3,2,1,3,Returning_Visitor,FALSE,FALSE 4,60.08333333,0,0,24,1204.416667,0.007692308,0.026923077,46.62801597,0,Dec,2,2,6,6,Returning_Visitor,FALSE,FALSE 0,0,0,0,2,58,0,0.1,0,0,Nov,2,2,2,6,Returning_Visitor,TRUE,FALSE 4,252.5,0,0,13,930.0535714,0,0.027083333,0,0,Nov,1,1,3,3,Returning_Visitor,FALSE,FALSE 0,0,0,0,8,190.1590909,0.05625,0.055357143,0,0,Dec,1,1,7,3,Returning_Visitor,FALSE,FALSE 4,45.83333333,0,0,70,1692.257143,0,0.014555556,0,0,Nov,2,2,1,2,Returning_Visitor,FALSE,FALSE 0,0,0,0,7,178.25,0.028571429,0.057142857,0,0,Dec,2,10,6,3,Returning_Visitor,FALSE,FALSE 0,0,0,0,79,2663.583333,0.002531646,0.017088608,0,0,Nov,2,2,1,1,Returning_Visitor,TRUE,TRUE 10,218.4027778,0,0,27,897.7837302,0.006060606,0.019805195,48.92856443,0,Dec,2,2,1,3,Returning_Visitor,FALSE,FALSE 0,0,0,0,40,3998.333333,0.002439024,0.041869919,0,0,Nov,4,2,4,1,Returning_Visitor,FALSE,FALSE 0,0,0,0,3,18,0.1,0.111111111,0,0,Nov,3,2,2,13,Returning_Visitor,FALSE,FALSE 3,67.75,0,0,2,19.25,0.04,0.053333333,0,0,Dec,1,1,3,20,New_Visitor,FALSE,TRUE 0,0,0,0,2,15,0,0.1,0,0,Dec,1,1,1,10,Returning_Visitor,FALSE,FALSE 9,141.75,2,310.5,9,123.5,0,0.017647059,0,0,Dec,1,1,1,2,Returning_Visitor,FALSE,FALSE 0,0,0,0,1,0,0.2,0.2,0,0,Nov,1,1,6,2,Returning_Visitor,FALSE,FALSE 8,203.4166667,0,0,116,3505.087955,0,0.009314826,62.83957669,0,Nov,2,2,1,6,Returning_Visitor,FALSE,TRUE 8,109.5714286,0,0,374,12499.37711,0.00198939,0.010782459,6.420746096,0,Nov,2,2,2,2,Returning_Visitor,TRUE,FALSE 2,9,4,31,401,12433.21865,0.006577408,0.022322606,0,0,Nov,2,2,1,2,Returning_Visitor,FALSE,TRUE 4,100.75,0,0,11,221.375,0,0.015384615,0,0,Nov,2,2,3,2,New_Visitor,TRUE,FALSE 0,0,0,0,96,5947.97123,0.0125,0.028333333,0,0,Nov,2,2,1,1,Returning_Visitor,TRUE,TRUE 0,0,0,0,5,134.5,0.04,0.024,0,0,Nov,3,2,9,3,Returning_Visitor,TRUE,FALSE 2,53.5,0,0,85,4478.699416,0.002352941,0.016205882,0,0,Nov,2,2,1,2,Returning_Visitor,FALSE,TRUE 0,0,2,26,90,4875.609432,0.01978022,0.044796184,0,0,Nov,2,2,7,1,Returning_Visitor,FALSE,TRUE 3,101.6428571,0,0,137,3978.626167,0.001449275,0.004746377,81.75845837,0,Dec,2,10,3,1,Returning_Visitor,FALSE,FALSE 3,48.5,0,0,10,138.1666667,0,0.015384615,0,0,Nov,2,2,1,2,New_Visitor,TRUE,FALSE 0,0,0,0,7,66.5,0.057142857,0.114285714,0,0,Nov,2,2,3,13,Returning_Visitor,FALSE,FALSE 1,26.5,0,0,6,110,0.066666667,0.1,0,0,Nov,3,2,1,2,Returning_Visitor,FALSE,TRUE 0,0,0,0,1,0,0.2,0.2,0,0,Dec,2,2,1,13,Returning_Visitor,FALSE,FALSE 0,0,2,7,16,794.5416667,0.015686275,0.071568627,0,0,Dec,1,1,3,2,Returning_Visitor,FALSE,FALSE 0,0,0,0,40,2426.266667,0.004878049,0.02296748,0,0,Nov,2,2,2,11,Returning_Visitor,FALSE,FALSE 11,188.8333333,3,27,64,4829.625,0.004054054,0.017194337,48.60623685,0,Nov,1,1,1,3,Returning_Visitor,FALSE,TRUE 2,15,0,0,35,1776.083333,0,0.011428571,0,0,Dec,2,2,4,1,Returning_Visitor,FALSE,FALSE 2,7,0,0,7,98.55,0.05,0.058333333,0,0,Nov,3,2,3,11,Returning_Visitor,TRUE,FALSE 0,0,0,0,4,66,0,0.025,0,0,Dec,2,2,4,2,Returning_Visitor,FALSE,FALSE 7,105.375,0,0,48,1268.116667,0.022902098,0.027788462,0,0,Nov,1,1,8,1,Returning_Visitor,TRUE,FALSE 2,157,0,0,5,85.5,0,0.025,0,0,Dec,2,4,6,2,New_Visitor,FALSE,TRUE 3,68.33333333,0,0,39,1263.864951,0.007906977,0.025721669,14.94379331,0,Nov,3,2,1,2,Returning_Visitor,FALSE,TRUE 7,95.75,0,0,41,1164.202381,0.004545455,0.010606061,27.07846934,0,Dec,3,2,5,1,Returning_Visitor,TRUE,TRUE 0,0,0,0,14,599.5833333,0,0.016326531,0,0,Nov,2,2,1,20,Returning_Visitor,FALSE,FALSE 1,0,4,154.8333333,224,5209.958128,0.008311643,0.021844997,0,0,Nov,1,2,3,2,Returning_Visitor,FALSE,TRUE 0,0,0,0,21,1005.833333,0.009090909,0.039393939,0,0,Dec,2,2,2,2,Returning_Visitor,FALSE,FALSE 12,1028.833333,1,3,155,6794.735417,0.002469136,0.015526696,3.726530231,0,Nov,2,2,1,2,Returning_Visitor,FALSE,TRUE 4,43,0,0,66,2544.053571,0,0.015920398,62.40576241,0,Nov,4,1,2,2,Returning_Visitor,FALSE,FALSE 0,0,0,0,7,56.625,0.028571429,0.066666667,0,0,Dec,3,2,1,20,Returning_Visitor,FALSE,FALSE 0,0,0,0,1,0,0.2,0.2,0,0,Nov,7,1,1,3,Returning_Visitor,FALSE,FALSE 8,279.25,2,1652,43,1717.740476,0,0.018472224,18.07802206,0,Dec,3,2,5,2,Returning_Visitor,FALSE,TRUE 10,302.0833333,0,0,81,1778.663263,0,0.002857143,16.43784656,0,Nov,1,1,1,8,New_Visitor,TRUE,FALSE 0,0,0,0,33,1246.708333,0.03030303,0.056986532,0,0,Dec,3,2,1,2,Returning_Visitor,FALSE,FALSE 0,0,0,0,13,342.9202381,0,0.011794872,0,0,Nov,3,2,3,2,New_Visitor,TRUE,FALSE 8,121.425,3,273.5,100,2565.797222,0.001869159,0.012071651,0,0,Nov,2,2,7,13,Returning_Visitor,TRUE,FALSE 0,0,0,0,29,488.4090909,0.014285714,0.02173913,0,0,Nov,1,8,1,11,New_Visitor,FALSE,TRUE 0,0,0,0,9,148.3333333,0.066666667,0.088888889,0,0,Dec,2,2,3,2,Returning_Visitor,FALSE,FALSE 6,81.75,0,0,152,4838.29406,0.003046557,0.022523524,0.789591795,0,Nov,2,2,2,2,Returning_Visitor,TRUE,FALSE 1,7,4,364.9166667,248,7445.957075,0.004316069,0.015735624,1.26665917,0,Nov,2,2,1,2,Returning_Visitor,FALSE,TRUE 0,0,1,14,119,2763.011332,0.005367232,0.007566642,0,0,Nov,3,2,3,13,Returning_Visitor,TRUE,FALSE 6,191,1,26.5,37,1874.841667,0.009302326,0.010077519,0,0,Nov,2,2,4,3,Returning_Visitor,FALSE,FALSE 0,0,0,0,22,493,0,0.00952381,55.5696756,0,Nov,4,1,9,8,Returning_Visitor,TRUE,TRUE 1,0,0,0,25,293.4166667,0,0.013461538,0,0,Nov,3,2,3,10,Returning_Visitor,TRUE,FALSE 13,123.875,0,0,87,2903.764417,0.00625,0.015280206,1.773369333,0,Dec,2,2,3,2,Returning_Visitor,FALSE,FALSE 7,191,0,0,237,8956.097502,0.003094233,0.012109705,0,0,Nov,2,2,1,2,Returning_Visitor,FALSE,FALSE 0,0,0,0,13,182.25,0.046153846,0.076923077,0,0,Dec,2,2,1,1,Returning_Visitor,FALSE,FALSE 1,206,2,69.5,65,1140.791667,0,0.020707071,6.641911419,0,Nov,2,2,4,2,Returning_Visitor,FALSE,TRUE 0,0,2,8,27,723.7666667,0.011494253,0.016387521,0,0,Nov,1,1,1,2,Returning_Visitor,TRUE,FALSE 0,0,1,0,10,1584.25,0.02,0.05,0,0,Dec,2,2,1,8,Returning_Visitor,FALSE,FALSE 0,0,0,0,23,723,0.015873016,0.026077098,0,0,Dec,3,2,2,13,Returning_Visitor,FALSE,FALSE 5,262.1212121,1,0,117,7468.860173,0.012900188,0.020948132,3.014025584,0,Nov,3,2,1,2,Returning_Visitor,FALSE,FALSE 1,17.75,0,0,22,610.975,0.031818182,0.072727273,0,0,Nov,1,1,1,2,Returning_Visitor,FALSE,FALSE 2,21,0,0,11,276.5,0,0.015384615,0,0,Nov,2,2,7,2,Returning_Visitor,FALSE,FALSE 0,0,0,0,6,47.25,0,0.033333333,0,0,Dec,2,5,1,1,Returning_Visitor,FALSE,FALSE 0,0,0,0,1,0.5,0,0.066666667,0,0,Dec,2,2,4,13,Returning_Visitor,FALSE,FALSE 0,0,0,0,11,431.1666667,0,0.004545455,0,0,Nov,1,1,1,2,Returning_Visitor,FALSE,FALSE 0,0,2,19.5,48,1249.75,0.008333333,0.02046131,0,0,Dec,2,2,1,2,Returning_Visitor,FALSE,FALSE 0,0,0,0,16,242.75,0,0.004910714,0,0,Nov,1,8,2,11,Returning_Visitor,TRUE,FALSE 0,0,0,0,22,366.75,0.018181818,0.040909091,0,0,Nov,2,2,6,1,Returning_Visitor,FALSE,FALSE 0,0,0,0,7,182.5,0.085714286,0.076190476,0,0,Nov,3,2,3,13,Returning_Visitor,FALSE,FALSE 11,88.375,1,0,81,3230.546573,0,0.017862319,15.38432962,0,Nov,1,1,7,2,Returning_Visitor,FALSE,TRUE 0,0,0,0,19,1195.375,0.004210526,0.036090226,0,0,Nov,2,2,4,13,Returning_Visitor,FALSE,FALSE 3,69.8,0,0,81,7514.790476,0.003821138,0.021883469,0,0,Nov,2,2,1,2,Returning_Visitor,FALSE,FALSE 10,720.5,2,88.725,220,10580.34479,0.004767742,0.022140924,0,0,Nov,2,2,1,1,Returning_Visitor,FALSE,FALSE 5,112.25,0,0,55,1170.711048,0.006779661,0.004699538,0,0,Nov,3,2,1,2,New_Visitor,TRUE,FALSE 3,50,5,248.75,39,1034.3125,0.013043478,0.017826087,0,0,Dec,1,1,1,2,Returning_Visitor,FALSE,FALSE 2,40.41666667,0,0,33,1477.734676,0,0.018970588,9.694154066,0,Dec,2,2,4,1,Returning_Visitor,TRUE,FALSE 0,0,2,18,1,0,0.066666667,0.133333333,0,0,Nov,1,1,1,2,Returning_Visitor,FALSE,FALSE 11,449.75,0,0,52,1786.109649,0,0.017118644,40.65671234,0,Nov,1,1,1,2,Returning_Visitor,FALSE,FALSE 0,0,0,0,2,1787.5,0.1,0.15,0,0,Nov,3,2,3,13,Returning_Visitor,FALSE,FALSE 0,0,0,0,1,0,0.2,0.2,0,0,Dec,8,13,9,20,Other,FALSE,FALSE 8,117.5,0,0,8,104.5,0,0.016666667,0,0,Nov,1,1,2,2,Returning_Visitor,TRUE,TRUE 4,107.75,0,0,15,369.05,0.005555556,0.029365079,0,0,Nov,3,2,8,1,Returning_Visitor,FALSE,FALSE 2,98,0,0,29,1407.375,0,0.006666667,39.59436227,0,Dec,2,2,4,1,Returning_Visitor,TRUE,TRUE 5,172.8492647,0,0,33,2002.814281,0,0.029566569,18.13388282,0,Nov,2,2,1,11,Returning_Visitor,FALSE,FALSE 0,0,2,75.5,109,4008.578283,0.003003003,0.01899841,0,0,Nov,2,2,1,2,Returning_Visitor,FALSE,TRUE 0,0,2,183,16,866.4166667,0,0.004444444,0,0,Nov,4,1,3,3,Returning_Visitor,TRUE,FALSE 0,0,0,0,9,436,0,0.022222222,0,0,Nov,2,2,3,2,Returning_Visitor,FALSE,FALSE 0,0,0,0,24,381.25,0.0125,0.0625,0,0,Nov,2,2,1,11,New_Visitor,TRUE,FALSE 1,18.25,0,0,82,2796.316667,0.005185185,0.019584736,0,0,Nov,2,2,1,6,Returning_Visitor,FALSE,FALSE 0,0,0,0,222,8068.742154,0.008325792,0.02329814,4.789722625,0,Nov,2,2,2,1,Returning_Visitor,TRUE,FALSE 0,0,0,0,8,160.25,0.0125,0.075,0,0,Nov,3,2,1,6,Returning_Visitor,FALSE,FALSE 2,229.5,0,0,31,1909.232143,0.0125,0.034375,17.15249937,0,Dec,2,2,3,8,Returning_Visitor,FALSE,TRUE 9,186.5,3,138.75,76,2002.230612,0.0025,0.023333333,0,0,Nov,2,2,1,1,Returning_Visitor,FALSE,FALSE 0,0,0,0,15,1677.178571,0.026666667,0.060444444,0,0,Nov,3,2,4,2,Returning_Visitor,TRUE,FALSE 5,98.35,0,0,279,9485.374568,0.003113824,0.018561894,0.651781814,0,Nov,2,2,1,2,Returning_Visitor,TRUE,FALSE 9,162.4911765,0,0,32,1162.797222,0,0.020175439,47.99566725,0,Nov,1,2,1,8,Returning_Visitor,FALSE,TRUE 16,164.925,5,66.875,64,1899.598899,0.010964912,0.029949875,83.78651166,0,Nov,3,2,1,3,Returning_Visitor,TRUE,FALSE 1,28.5,0,0,21,1089.625,0.01,0.033333333,0,0,Nov,2,2,1,20,Returning_Visitor,FALSE,FALSE 3,171.75,3,264.1,49,1435.772114,0.01509434,0.013579188,0,0,Nov,1,1,1,2,Returning_Visitor,FALSE,FALSE 18,514.075,4,67.5,178,6630.479365,0.003645833,0.010805067,0,0,Nov,3,2,1,8,Returning_Visitor,FALSE,TRUE 1,12,1,11,74,6194.852381,0.015540541,0.032152607,0,0,Nov,2,2,9,13,Returning_Visitor,FALSE,FALSE 6,172.125,0,0,122,2767.366071,0.000533333,0.004071111,39.75249992,0,Dec,4,1,1,2,Returning_Visitor,FALSE,FALSE 0,0,2,148.5,72,2271.553571,0.002702703,0.017905405,0,0,Dec,2,2,1,2,Returning_Visitor,FALSE,FALSE 0,0,0,0,5,325.1666667,0,0.056,0,0,Nov,1,1,3,3,Returning_Visitor,FALSE,FALSE 4,30.75,0,0,47,1477.394841,0.004166667,0.021527778,0,0,Nov,1,5,1,2,Returning_Visitor,FALSE,FALSE 0,0,0,0,2,56.5,0,0.05,0,0,Nov,8,2,1,3,Returning_Visitor,FALSE,FALSE 1,15,0,0,18,343.8333333,0,0.0125,38.3399435,0,Dec,2,13,9,20,New_Visitor,FALSE,TRUE 2,255,0,0,10,173.4333333,0.03,0.025714286,0,0,Dec,3,2,1,1,Returning_Visitor,TRUE,FALSE 0,0,0,0,47,1697.058333,0.004347826,0.019565217,1.990788672,0,Dec,2,2,7,1,Returning_Visitor,FALSE,FALSE 9,344,0,0,22,1860.25,0,0.013095238,12.94438665,0,Dec,2,2,7,1,Returning_Visitor,FALSE,TRUE 1,25.5,0,0,12,122.6666667,0,0.014285714,0,0,Nov,1,1,1,8,New_Visitor,TRUE,FALSE 7,127.6666667,0,0,12,107,0,0.033333333,0,0,Nov,1,1,4,2,New_Visitor,TRUE,FALSE 0,0,0,0,15,267.7333333,0.022857143,0.040606061,0,0,Nov,3,2,3,11,Returning_Visitor,TRUE,FALSE 3,22,0,0,10,53.83333333,0.025,0.0375,0,0,Dec,4,2,3,10,Returning_Visitor,TRUE,FALSE 1,154.5,0,0,9,308.3333333,0,0.0125,0,0,Nov,1,2,2,10,Returning_Visitor,TRUE,FALSE 0,0,0,0,2,0,0.2,0.2,0,0,Dec,1,1,2,1,Returning_Visitor,TRUE,FALSE 1,5.5,2,28,20,929.3333333,0,0.015333333,0,0,Dec,2,2,6,2,Returning_Visitor,FALSE,FALSE 4,181.5,0,0,34,2265.571429,0,0.018018018,28.11367347,0,Nov,4,1,3,1,Returning_Visitor,TRUE,TRUE 0,0,0,0,12,168,0.041666667,0.063888889,0,0,Nov,2,2,1,1,Returning_Visitor,FALSE,FALSE 0,0,0,0,1,0,0.2,0.2,0,0,Nov,2,2,1,6,New_Visitor,FALSE,FALSE 0,0,0,0,24,663,0.0375,0.066666667,0,0,Dec,2,2,1,1,Returning_Visitor,FALSE,FALSE 1,0,0,0,50,1009.430952,0.006,0.017238095,4.803305732,0,Nov,1,1,1,10,Returning_Visitor,TRUE,TRUE 0,0,0,0,1,0,0.2,0.2,0,0,Nov,3,2,1,1,Returning_Visitor,FALSE,FALSE 8,112,0,0,27,601.0864146,0.012643678,0.019984651,0,0,Dec,1,1,2,8,Returning_Visitor,FALSE,FALSE 0,0,0,0,6,529.5,0,0.04,44.443614,0,Dec,2,2,2,1,Returning_Visitor,FALSE,TRUE 0,0,0,0,10,640.875,0,0.011111111,18.85445822,0,Nov,2,2,1,2,Returning_Visitor,FALSE,TRUE 3,90.5,0,0,15,369.5,0,0.025,0,0,Nov,3,2,2,2,New_Visitor,TRUE,FALSE 1,57.5,0,0,32,534.2638889,0,0.011111111,87.74978701,0,Dec,1,2,1,8,Returning_Visitor,TRUE,TRUE 0,0,0,0,12,305.4,0,0.019444444,0,0,Dec,3,2,1,11,Returning_Visitor,FALSE,FALSE 2,18.6,0,0,85,2834.280117,0.004883721,0.013388945,37.42337649,0,Nov,2,2,1,1,Returning_Visitor,TRUE,FALSE 4,64.5,0,0,17,145.5,0.0125,0.025,0,0,Nov,2,2,4,8,Returning_Visitor,FALSE,FALSE 6,78.75,0,0,24,465.7321429,0,0.015384615,0,0,Dec,4,2,1,2,New_Visitor,FALSE,FALSE 0,0,0,0,29,1073.295779,0.013793103,0.017471264,0,0,Dec,1,1,1,3,Returning_Visitor,TRUE,FALSE 2,20.75,0,0,22,868.6785714,0.002777778,0.019722222,62.51664293,0,Dec,2,2,1,2,Returning_Visitor,FALSE,FALSE 0,0,0,0,4,39.25,0,0.05,0,0,Nov,4,2,1,1,Returning_Visitor,FALSE,FALSE 0,0,0,0,4,351.5,0.05,0.1,0,0,Dec,2,2,5,20,Returning_Visitor,FALSE,FALSE 3,138.5,6,292,26,807.0833333,0.002083333,0.011458333,0,0,Dec,3,2,1,8,Returning_Visitor,TRUE,FALSE 3,91.5,4,333.8333333,132,3041.676452,0.011764706,0.030932814,0,0,Nov,2,2,1,2,Returning_Visitor,FALSE,TRUE 8,163.375,0,0,18,403.4166667,0,0.005,0,0,Dec,2,2,9,2,New_Visitor,FALSE,FALSE 2,16,0,0,10,399,0.015384615,0.046153846,0,0,Nov,1,1,1,8,Returning_Visitor,FALSE,FALSE 7,93.5,0,0,101,2186.145833,0,0.004807692,32.13922216,0,Nov,2,2,1,1,Returning_Visitor,FALSE,FALSE 6,461,0,0,4,111,0,0.011111111,0,0,Dec,3,3,3,8,New_Visitor,FALSE,FALSE 0,0,1,6,39,720.15,0.005128205,0.017948718,0,0,Nov,2,4,7,2,Returning_Visitor,FALSE,FALSE 1,19,1,14,74,2408.579762,0,0.016621622,0,0,Nov,2,2,1,2,Returning_Visitor,FALSE,FALSE 3,13.33333333,4,68.5,72,1602.35,0.002747253,0.038988095,0,0,Nov,2,2,3,2,Returning_Visitor,FALSE,FALSE 0,0,0,0,5,150.25,0.04,0.12,0,0,Nov,1,1,3,3,Returning_Visitor,FALSE,FALSE 4,146,0,0,14,879.5,0,0.0125,0,0,Dec,2,5,4,2,New_Visitor,TRUE,FALSE 0,0,0,0,37,1234.525,0,0.020720721,0,0,Dec,1,2,5,3,Returning_Visitor,FALSE,FALSE 4,100,0,0,18,954.9083333,0,0.008521303,0,0,Nov,1,2,8,2,Returning_Visitor,TRUE,FALSE 0,0,2,13,45,3740.291667,0.012765957,0.041884498,0,0,Dec,2,10,1,1,Returning_Visitor,TRUE,FALSE 0,0,0,0,36,759.0833333,0.008333333,0.025925926,0,0,Nov,2,2,5,2,Returning_Visitor,TRUE,FALSE 2,76,0,0,33,831.1111111,0,0.0125,77.97458009,0,Dec,2,2,4,2,Returning_Visitor,TRUE,TRUE 0,0,0,0,6,168,0,0.011111111,0,0,Nov,1,1,1,2,New_Visitor,TRUE,FALSE 4,91,0,0,20,537.625,0,0.01,0,0,Dec,8,13,9,20,Other,FALSE,FALSE 1,37.5,0,0,5,65.5,0.033333333,0.066666667,0,0,Nov,1,1,3,3,New_Visitor,FALSE,FALSE 0,0,0,0,20,333,0.021052632,0.036842105,0,0,Dec,2,2,1,1,Returning_Visitor,FALSE,FALSE 0,0,0,0,1,0,0.2,0.2,0,0,Dec,1,1,1,1,Returning_Visitor,TRUE,FALSE 0,0,0,0,57,1234.5,0.001724138,0.020689655,0,0,Nov,2,7,2,2,Returning_Visitor,TRUE,FALSE 1,8,0,0,32,690.2541667,0.012121212,0.029834711,10.05608631,0,Dec,1,1,3,2,Returning_Visitor,TRUE,FALSE 0,0,0,0,7,152.75,0.014285714,0.047619048,0,0,Dec,3,2,3,2,New_Visitor,FALSE,FALSE 0,0,0,0,9,339.5,0,0.013888889,0,0,Dec,1,2,3,2,New_Visitor,TRUE,FALSE 2,37.33333333,1,5,30,797.7619048,0.012121212,0.034343434,0,0,Nov,1,1,1,2,Returning_Visitor,FALSE,FALSE 1,0,3,82,120,2895.129762,0.001626016,0.005813008,16.96317099,0,Nov,1,2,1,11,Returning_Visitor,FALSE,TRUE 1,6,0,0,44,2011.305556,0,0.012301587,0,0,Nov,2,2,1,3,Returning_Visitor,FALSE,FALSE 0,0,0,0,5,614.75,0,0.073333333,0,0,Dec,2,10,2,2,Returning_Visitor,FALSE,FALSE 1,89,0,0,11,417.75,0.01,0.03,0,0,Dec,2,2,1,2,New_Visitor,FALSE,FALSE 0,0,0,0,7,649.5,0.028571429,0.057142857,0,0,Dec,2,2,4,2,New_Visitor,FALSE,TRUE 2,26,1,6,103,3400.248897,0.014692557,0.025598706,0,0,Nov,3,2,9,13,Returning_Visitor,TRUE,FALSE 0,0,0,0,2,22,0.05,0.066666667,0,0,Dec,3,2,7,20,Returning_Visitor,FALSE,FALSE 2,42.75,0,0,31,2526.483333,0.027272727,0.055555556,40.1844879,0,Dec,2,4,6,1,Returning_Visitor,FALSE,FALSE 0,0,0,0,2,66.5,0.066666667,0.166666667,0,0,Nov,3,2,3,13,Returning_Visitor,FALSE,FALSE 0,0,0,0,8,185,0.025,0.033333333,0,0,Dec,3,2,3,10,Returning_Visitor,FALSE,FALSE 2,202.8333333,0,0,11,613.8958333,0,0.030769231,3.728742569,0,Dec,2,2,1,2,Returning_Visitor,FALSE,FALSE 0,0,0,0,29,1237,0.033333333,0.039761905,0,0,Nov,3,2,1,13,Returning_Visitor,FALSE,FALSE 3,122,0,0,34,651.8666667,0,0.011764706,0,0,Nov,3,3,2,11,New_Visitor,FALSE,FALSE 0,0,1,98,14,491.375,0,0.011904762,0,0,Nov,1,1,1,2,Returning_Visitor,FALSE,FALSE 0,0,0,0,20,469.9761905,0.02,0.064,0,0,Nov,2,2,1,2,Returning_Visitor,FALSE,FALSE 2,27.25,0,0,9,219.25,0,0.033333333,75.50833257,0,Dec,2,2,7,2,Returning_Visitor,FALSE,TRUE 0,0,0,0,21,607.5892857,0.028571429,0.051322751,0,0,Dec,3,2,1,2,Returning_Visitor,TRUE,FALSE 0,0,0,0,1,0,0.2,0.2,0,0,Nov,1,1,5,2,Returning_Visitor,FALSE,FALSE 6,213.5,0,0,11,303.5,0,0.016666667,87.9029606,0,Dec,8,13,9,20,Other,FALSE,TRUE 0,0,0,0,4,107.25,0,0.025,0,0,Dec,2,2,1,3,Returning_Visitor,FALSE,FALSE 3,86.33333333,0,0,14,1256.333333,0,0.023076923,0,0,Nov,1,1,4,2,Returning_Visitor,FALSE,FALSE 0,0,0,0,65,1624.941667,0,0.0203125,0,0,Nov,2,6,2,11,Returning_Visitor,TRUE,FALSE 1,45.5,0,0,10,333.25,0,0.018181818,9.905143745,0,Dec,2,2,1,2,New_Visitor,TRUE,TRUE 1,4,0,0,47,2386.066667,0.004444444,0.030864198,0,0,Nov,2,2,1,1,Returning_Visitor,FALSE,FALSE 0,0,0,0,73,3324.597222,0.017123288,0.040273973,0,0,Nov,2,2,1,13,Returning_Visitor,FALSE,TRUE 0,0,0,0,5,59.25,0.04,0.1,0,0,Nov,2,6,2,2,Returning_Visitor,FALSE,FALSE 0,0,0,0,2,29,0,0.05,0,0,Nov,1,1,3,3,Returning_Visitor,FALSE,FALSE 5,86.08333333,0,0,11,396.5,0,0.007692308,0,0,Nov,3,2,3,8,New_Visitor,TRUE,FALSE 2,11,0,0,14,249.6666667,0,0.033333333,0,0,Nov,4,2,1,3,Returning_Visitor,FALSE,TRUE 13,225.5386364,8,368.1547619,111,3874.240873,0.006494118,0.021731397,6.245397811,0,Nov,2,2,9,2,Returning_Visitor,FALSE,FALSE 6,57.25,0,0,36,1930.410714,0.0125,0.018083333,21.39566828,0,Nov,1,1,1,2,Returning_Visitor,FALSE,TRUE 5,29,0,0,94,3554.64902,0.002020202,0.013183349,0,0,Dec,2,2,4,2,Returning_Visitor,FALSE,FALSE 1,64.5,0,0,36,766.2166667,0,0.001851852,0,0,Dec,3,2,3,2,New_Visitor,FALSE,FALSE 0,0,0,0,1,0,0.2,0.2,0,0,Nov,2,2,2,3,Returning_Visitor,FALSE,FALSE 0,0,0,0,1,0,0.2,0.2,0,0,Dec,2,2,5,1,Returning_Visitor,FALSE,FALSE 4,101.75,2,65.5,37,1307.472222,0.011149826,0.027235772,0,0,Nov,3,2,3,2,Returning_Visitor,FALSE,FALSE 0,0,2,199,30,661.1388889,0.00625,0.027083333,0,0,Nov,2,2,3,10,Returning_Visitor,FALSE,TRUE 6,79.66666667,4,220,112,3556.346795,0.008403361,0.021415475,0,0,Dec,2,4,1,2,Returning_Visitor,FALSE,FALSE 5,66.5,0,0,29,1141.175,0.010416667,0.025902778,0,0,Nov,3,2,1,2,Returning_Visitor,FALSE,FALSE 2,22.25,0,0,27,2958.216667,0.007692308,0.033230985,7.868980948,0,Nov,2,2,6,2,Returning_Visitor,FALSE,TRUE 0,0,0,0,76,1553.269048,0,0.009586466,0,0,Nov,2,4,1,2,Returning_Visitor,FALSE,FALSE 0,0,0,0,1,39.5,0,0.1,0,0,Dec,1,2,1,2,Returning_Visitor,FALSE,FALSE 0,0,0,0,2,0,0.2,0.2,0,0,Dec,3,2,4,3,Returning_Visitor,FALSE,FALSE 0,0,0,0,8,221.5,0,0.00625,0,0,Dec,1,1,1,2,New_Visitor,FALSE,FALSE 1,6,0,0,15,579,0,0.0125,63.93506419,0,Dec,1,1,4,2,New_Visitor,FALSE,TRUE 3,44.5,4,376.5,49,1222.25,0.005185185,0.021604938,0,0,Dec,2,2,2,2,Returning_Visitor,TRUE,FALSE 0,0,0,0,14,1947.5,0,0.021428571,0,0,Nov,1,1,1,3,Returning_Visitor,FALSE,FALSE 0,0,0,0,12,166.0833333,0.016666667,0.025,0,0,Nov,1,1,1,2,Returning_Visitor,TRUE,FALSE 14,2137.112745,0,0,53,4223.409838,0.00877193,0.017126354,8.886648533,0,Nov,3,2,2,2,Returning_Visitor,FALSE,FALSE 5,107.6666667,10,505.1666667,281,14988.59151,0.008498915,0.029697173,0,0,Dec,2,2,1,2,Returning_Visitor,FALSE,FALSE 0,0,4,218.25,25,402.0430403,0.007142857,0.02202381,0,0,Nov,1,1,1,10,Returning_Visitor,FALSE,TRUE 0,0,0,0,23,935.9,0.004347826,0.024637681,0,0,Dec,2,2,1,2,New_Visitor,FALSE,FALSE 3,136.5,0,0,10,412.0833333,0,0.016666667,0,0,Dec,3,3,3,2,New_Visitor,FALSE,FALSE 0,0,0,0,13,215.952381,0,0.018181818,0,0,Nov,3,2,6,2,New_Visitor,FALSE,FALSE 0,0,0,0,14,275.1666667,0,0.021428571,0,0,Nov,4,2,6,3,Returning_Visitor,FALSE,FALSE 3,41.5,0,0,5,68.75,0,0.0375,0,0,Nov,1,2,1,8,New_Visitor,FALSE,FALSE 0,0,0,0,21,455.0833333,0,0.010526316,14.13268846,0,Dec,4,1,1,2,New_Visitor,FALSE,TRUE 1,44.5,0,0,52,1300.041667,0.001960784,0.023193277,6.028854102,0,Nov,2,2,2,2,Returning_Visitor,TRUE,TRUE 1,310,3,407,176,7767.32556,0.006045198,0.019212627,8.845689534,0,Nov,2,2,2,1,Returning_Visitor,TRUE,FALSE 0,0,0,0,5,199.5,0,0.01,0,0,Nov,4,2,1,3,Returning_Visitor,FALSE,FALSE 0,0,0,0,17,701.8032468,0,0.011111111,61.07207396,0,Nov,2,2,5,6,New_Visitor,FALSE,TRUE 1,32.25,0,0,18,434.5833333,0.011764706,0.011764706,0,0,Dec,3,2,1,2,Returning_Visitor,FALSE,FALSE 3,35.83333333,0,0,9,92.33333333,0.059259259,0.088888889,0,0,Nov,1,1,8,1,Returning_Visitor,FALSE,FALSE 0,0,0,0,23,1919.55,0.008695652,0.031884058,4.280302196,0,Nov,2,2,4,1,Returning_Visitor,FALSE,FALSE 0,0,0,0,38,1251.932143,0,0.011302211,0,0,Nov,1,1,1,1,Returning_Visitor,FALSE,FALSE 7,304.5,3,56.5,69,2350.905769,0.012088889,0.018292063,0,0,Dec,3,2,2,2,Returning_Visitor,FALSE,FALSE 0,0,0,0,13,210.5,0.030769231,0.046153846,0,0,Dec,2,2,1,1,Returning_Visitor,FALSE,FALSE 0,0,0,0,2,124.5,0,0.1,0,0,Dec,1,1,1,2,Returning_Visitor,FALSE,FALSE 0,0,0,0,6,12,0.033333333,0.133333333,0,0,Dec,2,2,1,3,Returning_Visitor,FALSE,FALSE 0,0,0,0,1,0,0.2,0.2,0,0,Nov,2,2,1,1,Returning_Visitor,FALSE,FALSE 0,0,0,0,9,171.5,0,0.022222222,0,0,Nov,2,2,2,13,Returning_Visitor,FALSE,FALSE 6,647.3333333,6,191.5833333,175,10886.87692,0.005110897,0.018243793,16.64099403,0,Nov,2,2,1,2,Returning_Visitor,FALSE,TRUE 2,51,3,68.75,227,12460.67006,0.007569141,0.014819782,0,0,Nov,2,2,1,2,Returning_Visitor,FALSE,FALSE 0,0,0,0,87,4403.382777,0.002941176,0.011552302,0,0,Dec,2,2,6,2,Returning_Visitor,FALSE,FALSE 5,96.75,0,0,113,2151.097436,0,0.005172414,0,0,Nov,2,2,2,11,New_Visitor,FALSE,FALSE 2,54.5,4,29.5,11,1055.75,0,0.026666667,0,0,Dec,2,2,1,2,New_Visitor,FALSE,FALSE 7,229.75,0,0,33,760.6190476,0,0.011111111,51.86660251,0,Nov,2,2,3,2,New_Visitor,FALSE,TRUE 1,58.5,1,0,11,1012.166667,0,0.041666667,62.86831138,0,Nov,2,2,1,1,Returning_Visitor,FALSE,TRUE 3,17.5,0,0,15,810.3333333,0,0.020512821,0,0,Nov,2,2,3,11,Returning_Visitor,TRUE,FALSE 0,0,2,139.75,141,4567.942696,0.002944942,0.02119383,0,0,Dec,2,5,3,2,Returning_Visitor,FALSE,FALSE 0,0,0,0,14,502.9166667,0,0.021428571,0,0,Dec,1,1,2,1,Returning_Visitor,FALSE,FALSE 2,37.5,0,0,3,45.25,0,0.02,0,0,Dec,4,1,3,3,Returning_Visitor,TRUE,FALSE 0,0,0,0,15,1251.316667,0,0.03452381,0,0,Nov,3,2,1,2,Returning_Visitor,TRUE,TRUE 0,0,0,0,1,0,0.2,0.2,0,0,Nov,1,1,3,3,Returning_Visitor,FALSE,FALSE 2,63.5,0,0,12,222.5,0,0.007142857,0,0,Dec,2,10,3,2,New_Visitor,TRUE,FALSE 0,0,0,0,2,0,0.2,0.2,0,0,Nov,1,8,3,1,Returning_Visitor,TRUE,FALSE 3,43.625,1,16,109,3624.056349,0.009489489,0.01406394,11.32556227,0,Nov,1,2,1,2,Returning_Visitor,FALSE,TRUE 0,0,0,0,5,155.25,0,0.12,0,0,Nov,1,1,1,1,Returning_Visitor,FALSE,FALSE 1,5,0,0,12,900.5,0.011538462,0.026923077,0,0,Nov,2,2,4,6,Returning_Visitor,FALSE,FALSE 3,281.9166667,2,6,83,2616.905952,0.002409639,0.033978772,0,0,Nov,2,2,3,2,Returning_Visitor,TRUE,TRUE 5,157.5,0,0,15,138.75,0.0125,0.025,0,0,Nov,1,1,1,2,New_Visitor,FALSE,FALSE 1,9,0,0,95,4334.815476,0.005531915,0.03962766,0,0,Dec,2,2,1,1,Returning_Visitor,FALSE,FALSE 2,57,0,0,15,772.9166667,0.017647059,0.045098039,0,0,Nov,3,2,3,13,Returning_Visitor,FALSE,FALSE 0,0,0,0,12,482.5,0.02,0.04,0,0,Nov,1,2,1,10,Returning_Visitor,FALSE,FALSE 3,15,0,0,12,1138.25,0.030769231,0.048717949,0,0,Dec,1,1,1,3,Returning_Visitor,FALSE,FALSE 0,0,0,0,3,61.5,0.066666667,0.1,0,0,Dec,3,2,3,8,New_Visitor,FALSE,FALSE 0,0,0,0,10,82.5,0,0.03,0,0,Nov,2,2,9,11,Returning_Visitor,FALSE,FALSE 0,0,0,0,3,36,0.066666667,0.1,0,0,Nov,2,2,3,2,Returning_Visitor,FALSE,FALSE 0,0,0,0,17,231,0.011764706,0.029411765,0,0,Nov,2,10,7,1,Returning_Visitor,FALSE,FALSE 0,0,0,0,3,0,0.2,0.2,0,0,Nov,2,2,1,13,Returning_Visitor,FALSE,FALSE 9,211.5,4,222,30,2266.270833,0.005263158,0.029874687,0,0,Nov,3,2,1,2,Returning_Visitor,FALSE,FALSE 0,0,0,0,42,1892.005556,0.02195122,0.040859466,0,0,Nov,1,1,1,13,Returning_Visitor,TRUE,TRUE 5,115.9666667,1,0,9,181.3,0,0.016666667,0,0,Dec,1,1,1,8,New_Visitor,FALSE,FALSE 3,14,0,0,36,947.8833333,0,0.006078431,0,0,Dec,2,2,1,10,Returning_Visitor,FALSE,FALSE 0,0,0,0,6,579,0,0.033333333,0,0,Dec,1,1,7,3,Returning_Visitor,TRUE,FALSE 5,187.875,1,32.5,34,2719.525974,0.016447876,0.029992691,3.222261703,0,Nov,3,2,3,2,Returning_Visitor,FALSE,FALSE 1,48.5,0,0,35,668.1,0.007843137,0.019117647,36.95500632,0,Nov,2,4,1,3,Returning_Visitor,FALSE,TRUE 2,50.5,1,7,27,916.9222222,0,0.009677419,0,0,Nov,1,2,1,8,New_Visitor,TRUE,TRUE 2,82,0,0,4,57,0,0.033333333,0,0,Dec,2,4,3,2,New_Visitor,FALSE,FALSE 0,0,2,40,152,5617.092505,0.003594771,0.02743431,0,0,Nov,2,2,4,2,Returning_Visitor,FALSE,FALSE 4,52.25,0,0,3,28,0,0.025,0,0,Nov,2,2,1,6,New_Visitor,FALSE,FALSE 0,0,0,0,9,38.75,0.12,0.120833333,0,0,Nov,3,2,1,13,Returning_Visitor,FALSE,FALSE 5,50,0,0,106,3095.200725,0.012612613,0.034684685,0,0,Dec,2,2,3,2,Returning_Visitor,FALSE,FALSE 0,0,0,0,2,70.5,0,0.05,0,0,Dec,3,2,7,2,Other,FALSE,FALSE 5,266,0,0,29,730.35,0.006060606,0.026262626,32.91300914,0,Nov,4,1,8,1,Returning_Visitor,FALSE,TRUE 9,255.4166667,2,94.16666667,22,612.8194444,0.015018315,0.025760906,22.31126324,0,Nov,3,2,4,2,Returning_Visitor,TRUE,TRUE 3,49.5,0,0,27,702.6431624,0.015517241,0.027155172,0,0,Nov,3,2,3,3,Returning_Visitor,FALSE,FALSE 0,0,0,0,12,461.1083333,0,0.007692308,45.05554866,0,Nov,3,2,1,1,Returning_Visitor,FALSE,FALSE 4,111,0,0,9,155,0,0.009090909,0,0,Dec,1,1,4,2,Returning_Visitor,FALSE,FALSE 5,76,0,0,20,535.25,0,0.015789474,0,0,Dec,1,1,1,3,Returning_Visitor,TRUE,FALSE 0,0,0,0,5,513.25,0,0.013333333,0,0,Nov,3,2,7,6,Returning_Visitor,FALSE,FALSE 0,0,0,0,12,115.25,0.066666667,0.108333333,0,0,Nov,2,2,1,1,Returning_Visitor,FALSE,FALSE 0,0,0,0,21,642.4642857,0.028571429,0.057142857,0,0,Dec,2,2,6,2,Returning_Visitor,TRUE,FALSE 11,190.3125,2,43.5,22,1259.872817,0.013333333,0.025185185,0,0,Nov,2,2,3,2,Returning_Visitor,FALSE,FALSE 3,28.125,0,0,50,1032.008333,0.008,0.014133333,0,0,Nov,2,2,1,2,Returning_Visitor,TRUE,FALSE 0,0,0,0,2,94,0,0.1,0,0,Nov,2,4,2,8,Returning_Visitor,FALSE,FALSE 0,0,0,0,1,0,0.2,0.2,0,0,Nov,2,5,2,1,Other,FALSE,FALSE 0,0,0,0,11,153.75,0.036363636,0.072727273,0,0,Nov,3,2,2,1,Returning_Visitor,FALSE,FALSE 0,0,0,0,19,380.9464286,0,0.013947368,0,0,Nov,3,2,4,2,Returning_Visitor,FALSE,TRUE 7,45.41666667,0,0,35,653.8595238,0,0.027350427,19.78456264,0,Dec,2,2,1,8,Returning_Visitor,FALSE,FALSE 6,254,0,0,48,1740.525631,0,0.004876543,0,0,Nov,1,1,3,2,New_Visitor,FALSE,TRUE 6,453.6,2,12,73,1889.833333,0.01025641,0.018883894,0,0,Dec,3,2,4,13,Returning_Visitor,FALSE,FALSE 0,0,0,0,7,984.5,0.042857143,0.071428571,0,0,Nov,2,2,2,3,Returning_Visitor,FALSE,FALSE 5,50,0,0,234,5246.435176,0.002616034,0.011613896,21.03233592,0,Nov,2,2,3,3,Returning_Visitor,FALSE,TRUE 0,0,0,0,6,81.75,0,0.066666667,0,0,Nov,2,2,1,3,Returning_Visitor,FALSE,TRUE 11,102.1666667,1,20,25,1222.625,0,0.035,0,0,Dec,2,2,1,2,Returning_Visitor,FALSE,FALSE 1,9,0,0,33,1597.10609,0,0.008754209,5.774785164,0,Dec,2,2,3,2,Returning_Visitor,FALSE,TRUE 0,0,0,0,1,0,0.2,0.2,0,0,Dec,1,1,4,1,Returning_Visitor,TRUE,FALSE 0,0,0,0,47,1204.664683,0,0.015,0,0,Dec,2,4,1,2,Returning_Visitor,TRUE,FALSE 0,0,1,30.5,24,1689.134524,0,0.008,0,0,Nov,2,2,9,2,Returning_Visitor,FALSE,FALSE 1,15.75,0,0,337,11231.41012,0.002472799,0.013233522,0,0,Nov,2,2,1,2,Returning_Visitor,FALSE,FALSE 2,45.5,0,0,2,317.25,0,0.066666667,0,0,Dec,2,2,1,2,Returning_Visitor,FALSE,FALSE 9,159.9166667,2,47.5,53,1262.488779,0,0.003448276,0,0,Dec,2,2,8,2,New_Visitor,TRUE,FALSE 0,0,0,0,4,160.5,0,0.05,0,0,Nov,1,1,1,3,Returning_Visitor,FALSE,FALSE 0,0,0,0,21,587.9166667,0.023333333,0.031666667,23.33239205,0,Nov,3,2,1,10,Returning_Visitor,FALSE,FALSE 0,0,0,0,4,302,0,0.04,46.06937312,0,Dec,8,13,9,20,Other,FALSE,TRUE 0,0,0,0,14,1324.133333,0.003571429,0.015986395,0,0,Nov,1,1,1,2,Returning_Visitor,TRUE,FALSE 0,0,0,0,15,378.5,0.004444444,0.037777778,0,0,Nov,2,2,1,2,Returning_Visitor,FALSE,FALSE 0,0,0,0,2,78,0,0.05,0,0,Dec,3,2,9,2,Returning_Visitor,FALSE,FALSE 0,0,0,0,1,0,0.2,0.2,0,0,Dec,2,2,1,1,Returning_Visitor,FALSE,FALSE 0,0,0,0,15,365.1666667,0.013333333,0.02,0,0,Nov,2,2,1,13,Returning_Visitor,FALSE,FALSE 5,103.875,0,0,6,81.375,0,0.025,0,0,Dec,1,1,3,2,Returning_Visitor,TRUE,FALSE 0,0,0,0,7,432,0,0.014285714,270.7846931,0,Nov,2,4,1,8,New_Visitor,FALSE,TRUE 1,29.25,1,0,3,67.66666667,0,0.04,0,0,Dec,8,13,9,20,Other,FALSE,FALSE 1,30.5,1,285,2,296,0,0.06,0,0,Dec,2,2,1,8,New_Visitor,TRUE,FALSE 0,0,0,0,18,315.6944444,0,0.002941176,0,0,Nov,1,1,8,8,New_Visitor,FALSE,FALSE 3,40,0,0,5,189.4166667,0,0.016666667,0,0,Dec,1,8,3,11,Returning_Visitor,TRUE,FALSE 0,0,0,0,1,0,0.2,0.2,0,0,Nov,1,1,6,1,Returning_Visitor,TRUE,FALSE 9,188.5638298,0,0,199,7750.537279,0.007911051,0.021772308,3.012181822,0,Nov,3,2,4,13,Returning_Visitor,FALSE,TRUE 2,53.5,0,0,16,681.5,0,0.022916667,0,0,Dec,2,4,1,2,Returning_Visitor,FALSE,FALSE 0,0,0,0,57,4312.225,0.004464286,0.031964286,0,0,Nov,2,2,1,2,Returning_Visitor,FALSE,FALSE 8,122.0416667,0,0,14,338.7916667,0,0.025,0,0,Dec,2,2,7,3,Returning_Visitor,FALSE,FALSE 0,0,4,117.25,96,5286.208333,0.011,0.024119048,0,0,Nov,2,2,1,1,Returning_Visitor,TRUE,FALSE 0,0,0,0,1,0,0.2,0.2,0,0,Dec,2,2,1,1,Returning_Visitor,FALSE,FALSE 1,28.66666667,0,0,148,3486.194444,0.008697765,0.017685455,0,0,Nov,3,2,4,1,Returning_Visitor,FALSE,FALSE 4,314.0833333,2,27,130,6560.00754,0,0.006376263,0,0,Nov,2,2,1,2,Returning_Visitor,FALSE,FALSE 9,152.875,2,54.91666667,85,2971.254167,0.002197802,0.013631606,1.022550578,0,Nov,2,2,3,1,Returning_Visitor,FALSE,TRUE 0,0,0,0,7,2612,0,0.033333333,0,0,Dec,8,13,9,20,Other,FALSE,FALSE 0,0,2,193,102,3085.620238,0.010851648,0.024392801,0,0,Nov,2,2,3,2,Returning_Visitor,TRUE,TRUE 4,105.75,2,152.5,129,4609.626389,0.001550388,0.016137507,0,0,Dec,2,2,6,2,Returning_Visitor,FALSE,FALSE 6,144.6666667,0,0,5,123.6666667,0.033333333,0.013333333,0,0,Nov,1,1,1,8,New_Visitor,TRUE,FALSE 0,0,1,0,41,446.4166667,0,0.027509158,0,0,Nov,2,2,1,2,Returning_Visitor,FALSE,FALSE 0,0,0,0,2,0,0.2,0.2,0,0,Nov,1,1,3,3,Returning_Visitor,FALSE,FALSE 4,72.5,0,0,28,1248.058333,0.0140625,0.024479167,0,0,Nov,2,6,4,2,Returning_Visitor,FALSE,FALSE 2,7,0,0,27,541.2071429,0.008888889,0.029320988,0,0,Nov,1,1,1,2,Returning_Visitor,TRUE,FALSE 5,879.5,0,0,43,2365.429545,0,0.013043478,0,0,Nov,4,1,1,10,Returning_Visitor,FALSE,FALSE 4,152,0,0,2,44.5,0,0.02,0,0,Dec,1,1,2,2,New_Visitor,FALSE,FALSE 1,4,0,0,22,2706.375,0.002083333,0.039814815,0,0,Nov,4,2,1,2,Returning_Visitor,FALSE,FALSE 0,0,0,0,33,783.6666667,0.006060606,0.027272727,0,0,Nov,2,2,1,3,Returning_Visitor,FALSE,FALSE 0,0,0,0,2,13,0,0.1,0,0,Dec,1,1,3,2,Returning_Visitor,TRUE,FALSE 2,10,1,19,24,845.8333333,0.004,0.022,0,0,Dec,1,1,4,3,Returning_Visitor,TRUE,FALSE 12,246.252381,2,56.5,102,4264.419078,0.007783883,0.019834499,0,0,Nov,3,2,1,10,Returning_Visitor,TRUE,FALSE 0,0,0,0,4,163,0.05,0.1,0,0,Dec,2,2,3,11,Returning_Visitor,FALSE,FALSE 4,143.8333333,0,0,43,997.7083333,0.002222222,0.021111111,0,0,Dec,2,5,3,2,Returning_Visitor,FALSE,FALSE 3,86.75,2,51,11,145.6666667,0.007692308,0.023076923,0,0,Dec,1,1,1,2,New_Visitor,FALSE,FALSE 0,0,0,0,12,548.1666667,0.016666667,0.071666667,0,0,Nov,2,2,2,3,Returning_Visitor,FALSE,FALSE 7,135.6666667,2,190.4166667,132,7356.402381,0.004512276,0.017577031,9.193989222,0,Dec,4,1,4,10,Returning_Visitor,FALSE,FALSE 3,183,0,0,190,6375.065753,0.00938918,0.020211611,0,0,Nov,2,2,1,2,Returning_Visitor,FALSE,FALSE 14,515.9821429,0,0,170,7128.75377,0.003370787,0.016081238,4.470297685,0,Nov,2,2,7,1,Returning_Visitor,TRUE,TRUE 7,56.5,0,0,96,4803.82717,0.012794613,0.02630097,0,0,Nov,3,2,3,2,Returning_Visitor,TRUE,FALSE 8,447.125,3,168.375,142,4777.812554,0.005666252,0.019707705,0,0,Nov,3,2,1,1,Returning_Visitor,TRUE,FALSE 12,878.7121212,4,256.5,86,4479.116226,0.005442177,0.026406456,2.356437292,0,Dec,1,1,1,2,Returning_Visitor,FALSE,FALSE 4,65,0,0,26,501.8,0,0.014285714,0,0,Nov,2,2,9,2,New_Visitor,FALSE,TRUE 11,598.8727273,0,0,60,2591.795346,0.009681373,0.019815044,20.32548134,0,Nov,3,2,2,11,Returning_Visitor,FALSE,FALSE 1,42.5,1,12.83333333,104,4054.755952,0.003883495,0.010097087,141.4590538,0,Nov,2,2,1,2,Returning_Visitor,FALSE,TRUE 0,0,0,0,14,471.4166667,0,0.002197802,0,0,Nov,3,2,3,6,New_Visitor,FALSE,FALSE 2,27.375,0,0,39,1157.416667,0.010833333,0.045714286,0,0,Dec,2,2,4,1,Returning_Visitor,TRUE,FALSE 6,165.9487179,2,114,42,2016.115385,0.016,0.028841026,26.65415979,0,Nov,1,1,3,2,Returning_Visitor,FALSE,FALSE 5,140,0,0,21,839.4166667,0.011594203,0.008695652,30.83748589,0,Dec,3,2,3,1,Returning_Visitor,FALSE,TRUE 1,13,5,425,116,2633.0849,0.004481793,0.010929463,0,0,Nov,2,2,1,2,Returning_Visitor,FALSE,TRUE 0,0,0,0,12,190.5,0.090909091,0.109090909,0,0,Dec,1,1,6,1,Returning_Visitor,TRUE,FALSE 0,0,0,0,0,0,0.2,0.2,0,0,Nov,1,1,8,8,Returning_Visitor,FALSE,FALSE 7,349.775,7,145.5,122,7532.511111,0.004615385,0.029612768,0,0,Nov,2,2,6,2,Returning_Visitor,FALSE,FALSE 0,0,0,0,12,76.66666667,0.016666667,0.0375,0,0,Nov,2,2,3,13,Returning_Visitor,FALSE,FALSE 5,218.5833333,0,0,11,370.4583333,0,0.003076923,0,0,Dec,3,7,4,2,Returning_Visitor,FALSE,FALSE 8,233.25,2,9,38,2277.555556,0,0.004761905,0,0,Dec,2,4,1,2,New_Visitor,TRUE,FALSE 1,150.5,0,0,18,635.3333333,0,0.011764706,57.5907585,0,Nov,2,2,1,2,New_Visitor,TRUE,TRUE 14,524.95,2,248.3333333,133,5948.998311,0.005957447,0.008289956,0,0,Dec,1,1,1,10,Returning_Visitor,FALSE,FALSE 0,0,0,0,7,228.5,0.028571429,0.023809524,0,0,Dec,3,2,6,1,Returning_Visitor,FALSE,FALSE 0,0,0,0,37,1089.208333,0,0.017117117,0,0,Dec,4,2,1,2,New_Visitor,FALSE,FALSE 0,0,0,0,3,51,0,0.1,0,0,Nov,2,2,3,2,Other,FALSE,FALSE 0,0,0,0,63,1320.189881,0,0.000468384,0,0,Nov,2,6,1,2,Returning_Visitor,FALSE,FALSE 1,100,0,0,9,246.1666667,0,0.025,0,0,Dec,3,2,8,2,Returning_Visitor,TRUE,FALSE 2,671,0,0,15,1106,0,0.04,0,0,Dec,2,2,4,1,Returning_Visitor,FALSE,FALSE 2,41.5,0,0,10,264.25,0.016666667,0.025,0,0,Nov,1,4,8,8,New_Visitor,TRUE,TRUE 4,76.125,0,0,13,487.4583333,0,0.024444444,0,0,Dec,2,4,8,2,Returning_Visitor,FALSE,FALSE 0,0,0,0,9,1352,0.022222222,0.044444444,0,0,Nov,4,1,7,2,Returning_Visitor,FALSE,FALSE 0,0,0,0,20,676.1666667,0,0.025,0,0,Nov,2,2,3,2,Returning_Visitor,FALSE,FALSE 0,0,0,0,25,644.875,0,0.044,0,0,Nov,2,2,3,2,Returning_Visitor,FALSE,FALSE 0,0,0,0,11,887,0.054545455,0.096969697,0,0,Nov,2,5,3,1,Returning_Visitor,FALSE,FALSE 0,0,0,0,6,161,0.033333333,0.1,0,0,Nov,1,8,1,11,Returning_Visitor,TRUE,FALSE 3,23.16666667,0,0,12,122.225,0,0.014285714,0,0,Dec,2,2,9,2,New_Visitor,TRUE,FALSE 1,153.5,0,0,20,1002.847222,0,0.00952381,41.13433129,0,Nov,2,2,4,2,Returning_Visitor,FALSE,FALSE 1,64.5,0,0,41,961.25,0.0025,0.020714286,0,0,Dec,2,2,1,2,New_Visitor,FALSE,FALSE 6,337,4,693,261,14150.54613,0.00598336,0.026011023,0.951933747,0,Nov,2,2,3,2,Returning_Visitor,FALSE,TRUE 0,0,0,0,4,137.5,0,0.066666667,0,0,Nov,2,2,2,11,Returning_Visitor,FALSE,FALSE 6,161.8333333,5,858.5708333,84,2701.337954,0.010771277,0.016381748,7.477388113,0,Nov,1,2,1,1,Returning_Visitor,FALSE,TRUE 2,79,0,0,7,823,0,0.028571429,0,0,Dec,1,1,3,2,Returning_Visitor,TRUE,FALSE 0,0,0,0,2,41.5,0,0.1,0,0,Dec,2,2,5,1,Returning_Visitor,FALSE,FALSE 7,84.83333333,0,0,57,1897.6875,0.009090909,0.017857143,0,0,Nov,1,2,3,3,Returning_Visitor,FALSE,TRUE 0,0,0,0,11,220.5,0.027272727,0.072727273,0,0,Dec,3,2,7,1,Returning_Visitor,TRUE,FALSE 0,0,0,0,112,2218.717208,0.000223214,0.018229167,0,0,Nov,2,2,1,2,Returning_Visitor,FALSE,FALSE 9,247.05,4,72,10,159.3,0.0125,0.058333333,0,0,Nov,1,1,1,10,Returning_Visitor,FALSE,FALSE 8,160.5,0,0,22,759.75,0.002564103,0.019871795,0,0,Dec,1,1,2,2,Returning_Visitor,FALSE,FALSE 2,213.5,0,0,6,108.9166667,0,0.025,0,0,Dec,1,1,1,2,New_Visitor,TRUE,FALSE 0,0,0,0,6,183.5,0,0.033333333,0,0,Dec,2,2,1,2,New_Visitor,FALSE,FALSE 0,0,0,0,10,146.9166667,0.06,0.085,0,0,Nov,3,2,4,13,Returning_Visitor,TRUE,FALSE 0,0,3,494,164,3269.256933,0.006586826,0.012909664,5.873000789,0,Dec,2,2,1,1,Returning_Visitor,FALSE,FALSE 0,0,0,0,7,444.25,0.028571429,0.021428571,0,0,Nov,3,2,3,3,Returning_Visitor,TRUE,FALSE 3,170,0,0,30,619.5208333,0,0.00625,0,0,Dec,3,2,4,1,Returning_Visitor,TRUE,FALSE 1,27.5,1,376,109,2925.677607,0.005504587,0.0156734,0,0,Nov,1,2,3,10,Returning_Visitor,FALSE,FALSE 0,0,1,13,116,4097.403993,0.016480331,0.022530582,0,0,Dec,3,2,1,13,Returning_Visitor,FALSE,FALSE 11,104.1944444,3,8,75,1753.761111,0.002380952,0.018278388,20.04842101,0,Nov,2,2,3,2,Returning_Visitor,FALSE,TRUE 3,216.5,3,127.5,151,7700.501461,0.002597403,0.012782022,4.074920999,0,Dec,2,2,3,2,Returning_Visitor,FALSE,FALSE 0,0,0,0,6,65.5,0.066666667,0.1,0,0,Nov,3,2,3,13,Returning_Visitor,FALSE,FALSE 0,0,0,0,13,787.75,0.015384615,0.046153846,0,0,Nov,2,2,4,10,Returning_Visitor,FALSE,FALSE 0,0,0,0,32,1589.625,0,0.012903226,15.63731434,0,Dec,2,2,6,1,Returning_Visitor,FALSE,FALSE 0,0,0,0,35,2994.827381,0,0.01969697,0,0,Nov,2,2,1,2,Returning_Visitor,TRUE,FALSE 0,0,0,0,118,3211.344872,0.006779661,0.016468927,3.05990612,0,Nov,2,2,9,2,Returning_Visitor,TRUE,TRUE 0,0,0,0,20,1958.833333,0.011111111,0.035185185,0,0,Nov,3,2,5,1,Returning_Visitor,FALSE,FALSE 2,98,0,0,81,2628,0.002469136,0.012345679,0.602232815,0,Dec,2,1,1,2,Returning_Visitor,FALSE,TRUE 0,0,0,0,14,124.75,0,0.028571429,0,0,Dec,4,6,4,2,Returning_Visitor,FALSE,FALSE 9,161.25,0,0,106,2709.522357,0,0.022650186,0,0,Nov,2,5,4,2,Returning_Visitor,FALSE,FALSE 3,35,3,32.5,290,11240.97669,0.012382578,0.024237631,0,0,Nov,3,2,1,13,Returning_Visitor,TRUE,FALSE 0,0,0,0,1,0,0.2,0.2,0,0,Nov,8,2,9,2,New_Visitor,FALSE,FALSE 0,0,0,0,4,790,0,0.05,0,0,Nov,8,1,4,3,Returning_Visitor,TRUE,FALSE 0,0,3,91,87,3006.883631,0.00462963,0.018585538,0,0,Dec,3,2,3,1,Returning_Visitor,FALSE,FALSE 0,0,0,0,17,376.5166667,0,0.01512605,0,0,Nov,2,2,6,2,Returning_Visitor,FALSE,FALSE 3,29.81515152,0,0,189,7190.547947,0.001396161,0.014604116,39.29216261,0,Nov,2,2,1,2,Returning_Visitor,FALSE,TRUE 0,0,0,0,42,772.625,0.004761905,0.019047619,0,0,Dec,2,2,1,2,Returning_Visitor,FALSE,FALSE 0,0,0,0,30,901.2888889,0,0.007333333,34.14841957,0,Dec,2,2,7,2,New_Visitor,FALSE,TRUE 0,0,0,0,5,54.33333333,0,0.02,0,0,Dec,1,2,1,3,Returning_Visitor,FALSE,FALSE 4,148.25,0,0,7,179.9583333,0,0.005,0,0,Nov,2,2,1,8,New_Visitor,TRUE,TRUE 4,140.675,1,97.75,70,2721.555108,0.001951952,0.017189707,10.53591515,0,Nov,1,2,1,10,Returning_Visitor,TRUE,TRUE 1,27.5,0,0,37,1673.593939,0.001851852,0.040162037,0,0,Nov,2,4,3,10,Returning_Visitor,TRUE,FALSE 4,153.75,0,0,17,670,0,0.010526316,0,0,Dec,2,2,1,2,New_Visitor,FALSE,FALSE 0,0,1,36.5,40,1121.416667,0,0.022195122,0,0,Nov,2,2,3,2,Returning_Visitor,FALSE,FALSE 4,55,3,17.25,100,1284.361822,0.001923077,0.011603935,0,0,Dec,1,1,1,10,Returning_Visitor,FALSE,FALSE 0,0,0,0,11,281.9,0.02,0.04,0,0,Dec,1,8,1,6,Returning_Visitor,FALSE,FALSE 0,0,2,20,28,1085.25,0,0.01,0,0,Dec,3,2,1,11,New_Visitor,FALSE,TRUE 0,0,0,0,1,0,0.2,0.2,0,0,Dec,1,1,1,2,New_Visitor,FALSE,FALSE 0,0,0,0,18,353.0833333,0.022222222,0.05,0,0,Nov,1,1,1,2,Returning_Visitor,TRUE,TRUE 1,321.5,0,0,103,5395.201794,0.010485437,0.03,0,0,Nov,2,2,1,2,Returning_Visitor,FALSE,FALSE 5,129.625,1,15,25,389.4107143,0.007692308,0.014102564,0,0,Dec,3,2,1,2,Returning_Visitor,TRUE,FALSE 0,0,0,0,1,0,0.2,0.2,0,0,Dec,1,1,4,1,Returning_Visitor,TRUE,FALSE 0,0,1,0,0,0,0.2,0.2,0,0,Nov,1,1,1,15,Returning_Visitor,FALSE,FALSE 0,0,0,0,12,483.2916667,0,0.041666667,0,0,Dec,2,5,1,1,Returning_Visitor,FALSE,FALSE 3,17,0,0,62,1493.353846,0.00952381,0.04031746,0,0,Dec,2,2,1,2,Returning_Visitor,FALSE,FALSE 0,0,0,0,11,350.375,0.018181818,0.011688312,0,0,Nov,3,2,3,2,New_Visitor,FALSE,FALSE 2,26.5,0,0,0,0,0,0.1,0,0,Dec,2,2,3,8,New_Visitor,FALSE,FALSE 2,50.875,0,0,32,1375.8,0.016129032,0.041935484,0,0,Nov,3,2,3,6,Returning_Visitor,TRUE,FALSE 1,44.5,1,66.5,8,568.25,0.018181818,0.033333333,26.45046371,0,Nov,1,1,2,7,Returning_Visitor,FALSE,FALSE 4,80,0,0,17,442.1666667,0,0.010526316,0,0,Nov,2,2,6,2,New_Visitor,TRUE,FALSE 0,0,0,0,30,1254.295238,0.006666667,0.014666667,0,0,Nov,1,1,3,2,Returning_Visitor,TRUE,FALSE 5,107.5,0,0,31,2221,0.002857143,0.01047619,0,0,Nov,2,2,9,3,Returning_Visitor,FALSE,FALSE 0,0,1,0,131,4597.575252,0.006766917,0.028754539,0,0,Dec,3,2,1,2,Returning_Visitor,FALSE,FALSE 0,0,0,0,6,234.5,0.1,0.133333333,0,0,Nov,1,1,7,10,Returning_Visitor,TRUE,FALSE 0,0,1,0,21,472.5,0.019047619,0.019047619,34.27708553,0,Nov,1,1,1,10,Returning_Visitor,FALSE,TRUE 0,0,0,0,9,146.375,0.075,0.0875,0,0,Nov,3,2,1,13,Returning_Visitor,FALSE,FALSE 0,0,0,0,2,18,0,0.1,0,0,Nov,1,1,1,1,Returning_Visitor,FALSE,FALSE 1,29.5,0,0,41,1119.541667,0.009756098,0.035656214,0,0,Nov,3,2,1,2,Returning_Visitor,FALSE,FALSE 0,0,0,0,9,329.25,0.05,0.075,38.83384563,0,Dec,3,2,4,1,Returning_Visitor,FALSE,TRUE 0,0,0,0,5,156.5,0,0.013333333,0,0,Nov,2,4,7,1,Returning_Visitor,FALSE,TRUE 0,0,0,0,14,429.9166667,0.014285714,0.033333333,0,0,Nov,1,8,4,11,Returning_Visitor,FALSE,FALSE 0,0,0,0,44,1127.566667,0.006818182,0.021969697,0,0,Nov,4,2,1,3,Returning_Visitor,FALSE,FALSE 0,0,0,0,16,1500.033333,0.00625,0.01467803,0,0,Nov,3,2,4,6,Returning_Visitor,FALSE,FALSE 0,0,0,0,17,1032.9,0,0.013333333,0,0,Nov,2,4,1,2,Returning_Visitor,FALSE,FALSE 9,183.7857143,1,90,95,3346.501984,0.00211827,0.018655904,5.932009013,0,Nov,3,2,2,2,Returning_Visitor,FALSE,TRUE 0,0,0,0,10,253.25,0,0.02,0,0,Dec,3,2,1,10,Returning_Visitor,FALSE,FALSE 1,14,0,0,1,0,0.1,0.15,0,0,Nov,1,1,1,10,Returning_Visitor,FALSE,FALSE 14,1005.608333,0,0,25,732.3448718,0,0.015675676,0,0,Nov,3,2,1,2,Returning_Visitor,FALSE,FALSE 13,442.6,6,164.625,107,4928.927389,0.016774043,0.028505395,1.451867861,0,Nov,3,2,1,2,Returning_Visitor,FALSE,TRUE 1,40.5,2,1415,56,1568.65,0.006896552,0.02183908,28.16893067,0,Nov,2,2,1,8,Returning_Visitor,TRUE,FALSE 0,0,1,30.5,14,327,0.013333333,0.026666667,0,0,Nov,1,1,4,3,Returning_Visitor,FALSE,FALSE 0,0,0,0,4,57,0.05,0.075,0,0,Nov,1,1,1,8,New_Visitor,FALSE,FALSE 0,0,0,0,1,0,0.2,0.2,0,0,Nov,1,8,1,11,Returning_Visitor,FALSE,FALSE 0,0,1,0,5,430.1666667,0,0.041666667,0,0,Nov,3,2,1,2,Returning_Visitor,FALSE,FALSE 3,126.5,0,0,36,576.8571429,0.005555556,0.01087963,0,0,Dec,1,1,4,3,Returning_Visitor,FALSE,FALSE 0,0,0,0,9,891.5,0,0.0125,0,0,Dec,1,1,1,2,Returning_Visitor,FALSE,FALSE 0,0,0,0,38,3779.444444,0,0.02814992,0,0,Dec,3,2,1,2,Returning_Visitor,FALSE,FALSE 0,0,0,0,19,557.35,0,0.029824561,0,0,Nov,1,1,3,3,Returning_Visitor,TRUE,FALSE 5,115.0833333,0,0,3,50.25,0,0.014285714,0,0,Nov,3,2,4,2,New_Visitor,FALSE,FALSE 0,0,0,0,27,1065.45,0,0.012345679,0,0,Nov,2,2,3,2,New_Visitor,FALSE,FALSE 10,155.8458333,4,155.375,128,4391.876232,0.008759124,0.022411525,0,0,Nov,1,2,1,2,Returning_Visitor,FALSE,FALSE 10,150.2083333,0,0,14,221.2083333,0,0.012698413,0,0,Dec,2,2,4,2,New_Visitor,FALSE,FALSE 0,0,0,0,15,593.75,0,0.014285714,92.9394993,0,Nov,1,8,3,11,Returning_Visitor,TRUE,TRUE 3,34.5,0,0,17,298.875,0,0.014035088,0,0,Dec,1,5,1,2,New_Visitor,FALSE,FALSE 0,0,0,0,2,166,0,0.05,0,0,Dec,8,13,9,20,Other,FALSE,FALSE 3,56,0,0,19,2117.654762,0,0.014444444,6.708900602,0,Dec,2,2,3,2,Returning_Visitor,FALSE,TRUE 14,345.8065476,3,195,75,5610.686899,0.008045977,0.019205169,4.452664494,0,Nov,2,2,3,2,Returning_Visitor,FALSE,FALSE 0,0,0,0,84,1460.256115,0.004703833,0.013205032,0,0,Nov,1,1,1,2,Returning_Visitor,TRUE,FALSE 0,0,0,0,8,764.6666667,0.025,0.04375,0,0,Nov,3,2,4,10,Returning_Visitor,FALSE,FALSE 12,397.7166667,3,431.3333333,336,12595.62776,0.002376812,0.01331553,1.178989949,0,Nov,2,2,1,11,Returning_Visitor,FALSE,TRUE 3,67.5,0,0,30,761.75,0,0.015053763,19.31425101,0,Dec,2,4,1,1,Returning_Visitor,TRUE,TRUE 14,228.748539,6,121.5833333,336,12166.69424,0,0.012143626,0.067049546,0,Nov,2,2,7,2,Returning_Visitor,FALSE,TRUE 0,0,0,0,1,0,0.2,0.2,0,0,Dec,2,2,1,2,Returning_Visitor,FALSE,FALSE 1,0,0,0,16,2007,0.01875,0.054166667,0,0,Nov,1,1,3,1,Returning_Visitor,FALSE,FALSE 1,47.5,0,0,123,3944.384921,0.00516129,0.017112135,3.303059969,0,Nov,2,2,1,2,Returning_Visitor,FALSE,FALSE 9,76.54166667,0,0,6,64.20833333,0,0.02,0,0,Nov,3,2,3,10,Returning_Visitor,TRUE,FALSE 0,0,0,0,1,0,0.2,0.2,0,0,Dec,3,2,1,1,Returning_Visitor,FALSE,FALSE 9,164.3333333,0,0,102,4227.094444,0,0.00849359,0,0,Nov,2,2,1,2,Returning_Visitor,TRUE,FALSE 0,0,0,0,29,631.775,0.021428571,0.05042517,0,0,Nov,2,2,1,1,Returning_Visitor,FALSE,FALSE 0,0,0,0,6,46,0,0.016666667,0,0,Dec,6,2,4,1,Returning_Visitor,FALSE,FALSE 0,0,0,0,10,416.5833333,0,0.036666667,0,0,Dec,1,1,3,2,Returning_Visitor,FALSE,FALSE 0,0,0,0,1,0,0.2,0.2,0,0,Nov,1,1,4,10,Returning_Visitor,FALSE,FALSE 5,89.625,2,11.5,31,1063.420238,0.011111111,0.019961874,0,0,Nov,3,2,1,13,Returning_Visitor,TRUE,FALSE 1,0,0,0,61,1934.225758,0.009677419,0.01662531,0,0,Nov,2,2,1,13,Returning_Visitor,FALSE,FALSE 8,96.75,1,73.75,64,2527.083333,0,0.005239899,0,0,Nov,2,2,4,2,Returning_Visitor,FALSE,FALSE 2,67.16666667,0,0,61,2495.615079,0.005649718,0.016000807,0,0,Dec,2,2,3,2,Returning_Visitor,TRUE,FALSE 0,0,0,0,33,655.875,0,0.015151515,0,0,Nov,2,2,2,13,Returning_Visitor,FALSE,FALSE 0,0,0,0,13,443,0.030769231,0.053846154,0,0,Nov,2,5,1,1,Returning_Visitor,FALSE,FALSE 5,76,0,0,66,1415.3875,0.00358209,0.014262023,9.829114577,0,Dec,2,2,5,2,Returning_Visitor,FALSE,FALSE 3,112.75,0,0,15,658.75,0,0.011111111,0,0,Nov,3,2,6,2,New_Visitor,FALSE,FALSE 5,29,0,0,8,233.5,0,0.027777778,0,0,Dec,2,2,4,2,Returning_Visitor,FALSE,FALSE 0,0,0,0,16,332.1666667,0,0.007142857,0,0,Dec,2,2,3,2,New_Visitor,FALSE,FALSE 9,57.95833333,0,0,32,498.2,0,0.010675991,27.61403036,0,Nov,2,2,1,2,Returning_Visitor,FALSE,FALSE 0,0,0,0,7,330.1666667,0,0.021428571,0,0,Dec,3,2,9,2,Returning_Visitor,FALSE,FALSE 0,0,0,0,6,97.83333333,0.033333333,0.075,0,0,Dec,3,2,3,1,Returning_Visitor,TRUE,FALSE 0,0,0,0,1,0,0.2,0.2,0,0,Dec,2,2,5,2,Returning_Visitor,TRUE,FALSE 8,363.05,0,0,8,345,0,0.016666667,13.64300463,0,Dec,2,2,3,3,Returning_Visitor,FALSE,TRUE 0,0,1,12,22,1668.95,0,0.00952381,12.18867006,0,Dec,2,2,1,2,New_Visitor,TRUE,TRUE 0,0,0,0,12,247,0,0.016666667,0,0,Dec,8,1,3,1,Returning_Visitor,TRUE,FALSE 0,0,0,0,22,1327.666667,0,0.032359307,0,0,Nov,2,2,1,2,New_Visitor,TRUE,TRUE 6,1922,0,0,30,941.672619,0.018181818,0.044444444,0,0,Nov,3,2,2,1,Returning_Visitor,FALSE,FALSE 0,0,0,0,33,735.4392857,0,0.002693603,0,0,Nov,4,2,4,2,Returning_Visitor,FALSE,FALSE 3,43,4,81,132,5091.890896,0.006521739,0.029863649,0,0,Nov,2,2,4,2,Returning_Visitor,TRUE,TRUE 0,0,0,0,3,131,0,0.033333333,0,0,Nov,1,8,4,6,New_Visitor,FALSE,FALSE 0,0,0,0,7,277.1666667,0.028571429,0.05,0,0,Nov,3,2,4,13,Returning_Visitor,FALSE,FALSE 0,0,0,0,11,242.9166667,0,0.02,16.16057924,0,Dec,2,10,7,2,Returning_Visitor,TRUE,TRUE 9,199.3856838,0,0,38,1342.434295,0,0.022542075,22.02071582,0,Nov,2,2,2,2,Returning_Visitor,TRUE,TRUE 3,32,0,0,15,371.25,0,0.011764706,0,0,Nov,1,8,4,11,Returning_Visitor,FALSE,FALSE 0,0,0,0,7,205.5,0.028571429,0.038095238,0,0,Nov,2,2,3,13,Returning_Visitor,TRUE,FALSE 2,29.5,0,0,36,1110.666667,0.005405405,0.035585586,0,0,Dec,2,4,9,2,Returning_Visitor,TRUE,FALSE 9,795.2083333,0,0,118,4947.358038,0.001138211,0.011200631,2.270358059,0,Nov,3,2,5,2,Returning_Visitor,FALSE,FALSE 2,11,0,0,72,1706.064885,0.011111111,0.021240391,0,0,Nov,1,1,3,2,Returning_Visitor,FALSE,FALSE 0,0,0,0,15,383.4583333,0,0.029333333,0,0,Dec,2,2,2,2,Returning_Visitor,FALSE,FALSE 0,0,0,0,7,82.75,0.028571429,0.085714286,0,0,Nov,2,2,7,3,Returning_Visitor,FALSE,FALSE 0,0,0,0,6,68.25,0.033333333,0.044444444,0,0,Nov,2,2,7,2,New_Visitor,FALSE,FALSE 1,58.5,0,0,23,1033,0.008333333,0.0375,0,0,Dec,2,2,3,2,Returning_Visitor,FALSE,FALSE 12,483.3333333,1,0,5,161.3333333,0,0.016666667,0,0,Dec,1,1,3,2,Returning_Visitor,TRUE,FALSE 0,0,0,0,12,687.4833333,0,0.0375,0,0,Nov,1,1,1,2,Returning_Visitor,FALSE,FALSE 1,3,1,302,7,539.5,0,0.0125,0,0,Dec,2,2,4,2,Other,TRUE,FALSE 7,381.75,0,0,69,2599.015278,0.011805556,0.034545686,5.560270538,0,Nov,1,1,1,2,Returning_Visitor,FALSE,TRUE 0,0,0,0,32,2371.122222,0,0.012903226,0,0,Nov,2,2,1,2,Returning_Visitor,FALSE,FALSE 3,64.5,0,0,14,671.625,0,0.030625,0,0,Nov,1,1,3,2,Returning_Visitor,TRUE,FALSE 0,0,0,0,13,1461.416667,0.021978022,0.047163947,0,0,Nov,3,2,3,11,Returning_Visitor,TRUE,FALSE 3,289,0,0,5,708.25,0,0.025,0,0,Nov,3,3,1,10,Returning_Visitor,FALSE,FALSE 0,0,0,0,9,386.25,0,0.022222222,0,0,Dec,3,2,6,6,New_Visitor,FALSE,FALSE 5,479.25,2,81,41,1355.483333,0,0.007142857,0,0,Dec,1,1,3,2,Returning_Visitor,TRUE,FALSE 0,0,0,0,21,331.0833333,0.00952381,0.042857143,0,0,Dec,2,2,1,2,Returning_Visitor,FALSE,FALSE 0,0,1,8,31,651.6666667,0,0.025,0,0,Dec,4,1,1,2,New_Visitor,TRUE,FALSE 5,200.8333333,0,0,44,1909.080556,0.004761905,0.018253968,0,0,Dec,1,8,1,10,Returning_Visitor,FALSE,FALSE 0,0,0,0,4,58.5,0,0.066666667,0,0,Dec,3,2,8,13,Returning_Visitor,FALSE,FALSE 0,0,0,0,51,874.4047619,0.008,0.0345,0,0,Nov,2,2,7,2,Returning_Visitor,FALSE,FALSE 4,255.5,0,0,14,770.5,0,0.002666667,0,0,Nov,3,2,7,8,Returning_Visitor,TRUE,FALSE 0,0,0,0,7,199.5,0.028571429,0.057142857,56.57877343,0,Nov,2,4,1,3,Returning_Visitor,FALSE,TRUE 2,53.125,4,184.5,8,612.625,0.015384615,0.041538462,0,0,Dec,1,1,1,2,Returning_Visitor,TRUE,FALSE 5,51,0,0,10,116,0,0.016666667,0,0,Nov,2,2,7,2,New_Visitor,FALSE,FALSE 4,323.5,0,0,10,320.75,0,0.015384615,0,0,Nov,2,2,6,2,New_Visitor,FALSE,FALSE 6,146.4166667,0,0,61,2405.652778,0.009375,0.032135417,0,0,Dec,2,1,1,2,Returning_Visitor,FALSE,TRUE 2,25.65,0,0,13,343.625,0,0.005128205,0,0,Dec,2,2,2,2,New_Visitor,FALSE,FALSE 0,0,0,0,3,22,0,0.066666667,0,0,Nov,2,2,3,3,Returning_Visitor,FALSE,FALSE 0,0,0,0,3,19,0.066666667,0.133333333,0,0,Nov,2,5,3,10,Returning_Visitor,FALSE,FALSE 0,0,0,0,5,182.625,0,0.008,0,0,Dec,3,2,8,13,Returning_Visitor,FALSE,FALSE 2,83.5,2,125.5,11,213.9472222,0.007692308,0.003247863,0,0,Dec,3,2,5,2,Returning_Visitor,FALSE,FALSE 2,20.15,0,0,60,1521.15,0.003333333,0.015277778,16.43637285,0,Nov,2,4,1,2,Returning_Visitor,FALSE,TRUE 0,0,0,0,28,1344.458333,0.007142857,0.032380952,0,0,Nov,2,2,7,2,Returning_Visitor,FALSE,FALSE 0,0,0,0,1,0,0.2,0.2,0,0,Nov,2,4,4,1,Returning_Visitor,FALSE,FALSE 2,233.5,0,0,36,1081.666667,0.025,0.046851852,0,0,Nov,2,2,9,1,Returning_Visitor,FALSE,FALSE 0,0,0,0,2,135.5,0.066666667,0.166666667,0,0,Dec,1,1,4,3,Returning_Visitor,FALSE,FALSE 0,0,0,0,11,296.5,0.036363636,0.081818182,0,0,Nov,2,2,1,1,Returning_Visitor,FALSE,FALSE 5,194.3333333,2,38,71,2632.189286,0.000694444,0.008611111,0,0,Nov,4,2,1,2,Returning_Visitor,TRUE,FALSE 0,0,0,0,44,2588.154762,0.009090909,0.027272727,0,0,Dec,2,2,1,1,Returning_Visitor,FALSE,FALSE 4,251,9,172,64,4326.666667,0.008552632,0.024502924,0,0,Dec,2,2,4,2,Returning_Visitor,FALSE,FALSE 0,0,0,0,55,1119.543056,0.005454545,0.008870523,0,0,Nov,1,1,1,3,Returning_Visitor,FALSE,FALSE 2,163,0,0,136,4201.508333,0,0.011265083,0,0,Dec,3,2,3,3,Returning_Visitor,FALSE,FALSE 3,118.5,0,0,11,241.625,0,0.012727273,0,0,Dec,3,2,3,1,Returning_Visitor,TRUE,FALSE 0,0,0,0,12,157,0.008333333,0.033333333,0,0,Nov,3,2,7,1,Returning_Visitor,FALSE,FALSE 1,10,0,0,11,753.5,0.02,0.04,0,0,Nov,2,2,2,2,Returning_Visitor,FALSE,FALSE 0,0,0,0,4,48.5,0.05,0.1,0,0,Dec,2,2,9,2,New_Visitor,FALSE,FALSE 3,127.5,1,46.5,33,1440.933333,0.011428571,0.027210884,0,0,Dec,2,2,1,1,Returning_Visitor,FALSE,FALSE 1,90,0,0,6,677,0,0.04,0,0,Dec,8,13,9,20,Other,FALSE,FALSE 0,0,0,0,1,0,0.2,0.2,0,0,Nov,2,2,2,13,Returning_Visitor,FALSE,FALSE 2,61.5,2,10,13,210,0.0125,0.03125,0,0,Dec,3,2,4,1,Returning_Visitor,FALSE,FALSE 8,822.6666667,3,225.3333333,45,1896.966667,0.011764706,0.02745098,7.521155028,0,Dec,1,1,3,1,Returning_Visitor,FALSE,FALSE 1,14.75,0,0,38,609.3488095,0,0.005263158,0,0,Dec,2,2,1,2,New_Visitor,FALSE,FALSE 0,0,0,0,11,437.8333333,0,0.024242424,0,0,Nov,3,3,3,11,New_Visitor,FALSE,FALSE 0,0,0,0,1,0,0.2,0.2,0,0,Nov,4,2,1,8,New_Visitor,FALSE,FALSE 1,20,0,0,30,1038.791667,0.006666667,0.026666667,0,0,Nov,3,2,1,2,Returning_Visitor,FALSE,FALSE 3,86.75,0,0,16,1102.5,0,0.014285714,0,0,Dec,1,1,2,2,Returning_Visitor,FALSE,FALSE 3,91.66666667,2,12,29,1347.266667,0,0.008854167,0,0,Dec,2,5,1,1,Returning_Visitor,FALSE,FALSE 0,0,0,0,16,370.6666667,0.033333333,0.051041667,0,0,Dec,3,2,2,3,Returning_Visitor,FALSE,FALSE 0,0,0,0,6,60.25,0.033333333,0.066666667,0,0,Nov,2,2,7,3,Returning_Visitor,FALSE,TRUE 1,30.25,2,0,20,543.9583333,0.00952381,0.026984127,0,0,Dec,3,2,1,2,Returning_Visitor,FALSE,FALSE 1,4,0,0,15,377.5,0,0.002040816,69.7132141,0,Nov,8,13,9,20,Other,FALSE,FALSE 6,127.4166667,0,0,162,5509.717928,0.001905069,0.021987823,0,0,Nov,2,2,1,1,Returning_Visitor,FALSE,FALSE 0,0,0,0,5,43.5,0.14,0.18,0,0,Nov,3,3,1,3,Returning_Visitor,FALSE,FALSE 1,54.5,0,0,26,449.25,0,0.022222222,0,0,Dec,4,2,3,2,Returning_Visitor,FALSE,FALSE 0,0,0,0,11,332.125,0.036363636,0.090909091,0,0,Nov,2,2,6,3,Returning_Visitor,FALSE,FALSE 10,260.25,0,0,31,1694.333333,0.017142857,0.039047619,15.108543,0,Nov,2,2,4,3,Returning_Visitor,FALSE,FALSE 4,441.1,0,0,20,249.8214286,0,0.0388,0,0,Dec,1,1,1,2,Returning_Visitor,FALSE,FALSE 8,889.1666667,0,0,137,5588.185426,0.000729167,0.026320008,6.898232461,0,Nov,2,2,1,3,Returning_Visitor,FALSE,TRUE 5,169.975,3,106,90,4792.380767,0.004166667,0.007479289,41.55801982,0,Nov,3,2,6,1,Returning_Visitor,FALSE,TRUE 1,0,0,0,7,174,0,0.05,0,0,Nov,4,2,3,20,Returning_Visitor,FALSE,FALSE 4,495.1666667,0,0,29,988.2595238,0.006451613,0.009677419,0,0,Dec,1,1,1,2,New_Visitor,FALSE,FALSE 0,0,0,0,7,331,0,0.014285714,0,0,Nov,1,1,7,10,Returning_Visitor,TRUE,FALSE 5,85.25,0,0,3,36.25,0,0.00952381,0,0,Nov,2,4,4,2,Returning_Visitor,FALSE,FALSE 1,21.25,1,0,23,1171.071429,0.007407407,0.039506173,11.07703164,0,Dec,2,2,7,1,Returning_Visitor,FALSE,FALSE 2,91,0,0,94,4698.796429,0,0.017659574,0,0,Nov,2,2,5,2,Returning_Visitor,TRUE,FALSE 12,275.5166667,2,288.375,60,1276.594048,0,0.00952381,5.45243266,0,Nov,4,1,1,2,Returning_Visitor,FALSE,TRUE 0,0,0,0,10,392.5,0,0.02,42.83887416,0,Nov,1,1,2,2,New_Visitor,FALSE,TRUE 0,0,0,0,2,16,0,0.1,0,0,Nov,2,2,4,8,Returning_Visitor,FALSE,FALSE 5,57.33333333,3,167.8,222,6942.783547,0.002212389,0.015536555,2.780937777,0,Dec,2,2,3,2,Returning_Visitor,FALSE,FALSE 0,0,0,0,23,966.3583333,0,0.004081633,0,0,Nov,3,2,7,2,Returning_Visitor,FALSE,FALSE 0,0,0,0,28,1107.922619,0.014814815,0.009876543,226.6777017,0,Dec,8,13,9,20,Other,FALSE,TRUE 5,105.5,0,0,32,1403.541667,0,0.00125,0,0,Dec,8,13,9,20,Other,FALSE,FALSE 0,0,10,518.25,60,2068.248473,0.002857143,0.034058957,0,0,Nov,1,1,1,2,Returning_Visitor,FALSE,FALSE 10,141.0357143,2,213,50,829.5190476,0.001149425,0.025172414,0,0,Dec,2,2,4,10,Returning_Visitor,FALSE,FALSE 1,10,0,0,30,602.5,0.009677419,0.014910394,0,0,Dec,2,2,2,10,Returning_Visitor,FALSE,FALSE 5,121.425,1,8,16,792.75,0.005,0.008333333,38.41401623,0,Nov,2,2,1,3,Returning_Visitor,FALSE,FALSE 4,60,3,37.83333333,21,868.0166667,0,0.008,40.72562976,0,Dec,1,1,1,2,New_Visitor,FALSE,TRUE 2,113.75,4,125,20,933.55,0.008333333,0.035416667,60.97758829,0,Dec,3,2,3,2,Returning_Visitor,FALSE,FALSE 0,0,0,0,1,0,0.2,0.2,0,0,Nov,2,10,3,1,Returning_Visitor,FALSE,FALSE 1,46.5,0,0,14,398.9791667,0,0.014285714,116.3378652,0,Dec,8,13,9,20,Other,FALSE,TRUE 0,0,0,0,2,3,0,0.1,0,0,Dec,2,2,4,2,Returning_Visitor,FALSE,FALSE 0,0,0,0,14,152,0.057142857,0.057142857,0,0,Dec,1,1,4,3,Returning_Visitor,FALSE,FALSE 7,91.25,1,0,95,2900.791667,0.003960396,0.023267327,4.48567854,0,Nov,2,2,1,2,Returning_Visitor,TRUE,TRUE 0,0,0,0,11,1232.083333,0,0.004,0,0,Nov,1,1,1,2,New_Visitor,FALSE,FALSE 7,441.8333333,3,102.5,231,9230.890838,0.006458333,0.021838687,0,0,Nov,3,2,1,2,Returning_Visitor,TRUE,FALSE 0,0,0,0,10,761.75,0.022222222,0.022222222,0,0,Nov,3,2,3,2,Returning_Visitor,TRUE,FALSE 2,27.25,0,0,1,31.5,0.05,0.066666667,0,0,Dec,3,2,2,3,Returning_Visitor,FALSE,TRUE 0,0,0,0,3,105,0,0.033333333,0,0,Dec,2,2,2,6,Returning_Visitor,FALSE,FALSE 0,0,0,0,18,636.5,0,0.027777778,0,0,Dec,2,2,3,2,Returning_Visitor,FALSE,FALSE 1,46.5,0,0,40,2338.95,0.017073171,0.030325203,9.472914356,0,Nov,1,1,1,3,Returning_Visitor,FALSE,TRUE 6,77.75,0,0,18,941.9166667,0,0.001428571,0,0,Nov,2,2,1,2,New_Visitor,FALSE,FALSE 6,141.9166667,2,1060.75,136,8777.613879,0.007159986,0.016360941,21.3825758,0,Dec,3,2,1,2,Returning_Visitor,FALSE,TRUE 6,183.25,2,80.5,60,1883.444231,0,0.020858475,0,0,Nov,3,12,1,6,Returning_Visitor,TRUE,TRUE 10,297.8333333,12,290.225,33,1467.654221,0.008,0.023038462,13.20331045,0,Dec,3,2,1,2,Returning_Visitor,TRUE,FALSE 0,0,2,407.5,14,1043.15,0.05,0.08125,0,0,Nov,1,1,1,2,Returning_Visitor,TRUE,FALSE 7,97,3,103.25,16,1509.5,0,0.018933333,0,0,Nov,2,2,1,2,Returning_Visitor,FALSE,FALSE 1,14.5,0,0,24,1277.842857,0,0.016073781,0,0,Nov,3,2,1,2,New_Visitor,TRUE,FALSE 0,0,0,0,12,282.75,0.016666667,0.052777778,0,0,Nov,2,2,1,13,Returning_Visitor,FALSE,FALSE 0,0,0,0,14,654.875,0.028571429,0.042857143,0,0,Nov,2,2,1,13,Returning_Visitor,FALSE,FALSE 0,0,0,0,25,610.3095238,0,0.039,0,0,Nov,2,2,3,2,Returning_Visitor,FALSE,FALSE 0,0,0,0,5,586,0,0.06,0,0,Nov,2,4,9,10,Returning_Visitor,TRUE,FALSE 7,172.0595238,0,0,104,5155.351132,0.006893004,0.031570985,8.805138964,0,Nov,2,5,1,1,Returning_Visitor,FALSE,FALSE 0,0,0,0,12,640.375,0.023076923,0.041538462,0,0,Dec,1,1,5,1,Returning_Visitor,TRUE,FALSE 4,28,3,37.83333333,378,12003.31758,0.003655352,0.023352993,0,0,Nov,2,2,1,2,Returning_Visitor,FALSE,FALSE 0,0,0,0,4,161.25,0,0.05,0,0,Nov,1,1,3,2,New_Visitor,TRUE,FALSE 5,44,0,0,131,3865.838889,0.006458094,0.023900203,0.818306551,0,Nov,2,2,3,2,Returning_Visitor,TRUE,TRUE 0,0,0,0,9,348.75,0,0.02962963,0,0,Dec,2,5,1,1,Returning_Visitor,FALSE,FALSE 3,143.625,0,0,63,2801.131746,0.003030303,0.012987013,9.078681618,0,Nov,3,2,1,2,Returning_Visitor,FALSE,TRUE 0,0,0,0,13,979.0833333,0,0.006060606,0,0,Dec,3,2,6,2,New_Visitor,TRUE,FALSE 4,62.5,0,0,53,1840.369156,0.007407407,0.023148148,46.77831787,0,Dec,1,1,2,2,Returning_Visitor,FALSE,TRUE 11,326.4946809,0,0,92,4637.53842,0.0005,0.011495239,17.28402947,0,Nov,4,1,4,3,Returning_Visitor,FALSE,FALSE 1,58,0,0,33,647.625,0,0.00625,0,0,Dec,3,2,3,2,New_Visitor,FALSE,FALSE 0,0,0,0,64,2018.762066,0.009895833,0.027936058,0,0,Nov,3,2,1,2,Returning_Visitor,FALSE,FALSE 4,63.25,2,7,9,297.0833333,0,0.014285714,0,0,Nov,2,2,6,8,New_Visitor,TRUE,FALSE 1,10,0,0,254,6308.063793,0.002513228,0.01518288,0,0,Nov,2,2,3,2,Returning_Visitor,FALSE,FALSE 0,0,0,0,7,278.75,0.028571429,0.042857143,0,0,Dec,3,2,1,20,New_Visitor,FALSE,FALSE 0,0,0,0,6,85.25,0,0.011111111,0,0,Dec,2,2,3,10,Returning_Visitor,FALSE,FALSE 2,21,6,182.7916667,82,1836.214249,0.004545455,0.025181135,0,0,Dec,3,2,3,2,Returning_Visitor,FALSE,FALSE 0,0,0,0,1,0,0,0.1,0,0,Dec,8,13,9,20,Other,FALSE,FALSE 0,0,0,0,17,289.5555556,0.017647059,0.031372549,0,0,Dec,1,1,1,10,Returning_Visitor,FALSE,FALSE 6,189,0,0,21,704.3,0,0.018478261,0,0,Dec,1,2,8,2,New_Visitor,FALSE,TRUE 0,0,0,0,1,0,0.2,0.2,0,0,Dec,1,1,1,3,Returning_Visitor,FALSE,FALSE 0,0,0,0,16,316,0.025,0.0375,0,0,Nov,1,8,9,2,Returning_Visitor,FALSE,FALSE 0,0,0,0,145,4108.808442,0.007552448,0.023216783,0,0,Nov,2,2,4,2,Returning_Visitor,FALSE,TRUE 0,0,0,0,3,44.5,0.066666667,0.133333333,0,0,Dec,2,2,9,3,Returning_Visitor,FALSE,FALSE 2,43.72727273,0,0,59,3354.078463,0.012307692,0.026494505,9.005310781,0,Nov,2,2,3,1,Returning_Visitor,FALSE,TRUE 0,0,0,0,2,1121.5,0,0.1,0,0,Nov,8,2,4,3,Returning_Visitor,FALSE,FALSE 6,84.66666667,0,0,24,1387.5,0,0.017261905,11.30850046,0,Nov,2,2,1,3,Returning_Visitor,FALSE,TRUE 0,0,0,0,89,2924.432937,0.006741573,0.024478493,0,0,Nov,2,2,1,10,Returning_Visitor,FALSE,TRUE 7,134.625,0,0,14,1010.375,0.011764706,0.058823529,0,0,Nov,2,2,1,1,Returning_Visitor,FALSE,FALSE 3,109,0,0,31,3116.5,0,0.005208333,0,0,Dec,3,2,1,10,Returning_Visitor,FALSE,TRUE 6,888.8333333,0,0,39,1570.791667,0.00952381,0.011156463,16.32930757,0,Dec,1,1,3,2,Returning_Visitor,TRUE,FALSE 2,112,4,231.875,73,2163.5,0.007692308,0.02991453,11.62573653,0,Nov,2,2,1,1,Returning_Visitor,TRUE,FALSE 1,11.5,1,94,98,6212.779517,0.024162985,0.033991575,0,0,Nov,3,2,4,10,Returning_Visitor,TRUE,FALSE 2,37.5,0,0,70,6384.366987,0.016666667,0.027790836,0,0,Nov,1,1,3,8,Returning_Visitor,FALSE,TRUE 0,0,0,0,28,1023.125,0.007142857,0.028571429,0,0,Nov,2,2,3,2,Returning_Visitor,FALSE,FALSE 0,0,0,0,1,0,0.2,0.2,0,0,Dec,3,2,9,1,Returning_Visitor,TRUE,FALSE 0,0,0,0,1,0,0.2,0.2,0,0,Dec,1,1,6,2,Returning_Visitor,FALSE,FALSE 6,1489.041667,0,0,12,879.8,0,0.005555556,0,0,Nov,2,2,4,2,New_Visitor,TRUE,FALSE 0,0,0,0,4,86,0,0.025,0,0,Dec,2,5,1,8,New_Visitor,TRUE,FALSE 26,1561.717567,9,503.7222222,183,9676.09318,0.011054834,0.014200442,19.56746389,0,Nov,3,2,2,13,Returning_Visitor,FALSE,TRUE 10,298.581761,0,0,210,8115.845852,0.008398675,0.030193184,0,0,Nov,2,2,1,2,Returning_Visitor,FALSE,FALSE 0,0,0,0,1,0,0.2,0.2,0,0,Dec,1,13,9,20,Returning_Visitor,FALSE,FALSE 0,0,0,0,2,25.5,0,0.1,0,0,Dec,1,1,1,3,Returning_Visitor,FALSE,FALSE 9,102.8666667,3,339.5,29,693.2541667,0,0.014705882,77.64914484,0,Dec,2,10,1,2,Returning_Visitor,FALSE,TRUE 0,0,0,0,17,278.6666667,0.015686275,0.042666157,0,0,Nov,3,2,1,6,Returning_Visitor,TRUE,FALSE 9,1294.9,1,32.66666667,37,1012.233333,0,0.006910299,33.31220592,0,Nov,2,2,1,2,Returning_Visitor,FALSE,FALSE 0,0,0,0,1,0,0.2,0.2,0,0,Dec,8,13,9,20,Other,FALSE,FALSE 0,0,0,0,46,1014.375,0.008695652,0.026449275,0,0,Dec,2,2,3,2,Returning_Visitor,TRUE,FALSE 0,0,0,0,23,1107.267857,0.058181818,0.069393939,39.038442,0,Dec,2,2,3,2,Returning_Visitor,FALSE,FALSE 7,533.5913534,4,333.5,85,5496.630355,0.010192616,0.025284123,5.145928873,0,Nov,3,2,1,13,Returning_Visitor,FALSE,FALSE 4,140.5833333,2,385,53,1609.990079,0.003571429,0.028928571,7.209305119,0,Dec,2,2,5,2,Returning_Visitor,TRUE,FALSE 6,76.3125,0,0,187,6338.256636,0.003076923,0.024062504,2.122731861,0,Nov,2,4,1,2,Returning_Visitor,FALSE,FALSE 6,112.125,8,224,114,3459.479978,0.001574803,0.009848144,5.648688126,0,Nov,2,2,3,8,Returning_Visitor,TRUE,TRUE 6,344.5,0,0,7,186,0.046153846,0.033333333,0,0,Nov,3,2,1,2,Returning_Visitor,TRUE,FALSE 0,0,0,0,5,52.66666667,0.04,0.05,0,0,Nov,3,2,2,13,Returning_Visitor,FALSE,FALSE 2,698.5,0,0,41,1053.841565,0.002990033,0.014327278,0,0,Nov,2,5,6,1,Returning_Visitor,TRUE,FALSE 6,60.875,0,0,57,3671.675,0.003278689,0.027595628,0,0,Nov,2,2,1,2,Returning_Visitor,FALSE,FALSE 2,7,0,0,63,2219.866667,0.003174603,0.023129252,0,0,Nov,2,2,4,2,Returning_Visitor,TRUE,FALSE 0,0,0,0,3,0,0.2,0.2,0,0,Nov,3,2,1,10,Returning_Visitor,FALSE,FALSE 3,81.5,0,0,3,40.5,0,0.04,0,0,Nov,1,1,1,8,New_Visitor,FALSE,FALSE 3,97.5,0,0,5,155.3333333,0,0.028571429,0,0,Dec,3,2,1,2,New_Visitor,FALSE,FALSE 0,0,0,0,1,0,0.2,0.2,0,0,Dec,2,2,6,1,Returning_Visitor,TRUE,FALSE 0,0,0,0,15,1134.455556,0,0.001333333,30.30681247,0,Dec,2,4,1,2,Returning_Visitor,FALSE,TRUE 0,0,0,0,7,389.1666667,0,0.021428571,0,0,Nov,3,2,3,2,Returning_Visitor,TRUE,TRUE 0,0,0,0,10,902.6666667,0,0.03,0,0,Nov,2,2,3,20,Returning_Visitor,TRUE,FALSE 9,500,0,0,54,2201.710714,0.000595238,0.011252834,2.82745293,0,Dec,2,5,1,1,Returning_Visitor,FALSE,FALSE 1,7,0,0,21,803,0.031578947,0.052631579,7.014753437,0,Nov,3,2,3,3,Returning_Visitor,FALSE,TRUE 0,0,0,0,8,102.75,0,0.066666667,0,0,Dec,2,2,9,6,Returning_Visitor,FALSE,FALSE 3,117,0,0,98,3653.641667,0.002680653,0.0296601,0,0,Nov,2,5,9,2,Returning_Visitor,FALSE,FALSE 0,0,0,0,15,264.0833333,0,0.02,0,0,Nov,2,2,2,1,Returning_Visitor,FALSE,FALSE 12,257.75,0,0,3,97.5,0,0.038461538,0,0,Dec,3,2,4,8,Returning_Visitor,FALSE,TRUE 0,0,0,0,16,853.4166667,0.013333333,0.037777778,50.81417971,0,Dec,1,1,7,2,New_Visitor,FALSE,TRUE 6,270.75,0,0,6,217.5,0,0.02,0,0,Dec,3,2,4,2,New_Visitor,TRUE,FALSE 0,0,0,0,13,223.4166667,0,0.023076923,0,0,Dec,2,4,1,2,Returning_Visitor,FALSE,FALSE 1,8,0,0,55,1893.650488,0.012727273,0.038483516,0,0,Nov,3,2,1,13,Returning_Visitor,TRUE,FALSE 0,0,0,0,69,2077.806007,0.004830918,0.018561873,0,0,Nov,2,2,4,2,Returning_Visitor,FALSE,FALSE 0,0,2,120.5,54,1389.216667,0.008928571,0.025119048,0,0,Dec,1,1,1,2,Returning_Visitor,FALSE,FALSE 1,0,2,23.75,193,6784.671131,0.004931973,0.021157516,0,0,Nov,2,2,7,2,Returning_Visitor,TRUE,TRUE 1,43.5,0,0,6,183.875,0,0.014285714,0,0,Dec,2,2,1,2,New_Visitor,FALSE,FALSE 4,17.5,3,171,6,101,0.02,0.03,0,0,Nov,1,2,1,8,New_Visitor,TRUE,FALSE 0,0,0,0,6,199.25,0,0.033333333,0,0,Dec,2,4,1,3,Other,FALSE,FALSE 0,0,0,0,2,0,0.2,0.2,0,0,Nov,2,5,1,2,Returning_Visitor,FALSE,FALSE 12,317.6282051,2,256.5,81,3675.940705,0.006741573,0.017420532,11.62288468,0,Dec,2,2,1,10,Returning_Visitor,TRUE,TRUE 0,0,0,0,18,541.9166667,0,0.015686275,0,0,Nov,3,2,1,2,New_Visitor,TRUE,FALSE 5,40.13636364,0,0,35,840.9952922,0.005263158,0.012593985,64.72238907,0,Nov,2,2,1,1,Returning_Visitor,FALSE,TRUE 0,0,6,97.16666667,81,3466.794048,0.018431373,0.022050322,0,0,Nov,3,2,1,13,Returning_Visitor,TRUE,FALSE 0,0,0,0,3,52.5,0.066666667,0.1,0,0,Dec,3,2,3,3,Returning_Visitor,FALSE,FALSE 0,0,0,0,8,833.75,0.0125,0.0375,0,0,Dec,2,5,1,3,Returning_Visitor,FALSE,FALSE 5,116,0,0,31,1350.258333,0.012903226,0.023870968,0,0,Dec,3,2,8,2,Returning_Visitor,FALSE,FALSE 0,0,0,0,14,999,0,0.023076923,8.482951523,0,Dec,2,2,3,6,Returning_Visitor,FALSE,TRUE 0,0,0,0,111,1624.75,0.013636364,0.025515152,0,0,Dec,2,2,1,2,Returning_Visitor,FALSE,FALSE 6,580.3333333,0,0,43,1326.107143,0.018367347,0.018160173,0,0,Nov,1,1,3,2,Returning_Visitor,FALSE,TRUE 1,14,0,0,11,239.5833333,0,0.016666667,0,0,Nov,2,2,6,1,Returning_Visitor,TRUE,FALSE 0,0,0,0,18,413.6666667,0,0.047222222,0,0,Dec,2,2,6,1,Returning_Visitor,FALSE,FALSE 13,399.75,3,47.75,61,1458.993056,0.001785714,0.01655198,17.49721917,0,Nov,1,1,1,2,New_Visitor,FALSE,FALSE 0,0,0,0,36,872.8333333,0,0.007058824,81.02729581,0,Nov,2,4,1,8,New_Visitor,FALSE,TRUE 8,108.5,0,0,53,1287.852778,0.003389831,0.027514124,0,0,Dec,2,2,1,2,Returning_Visitor,FALSE,FALSE 12,219.274359,4,857.1666667,107,2640.135697,0,0.015489592,6.479206264,0,Nov,1,2,1,2,Returning_Visitor,TRUE,TRUE 0,0,0,0,112,6673.291392,0,0.008744589,5.468093499,0,Nov,2,4,1,3,Returning_Visitor,FALSE,FALSE 1,0,1,46.5,22,671.8666667,0.017391304,0.02057971,0,0,Nov,3,2,7,10,Returning_Visitor,FALSE,FALSE 0,0,0,0,7,486.5,0,0.028571429,0,0,Nov,2,4,1,1,Returning_Visitor,FALSE,FALSE 1,8,0,0,6,189.75,0,0.019047619,0,0,Nov,1,1,7,2,Returning_Visitor,FALSE,FALSE 0,0,0,0,33,472.3611111,0.012903226,0.014721408,0,0,Nov,1,1,9,2,Returning_Visitor,FALSE,FALSE 6,243.75,0,0,8,171.25,0.038888889,0.067222222,0,0,Dec,3,2,3,1,Returning_Visitor,FALSE,FALSE 3,18,0,0,2,8,0,0.05,0,0,Dec,8,13,9,20,Other,FALSE,FALSE 19,739.1948718,1,8,114,4269.493146,0.001574803,0.016980377,8.466384757,0,Nov,2,2,2,1,Returning_Visitor,FALSE,FALSE 0,0,0,0,74,3109.404167,0.009459459,0.042859175,0,0,Nov,2,2,1,2,Returning_Visitor,FALSE,FALSE 0,0,0,0,44,1236.638095,0,0.014728682,0,0,Dec,2,4,1,2,Returning_Visitor,FALSE,FALSE 7,311.1361111,0,0,36,1699.073611,0.010087719,0.035614035,133.2813786,0,Dec,3,2,1,13,Returning_Visitor,FALSE,FALSE 0,0,0,0,7,1749,0.047619048,0.104761905,0,0,Dec,2,2,1,2,Returning_Visitor,FALSE,FALSE 0,0,0,0,13,420,0,0.030769231,0,0,Dec,2,5,6,2,New_Visitor,FALSE,FALSE 1,41.125,0,0,126,4310.004668,0.000687831,0.012822843,3.451072113,0,Nov,2,2,2,2,Returning_Visitor,FALSE,FALSE 0,0,1,5,9,279,0.04,0.041666667,0,0,Nov,3,2,7,8,New_Visitor,TRUE,FALSE 0,0,0,0,24,1010.25,0.008333333,0.016666667,31.81967723,0,Dec,3,2,8,2,Returning_Visitor,FALSE,TRUE 0,0,0,0,3,178.25,0,0.066666667,0,0,Nov,3,2,1,10,Returning_Visitor,FALSE,FALSE 0,0,0,0,19,540.3333333,0,0.044736842,0,0,Nov,2,2,1,10,Returning_Visitor,TRUE,FALSE 5,127.2,2,33,93,2720.265368,0.007142857,0.034701976,0,0,Nov,3,2,4,1,Returning_Visitor,TRUE,FALSE 0,0,0,0,9,867,0.022222222,0.051851852,0,0,Nov,1,1,1,3,Returning_Visitor,FALSE,FALSE 0,0,1,0,22,812.3,0.030848861,0.057391304,0,0,Nov,1,1,4,10,Returning_Visitor,TRUE,FALSE 2,50.5,0,0,70,2802.116667,0.002898551,0.017230274,0,0,Nov,2,2,3,3,Returning_Visitor,FALSE,FALSE 4,91,0,0,2,22,0.033333333,0.022222222,0,0,Dec,1,1,1,2,New_Visitor,FALSE,FALSE 2,262.6666667,3,112,127,4214.779822,0.008396947,0.018035252,0,0,Nov,3,2,1,3,Returning_Visitor,FALSE,FALSE 5,157.3333333,0,0,76,3923.586111,0.000865801,0.013675718,0,0,Nov,2,2,8,2,Returning_Visitor,TRUE,FALSE 0,0,0,0,49,3414.875,0.012244898,0.027210884,0,0,Nov,2,2,7,2,Returning_Visitor,FALSE,FALSE 2,23,0,0,12,284.5,0,0.014285714,0,0,Dec,2,2,3,2,Returning_Visitor,FALSE,FALSE 0,0,0,0,7,53,0.028571429,0.057142857,0,0,Dec,1,1,1,2,Returning_Visitor,FALSE,FALSE 6,413.4996124,0,0,83,6072.032391,0.007058824,0.024023709,1.240071108,0,Nov,3,2,8,1,Returning_Visitor,TRUE,TRUE 2,223.75,3,165,51,2159.004167,0.013684211,0.037347156,10.22993737,0,Nov,1,1,1,2,Returning_Visitor,FALSE,FALSE 0,0,0,0,5,277.5,0,0.04,42.03044096,0,Nov,2,2,3,2,Returning_Visitor,FALSE,TRUE 6,95.91666667,0,0,19,671.55,0,0.020454545,28.12876638,0,Dec,2,2,7,2,Returning_Visitor,FALSE,FALSE 7,111.0833333,0,0,24,431.5,0.008,0.012,0,0,Dec,1,1,1,3,Returning_Visitor,FALSE,FALSE 4,81.5,0,0,93,2773.010873,0.011827957,0.017573541,0,0,Nov,3,2,3,3,Returning_Visitor,TRUE,FALSE 3,52.75,0,0,69,909.1548701,0.002857143,0.012225275,8.086657063,0,Dec,2,2,1,7,Returning_Visitor,FALSE,FALSE 6,133.4666667,0,0,44,2664.445833,0.002040816,0.010884354,97.86083623,0,Nov,2,2,1,3,Returning_Visitor,TRUE,TRUE 5,134.3333333,0,0,56,959.8117424,0,0.011420635,11.61864445,0,Nov,2,2,1,1,Returning_Visitor,TRUE,FALSE 0,0,0,0,7,205.5,0,0.042857143,0,0,Nov,3,2,9,13,Returning_Visitor,FALSE,FALSE 0,0,0,0,20,2001.833333,0.00952381,0.013492063,0,0,Nov,2,2,9,6,Returning_Visitor,FALSE,FALSE 7,139.575,0,0,30,986.5,0,0.011428571,36.39286104,0,Dec,2,10,1,2,New_Visitor,FALSE,TRUE 0,0,0,0,2,40.5,0,0.1,0,0,Dec,1,1,3,3,Other,FALSE,FALSE 5,181.8666667,0,0,192,4845.989214,0.005412371,0.022099252,18.67564853,0,Nov,2,2,1,1,Returning_Visitor,FALSE,FALSE 0,0,0,0,40,1445.840909,0.003827751,0.032192982,0,0,Nov,3,2,6,8,Returning_Visitor,TRUE,FALSE 1,73.75,0,0,45,2674.339216,0.000869565,0.020139353,0,0,Nov,1,1,8,1,Returning_Visitor,FALSE,FALSE 1,88,3,540.3333333,57,1656.410714,0,0.002259887,0,0,Nov,2,4,9,3,Returning_Visitor,FALSE,FALSE 0,0,0,0,125,7453.766667,0.001626016,0.013132473,0,0,Nov,2,2,3,2,Returning_Visitor,FALSE,FALSE 0,0,0,0,37,854.55,0.005405405,0.023166023,0,0,Dec,2,2,6,3,Returning_Visitor,FALSE,FALSE 0,0,0,0,45,2650.416667,0.013333333,0.034074074,0,0,Dec,3,7,2,1,Returning_Visitor,TRUE,FALSE 6,369.3333333,2,225.5,133,3918.363736,0,0.009275362,7.147603773,0,Nov,2,2,2,2,Returning_Visitor,FALSE,FALSE 0,0,0,0,4,197.4166667,0.016666667,0.0875,0,0,Nov,4,1,5,1,Returning_Visitor,FALSE,FALSE 8,167.9107143,6,547.75,111,6340.152381,0.003361345,0.009431773,44.21979406,0,Dec,3,2,6,2,Returning_Visitor,FALSE,FALSE 3,100,0,0,27,730.7916667,0.016,0.027555556,0,0,Nov,3,2,3,11,Returning_Visitor,FALSE,FALSE 0,0,0,0,3,88.5,0,0.033333333,0,0,Nov,3,2,1,3,Returning_Visitor,TRUE,FALSE 0,0,0,0,20,1008.5,0.08,0.12,0,0,Dec,2,2,3,1,Returning_Visitor,TRUE,FALSE 0,0,0,0,3,35,0,0.066666667,0,0,Nov,4,1,1,1,Returning_Visitor,FALSE,FALSE 0,0,0,0,8,189.5,0.075,0.1,0,0,Dec,3,2,1,13,Returning_Visitor,FALSE,FALSE 0,0,2,21,12,144.5,0.014285714,0.05,0,0,Dec,2,2,2,1,Returning_Visitor,FALSE,FALSE 0,0,0,0,23,1422.85,0.017391304,0.049275362,0,0,Nov,2,2,7,1,Returning_Visitor,FALSE,FALSE 0,0,3,33.75,9,313.9285714,0.033333333,0.027777778,0,0,Dec,3,2,2,10,Returning_Visitor,FALSE,FALSE 0,0,0,0,5,108,0,0.04,0,0,Dec,2,2,1,2,New_Visitor,FALSE,FALSE 0,0,0,0,6,139.75,0.033333333,0.066666667,0,0,Nov,2,2,3,2,New_Visitor,FALSE,FALSE 0,0,0,0,21,472.875,0.00952381,0.041269841,0,0,Nov,2,2,1,2,Returning_Visitor,FALSE,FALSE 3,9,0,0,17,372.25,0,0.016666667,0,0,Nov,4,1,3,1,Returning_Visitor,FALSE,FALSE 0,0,0,0,9,719.5833333,0.022222222,0.055555556,0,0,Nov,2,4,4,2,Returning_Visitor,FALSE,FALSE 0,0,0,0,2,0,0.2,0.2,0,0,Nov,1,1,4,1,Returning_Visitor,FALSE,FALSE 0,0,0,0,22,185.25,0.021212121,0.075757576,0,0,Nov,2,7,1,1,Returning_Visitor,TRUE,FALSE 0,0,0,0,3,172,0,0.066666667,0,0,Dec,2,13,9,20,Returning_Visitor,FALSE,FALSE 0,0,0,0,30,655.5428571,0.006666667,0.019666667,0,0,Dec,3,2,1,2,Returning_Visitor,TRUE,FALSE 0,0,0,0,39,1168.619841,0,0.009064449,0,0,Nov,1,1,4,3,Returning_Visitor,FALSE,FALSE 0,0,0,0,13,1041.75,0.018181818,0.027272727,0,0,Dec,2,2,5,2,Returning_Visitor,FALSE,FALSE 2,305.125,3,368.25,27,1121.25,0.02,0.042857143,39.51980679,0,Dec,3,2,1,2,Returning_Visitor,FALSE,FALSE 1,19,0,0,45,4018.45,0.009090909,0.021969697,0,0,Nov,2,2,3,1,Returning_Visitor,TRUE,FALSE 3,54,0,0,20,262.9,0.008333333,0.055555556,0,0,Dec,2,2,1,2,Returning_Visitor,FALSE,FALSE 1,5,0,0,21,411.0833333,0.015789474,0.016140351,0,0,Nov,3,2,7,3,Returning_Visitor,FALSE,FALSE 1,0,2,211.25,144,4627.489571,0.001360544,0.020664031,0,0,Nov,2,2,1,2,Returning_Visitor,FALSE,TRUE 7,150.3571429,1,9,221,11431.00124,0.011148992,0.021904109,1.582473154,0,Nov,2,5,1,2,Returning_Visitor,TRUE,TRUE 3,16,3,86,15,2773.5,0,0.03,78.81172527,0,Dec,2,2,1,2,Returning_Visitor,FALSE,TRUE 0,0,0,0,7,705.8333333,0.028571429,0.085714286,0,0,Dec,5,11,4,1,Returning_Visitor,TRUE,FALSE 0,0,0,0,7,212.5,0,0.033333333,0,0,Nov,3,2,1,11,Returning_Visitor,TRUE,FALSE 0,0,0,0,44,615,0.013636364,0.036363636,0,0,Dec,4,1,3,1,Returning_Visitor,FALSE,FALSE 3,100.5,0,0,95,1453.640882,0.002105263,0.012761333,0,0,Dec,2,2,3,2,New_Visitor,FALSE,FALSE 0,0,0,0,6,279,0,0.033333333,0,0,Dec,1,2,3,10,Returning_Visitor,TRUE,FALSE 0,0,0,0,21,1128.583333,0,0.013043478,3.685400817,0,Dec,2,2,1,2,Returning_Visitor,FALSE,FALSE 0,0,0,0,8,143.5833333,0.014285714,0.05,0,0,Nov,2,2,3,1,Returning_Visitor,FALSE,FALSE 0,0,0,0,6,0,0.2,0.2,0,0,Nov,1,8,4,1,Returning_Visitor,FALSE,FALSE 6,76.25,0,0,22,1075.25,0,0.004166667,0,0,Dec,2,2,4,2,Returning_Visitor,FALSE,FALSE 2,64.75,0,0,44,1157.97619,0,0.013953488,0,0,Nov,2,2,1,10,Returning_Visitor,FALSE,FALSE 0,0,1,0,16,503,0,0.037647059,0,0,Nov,2,2,1,1,Returning_Visitor,FALSE,FALSE 3,145,0,0,53,1783.791667,0.007142857,0.029030612,12.24171745,0,Dec,4,6,1,1,Returning_Visitor,TRUE,FALSE 0,0,0,0,5,465.75,0,0.021333333,0,0,Nov,3,2,1,8,Returning_Visitor,TRUE,FALSE 0,0,0,0,6,184.25,0.083333333,0.086666667,0,0,Nov,3,2,1,13,Returning_Visitor,TRUE,FALSE 4,75,0,0,15,346,0,0.021052632,0,0,Nov,2,2,3,11,Returning_Visitor,FALSE,FALSE 0,0,0,0,3,21.25,0,0.066666667,0,0,Nov,3,2,1,2,New_Visitor,TRUE,FALSE ================================================ FILE: week4/shopping/shopping.py ================================================ import csv import pandas import sys from sklearn.model_selection import train_test_split from sklearn.neighbors import KNeighborsClassifier from sklearn.metrics import confusion_matrix TEST_SIZE = 0.4 def main(): # Check command-line arguments if len(sys.argv) != 2: sys.exit("Usage: python shopping.py data") # Load data from spreadsheet and split into train and test sets evidence, labels = load_data(sys.argv[1]) X_train, X_test, y_train, y_test = train_test_split( evidence, labels, test_size=TEST_SIZE ) # Train model and make predictions model = train_model(X_train, y_train) predictions = model.predict(X_test) sensitivity, specificity = evaluate(y_test, predictions) # Print results print(f"Correct: {(y_test == predictions).sum()}") print(f"Incorrect: {(y_test != predictions).sum()}") print(f"True Positive Rate: {100 * sensitivity:.2f}%") print(f"True Negative Rate: {100 * specificity:.2f}%") def load_data(filename): """ Load shopping data from a CSV file `filename` and convert into a list of evidence lists and a list of labels. Return a tuple (evidence, labels). evidence should be a list of lists, where each list contains the following values, in order: - Administrative, an integer - Administrative_Duration, a floating point number - Informational, an integer - Informational_Duration, a floating point number - ProductRelated, an integer - ProductRelated_Duration, a floating point number - BounceRates, a floating point number - ExitRates, a floating point number - PageValues, a floating point number - SpecialDay, a floating point number - Month, an index from 0 (January) to 11 (December) - OperatingSystems, an integer - Browser, an integer - Region, an integer - TrafficType, an integer - VisitorType, an integer 0 (not returning) or 1 (returning) - Weekend, an integer 0 (if false) or 1 (if true) labels should be the corresponding list of labels, where each label is 1 if Revenue is true, and 0 otherwise. """ evidence = [] labels = [] months = {'Jan': 0, 'Feb': 1, 'Mar': 2, 'Apr': 3, 'May': 4, 'June': 5, 'Jul': 6, 'Aug': 7, 'Sep': 8, 'Oct': 9, 'Nov': 10, 'Dec': 11} # read csv file csv_file = pandas.read_csv(filename) # prepare labels dataframe labels_df = csv_file['Revenue'] # prepare evidence dataframe evidence_df = csv_file.drop(columns=['Revenue']) # replace month names with numerical values evidence_df = evidence_df.replace(months) # replace boolean with 0/1 values evidence_df['VisitorType'] = evidence_df['VisitorType'].apply(lambda x: 1 if x == 'Returning_Visitor' else 0) evidence_df['Weekend'] = evidence_df['Weekend'].apply(lambda x: 1 if x == 'True' else 0) labels_df = labels_df.apply(lambda x: 1 if x is True else 0) # convert dataframes to lists evidence_list = evidence_df.values.tolist() labels_list = labels_df.values.tolist() return evidence_list, labels_list def train_model(evidence, labels): """ Given a list of evidence lists and a list of labels, return a fitted k-nearest neighbor model (k=1) trained on the data. """ neigh = KNeighborsClassifier(n_neighbors=1) neigh.fit(evidence, labels) return neigh def evaluate(labels, predictions): """ Given a list of actual labels and a list of predicted labels, return a tuple (sensitivity, specificty). Assume each label is either a 1 (positive) or 0 (negative). `sensitivity` should be a floating-point value from 0 to 1 representing the "true positive rate": the proportion of actual positive labels that were accurately identified. `specificity` should be a floating-point value from 0 to 1 representing the "true negative rate": the proportion of actual negative labels that were accurately identified. """ tn, fp, fn, tp = confusion_matrix(labels, predictions).ravel() sensitivity = tp / (tp + fn) specificity = tn / (tn + fp) return sensitivity, specificity if __name__ == "__main__": main() ================================================ FILE: week5/traffic/traffic.py ================================================ import cv2 import numpy as np import os import sys import tensorflow as tf from sklearn.model_selection import train_test_split EPOCHS = 10 IMG_WIDTH = 30 IMG_HEIGHT = 30 NUM_CATEGORIES = 43 TEST_SIZE = 0.4 def main(): # Check command-line arguments if len(sys.argv) not in [2, 3]: sys.exit("Usage: python traffic.py data_directory [model.h5]") # Get image arrays and labels for all image files images, labels = load_data(sys.argv[1]) # Split data into training and testing sets labels = tf.keras.utils.to_categorical(labels) x_train, x_test, y_train, y_test = train_test_split( np.array(images), np.array(labels), test_size=TEST_SIZE ) x_train, x_test = x_train / 255.0, x_test / 255.0 # Get a compiled neural network model = get_model() # Fit model on training data model.fit(x_train, y_train, epochs=EPOCHS) # Evaluate neural network performance model.evaluate(x_test, y_test, verbose=2) # Save model to file if len(sys.argv) == 3: filename = sys.argv[2] model.save(filename) print(f"Model saved to {filename}.") def load_data(data_dir): """ Load image data from directory `data_dir`. Assume `data_dir` has one directory named after each category, numbered 0 through NUM_CATEGORIES - 1. Inside each category directory will be some number of image files. Return tuple `(images, labels)`. `images` should be a list of all of the images in the data directory, where each image is formatted as a numpy ndarray with dimensions IMG_WIDTH x IMG_HEIGHT x 3. `labels` should be a list of integer labels, representing the categories for each of the corresponding `images`. """ images = [] labels = [] # iterate through data set directories for directory in os.listdir(data_dir): # iterate through single image files print(f"Started loading files from {directory} directory") for file in os.listdir(os.path.join(data_dir, directory)): image = cv2.imread(os.path.join(data_dir, directory, file)) resized = cv2.resize(image, (IMG_WIDTH, IMG_HEIGHT)) images.append(resized) labels.append(int(directory)) print(f"Ended loading files from {directory} directory") return images, labels def get_model(): """ Returns a compiled convolutional neural network model. Assume that the `input_shape` of the first layer is `(IMG_WIDTH, IMG_HEIGHT, 3)`. The output layer should have `NUM_CATEGORIES` units, one for each category. """ model = tf.keras.models.Sequential([ # Convolutional layer. Learn 32 filters using a 3x3 kernel tf.keras.layers.Conv2D( 32, (3, 3), activation="relu", input_shape=(IMG_WIDTH, IMG_HEIGHT, 3) ), # Max-pooling layer, using 2x2 pool size tf.keras.layers.MaxPooling2D(pool_size=(2, 2)), tf.keras.layers.Conv2D( 32, (3, 3), activation="relu", input_shape=(IMG_WIDTH, IMG_HEIGHT, 3) ), # Max-pooling layer, using 2x2 pool size tf.keras.layers.MaxPooling2D(pool_size=(2, 2)), # Flatten units tf.keras.layers.Flatten(), # Add a hidden layer with dropout tf.keras.layers.Dense(128, activation="relu"), tf.keras.layers.Dropout(0.5), # Add an output layer with output units for all 10 digits tf.keras.layers.Dense(NUM_CATEGORIES, activation="softmax") ]) model.compile( optimizer="adam", loss="categorical_crossentropy", metrics=["accuracy"] ) model.summary() return model if __name__ == "__main__": main() ================================================ FILE: week6/parser/parser.py ================================================ import nltk import sys import string TERMINALS = """ Adj -> "country" | "dreadful" | "enigmatical" | "little" | "moist" | "red" Adv -> "down" | "here" | "never" Conj -> "and" | "until" Det -> "a" | "an" | "his" | "my" | "the" N -> "armchair" | "companion" | "day" | "door" | "hand" | "he" | "himself" N -> "holmes" | "home" | "i" | "mess" | "paint" | "palm" | "pipe" | "she" N -> "smile" | "thursday" | "walk" | "we" | "word" P -> "at" | "before" | "in" | "of" | "on" | "to" V -> "arrived" | "came" | "chuckled" | "had" | "lit" | "said" | "sat" V -> "smiled" | "tell" | "were" """ NONTERMINALS = """ S -> PART | PART Conj PART PART -> NP VP | NP Adv VP | VP NP -> N | NA N NA -> Det | Adj | NA NA VP -> V | V SUPP SUPP -> NP | P | Adv | SUPP SUPP | SUPP SUPP SUPP """ grammar = nltk.CFG.fromstring(NONTERMINALS + TERMINALS) parser = nltk.ChartParser(grammar) def main(): # If filename specified, read sentence from file if len(sys.argv) == 2: with open(sys.argv[1]) as f: s = f.read() # Otherwise, get sentence as input else: s = input("Sentence: ") # Convert input into list of words s = preprocess(s) # Attempt to parse sentence try: trees = list(parser.parse(s)) except ValueError as e: print(e) return if not trees: print("Could not parse sentence.") return # Print each tree with noun phrase chunks for tree in trees: tree.pretty_print() print("Noun Phrase Chunks") for np in np_chunk(tree): print(" ".join(np.flatten())) def preprocess(sentence): """ Convert `sentence` to a list of its words. Pre-process sentence by converting all characters to lowercase and removing any word that does not contain at least one alphabetic character. """ tokenized = nltk.tokenize.word_tokenize(sentence) return [x.lower() for x in tokenized if x.isalpha()] def np_chunk(tree): """ Return a list of all noun phrase chunks in the sentence tree. A noun phrase chunk is defined as any subtree of the sentence whose label is "NP" that does not itself contain any other noun phrases as subtrees. """ retlst = [] for subtree in tree.subtrees(): if subtree.label() == "NP": retlst.append(subtree) return retlst if __name__ == "__main__": main() ================================================ FILE: week6/parser/sentences/1.txt ================================================ Holmes sat. ================================================ FILE: week6/parser/sentences/10.txt ================================================ I had a little moist red paint in the palm of my hand. ================================================ FILE: week6/parser/sentences/2.txt ================================================ Holmes lit a pipe. ================================================ FILE: week6/parser/sentences/3.txt ================================================ We arrived the day before Thursday. ================================================ FILE: week6/parser/sentences/4.txt ================================================ Holmes sat in the red armchair and he chuckled. ================================================ FILE: week6/parser/sentences/5.txt ================================================ My companion smiled an enigmatical smile. ================================================ FILE: week6/parser/sentences/6.txt ================================================ Holmes chuckled to himself. ================================================ FILE: week6/parser/sentences/7.txt ================================================ She never said a word until we were at the door here. ================================================ FILE: week6/parser/sentences/8.txt ================================================ Holmes sat down and lit his pipe. ================================================ FILE: week6/parser/sentences/9.txt ================================================ I had a country walk on Thursday and came home in a dreadful mess. ================================================ FILE: week6/parser/sentences/test.txt ================================================ ================================================ FILE: week6/questions/corpus/artificial_intelligence.txt ================================================ https://en.wikipedia.org/wiki/Artificial_intelligence In computer science, artificial intelligence (AI), sometimes called machine intelligence, is intelligence demonstrated by machines, in contrast to the natural intelligence displayed by humans and animals. Leading AI textbooks define the field as the study of "intelligent agents": any device that perceives its environment and takes actions that maximize its chance of successfully achieving its goals. Colloquially, the term "artificial intelligence" is often used to describe machines (or computers) that mimic "cognitive" functions that humans associate with the human mind, such as "learning" and "problem solving".As machines become increasingly capable, tasks considered to require "intelligence" are often removed from the definition of AI, a phenomenon known as the AI effect. A quip in Tesler's Theorem says "AI is whatever hasn't been done yet." For instance, optical character recognition is frequently excluded from things considered to be AI, having become a routine technology. Modern machine capabilities generally classified as AI include successfully understanding human speech, competing at the highest level in strategic game systems (such as chess and Go), autonomously operating cars, intelligent routing in content delivery networks, and military simulations. Artificial intelligence was founded as an academic discipline in 1955, and in the years since has experienced several waves of optimism, followed by disappointment and the loss of funding (known as an "AI winter"), followed by new approaches, success and renewed funding. For most of its history, AI research has been divided into subfields that often fail to communicate with each other. These sub-fields are based on technical considerations, such as particular goals (e.g. "robotics" or "machine learning"), the use of particular tools ("logic" or artificial neural networks), or deep philosophical differences. Subfields have also been based on social factors (particular institutions or the work of particular researchers).The traditional problems (or goals) of AI research include reasoning, knowledge representation, planning, learning, natural language processing, perception and the ability to move and manipulate objects. General intelligence is among the field's long-term goals. Approaches include statistical methods, computational intelligence, and traditional symbolic AI. Many tools are used in AI, including versions of search and mathematical optimization, artificial neural networks, and methods based on statistics, probability and economics. The AI field draws upon computer science, information engineering, mathematics, psychology, linguistics, philosophy, and many other fields. The field was founded on the assumption that human intelligence "can be so precisely described that a machine can be made to simulate it". This raises philosophical arguments about the nature of the mind and the ethics of creating artificial beings endowed with human-like intelligence. These issues have been explored by myth, fiction and philosophy since antiquity. Some people also consider AI to be a danger to humanity if it progresses unabated. Others believe that AI, unlike previous technological revolutions, will create a risk of mass unemployment. In the twenty-first century, AI techniques have experienced a resurgence following concurrent advances in computer power, large amounts of data, and theoretical understanding; and AI techniques have become an essential part of the technology industry, helping to solve many challenging problems in computer science, software engineering and operations research. == History == Thought-capable artificial beings appeared as storytelling devices in antiquity, and have been common in fiction, as in Mary Shelley's Frankenstein or Karel Čapek's R.U.R. (Rossum's Universal Robots). These characters and their fates raised many of the same issues now discussed in the ethics of artificial intelligence. The study of mechanical or "formal" reasoning began with philosophers and mathematicians in antiquity. The study of mathematical logic led directly to Alan Turing's theory of computation, which suggested that a machine, by shuffling symbols as simple as "0" and "1", could simulate any conceivable act of mathematical deduction. This insight, that digital computers can simulate any process of formal reasoning, is known as the Church–Turing thesis. Along with concurrent discoveries in neurobiology, information theory and cybernetics, this led researchers to consider the possibility of building an electronic brain. Turing proposed changing the question from whether a machine was intelligent, to "whether or not it is possible for machinery to show intelligent behaviour". The first work that is now generally recognized as AI was McCullouch and Pitts' 1943 formal design for Turing-complete "artificial neurons".The field of AI research was born at a workshop at Dartmouth College in 1956, where the term "Artificial Intelligence" was coined by John McCarthy to distinguish the field from cybernetics and escape the influence of the cyberneticist Norbert Wiener. Attendees Allen Newell (CMU), Herbert Simon (CMU), John McCarthy (MIT), Marvin Minsky (MIT) and Arthur Samuel (IBM) became the founders and leaders of AI research. They and their students produced programs that the press described as "astonishing": computers were learning checkers strategies (c. 1954) (and by 1959 were reportedly playing better than the average human), solving word problems in algebra, proving logical theorems (Logic Theorist, first run c. 1956) and speaking English. By the middle of the 1960s, research in the U.S. was heavily funded by the Department of Defense and laboratories had been established around the world. AI's founders were optimistic about the future: Herbert Simon predicted, "machines will be capable, within twenty years, of doing any work a man can do". Marvin Minsky agreed, writing, "within a generation ... the problem of creating 'artificial intelligence' will substantially be solved".They failed to recognize the difficulty of some of the remaining tasks. Progress slowed and in 1974, in response to the criticism of Sir James Lighthill and ongoing pressure from the US Congress to fund more productive projects, both the U.S. and British governments cut off exploratory research in AI. The next few years would later be called an "AI winter", a period when obtaining funding for AI projects was difficult. In the early 1980s, AI research was revived by the commercial success of expert systems, a form of AI program that simulated the knowledge and analytical skills of human experts. By 1985, the market for AI had reached over a billion dollars. At the same time, Japan's fifth generation computer project inspired the U.S and British governments to restore funding for academic research. However, beginning with the collapse of the Lisp Machine market in 1987, AI once again fell into disrepute, and a second, longer-lasting hiatus began. The development of metal–oxide–semiconductor (MOS) very-large-scale integration (VLSI), in the form of complementary MOS (CMOS) transistor technology, enabled the development of practical artificial neural network (ANN) technology in the 1980s. A landmark publication in the field was the 1989 book Analog VLSI Implementation of Neural Systems by Carver A. Mead and Mohammed Ismail. In the late 1990s and early 21st century, AI began to be used for logistics, data mining, medical diagnosis and other areas. The success was due to increasing computational power (see Moore's law and transistor count), greater emphasis on solving specific problems, new ties between AI and other fields (such as statistics, economics and mathematics), and a commitment by researchers to mathematical methods and scientific standards. Deep Blue became the first computer chess-playing system to beat a reigning world chess champion, Garry Kasparov, on 11 May 1997.In 2011, a Jeopardy! quiz show exhibition match, IBM's question answering system, Watson, defeated the two greatest Jeopardy! champions, Brad Rutter and Ken Jennings, by a significant margin. Faster computers, algorithmic improvements, and access to large amounts of data enabled advances in machine learning and perception; data-hungry deep learning methods started to dominate accuracy benchmarks around 2012. The Kinect, which provides a 3D body–motion interface for the Xbox 360 and the Xbox One, uses algorithms that emerged from lengthy AI research as do intelligent personal assistants in smartphones. In March 2016, AlphaGo won 4 out of 5 games of Go in a match with Go champion Lee Sedol, becoming the first computer Go-playing system to beat a professional Go player without handicaps. In the 2017 Future of Go Summit, AlphaGo won a three-game match with Ke Jie, who at the time continuously held the world No. 1 ranking for two years. This marked the completion of a significant milestone in the development of Artificial Intelligence as Go is a relatively complex game, more so than Chess. According to Bloomberg's Jack Clark, 2015 was a landmark year for artificial intelligence, with the number of software projects that use AI Google increased from a "sporadic usage" in 2012 to more than 2,700 projects. Clark also presents factual data indicating the improvements of AI since 2012 supported by lower error rates in image processing tasks. He attributes this to an increase in affordable neural networks, due to a rise in cloud computing infrastructure and to an increase in research tools and datasets. Other cited examples include Microsoft's development of a Skype system that can automatically translate from one language to another and Facebook's system that can describe images to blind people. In a 2017 survey, one in five companies reported they had "incorporated AI in some offerings or processes". Around 2016, China greatly accelerated its government funding; given its large supply of data and its rapidly increasing research output, some observers believe it may be on track to becoming an "AI superpower". However, it has been acknowledged that reports regarding artificial intelligence have tended to be exaggerated. == Definitions == Computer science defines AI research as the study of "intelligent agents": any device that perceives its environment and takes actions that maximize its chance of successfully achieving its goals. A more elaborate definition characterizes AI as "a system's ability to correctly interpret external data, to learn from such data, and to use those learnings to achieve specific goals and tasks through flexible adaptation." == Basics == A typical AI analyzes its environment and takes actions that maximize its chance of success. An AI's intended utility function (or goal) can be simple ("1 if the AI wins a game of Go, 0 otherwise") or complex ("Do mathematically similar actions to the ones succeeded in the past"). Goals can be explicitly defined or induced. If the AI is programmed for "reinforcement learning", goals can be implicitly induced by rewarding some types of behavior or punishing others. Alternatively, an evolutionary system can induce goals by using a "fitness function" to mutate and preferentially replicate high-scoring AI systems, similar to how animals evolved to innately desire certain goals such as finding food. Some AI systems, such as nearest-neighbor, instead of reason by analogy, these systems are not generally given goals, except to the degree that goals are implicit in their training data. Such systems can still be benchmarked if the non-goal system is framed as a system whose "goal" is to successfully accomplish its narrow classification task. AI often revolves around the use of algorithms. An algorithm is a set of unambiguous instructions that a mechanical computer can execute. A complex algorithm is often built on top of other, simpler, algorithms. A simple example of an algorithm is the following (optimal for first player) recipe for play at tic-tac-toe: If someone has a "threat" (that is, two in a row), take the remaining square. Otherwise, if a move "forks" to create two threats at once, play that move. Otherwise, take the center square if it is free. Otherwise, if your opponent has played in a corner, take the opposite corner. Otherwise, take an empty corner if one exists. Otherwise, take any empty square. Many AI algorithms are capable of learning from data; they can enhance themselves by learning new heuristics (strategies, or "rules of thumb", that have worked well in the past), or can themselves write other algorithms. Some of the "learners" described below, including Bayesian networks, decision trees, and nearest-neighbor, could theoretically, (given infinite data, time, and memory) learn to approximate any function, including which combination of mathematical functions would best describe the world. These learners could therefore, derive all possible knowledge, by considering every possible hypothesis and matching them against the data. In practice, it is almost never possible to consider every possibility, because of the phenomenon of "combinatorial explosion", where the amount of time needed to solve a problem grows exponentially. Much of AI research involves figuring out how to identify and avoid considering broad range of possibilities that are unlikely to be beneficial. For example, when viewing a map and looking for the shortest driving route from Denver to New York in the East, one can in most cases skip looking at any path through San Francisco or other areas far to the West; thus, an AI wielding a pathfinding algorithm like A* can avoid the combinatorial explosion that would ensue if every possible route had to be ponderously considered in turn. The earliest (and easiest to understand) approach to AI was symbolism (such as formal logic): "If an otherwise healthy adult has a fever, then they may have influenza". A second, more general, approach is Bayesian inference: "If the current patient has a fever, adjust the probability they have influenza in such-and-such way". The third major approach, extremely popular in routine business AI applications, are analogizers such as SVM and nearest-neighbor: "After examining the records of known past patients whose temperature, symptoms, age, and other factors mostly match the current patient, X% of those patients turned out to have influenza". A fourth approach is harder to intuitively understand, but is inspired by how the brain's machinery works: the artificial neural network approach uses artificial "neurons" that can learn by comparing itself to the desired output and altering the strengths of the connections between its internal neurons to "reinforce" connections that seemed to be useful. These four main approaches can overlap with each other and with evolutionary systems; for example, neural nets can learn to make inferences, to generalize, and to make analogies. Some systems implicitly or explicitly use multiple of these approaches, alongside many other AI and non-AI algorithms; the best approach is often different depending on the problem. Learning algorithms work on the basis that strategies, algorithms, and inferences that worked well in the past are likely to continue working well in the future. These inferences can be obvious, such as "since the sun rose every morning for the last 10,000 days, it will probably rise tomorrow morning as well". They can be nuanced, such as "X% of families have geographically separate species with color variants, so there is a Y% chance that undiscovered black swans exist". Learners also work on the basis of "Occam's razor": The simplest theory that explains the data is the likeliest. Therefore, according to Occam's razor principle, a learner must be designed such that it prefers simpler theories to complex theories, except in cases where the complex theory is proven substantially better. Settling on a bad, overly complex theory gerrymandered to fit all the past training data is known as overfitting. Many systems attempt to reduce overfitting by rewarding a theory in accordance with how well it fits the data, but penalizing the theory in accordance with how complex the theory is. Besides classic overfitting, learners can also disappoint by "learning the wrong lesson". A toy example is that an image classifier trained only on pictures of brown horses and black cats might conclude that all brown patches are likely to be horses. A real-world example is that, unlike humans, current image classifiers don't determine the spatial relationship between components of the picture; instead, they learn abstract patterns of pixels that humans are oblivious to, but that linearly correlate with images of certain types of real objects. Faintly superimposing such a pattern on a legitimate image results in an "adversarial" image that the system misclassifies. Compared with humans, existing AI lacks several features of human "commonsense reasoning"; most notably, humans have powerful mechanisms for reasoning about "naïve physics" such as space, time, and physical interactions. This enables even young children to easily make inferences like "If I roll this pen off a table, it will fall on the floor". Humans also have a powerful mechanism of "folk psychology" that helps them to interpret natural-language sentences such as "The city councilmen refused the demonstrators a permit because they advocated violence". (A generic AI has difficulty discerning whether the ones alleged to be advocating violence are the councilmen or the demonstrators.) This lack of "common knowledge" means that AI often makes different mistakes than humans make, in ways that can seem incomprehensible. For example, existing self-driving cars cannot reason about the location nor the intentions of pedestrians in the exact way that humans do, and instead must use non-human modes of reasoning to avoid accidents. == Challenges == The cognitive capabilities of current architectures are very limited, using only a simplified version of what intelligence is really capable of. For instance, the human mind has come up with ways to reason beyond measure and logical explanations to different occurrences in life. What would have been otherwise straightforward, an equivalently difficult problem may be challenging to solve computationally as opposed to using the human mind. This gives rise to two classes of models: structuralist and functionalist. The structural models aim to loosely mimic the basic intelligence operations of the mind such as reasoning and logic. The functional model refers to the correlating data to its computed counterpart. The overall research goal of artificial intelligence is to create technology that allows computers and machines to function in an intelligent manner. The general problem of simulating (or creating) intelligence has been broken down into sub-problems. These consist of particular traits or capabilities that researchers expect an intelligent system to display. The traits described below have received the most attention. === Reasoning, problem solving === Early researchers developed algorithms that imitated step-by-step reasoning that humans use when they solve puzzles or make logical deductions. By the late 1980s and 1990s, AI research had developed methods for dealing with uncertain or incomplete information, employing concepts from probability and economics. These algorithms proved to be insufficient for solving large reasoning problems because they experienced a "combinatorial explosion": they became exponentially slower as the problems grew larger. In fact, even humans rarely use the step-by-step deduction that early AI research was able to model. They solve most of their problems using fast, intuitive judgments. === Knowledge representation === Knowledge representation and knowledge engineering are central to classical AI research. Some "expert systems" attempt to gather together explicit knowledge possessed by experts in some narrow domain. In addition, some projects attempt to gather the "commonsense knowledge" known to the average person into a database containing extensive knowledge about the world. Among the things a comprehensive commonsense knowledge base would contain are: objects, properties, categories and relations between objects; situations, events, states and time; causes and effects; knowledge about knowledge (what we know about what other people know); and many other, less well researched domains. A representation of "what exists" is an ontology: the set of objects, relations, concepts, and properties formally described so that software agents can interpret them. The semantics of these are captured as description logic concepts, roles, and individuals, and typically implemented as classes, properties, and individuals in the Web Ontology Language. The most general ontologies are called upper ontologies, which attempt to provide a foundation for all other knowledge by acting as mediators between domain ontologies that cover specific knowledge about a particular knowledge domain (field of interest or area of concern). Such formal knowledge representations can be used in content-based indexing and retrieval, scene interpretation, clinical decision support, knowledge discovery (mining "interesting" and actionable inferences from large databases), and other areas. Among the most difficult problems in knowledge representation are: Default reasoning and the qualification problem Many of the things people know take the form of "working assumptions". For example, if a bird comes up in conversation, people typically picture an animal that is fist-sized, sings, and flies. None of these things are true about all birds. John McCarthy identified this problem in 1969 as the qualification problem: for any commonsense rule that AI researchers care to represent, there tend to be a huge number of exceptions. Almost nothing is simply true or false in the way that abstract logic requires. AI research has explored a number of solutions to this problem. Breadth of commonsense knowledge The number of atomic facts that the average person knows is very large. Research projects that attempt to build a complete knowledge base of commonsense knowledge (e.g., Cyc) require enormous amounts of laborious ontological engineering—they must be built, by hand, one complicated concept at a time. Subsymbolic form of some commonsense knowledge Much of what people know is not represented as "facts" or "statements" that they could express verbally. For example, a chess master will avoid a particular chess position because it "feels too exposed" or an art critic can take one look at a statue and realize that it is a fake. These are non-conscious and sub-symbolic intuitions or tendencies in the human brain. Knowledge like this informs, supports and provides a context for symbolic, conscious knowledge. As with the related problem of sub-symbolic reasoning, it is hoped that situated AI, computational intelligence, or statistical AI will provide ways to represent this kind of knowledge. === Planning === Intelligent agents must be able to set goals and achieve them. They need a way to visualize the future—a representation of the state of the world and be able to make predictions about how their actions will change it—and be able to make choices that maximize the utility (or "value") of available choices. In classical planning problems, the agent can assume that it is the only system acting in the world, allowing the agent to be certain of the consequences of its actions. However, if the agent is not the only actor, then it requires that the agent can reason under uncertainty. This calls for an agent that can not only assess its environment and make predictions but also evaluate its predictions and adapt based on its assessment. Multi-agent planning uses the cooperation and competition of many agents to achieve a given goal. Emergent behavior such as this is used by evolutionary algorithms and swarm intelligence. === Learning === Machine learning (ML), a fundamental concept of AI research since the field's inception, is the study of computer algorithms that improve automatically through experience. Unsupervised learning is the ability to find patterns in a stream of input, without requiring a human to label the inputs first. Supervised learning includes both classification and numerical regression, which requires a human to label the input data first. Classification is used to determine what category something belongs in, and occurs after a program sees a number of examples of things from several categories. Regression is the attempt to produce a function that describes the relationship between inputs and outputs and predicts how the outputs should change as the inputs change. Both classifiers and regression learners can be viewed as "function approximators" trying to learn an unknown (possibly implicit) function; for example, a spam classifier can be viewed as learning a function that maps from the text of an email to one of two categories, "spam" or "not spam". Computational learning theory can assess learners by computational complexity, by sample complexity (how much data is required), or by other notions of optimization. In reinforcement learning the agent is rewarded for good responses and punished for bad ones. The agent uses this sequence of rewards and punishments to form a strategy for operating in its problem space. === Natural language processing === Natural language processing (NLP) gives machines the ability to read and understand human language. A sufficiently powerful natural language processing system would enable natural-language user interfaces and the acquisition of knowledge directly from human-written sources, such as newswire texts. Some straightforward applications of natural language processing include information retrieval, text mining, question answering and machine translation. Many current approaches use word co-occurrence frequencies to construct syntactic representations of text. "Keyword spotting" strategies for search are popular and scalable but dumb; a search query for "dog" might only match documents with the literal word "dog" and miss a document with the word "poodle". "Lexical affinity" strategies use the occurrence of words such as "accident" to assess the sentiment of a document. Modern statistical NLP approaches can combine all these strategies as well as others, and often achieve acceptable accuracy at the page or paragraph level, but continue to lack the semantic understanding required to classify isolated sentences well. Besides the usual difficulties with encoding semantic commonsense knowledge, existing semantic NLP sometimes scales too poorly to be viable in business applications. Beyond semantic NLP, the ultimate goal of "narrative" NLP is to embody a full understanding of commonsense reasoning. === Perception === Machine perception is the ability to use input from sensors (such as cameras (visible spectrum or infrared), microphones, wireless signals, and active lidar, sonar, radar, and tactile sensors) to deduce aspects of the world. Applications include speech recognition, facial recognition, and object recognition. Computer vision is the ability to analyze visual input. Such input is usually ambiguous; a giant, fifty-meter-tall pedestrian far away may produce exactly the same pixels as a nearby normal-sized pedestrian, requiring the AI to judge the relative likelihood and reasonableness of different interpretations, for example by using its "object model" to assess that fifty-meter pedestrians do not exist. === Motion and manipulation === AI is heavily used in robotics. Advanced robotic arms and other industrial robots, widely used in modern factories, can learn from experience how to move efficiently despite the presence of friction and gear slippage. A modern mobile robot, when given a small, static, and visible environment, can easily determine its location and map its environment; however, dynamic environments, such as (in endoscopy) the interior of a patient's breathing body, pose a greater challenge. Motion planning is the process of breaking down a movement task into "primitives" such as individual joint movements. Such movement often involves compliant motion, a process where movement requires maintaining physical contact with an object. Moravec's paradox generalizes that low-level sensorimotor skills that humans take for granted are, counterintuitively, difficult to program into a robot; the paradox is named after Hans Moravec, who stated in 1988 that "it is comparatively easy to make computers exhibit adult level performance on intelligence tests or playing checkers, and difficult or impossible to give them the skills of a one-year-old when it comes to perception and mobility". This is attributed to the fact that, unlike checkers, physical dexterity has been a direct target of natural selection for millions of years. === Social intelligence === Moravec's paradox can be extended to many forms of social intelligence. Distributed multi-agent coordination of autonomous vehicles remains a difficult problem. Affective computing is an interdisciplinary umbrella that comprises systems which recognize, interpret, process, or simulate human affects. Moderate successes related to affective computing include textual sentiment analysis and, more recently, multimodal affect analysis (see multimodal sentiment analysis), wherein AI classifies the affects displayed by a videotaped subject. In the long run, social skills and an understanding of human emotion and game theory would be valuable to a social agent. Being able to predict the actions of others by understanding their motives and emotional states would allow an agent to make better decisions. Some computer systems mimic human emotion and expressions to appear more sensitive to the emotional dynamics of human interaction, or to otherwise facilitate human–computer interaction. Similarly, some virtual assistants are programmed to speak conversationally or even to banter humorously; this tends to give naïve users an unrealistic conception of how intelligent existing computer agents actually are. === General intelligence === Historically, projects such as the Cyc knowledge base (1984–) and the massive Japanese Fifth Generation Computer Systems initiative (1982–1992) attempted to cover the breadth of human cognition. These early projects failed to escape the limitations of non-quantitative symbolic logic models and, in retrospect, greatly underestimated the difficulty of cross-domain AI. Nowadays, the vast majority of current AI researchers work instead on tractable "narrow AI" applications (such as medical diagnosis or automobile navigation). Many researchers predict that such "narrow AI" work in different individual domains will eventually be incorporated into a machine with artificial general intelligence (AGI), combining most of the narrow skills mentioned in this article and at some point even exceeding human ability in most or all these areas. Many advances have general, cross-domain significance. One high-profile example is that DeepMind in the 2010s developed a "generalized artificial intelligence" that could learn many diverse Atari games on its own, and later developed a variant of the system which succeeds at sequential learning. Besides transfer learning, hypothetical AGI breakthroughs could include the development of reflective architectures that can engage in decision-theoretic metareasoning, and figuring out how to "slurp up" a comprehensive knowledge base from the entire unstructured Web. Some argue that some kind of (currently-undiscovered) conceptually straightforward, but mathematically difficult, "Master Algorithm" could lead to AGI. Finally, a few "emergent" approaches look to simulating human intelligence extremely closely, and believe that anthropomorphic features like an artificial brain or simulated child development may someday reach a critical point where general intelligence emerges. Many of the problems in this article may also require general intelligence, if machines are to solve the problems as well as people do. For example, even specific straightforward tasks, like machine translation, require that a machine read and write in both languages (NLP), follow the author's argument (reason), know what is being talked about (knowledge), and faithfully reproduce the author's original intent (social intelligence). A problem like machine translation is considered "AI-complete", because all of these problems need to be solved simultaneously in order to reach human-level machine performance. == Approaches == There is no established unifying theory or paradigm that guides AI research. Researchers disagree about many issues. A few of the most long standing questions that have remained unanswered are these: should artificial intelligence simulate natural intelligence by studying psychology or neurobiology? Or is human biology as irrelevant to AI research as bird biology is to aeronautical engineering? Can intelligent behavior be described using simple, elegant principles (such as logic or optimization)? Or does it necessarily require solving a large number of completely unrelated problems? === Cybernetics and brain simulation === In the 1940s and 1950s, a number of researchers explored the connection between neurobiology, information theory, and cybernetics. Some of them built machines that used electronic networks to exhibit rudimentary intelligence, such as W. Grey Walter's turtles and the Johns Hopkins Beast. Many of these researchers gathered for meetings of the Teleological Society at Princeton University and the Ratio Club in England. By 1960, this approach was largely abandoned, although elements of it would be revived in the 1980s. === Symbolic === When access to digital computers became possible in the mid-1950s, AI research began to explore the possibility that human intelligence could be reduced to symbol manipulation. The research was centered in three institutions: Carnegie Mellon University, Stanford and MIT, and as described below, each one developed its own style of research. John Haugeland named these symbolic approaches to AI "good old fashioned AI" or "GOFAI". During the 1960s, symbolic approaches had achieved great success at simulating high-level "thinking" in small demonstration programs. Approaches based on cybernetics or artificial neural networks were abandoned or pushed into the background. Researchers in the 1960s and the 1970s were convinced that symbolic approaches would eventually succeed in creating a machine with artificial general intelligence and considered this the goal of their field. ==== Cognitive simulation ==== Economist Herbert Simon and Allen Newell studied human problem-solving skills and attempted to formalize them, and their work laid the foundations of the field of artificial intelligence, as well as cognitive science, operations research and management science. Their research team used the results of psychological experiments to develop programs that simulated the techniques that people used to solve problems. This tradition, centered at Carnegie Mellon University would eventually culminate in the development of the Soar architecture in the middle 1980s. ==== Logic-based ==== Unlike Simon and Newell, John McCarthy felt that machines did not need to simulate human thought, but should instead try to find the essence of abstract reasoning and problem-solving, regardless whether people used the same algorithms. His laboratory at Stanford (SAIL) focused on using formal logic to solve a wide variety of problems, including knowledge representation, planning and learning. Logic was also the focus of the work at the University of Edinburgh and elsewhere in Europe which led to the development of the programming language Prolog and the science of logic programming. ==== Anti-logic or scruffy ==== Researchers at MIT (such as Marvin Minsky and Seymour Papert) found that solving difficult problems in vision and natural language processing required ad-hoc solutions—they argued that there was no simple and general principle (like logic) that would capture all the aspects of intelligent behavior. Roger Schank described their "anti-logic" approaches as "scruffy" (as opposed to the "neat" paradigms at CMU and Stanford). Commonsense knowledge bases (such as Doug Lenat's Cyc) are an example of "scruffy" AI, since they must be built by hand, one complicated concept at a time. ==== Knowledge-based ==== When computers with large memories became available around 1970, researchers from all three traditions began to build knowledge into AI applications. This "knowledge revolution" led to the development and deployment of expert systems (introduced by Edward Feigenbaum), the first truly successful form of AI software. A key component of the system architecture for all expert systems is the knowledge base, which stores facts and rules that illustrate AI. The knowledge revolution was also driven by the realization that enormous amounts of knowledge would be required by many simple AI applications. === Sub-symbolic === By the 1980s, progress in symbolic AI seemed to stall and many believed that symbolic systems would never be able to imitate all the processes of human cognition, especially perception, robotics, learning and pattern recognition. A number of researchers began to look into "sub-symbolic" approaches to specific AI problems. Sub-symbolic methods manage to approach intelligence without specific representations of knowledge. ==== Embodied intelligence ==== This includes embodied, situated, behavior-based, and nouvelle AI. Researchers from the related field of robotics, such as Rodney Brooks, rejected symbolic AI and focused on the basic engineering problems that would allow robots to move and survive. Their work revived the non-symbolic point of view of the early cybernetics researchers of the 1950s and reintroduced the use of control theory in AI. This coincided with the development of the embodied mind thesis in the related field of cognitive science: the idea that aspects of the body (such as movement, perception and visualization) are required for higher intelligence. Within developmental robotics, developmental learning approaches are elaborated upon to allow robots to accumulate repertoires of novel skills through autonomous self-exploration, social interaction with human teachers, and the use of guidance mechanisms (active learning, maturation, motor synergies, etc.). ==== Computational intelligence and soft computing ==== Interest in neural networks and "connectionism" was revived by David Rumelhart and others in the middle of the 1980s. Artificial neural networks are an example of soft computing—they are solutions to problems which cannot be solved with complete logical certainty, and where an approximate solution is often sufficient. Other soft computing approaches to AI include fuzzy systems, Grey system theory, evolutionary computation and many statistical tools. The application of soft computing to AI is studied collectively by the emerging discipline of computational intelligence. === Statistical learning === Much of traditional GOFAI got bogged down on ad hoc patches to symbolic computation that worked on their own toy models but failed to generalize to real-world results. However, around the 1990s, AI researchers adopted sophisticated mathematical tools, such as hidden Markov models (HMM), information theory, and normative Bayesian decision theory to compare or to unify competing architectures. The shared mathematical language permitted a high level of collaboration with more established fields (like mathematics, economics or operations research). Compared with GOFAI, new "statistical learning" techniques such as HMM and neural networks were gaining higher levels of accuracy in many practical domains such as data mining, without necessarily acquiring a semantic understanding of the datasets. The increased successes with real-world data led to increasing emphasis on comparing different approaches against shared test data to see which approach performed best in a broader context than that provided by idiosyncratic toy models; AI research was becoming more scientific. Nowadays results of experiments are often rigorously measurable, and are sometimes (with difficulty) reproducible. Different statistical learning techniques have different limitations; for example, basic HMM cannot model the infinite possible combinations of natural language. Critics note that the shift from GOFAI to statistical learning is often also a shift away from explainable AI. In AGI research, some scholars caution against over-reliance on statistical learning, and argue that continuing research into GOFAI will still be necessary to attain general intelligence. === Integrating the approaches === Intelligent agent paradigm An intelligent agent is a system that perceives its environment and takes actions that maximize its chances of success. The simplest intelligent agents are programs that solve specific problems. More complicated agents include human beings and organizations of human beings (such as firms). The paradigm allows researchers to directly compare or even combine different approaches to isolated problems, by asking which agent is best at maximizing a given "goal function". An agent that solves a specific problem can use any approach that works—some agents are symbolic and logical, some are sub-symbolic artificial neural networks and others may use new approaches. The paradigm also gives researchers a common language to communicate with other fields—such as decision theory and economics—that also use concepts of abstract agents. Building a complete agent requires researchers to address realistic problems of integration; for example, because sensory systems give uncertain information about the environment, planning systems must be able to function in the presence of uncertainty. The intelligent agent paradigm became widely accepted during the 1990s. Agent architectures and cognitive architectures Researchers have designed systems to build intelligent systems out of interacting intelligent agents in a multi-agent system. A hierarchical control system provides a bridge between sub-symbolic AI at its lowest, reactive levels and traditional symbolic AI at its highest levels, where relaxed time constraints permit planning and world modeling. Some cognitive architectures are custom-built to solve a narrow problem; others, such as Soar, are designed to mimic human cognition and to provide insight into general intelligence. Modern extensions of Soar are hybrid intelligent systems that include both symbolic and sub-symbolic components. == Tools == AI has developed many tools to solve the most difficult problems in computer science. A few of the most general of these methods are discussed below. === Search and optimization === Many problems in AI can be solved in theory by intelligently searching through many possible solutions: Reasoning can be reduced to performing a search. For example, logical proof can be viewed as searching for a path that leads from premises to conclusions, where each step is the application of an inference rule. Planning algorithms search through trees of goals and subgoals, attempting to find a path to a target goal, a process called means-ends analysis. Robotics algorithms for moving limbs and grasping objects use local searches in configuration space. Many learning algorithms use search algorithms based on optimization. Simple exhaustive searches are rarely sufficient for most real-world problems: the search space (the number of places to search) quickly grows to astronomical numbers. The result is a search that is too slow or never completes. The solution, for many problems, is to use "heuristics" or "rules of thumb" that prioritize choices in favor of those that are more likely to reach a goal and to do so in a shorter number of steps. In some search methodologies heuristics can also serve to entirely eliminate some choices that are unlikely to lead to a goal (called "pruning the search tree"). Heuristics supply the program with a "best guess" for the path on which the solution lies. Heuristics limit the search for solutions into a smaller sample size. A very different kind of search came to prominence in the 1990s, based on the mathematical theory of optimization. For many problems, it is possible to begin the search with some form of a guess and then refine the guess incrementally until no more refinements can be made. These algorithms can be visualized as blind hill climbing: we begin the search at a random point on the landscape, and then, by jumps or steps, we keep moving our guess uphill, until we reach the top. Other optimization algorithms are simulated annealing, beam search and random optimization. Evolutionary computation uses a form of optimization search. For example, they may begin with a population of organisms (the guesses) and then allow them to mutate and recombine, selecting only the fittest to survive each generation (refining the guesses). Classic evolutionary algorithms include genetic algorithms, gene expression programming, and genetic programming. Alternatively, distributed search processes can coordinate via swarm intelligence algorithms. Two popular swarm algorithms used in search are particle swarm optimization (inspired by bird flocking) and ant colony optimization (inspired by ant trails). === Logic === Logic is used for knowledge representation and problem solving, but it can be applied to other problems as well. For example, the satplan algorithm uses logic for planning and inductive logic programming is a method for learning. Several different forms of logic are used in AI research. Propositional logic involves truth functions such as "or" and "not". First-order logic adds quantifiers and predicates, and can express facts about objects, their properties, and their relations with each other. Fuzzy set theory assigns a "degree of truth" (between 0 and 1) to vague statements such as "Alice is old" (or rich, or tall, or hungry) that are too linguistically imprecise to be completely true or false. Fuzzy logic is successfully used in control systems to allow experts to contribute vague rules such as "if you are close to the destination station and moving fast, increase the train's brake pressure"; these vague rules can then be numerically refined within the system. Fuzzy logic fails to scale well in knowledge bases; many AI researchers question the validity of chaining fuzzy-logic inferences. Default logics, non-monotonic logics and circumscription are forms of logic designed to help with default reasoning and the qualification problem. Several extensions of logic have been designed to handle specific domains of knowledge, such as: description logics; situation calculus, event calculus and fluent calculus (for representing events and time); causal calculus; belief calculus (belief revision); and modal logics. Logics to model contradictory or inconsistent statements arising in multi-agent systems have also been designed, such as paraconsistent logics. === Probabilistic methods for uncertain reasoning === Many problems in AI (in reasoning, planning, learning, perception, and robotics) require the agent to operate with incomplete or uncertain information. AI researchers have devised a number of powerful tools to solve these problems using methods from probability theory and economics. Bayesian networks are a very general tool that can be used for various problems: reasoning (using the Bayesian inference algorithm), learning (using the expectation-maximization algorithm), planning (using decision networks) and perception (using dynamic Bayesian networks). Probabilistic algorithms can also be used for filtering, prediction, smoothing and finding explanations for streams of data, helping perception systems to analyze processes that occur over time (e.g., hidden Markov models or Kalman filters). Compared with symbolic logic, formal Bayesian inference is computationally expensive. For inference to be tractable, most observations must be conditionally independent of one another. Complicated graphs with diamonds or other "loops" (undirected cycles) can require a sophisticated method such as Markov chain Monte Carlo, which spreads an ensemble of random walkers throughout the Bayesian network and attempts to converge to an assessment of the conditional probabilities. Bayesian networks are used on Xbox Live to rate and match players; wins and losses are "evidence" of how good a player is. AdSense uses a Bayesian network with over 300 million edges to learn which ads to serve. A key concept from the science of economics is "utility": a measure of how valuable something is to an intelligent agent. Precise mathematical tools have been developed that analyze how an agent can make choices and plan, using decision theory, decision analysis, and information value theory. These tools include models such as Markov decision processes, dynamic decision networks, game theory and mechanism design. === Classifiers and statistical learning methods === The simplest AI applications can be divided into two types: classifiers ("if shiny then diamond") and controllers ("if shiny then pick up"). Controllers do, however, also classify conditions before inferring actions, and therefore classification forms a central part of many AI systems. Classifiers are functions that use pattern matching to determine a closest match. They can be tuned according to examples, making them very attractive for use in AI. These examples are known as observations or patterns. In supervised learning, each pattern belongs to a certain predefined class. A class can be seen as a decision that has to be made. All the observations combined with their class labels are known as a data set. When a new observation is received, that observation is classified based on previous experience. A classifier can be trained in various ways; there are many statistical and machine learning approaches. The decision tree is perhaps the most widely used machine learning algorithm. Other widely used classifiers are the neural network,k-nearest neighbor algorithm,kernel methods such as the support vector machine (SVM),Gaussian mixture model, and the extremely popular naive Bayes classifier. Classifier performance depends greatly on the characteristics of the data to be classified, such as the dataset size, distribution of samples across classes, the dimensionality, and the level of noise. Model-based classifiers perform well if the assumed model is an extremely good fit for the actual data. Otherwise, if no matching model is available, and if accuracy (rather than speed or scalability) is the sole concern, conventional wisdom is that discriminative classifiers (especially SVM) tend to be more accurate than model-based classifiers such as "naive Bayes" on most practical data sets. === Artificial neural networks === Neural networks were inspired by the architecture of neurons in the human brain. A simple "neuron" N accepts input from other neurons, each of which, when activated (or "fired"), cast a weighted "vote" for or against whether neuron N should itself activate. Learning requires an algorithm to adjust these weights based on the training data; one simple algorithm (dubbed "fire together, wire together") is to increase the weight between two connected neurons when the activation of one triggers the successful activation of another. The neural network forms "concepts" that are distributed among a subnetwork of shared neurons that tend to fire together; a concept meaning "leg" might be coupled with a subnetwork meaning "foot" that includes the sound for "foot". Neurons have a continuous spectrum of activation; in addition, neurons can process inputs in a nonlinear way rather than weighing straightforward votes. Modern neural networks can learn both continuous functions and, surprisingly, digital logical operations. Neural networks' early successes included predicting the stock market and (in 1995) a mostly self-driving car. In the 2010s, advances in neural networks using deep learning thrust AI into widespread public consciousness and contributed to an enormous upshift in corporate AI spending; for example, AI-related M&A in 2017 was over 25 times as large as in 2015.The study of non-learning artificial neural networks began in the decade before the field of AI research was founded, in the work of Walter Pitts and Warren McCullouch. Frank Rosenblatt invented the perceptron, a learning network with a single layer, similar to the old concept of linear regression. Early pioneers also include Alexey Grigorevich Ivakhnenko, Teuvo Kohonen, Stephen Grossberg, Kunihiko Fukushima, Christoph von der Malsburg, David Willshaw, Shun-Ichi Amari, Bernard Widrow, John Hopfield, Eduardo R. Caianiello, and others. The main categories of networks are acyclic or feedforward neural networks (where the signal passes in only one direction) and recurrent neural networks (which allow feedback and short-term memories of previous input events). Among the most popular feedforward networks are perceptrons, multi-layer perceptrons and radial basis networks. Neural networks can be applied to the problem of intelligent control (for robotics) or learning, using such techniques as Hebbian learning ("fire together, wire together"), GMDH or competitive learning. Today, neural networks are often trained by the backpropagation algorithm, which had been around since 1970 as the reverse mode of automatic differentiation published by Seppo Linnainmaa, and was introduced to neural networks by Paul Werbos. Hierarchical temporal memory is an approach that models some of the structural and algorithmic properties of the neocortex. To summarize, most neural networks use some form of gradient descent on a hand-created neural topology. However, some research groups, such as Uber, argue that simple neuroevolution to mutate new neural network topologies and weights may be competitive with sophisticated gradient descent approaches. One advantage of neuroevolution is that it may be less prone to get caught in "dead ends". ==== Deep feedforward neural networks ==== Deep learning is any artificial neural network that can learn a long chain of causal links. For example, a feedforward network with six hidden layers can learn a seven-link causal chain (six hidden layers + output layer) and has a "credit assignment path" (CAP) depth of seven. Many deep learning systems need to be able to learn chains ten or more causal links in length. Deep learning has transformed many important subfields of artificial intelligence, including computer vision, speech recognition, natural language processing and others. According to one overview, the expression "Deep Learning" was introduced to the machine learning community by Rina Dechter in 1986 and gained traction after Igor Aizenberg and colleagues introduced it to artificial neural networks in 2000. The first functional Deep Learning networks were published by Alexey Grigorevich Ivakhnenko and V. G. Lapa in 1965. These networks are trained one layer at a time. Ivakhnenko's 1971 paper describes the learning of a deep feedforward multilayer perceptron with eight layers, already much deeper than many later networks. In 2006, a publication by Geoffrey Hinton and Ruslan Salakhutdinov introduced another way of pre-training many-layered feedforward neural networks (FNNs) one layer at a time, treating each layer in turn as an unsupervised restricted Boltzmann machine, then using supervised backpropagation for fine-tuning. Similar to shallow artificial neural networks, deep neural networks can model complex non-linear relationships. Over the last few years, advances in both machine learning algorithms and computer hardware have led to more efficient methods for training deep neural networks that contain many layers of non-linear hidden units and a very large output layer. Deep learning often uses convolutional neural networks (CNNs), whose origins can be traced back to the Neocognitron introduced by Kunihiko Fukushima in 1980. In 1989, Yann LeCun and colleagues applied backpropagation to such an architecture. In the early 2000s, in an industrial application, CNNs already processed an estimated 10% to 20% of all the checks written in the US. Since 2011, fast implementations of CNNs on GPUs have won many visual pattern recognition competitions. CNNs with 12 convolutional layers were used in conjunction with reinforcement learning by Deepmind's "AlphaGo Lee", the program that beat a top Go champion in 2016. ==== Deep recurrent neural networks ==== Early on, deep learning was also applied to sequence learning with recurrent neural networks (RNNs) which are in theory Turing complete and can run arbitrary programs to process arbitrary sequences of inputs. The depth of an RNN is unlimited and depends on the length of its input sequence; thus, an RNN is an example of deep learning. RNNs can be trained by gradient descent but suffer from the vanishing gradient problem. In 1992, it was shown that unsupervised pre-training of a stack of recurrent neural networks can speed up subsequent supervised learning of deep sequential problems. Numerous researchers now use variants of a deep learning recurrent NN called the long short-term memory (LSTM) network published by Hochreiter & Schmidhuber in 1997. LSTM is often trained by Connectionist Temporal Classification (CTC). At Google, Microsoft and Baidu this approach has revolutionized speech recognition. For example, in 2015, Google's speech recognition experienced a dramatic performance jump of 49% through CTC-trained LSTM, which is now available through Google Voice to billions of smartphone users. Google also used LSTM to improve machine translation, Language Modeling and Multilingual Language Processing. LSTM combined with CNNs also improved automatic image captioning and a plethora of other applications. === Evaluating progress === AI, like electricity or the steam engine, is a general purpose technology. There is no consensus on how to characterize which tasks AI tends to excel at. While projects such as AlphaZero have succeeded in generating their own knowledge from scratch, many other machine learning projects require large training datasets. Researcher Andrew Ng has suggested, as a "highly imperfect rule of thumb", that "almost anything a typical human can do with less than one second of mental thought, we can probably now or in the near future automate using AI." Moravec's paradox suggests that AI lags humans at many tasks that the human brain has specifically evolved to perform well. Games provide a well-publicized benchmark for assessing rates of progress. AlphaGo around 2016 brought the era of classical board-game benchmarks to a close. Games of imperfect knowledge provide new challenges to AI in the area of game theory. E-sports such as StarCraft continue to provide additional public benchmarks. There are many competitions and prizes, such as the Imagenet Challenge, to promote research in artificial intelligence. The most common areas of competition include general machine intelligence, conversational behavior, data-mining, robotic cars, and robot soccer as well as conventional games. The "imitation game" (an interpretation of the 1950 Turing test that assesses whether a computer can imitate a human) is nowadays considered too exploitable to be a meaningful benchmark. A derivative of the Turing test is the Completely Automated Public Turing test to tell Computers and Humans Apart (CAPTCHA). As the name implies, this helps to determine that a user is an actual person and not a computer posing as a human. In contrast to the standard Turing test, CAPTCHA is administered by a machine and targeted to a human as opposed to being administered by a human and targeted to a machine. A computer asks a user to complete a simple test then generates a grade for that test. Computers are unable to solve the problem, so correct solutions are deemed to be the result of a person taking the test. A common type of CAPTCHA is the test that requires the typing of distorted letters, numbers or symbols that appear in an image undecipherable by a computer. Proposed "universal intelligence" tests aim to compare how well machines, humans, and even non-human animals perform on problem sets that are generic as possible. At an extreme, the test suite can contain every possible problem, weighted by Kolmogorov complexity; unfortunately, these problem sets tend to be dominated by impoverished pattern-matching exercises where a tuned AI can easily exceed human performance levels. == Applications == AI is relevant to any intellectual task. Modern artificial intelligence techniques are pervasive and are too numerous to list here. Frequently, when a technique reaches mainstream use, it is no longer considered artificial intelligence; this phenomenon is described as the AI effect. High-profile examples of AI include autonomous vehicles (such as drones and self-driving cars), medical diagnosis, creating art (such as poetry), proving mathematical theorems, playing games (such as Chess or Go), search engines (such as Google search), online assistants (such as Siri), image recognition in photographs, spam filtering, predicting flight delays, prediction of judicial decisions, targeting online advertisements, and energy storageWith social media sites overtaking TV as a source for news for young people and news organizations increasingly reliant on social media platforms for generating distribution, major publishers now use artificial intelligence (AI) technology to post stories more effectively and generate higher volumes of traffic. AI can also produce Deepfakes, a content-altering technology. ZDNet reports, "It presents something that did not actually occur," Though 88% of Americans believe Deepfakes can cause more harm than good, only 47% of them believe they can be targeted. The boom of election year also opens public discourse to threats of videos of falsified politician media. === Healthcare === AI in healthcare is often used for classification, whether to automate initial evaluation of a CT scan or EKG or to identify high-risk patients for population health. The breadth of applications is rapidly increasing. As an example, AI is being applied to the high-cost problem of dosage issues—where findings suggested that AI could save $16 billion. In 2016, a groundbreaking study in California found that a mathematical formula developed with the help of AI correctly determined the accurate dose of immunosuppressant drugs to give to organ patients. Artificial intelligence is assisting doctors. According to Bloomberg Technology, Microsoft has developed AI to help doctors find the right treatments for cancer. There is a great amount of research and drugs developed relating to cancer. In detail, there are more than 800 medicines and vaccines to treat cancer. This negatively affects the doctors, because there are too many options to choose from, making it more difficult to choose the right drugs for the patients. Microsoft is working on a project to develop a machine called "Hanover". Its goal is to memorize all the papers necessary to cancer and help predict which combinations of drugs will be most effective for each patient. One project that is being worked on at the moment is fighting myeloid leukemia, a fatal cancer where the treatment has not improved in decades. Another study was reported to have found that artificial intelligence was as good as trained doctors in identifying skin cancers. Another study is using artificial intelligence to try to monitor multiple high-risk patients, and this is done by asking each patient numerous questions based on data acquired from live doctor to patient interactions. One study was done with transfer learning, the machine performed a diagnosis similarly to a well-trained ophthalmologist, and could generate a decision within 30 seconds on whether or not the patient should be referred for treatment, with more than 95% accuracy. According to CNN, a recent study by surgeons at the Children's National Medical Center in Washington successfully demonstrated surgery with an autonomous robot. The team supervised the robot while it performed soft-tissue surgery, stitching together a pig's bowel during open surgery, and doing so better than a human surgeon, the team claimed. IBM has created its own artificial intelligence computer, the IBM Watson, which has beaten human intelligence (at some levels). Watson has struggled to achieve success and adoption in healthcare. === Automotive === Advancements in AI have contributed to the growth of the automotive industry through the creation and evolution of self-driving vehicles. As of 2016, there are over 30 companies utilizing AI into the creation of self-driving cars. A few companies involved with AI include Tesla, Google, and Apple. Many components contribute to the functioning of self-driving cars. These vehicles incorporate systems such as braking, lane changing, collision prevention, navigation and mapping. Together, these systems, as well as high-performance computers, are integrated into one complex vehicle. Recent developments in autonomous automobiles have made the innovation of self-driving trucks possible, though they are still in the testing phase. The UK government has passed legislation to begin testing of self-driving truck platoons in 2018. Self-driving truck platoons are a fleet of self-driving trucks following the lead of one non-self-driving truck, so the truck platoons aren't entirely autonomous yet. Meanwhile, the Daimler, a German automobile corporation, is testing the Freightliner Inspiration which is a semi-autonomous truck that will only be used on the highway. One main factor that influences the ability for a driver-less automobile to function is mapping. In general, the vehicle would be pre-programmed with a map of the area being driven. This map would include data on the approximations of street light and curb heights in order for the vehicle to be aware of its surroundings. However, Google has been working on an algorithm with the purpose of eliminating the need for pre-programmed maps and instead, creating a device that would be able to adjust to a variety of new surroundings. Some self-driving cars are not equipped with steering wheels or brake pedals, so there has also been research focused on creating an algorithm that is capable of maintaining a safe environment for the passengers in the vehicle through awareness of speed and driving conditions. Another factor that is influencing the ability of a driver-less automobile is the safety of the passenger. To make a driver-less automobile, engineers must program it to handle high-risk situations. These situations could include a head-on collision with pedestrians. The car's main goal should be to make a decision that would avoid hitting the pedestrians and saving the passengers in the car. But there is a possibility the car would need to make a decision that would put someone in danger. In other words, the car would need to decide to save the pedestrians or the passengers. The programming of the car in these situations is crucial to a successful driver-less automobile. === Finance and economics === Financial institutions have long used artificial neural network systems to detect charges or claims outside of the norm, flagging these for human investigation. The use of AI in banking can be traced back to 1987 when Security Pacific National Bank in US set-up a Fraud Prevention Task force to counter the unauthorized use of debit cards. Programs like Kasisto and Moneystream are using AI in financial services. Banks use artificial intelligence systems today to organize operations, maintain book-keeping, invest in stocks, and manage properties. AI can react to changes overnight or when business is not taking place. In August 2001, robots beat humans in a simulated financial trading competition. AI has also reduced fraud and financial crimes by monitoring behavioral patterns of users for any abnormal changes or anomalies. AI is increasingly being used by corporations. Jack Ma has controversially predicted that AI CEO's are 30 years away. The use of AI machines in the market in applications such as online trading and decision making has changed major economic theories. For example, AI-based buying and selling platforms have changed the law of supply and demand in that it is now possible to easily estimate individualized demand and supply curves and thus individualized pricing. Furthermore, AI machines reduce information asymmetry in the market and thus making markets more efficient while reducing the volume of trades. Furthermore, AI in the markets limits the consequences of behavior in the markets again making markets more efficient. Other theories where AI has had impact include in rational choice, rational expectations, game theory, Lewis turning point, portfolio optimization and counterfactual thinking.. In August 2019, the AICPA introduced AI training course for accounting professionals. === Cybersecurity === The cybersecurity arena faces significant challenges in the form of large-scale hacking attacks of different types that harm organizations of all kinds and create billions of dollars in business damage. Artificial intelligence and Natural Language Processing (NLP) has begun to be used by security companies - for example, SIEM (Security Information and Event Management) solutions. The more advanced of these solutions use AI and NLP to automatically sort the data in networks into high risk and low-risk information. This enables security teams to focus on the attacks that have the potential to do real harm to the organization, and not become victims of attacks such as Denial of Service (DoS), Malware and others. === Government === Artificial intelligence in government consists of applications and regulation. Artificial intelligence paired with facial recognition systems may be used for mass surveillance. This is already the case in some parts of China. An artificial intelligence has also competed in the Tama City mayoral elections in 2018. In 2019, the tech city of Bengaluru in India is set to deploy AI managed traffic signal systems across the 387 traffic signals in the city. This system will involve use of cameras to ascertain traffic density and accordingly calculate the time needed to clear the traffic volume which will determine the signal duration for vehicular traffic across streets. === Law-related professions === Artificial intelligence (AI) is becoming a mainstay component of law-related professions. In some circumstances, this analytics-crunching technology is using algorithms and machine learning to do work that was previously done by entry-level lawyers. In Electronic Discovery (eDiscovery), the industry has been focused on machine learning (predictive coding/technology assisted review), which is a subset of AI. To add to the soup of applications, Natural Language Processing (NLP) and Automated Speech Recognition (ASR) are also in vogue in the industry. === Video games === In video games, artificial intelligence is routinely used to generate dynamic purposeful behavior in non-player characters (NPCs). In addition, well-understood AI techniques are routinely used for pathfinding. Some researchers consider NPC AI in games to be a "solved problem" for most production tasks. Games with more atypical AI include the AI director of Left 4 Dead (2008) and the neuroevolutionary training of platoons in Supreme Commander 2 (2010). === Military === The main military applications of Artificial Intelligence and Machine Learning are to enhance C2, Communications, Sensors, Integration and Interoperability. Artificial Intelligence technologies enable coordination of sensors and effectors, threat detection and identification, marking of enemy positions, target acquisition, coordination and deconfliction of distributed Join Fires between networked combat vehicles and tanks also inside Manned and Unmanned Teams (MUM-T).Worldwide annual military spending on robotics rose from US$5.1 billion in 2010 to US$7.5 billion in 2015. Military drones capable of autonomous action are widely considered a useful asset. Many artificial intelligence researchers seek to distance themselves from military applications of AI. === Hospitality === In the hospitality industry, Artificial Intelligence based solutions are used to reduce staff load and increase efficiency by cutting repetitive tasks frequency, trends analysis, guest interaction, and customer needs prediction. Hotel services backed by Artificial Intelligence are represented in the form of a chatbot, application, virtual voice assistant and service robots. === Audit === For financial statements audit, AI makes continuous audit possible. AI tools could analyze many sets of different information immediately. The potential benefit would be the overall audit risk will be reduced, the level of assurance will be increased and the time duration of audit will be reduced. === Advertising === It is possible to use AI to predict or generalize the behavior of customers from their digital footprints in order to target them with personalized promotions or build customer personas automatically. A documented case reports that online gambling companies were using AI to improve customer targeting. Moreover, the application of Personality computing AI models can help reducing the cost of advertising campaigns by adding psychological targeting to more traditional sociodemographic or behavioral targeting. === Art === Artificial Intelligence has inspired numerous creative applications including its usage to produce visual art. The exhibition "Thinking Machines: Art and Design in the Computer Age, 1959–1989" at MoMA provides a good overview of the historical applications of AI for art, architecture, and design. Recent exhibitions showcasing the usage of AI to produce art include the Google-sponsored benefit and auction at the Gray Area Foundation in San Francisco, where artists experimented with the DeepDream algorithm and the exhibition "Unhuman: Art in the Age of AI," which took place in Los Angeles and Frankfurt in the fall of 2017. In the spring of 2018, the Association of Computing Machinery dedicated a special magazine issue to the subject of computers and art highlighting the role of machine learning in the arts. The Austrian Ars Electronica and Museum of Applied Arts, Vienna opened exhibitions on AI in 2019. The Ars Electronica's 2019 festival "Out of the box" extensively thematized the role of arts for a sustainable societal transformation with AI. == Philosophy and ethics == There are three philosophical questions related to AI: Is artificial general intelligence possible? Can a machine solve any problem that a human being can solve using intelligence? Or are there hard limits to what a machine can accomplish? Are intelligent machines dangerous? How can we ensure that machines behave ethically and that they are used ethically? Can a machine have a mind, consciousness and mental states in exactly the same sense that human beings do? Can a machine be sentient, and thus deserve certain rights? Can a machine intentionally cause harm? === The limits of artificial general intelligence === Can a machine be intelligent? Can it "think"? Alan Turing's "polite convention" We need not decide if a machine can "think"; we need only decide if a machine can act as intelligently as a human being. This approach to the philosophical problems associated with artificial intelligence forms the basis of the Turing test. The Dartmouth proposal "Every aspect of learning or any other feature of intelligence can be so precisely described that a machine can be made to simulate it." This conjecture was printed in the proposal for the Dartmouth Conference of 1956.Newell and Simon's physical symbol system hypothesis "A physical symbol system has the necessary and sufficient means of general intelligent action." Newell and Simon argue that intelligence consists of formal operations on symbols. Hubert Dreyfus argued that, on the contrary, human expertise depends on unconscious instinct rather than conscious symbol manipulation and on having a "feel" for the situation rather than explicit symbolic knowledge. (See Dreyfus' critique of AI.)Gödelian arguments Gödel himself, John Lucas (in 1961) and Roger Penrose (in a more detailed argument from 1989 onwards) made highly technical arguments that human mathematicians can consistently see the truth of their own "Gödel statements" and therefore have computational abilities beyond that of mechanical Turing machines. However, some people do not agree with the "Gödelian arguments".The artificial brain argument The brain can be simulated by machines and because brains are intelligent, simulated brains must also be intelligent; thus machines can be intelligent. Hans Moravec, Ray Kurzweil and others have argued that it is technologically feasible to copy the brain directly into hardware and software and that such a simulation will be essentially identical to the original. The AI effect Machines are already intelligent, but observers have failed to recognize it. When Deep Blue beat Garry Kasparov in chess, the machine was acting intelligently. However, onlookers commonly discount the behavior of an artificial intelligence program by arguing that it is not "real" intelligence after all; thus "real" intelligence is whatever intelligent behavior people can do that machines still cannot. This is known as the AI Effect: "AI is whatever hasn't been done yet." === Potential harm === Widespread use of artificial intelligence could have unintended consequences that are dangerous or undesirable. Scientists from the Future of Life Institute, among others, described some short-term research goals to see how AI influences the economy, the laws and ethics that are involved with AI and how to minimize AI security risks. In the long-term, the scientists have proposed to continue optimizing function while minimizing possible security risks that come along with new technologies. The potential negative effects of AI and automation were a major issue for Andrew Yang's 2020 presidential campaign in the United States. Irakli Beridze, Head of the Centre for Artificial Intelligence and Robotics at UNICRI, United Nations, has expressed that "I think the dangerous applications for AI, from my point of view, would be criminals or large terrorist organizations using it to disrupt large processes or simply do pure harm. [Terrorists could cause harm] via digital warfare, or it could be a combination of robotics, drones, with AI and other things as well that could be really dangerous. And, of course, other risks come from things like job losses. If we have massive numbers of people losing jobs and don't find a solution, it will be extremely dangerous. Things like lethal autonomous weapons systems should be properly governed — otherwise there's massive potential of misuse." ==== Existential risk ==== Physicist Stephen Hawking, Microsoft founder Bill Gates, and SpaceX founder Elon Musk have expressed concerns about the possibility that AI could evolve to the point that humans could not control it, with Hawking theorizing that this could "spell the end of the human race". The development of full artificial intelligence could spell the end of the human race. Once humans develop artificial intelligence, it will take off on its own and redesign itself at an ever-increasing rate. Humans, who are limited by slow biological evolution, couldn't compete and would be superseded. In his book Superintelligence, philosopher Nick Bostrom provides an argument that artificial intelligence will pose a threat to humankind. He argues that sufficiently intelligent AI, if it chooses actions based on achieving some goal, will exhibit convergent behavior such as acquiring resources or protecting itself from being shut down. If this AI's goals do not fully reflect humanity's—one example is an AI told to compute as many digits of pi as possible—it might harm humanity in order to acquire more resources or prevent itself from being shut down, ultimately to better achieve its goal. Bostrom also emphasizes the difficulty of fully conveying humanity's values to an advanced AI. He uses the hypothetical example of giving an AI the goal to make humans smile to illustrate a misguided attempt. If the AI in that scenario were to become superintelligent, Bostrom argues, it may resort to methods that most humans would find horrifying, such as inserting "electrodes into the facial muscles of humans to cause constant, beaming grins" because that would be an efficient way to achieve its goal of making humans smile. In his book Human Compatible, AI researcher Stuart J. Russell echoes some of Bostrom's concerns while also proposing an approach to developing provably beneficial machines focused on uncertainty and deference to humans, possibly involving inverse reinforcement learning. Concern over risk from artificial intelligence has led to some high-profile donations and investments. A group of prominent tech titans including Peter Thiel, Amazon Web Services and Musk have committed $1 billion to OpenAI, a nonprofit company aimed at championing responsible AI development. The opinion of experts within the field of artificial intelligence is mixed, with sizable fractions both concerned and unconcerned by risk from eventual superhumanly-capable AI. Other technology industry leaders believe that artificial intelligence is helpful in its current form and will continue to assist humans. Oracle CEO Mark Hurd has stated that AI "will actually create more jobs, not less jobs" as humans will be needed to manage AI systems. Facebook CEO Mark Zuckerberg believes AI will "unlock a huge amount of positive things," such as curing disease and increasing the safety of autonomous cars. In January 2015, Musk donated $10 million to the Future of Life Institute to fund research on understanding AI decision making. The goal of the institute is to "grow wisdom with which we manage" the growing power of technology. Musk also funds companies developing artificial intelligence such as DeepMind and Vicarious to "just keep an eye on what's going on with artificial intelligence. I think there is potentially a dangerous outcome there."For the danger of uncontrolled advanced AI to be realized, the hypothetical AI would have to overpower or out-think all of humanity, which a minority of experts argue is a possibility far enough in the future to not be worth researching. Other counterarguments revolve around humans being either intrinsically or convergently valuable from the perspective of an artificial intelligence. ==== Devaluation of humanity ==== Joseph Weizenbaum wrote that AI applications cannot, by definition, successfully simulate genuine human empathy and that the use of AI technology in fields such as customer service or psychotherapy was deeply misguided. Weizenbaum was also bothered that AI researchers (and some philosophers) were willing to view the human mind as nothing more than a computer program (a position now known as computationalism). To Weizenbaum these points suggest that AI research devalues human life. ==== Social justice ==== One concern is that AI programs may be programmed to be biased against certain groups, such as women and minorities, because most of the developers are wealthy Caucasian men. Support for artificial intelligence is higher among men (with 47% approving) than women (35% approving). Algorithms have a host of applications in today's legal system already, assisting officials ranging from judges to parole officers and public defenders in gauging the predicted likelihood of recidivism of defendants. COMPAS (an acronym for Correctional Offender Management Profiling for Alternative Sanctions) counts among the most widely utilized commercially available solutions. It has been suggested that COMPAS assigns an exceptionally elevated risk of recidivism to black defendants while, conversely, ascribing low risk estimate to white defendants significantly more often than statistically expected. ==== Decrease in demand for human labor ==== The relationship between automation and employment is complicated. While automation eliminates old jobs, it also creates new jobs through micro-economic and macro-economic effects. Unlike previous waves of automation, many middle-class jobs may be eliminated by artificial intelligence; The Economist states that "the worry that AI could do to white-collar jobs what steam power did to blue-collar ones during the Industrial Revolution" is "worth taking seriously". Subjective estimates of the risk vary widely; for example, Michael Osborne and Carl Benedikt Frey estimate 47% of U.S. jobs are at "high risk" of potential automation, while an OECD report classifies only 9% of U.S. jobs as "high risk". Jobs at extreme risk range from paralegals to fast food cooks, while job demand is likely to increase for care-related professions ranging from personal healthcare to the clergy. Author Martin Ford and others go further and argue that many jobs are routine, repetitive and (to an AI) predictable; Ford warns that these jobs may be automated in the next couple of decades, and that many of the new jobs may not be "accessible to people with average capability", even with retraining. Economists point out that in the past technology has tended to increase rather than reduce total employment, but acknowledge that "we're in uncharted territory" with AI. ==== Autonomous weapons ==== Currently, 50+ countries are researching battlefield robots, including the United States, China, Russia, and the United Kingdom. Many people concerned about risk from superintelligent AI also want to limit the use of artificial soldiers and drones. === Ethical machines === Machines with intelligence have the potential to use their intelligence to prevent harm and minimize the risks; they may have the ability to use ethical reasoning to better choose their actions in the world. As such, there is a need for policy making to devise policies for and regulate artificial intelligence and robotics. Research in this area includes machine ethics, artificial moral agents, friendly AI and discussion towards building a human rights framework is also in talks. ==== Artificial moral agents ==== Wendell Wallach introduced the concept of artificial moral agents (AMA) in his book Moral Machines For Wallach, AMAs have become a part of the research landscape of artificial intelligence as guided by its two central questions which he identifies as "Does Humanity Want Computers Making Moral Decisions" and "Can (Ro)bots Really Be Moral". For Wallach, the question is not centered on the issue of whether machines can demonstrate the equivalent of moral behavior in contrast to the constraints which society may place on the development of AMAs. ==== Machine ethics ==== The field of machine ethics is concerned with giving machines ethical principles, or a procedure for discovering a way to resolve the ethical dilemmas they might encounter, enabling them to function in an ethically responsible manner through their own ethical decision making. The field was delineated in the AAAI Fall 2005 Symposium on Machine Ethics: "Past research concerning the relationship between technology and ethics has largely focused on responsible and irresponsible use of technology by human beings, with a few people being interested in how human beings ought to treat machines. In all cases, only human beings have engaged in ethical reasoning. The time has come for adding an ethical dimension to at least some machines. Recognition of the ethical ramifications of behavior involving machines, as well as recent and potential developments in machine autonomy, necessitate this. In contrast to computer hacking, software property issues, privacy issues and other topics normally ascribed to computer ethics, machine ethics is concerned with the behavior of machines towards human users and other machines. Research in machine ethics is key to alleviating concerns with autonomous systems—it could be argued that the notion of autonomous machines without such a dimension is at the root of all fear concerning machine intelligence. Further, investigation of machine ethics could enable the discovery of problems with current ethical theories, advancing our thinking about Ethics." Machine ethics is sometimes referred to as machine morality, computational ethics or computational morality. A variety of perspectives of this nascent field can be found in the collected edition "Machine Ethics" that stems from the AAAI Fall 2005 Symposium on Machine Ethics. ==== Malevolent and friendly AI ==== Political scientist Charles T. Rubin believes that AI can be neither designed nor guaranteed to be benevolent. He argues that "any sufficiently advanced benevolence may be indistinguishable from malevolence." Humans should not assume machines or robots would treat us favorably because there is no a priori reason to believe that they would be sympathetic to our system of morality, which has evolved along with our particular biology (which AIs would not share). Hyper-intelligent software may not necessarily decide to support the continued existence of humanity and would be extremely difficult to stop. This topic has also recently begun to be discussed in academic publications as a real source of risks to civilization, humans, and planet Earth. One proposal to deal with this is to ensure that the first generally intelligent AI is 'Friendly AI' and will be able to control subsequently developed AIs. Some question whether this kind of check could actually remain in place. Leading AI researcher Rodney Brooks writes, "I think it is a mistake to be worrying about us developing malevolent AI anytime in the next few hundred years. I think the worry stems from a fundamental error in not distinguishing the difference between the very real recent advances in a particular aspect of AI and the enormity and complexity of building sentient volitional intelligence." === Machine consciousness, sentience and mind === If an AI system replicates all key aspects of human intelligence, will that system also be sentient—will it have a mind which has conscious experiences? This question is closely related to the philosophical problem as to the nature of human consciousness, generally referred to as the hard problem of consciousness. ==== Consciousness ==== David Chalmers identified two problems in understanding the mind, which he named the "hard" and "easy" problems of consciousness. The easy problem is understanding how the brain processes signals, makes plans and controls behavior. The hard problem is explaining how this feels or why it should feel like anything at all. Human information processing is easy to explain, however human subjective experience is difficult to explain. For example, consider what happens when a person is shown a color swatch and identifies it, saying "it's red". The easy problem only requires understanding the machinery in the brain that makes it possible for a person to know that the color swatch is red. The hard problem is that people also know something else—they also know what red looks like. (Consider that a person born blind can know that something is red without knowing what red looks like.) Everyone knows subjective experience exists, because they do it every day (e.g., all sighted people know what red looks like). The hard problem is explaining how the brain creates it, why it exists, and how it is different from knowledge and other aspects of the brain. ==== Computationalism and functionalism ==== Computationalism is the position in the philosophy of mind that the human mind or the human brain (or both) is an information processing system and that thinking is a form of computing. Computationalism argues that the relationship between mind and body is similar or identical to the relationship between software and hardware and thus may be a solution to the mind-body problem. This philosophical position was inspired by the work of AI researchers and cognitive scientists in the 1960s and was originally proposed by philosophers Jerry Fodor and Hilary Putnam. ==== Strong AI hypothesis ==== The philosophical position that John Searle has named "strong AI" states: "The appropriately programmed computer with the right inputs and outputs would thereby have a mind in exactly the same sense human beings have minds." Searle counters this assertion with his Chinese room argument, which asks us to look inside the computer and try to find where the "mind" might be. ==== Robot rights ==== If a machine can be created that has intelligence, could it also feel? If it can feel, does it have the same rights as a human? This issue, now known as "robot rights", is currently being considered by, for example, California's Institute for the Future, although many critics believe that the discussion is premature. Some critics of transhumanism argue that any hypothetical robot rights would lie on a spectrum with animal rights and human rights. The subject is profoundly discussed in the 2010 documentary film Plug & Pray, and many sci fi media such as Star Trek Next Generation, with the character of Commander Data, who fought being disassembled for research, and wanted to "become human", and the robotic holograms in Voyager. === Superintelligence === Are there limits to how intelligent machines—or human-machine hybrids—can be? A superintelligence, hyperintelligence, or superhuman intelligence is a hypothetical agent that would possess intelligence far surpassing that of the brightest and most gifted human mind. Superintelligence may also refer to the form or degree of intelligence possessed by such an agent. ==== Technological singularity ==== If research into Strong AI produced sufficiently intelligent software, it might be able to reprogram and improve itself. The improved software would be even better at improving itself, leading to recursive self-improvement. The new intelligence could thus increase exponentially and dramatically surpass humans. Science fiction writer Vernor Vinge named this scenario "singularity". Technological singularity is when accelerating progress in technologies will cause a runaway effect wherein artificial intelligence will exceed human intellectual capacity and control, thus radically changing or even ending civilization. Because the capabilities of such an intelligence may be impossible to comprehend, the technological singularity is an occurrence beyond which events are unpredictable or even unfathomable. Ray Kurzweil has used Moore's law (which describes the relentless exponential improvement in digital technology) to calculate that desktop computers will have the same processing power as human brains by the year 2029, and predicts that the singularity will occur in 2045. ==== Transhumanism ==== Robot designer Hans Moravec, cyberneticist Kevin Warwick and inventor Ray Kurzweil have predicted that humans and machines will merge in the future into cyborgs that are more capable and powerful than either. This idea, called transhumanism, has roots in Aldous Huxley and Robert Ettinger. Edward Fredkin argues that "artificial intelligence is the next stage in evolution", an idea first proposed by Samuel Butler's "Darwin among the Machines" as far back as 1863, and expanded upon by George Dyson in his book of the same name in 1998. == Economics == The long-term economic effects of AI are uncertain. A survey of economists showed disagreement about whether the increasing use of robots and AI will cause a substantial increase in long-term unemployment, but they generally agree that it could be a net benefit, if productivity gains are redistributed. A February 2020 European Union white paper on artificial intelligence advocated for artificial intelligence for economic benefits, including "improving healthcare (e.g. making diagnosis more precise, enabling better prevention of diseases), increasing the efficiency of farming, contributing to climate change mitigation and adaptation, [and] improving the efficiency of production systems through predictive maintenance", while acknowledging potential risks. == Regulation == The development of public sector policies for promoting and regulating artificial intelligence (AI) is considered necessary to both encourage AI and manage associated risks, but challenging. In 2017 Elon Musk called for regulation of AI development. In February 2020, the European Union published its draft strategy paper for promoting and regulating AI. == In fiction == Thought-capable artificial beings appeared as storytelling devices since antiquity, and have been a persistent theme in science fiction. A common trope in these works began with Mary Shelley's Frankenstein, where a human creation becomes a threat to its masters. This includes such works as Arthur C. Clarke's and Stanley Kubrick's 2001: A Space Odyssey (both 1968), with HAL 9000, the murderous computer in charge of the Discovery One spaceship, as well as The Terminator (1984) and The Matrix (1999). In contrast, the rare loyal robots such as Gort from The Day the Earth Stood Still (1951) and Bishop from Aliens (1986) are less prominent in popular culture. Isaac Asimov introduced the Three Laws of Robotics in many books and stories, most notably the "Multivac" series about a super-intelligent computer of the same name. Asimov's laws are often brought up during lay discussions of machine ethics; while almost all artificial intelligence researchers are familiar with Asimov's laws through popular culture, they generally consider the laws useless for many reasons, one of which is their ambiguity. Transhumanism (the merging of humans and machines) is explored in the manga Ghost in the Shell and the science-fiction series Dune. In the 1980s, artist Hajime Sorayama's Sexy Robots series were painted and published in Japan depicting the actual organic human form with lifelike muscular metallic skins and later "the Gynoids" book followed that was used by or influenced movie makers including George Lucas and other creatives. Sorayama never considered these organic robots to be real part of nature but always unnatural product of the human mind, a fantasy existing in the mind even when realized in actual form. Several works use AI to force us to confront the fundamental question of what makes us human, showing us artificial beings that have the ability to feel, and thus to suffer. This appears in Karel Čapek's R.U.R., the films A.I. Artificial Intelligence and Ex Machina, as well as the novel Do Androids Dream of Electric Sheep?, by Philip K. Dick. Dick considers the idea that our understanding of human subjectivity is altered by technology created with artificial intelligence. == See also == == Explanatory notes == == References == === AI textbooks === === History of AI === === Other sources === == Further reading == == External links == "Artificial Intelligence". Internet Encyclopedia of Philosophy. Thomason, Richmond. "Logic and Artificial Intelligence". In Zalta, Edward N. (ed.). Stanford Encyclopedia of Philosophy. AITopics – A large directory of links and other resources maintained by the Association for the Advancement of Artificial Intelligence, the leading organization of academic AI researchers. Artificial Intelligence, BBC Radio 4 discussion with John Agar, Alison Adam & Igor Aleksander (In Our Time, Dec. 8, 2005) ================================================ FILE: week6/questions/corpus/machine_learning.txt ================================================ https://en.wikipedia.org/wiki/Machine_learning Machine learning (ML) is the study of computer algorithms that improve automatically through experience. It is seen as a subset of artificial intelligence. Machine learning algorithms build a mathematical model based on sample data, known as "training data", in order to make predictions or decisions without being explicitly programmed to do so. Machine learning algorithms are used in a wide variety of applications, such as email filtering and computer vision, where it is difficult or infeasible to develop conventional algorithms to perform the needed tasks. Machine learning is closely related to computational statistics, which focuses on making predictions using computers. The study of mathematical optimization delivers methods, theory and application domains to the field of machine learning. Data mining is a related field of study, focusing on exploratory data analysis through unsupervised learning. In its application across business problems, machine learning is also referred to as predictive analytics. == Overview == Machine learning involves computers discovering how they can perform tasks without being explicitly programmed to do so. For early tasks that humans assigned to computers, it was possible to create algorithms telling the machine how to execute all needed steps to solve the problem in hand. So on the computer's part, no learning was needed. For more advanced tasks, it can be challenging for a human to manually create the needed algorithms. In practice, it can turn out to be more effective to help the machine develop its own algorithm, rather than have human programmers specify every needed step. The discipline of machine learning employs various approaches to help computers learn to accomplish tasks for which no fully satisfactory algorithm exists. For example, for cases where vast numbers of potential answers exist, some of the correct ones can be labelled as valid, and this can be used as training data for the computer to improve the algorithm(s) it uses to determine correct answers. === Machine learning approaches === Early classifications for machine learning approaches sometimes divided them into three broad categories, depending on the nature of the "signal" or "feedback" available to the learning system. These were: Supervised learning: The computer is presented with example inputs and their desired outputs, given by a "teacher", and the goal is to learn a general rule that maps inputs to outputs. Unsupervised learning: No labels are given to the learning algorithm, leaving it on its own to find structure in its input. Unsupervised learning can be a goal in itself (discovering hidden patterns in data) or a means towards an end (feature learning). Reinforcement learning: A computer program interacts with a dynamic environment in which it must perform a certain goal (such as driving a vehicle or playing a game against an opponent) As it navigates its problem space, the program is provided feedback that's analogous to rewards, which it tries to maximise. Other approaches or processes have since developed that don't fit neatly into this three-fold categorisation, and sometimes more than one is used by the same machine learning system. For example topic modeling, dimensionality reduction or meta learning. As of 2020, deep learning had become the dominant approach for much ongoing work in the field of machine learning . == History and relationships to other fields == The term machine learning was coined in 1959 by Arthur Samuel, an American IBMer and pioneer in the field of computer gaming and artificial intelligence. A representative book of the machine learning research during the 1960s was the Nilsson's book on Learning Machines, dealing mostly with machine learning for pattern classification. Interest related to pattern recognition continued into the 1970s, as described by Duda and Hart in 1973. In 1981 a report was given on using teaching strategies so that a neural network learns to recognize 40 characters (26 letters, 10 digits, and 4 special symbols) from a computer terminal. Tom M. Mitchell provided a widely quoted, more formal definition of the algorithms studied in the machine learning field: "A computer program is said to learn from experience E with respect to some class of tasks T and performance measure P if its performance at tasks in T, as measured by P, improves with experience E." This definition of the tasks in which machine learning is concerned offers a fundamentally operational definition rather than defining the field in cognitive terms. This follows Alan Turing's proposal in his paper "Computing Machinery and Intelligence", in which the question "Can machines think?" is replaced with the question "Can machines do what we (as thinking entities) can do?". === Relation to artificial intelligence === As a scientific endeavor, machine learning grew out of the quest for artificial intelligence. In the early days of AI as an academic discipline, some researchers were interested in having machines learn from data. They attempted to approach the problem with various symbolic methods, as well as what were then termed "neural networks"; these were mostly perceptrons and other models that were later found to be reinventions of the generalized linear models of statistics. Probabilistic reasoning was also employed, especially in automated medical diagnosis. However, an increasing emphasis on the logical, knowledge-based approach caused a rift between AI and machine learning. Probabilistic systems were plagued by theoretical and practical problems of data acquisition and representation. By 1980, expert systems had come to dominate AI, and statistics was out of favor. Work on symbolic/knowledge-based learning did continue within AI, leading to inductive logic programming, but the more statistical line of research was now outside the field of AI proper, in pattern recognition and information retrieval. Neural networks research had been abandoned by AI and computer science around the same time. This line, too, was continued outside the AI/CS field, as "connectionism", by researchers from other disciplines including Hopfield, Rumelhart and Hinton. Their main success came in the mid-1980s with the reinvention of backpropagation. Machine learning, reorganized as a separate field, started to flourish in the 1990s. The field changed its goal from achieving artificial intelligence to tackling solvable problems of a practical nature. It shifted focus away from the symbolic approaches it had inherited from AI, and toward methods and models borrowed from statistics and probability theory. As of 2019, many sources continue to assert that machine learning remains a sub field of AI. Yet some practitioners, for example Dr Daniel Hulme, who both teaches AI and runs a company operating in the field, argues that machine learning and AI are separate. === Relation to data mining === Machine learning and data mining often employ the same methods and overlap significantly, but while machine learning focuses on prediction, based on known properties learned from the training data, data mining focuses on the discovery of (previously) unknown properties in the data (this is the analysis step of knowledge discovery in databases). Data mining uses many machine learning methods, but with different goals; on the other hand, machine learning also employs data mining methods as "unsupervised learning" or as a preprocessing step to improve learner accuracy. Much of the confusion between these two research communities (which do often have separate conferences and separate journals, ECML PKDD being a major exception) comes from the basic assumptions they work with: in machine learning, performance is usually evaluated with respect to the ability to reproduce known knowledge, while in knowledge discovery and data mining (KDD) the key task is the discovery of previously unknown knowledge. Evaluated with respect to known knowledge, an uninformed (unsupervised) method will easily be outperformed by other supervised methods, while in a typical KDD task, supervised methods cannot be used due to the unavailability of training data. === Relation to optimization === Machine learning also has intimate ties to optimization: many learning problems are formulated as minimization of some loss function on a training set of examples. Loss functions express the discrepancy between the predictions of the model being trained and the actual problem instances (for example, in classification, one wants to assign a label to instances, and models are trained to correctly predict the pre-assigned labels of a set of examples). The difference between the two fields arises from the goal of generalization: while optimization algorithms can minimize the loss on a training set, machine learning is concerned with minimizing the loss on unseen samples. === Relation to statistics === Machine learning and statistics are closely related fields in terms of methods, but distinct in their principal goal: statistics draws population inferences from a sample, while machine learning finds generalizable predictive patterns. According to Michael I. Jordan, the ideas of machine learning, from methodological principles to theoretical tools, have had a long pre-history in statistics. He also suggested the term data science as a placeholder to call the overall field. Leo Breiman distinguished two statistical modeling paradigms: data model and algorithmic model, wherein "algorithmic model" means more or less the machine learning algorithms like Random forest. Some statisticians have adopted methods from machine learning, leading to a combined field that they call statistical learning. == Theory == A core objective of a learner is to generalize from its experience. Generalization in this context is the ability of a learning machine to perform accurately on new, unseen examples/tasks after having experienced a learning data set. The training examples come from some generally unknown probability distribution (considered representative of the space of occurrences) and the learner has to build a general model about this space that enables it to produce sufficiently accurate predictions in new cases. The computational analysis of machine learning algorithms and their performance is a branch of theoretical computer science known as computational learning theory. Because training sets are finite and the future is uncertain, learning theory usually does not yield guarantees of the performance of algorithms. Instead, probabilistic bounds on the performance are quite common. The bias–variance decomposition is one way to quantify generalization error. For the best performance in the context of generalization, the complexity of the hypothesis should match the complexity of the function underlying the data. If the hypothesis is less complex than the function, then the model has under fitted the data. If the complexity of the model is increased in response, then the training error decreases. But if the hypothesis is too complex, then the model is subject to overfitting and generalization will be poorer. In addition to performance bounds, learning theorists study the time complexity and feasibility of learning. In computational learning theory, a computation is considered feasible if it can be done in polynomial time. There are two kinds of time complexity results. Positive results show that a certain class of functions can be learned in polynomial time. Negative results show that certain classes cannot be learned in polynomial time. == Approaches == === Types of learning algorithms === The types of machine learning algorithms differ in their approach, the type of data they input and output, and the type of task or problem that they are intended to solve. ==== Supervised learning ==== Supervised learning algorithms build a mathematical model of a set of data that contains both the inputs and the desired outputs. The data is known as training data, and consists of a set of training examples. Each training example has one or more inputs and the desired output, also known as a supervisory signal. In the mathematical model, each training example is represented by an array or vector, sometimes called a feature vector, and the training data is represented by a matrix. Through iterative optimization of an objective function, supervised learning algorithms learn a function that can be used to predict the output associated with new inputs. An optimal function will allow the algorithm to correctly determine the output for inputs that were not a part of the training data. An algorithm that improves the accuracy of its outputs or predictions over time is said to have learned to perform that task. Types of supervised learning algorithms include Active learning , classification and regression. Classification algorithms are used when the outputs are restricted to a limited set of values, and regression algorithms are used when the outputs may have any numerical value within a range. As an example, for a classification algorithm that filters emails, the input would be an incoming email, and the output would be the name of the folder in which to file the email. Similarity learning is an area of supervised machine learning closely related to regression and classification, but the goal is to learn from examples using a similarity function that measures how similar or related two objects are. It has applications in ranking, recommendation systems, visual identity tracking, face verification, and speaker verification. ==== Unsupervised learning ==== Unsupervised learning algorithms take a set of data that contains only inputs, and find structure in the data, like grouping or clustering of data points. The algorithms, therefore, learn from test data that has not been labeled, classified or categorized. Instead of responding to feedback, unsupervised learning algorithms identify commonalities in the data and react based on the presence or absence of such commonalities in each new piece of data. A central application of unsupervised learning is in the field of density estimation in statistics, such as finding the probability density function. Though unsupervised learning encompasses other domains involving summarizing and explaining data features. Cluster analysis is the assignment of a set of observations into subsets (called clusters) so that observations within the same cluster are similar according to one or more predesignated criteria, while observations drawn from different clusters are dissimilar. Different clustering techniques make different assumptions on the structure of the data, often defined by some similarity metric and evaluated, for example, by internal compactness, or the similarity between members of the same cluster, and separation, the difference between clusters. Other methods are based on estimated density and graph connectivity. ==== Semi-supervised learning ==== Semi-supervised learning falls between unsupervised learning (without any labeled training data) and supervised learning (with completely labeled training data). Some of the training examples are missing training labels, yet many machine-learning researchers have found that unlabeled data, when used in conjunction with a small amount of labeled data, can produce a considerable improvement in learning accuracy. In weakly supervised learning, the training labels are noisy, limited, or imprecise; however, these labels are often cheaper to obtain, resulting in larger effective training sets. ==== Reinforcement learning ==== Reinforcement learning is an area of machine learning concerned with how software agents ought to take actions in an environment so as to maximize some notion of cumulative reward. Due to its generality, the field is studied in many other disciplines, such as game theory, control theory, operations research, information theory, simulation-based optimization, multi-agent systems, swarm intelligence, statistics and genetic algorithms. In machine learning, the environment is typically represented as a Markov Decision Process (MDP). Many reinforcement learning algorithms use dynamic programming techniques. Reinforcement learning algorithms do not assume knowledge of an exact mathematical model of the MDP, and are used when exact models are infeasible. Reinforcement learning algorithms are used in autonomous vehicles or in learning to play a game against a human opponent. ==== Self learning ==== Self-learning as machine learning paradigm was introduced in 1982 along with a neural network capable of self-learning named Crossbar Adaptive Array (CAA). It is a learning with no external rewards and no external teacher advices. The CAA self-learning algorithm computes, in a crossbar fashion, both decisions about actions and emotions (feelings) about consequence situations. The system is driven by the interaction between cognition and emotion. The self-learning algorithm updates a memory matrix W =||w(a,s)|| such that in each iteration executes the following machine learning routine: In situation s perform action a; Receive consequence situation s’; Compute emotion of being in consequence situation v(s’); Update crossbar memory w’(a,s) = w(a,s) + v(s’). It is a system with only one input, situation s, and only one output, action (or behavior) a. There is neither a separate reinforcement input nor an advice input from the environment. The backpropagated value (secondary reinforcement) is the emotion toward the consequence situation. The CAA exists in two environments, one is behavioral environment where it behaves, and the other is genetic environment, wherefrom it initially and only once receives initial emotions about situations to be encountered in the behavioral environment. After receiving the genome (species) vector from the genetic environment, the CAA learns a goal seeking behavior, in an environment that contains both desirable and undesirable situations. ==== Feature learning ==== Several learning algorithms aim at discovering better representations of the inputs provided during training. Classic examples include principal components analysis and cluster analysis. Feature learning algorithms, also called representation learning algorithms, often attempt to preserve the information in their input but also transform it in a way that makes it useful, often as a pre-processing step before performing classification or predictions. This technique allows reconstruction of the inputs coming from the unknown data-generating distribution, while not being necessarily faithful to configurations that are implausible under that distribution. This replaces manual feature engineering, and allows a machine to both learn the features and use them to perform a specific task. Feature learning can be either supervised or unsupervised. In supervised feature learning, features are learned using labeled input data. Examples include artificial neural networks, multilayer perceptrons, and supervised dictionary learning. In unsupervised feature learning, features are learned with unlabeled input data. Examples include dictionary learning, independent component analysis, autoencoders, matrix factorization and various forms of clustering. Manifold learning algorithms attempt to do so under the constraint that the learned representation is low-dimensional. Sparse coding algorithms attempt to do so under the constraint that the learned representation is sparse, meaning that the mathematical model has many zeros. Multilinear subspace learning algorithms aim to learn low-dimensional representations directly from tensor representations for multidimensional data, without reshaping them into higher-dimensional vectors. Deep learning algorithms discover multiple levels of representation, or a hierarchy of features, with higher-level, more abstract features defined in terms of (or generating) lower-level features. It has been argued that an intelligent machine is one that learns a representation that disentangles the underlying factors of variation that explain the observed data. Feature learning is motivated by the fact that machine learning tasks such as classification often require input that is mathematically and computationally convenient to process. However, real-world data such as images, video, and sensory data has not yielded to attempts to algorithmically define specific features. An alternative is to discover such features or representations through examination, without relying on explicit algorithms. ==== Sparse dictionary learning ==== Sparse dictionary learning is a feature learning method where a training example is represented as a linear combination of basis functions, and is assumed to be a sparse matrix. The method is strongly NP-hard and difficult to solve approximately. A popular heuristic method for sparse dictionary learning is the K-SVD algorithm. Sparse dictionary learning has been applied in several contexts. In classification, the problem is to determine the class to which a previously unseen training example belongs. For a dictionary where each class has already been built, a new training example is associated with the class that is best sparsely represented by the corresponding dictionary. Sparse dictionary learning has also been applied in image de-noising. The key idea is that a clean image patch can be sparsely represented by an image dictionary, but the noise cannot. ==== Anomaly detection ==== In data mining, anomaly detection, also known as outlier detection, is the identification of rare items, events or observations which raise suspicions by differing significantly from the majority of the data. Typically, the anomalous items represent an issue such as bank fraud, a structural defect, medical problems or errors in a text. Anomalies are referred to as outliers, novelties, noise, deviations and exceptions. In particular, in the context of abuse and network intrusion detection, the interesting objects are often not rare objects, but unexpected bursts in activity. This pattern does not adhere to the common statistical definition of an outlier as a rare object, and many outlier detection methods (in particular, unsupervised algorithms) will fail on such data, unless it has been aggregated appropriately. Instead, a cluster analysis algorithm may be able to detect the micro-clusters formed by these patterns. Three broad categories of anomaly detection techniques exist. Unsupervised anomaly detection techniques detect anomalies in an unlabeled test data set under the assumption that the majority of the instances in the data set are normal, by looking for instances that seem to fit least to the remainder of the data set. Supervised anomaly detection techniques require a data set that has been labeled as "normal" and "abnormal" and involves training a classifier (the key difference to many other statistical classification problems is the inherently unbalanced nature of outlier detection). Semi-supervised anomaly detection techniques construct a model representing normal behavior from a given normal training data set and then test the likelihood of a test instance to be generated by the model. ==== Robot learning ==== In developmental robotics, robot learning algorithms generate their own sequences of learning experiences, also known as a curriculum, to cumulatively acquire new skills through self-guided exploration and social interaction with humans. These robots use guidance mechanisms such as active learning, maturation, motor synergies and imitation. ==== Association rules ==== Association rule learning is a rule-based machine learning method for discovering relationships between variables in large databases. It is intended to identify strong rules discovered in databases using some measure of "interestingness".Rule-based machine learning is a general term for any machine learning method that identifies, learns, or evolves "rules" to store, manipulate or apply knowledge. The defining characteristic of a rule-based machine learning algorithm is the identification and utilization of a set of relational rules that collectively represent the knowledge captured by the system. This is in contrast to other machine learning algorithms that commonly identify a singular model that can be universally applied to any instance in order to make a prediction. Rule-based machine learning approaches include learning classifier systems, association rule learning, and artificial immune systems. Based on the concept of strong rules, Rakesh Agrawal, Tomasz Imieliński and Arun Swami introduced association rules for discovering regularities between products in large-scale transaction data recorded by point-of-sale (POS) systems in supermarkets. For example, the rule { o n i o n s , p o t a t o e s } ⇒ { b u r g e r } {\displaystyle \{\mathrm {onions,potatoes} \}\Rightarrow \{\mathrm {burger} \}} found in the sales data of a supermarket would indicate that if a customer buys onions and potatoes together, they are likely to also buy hamburger meat. Such information can be used as the basis for decisions about marketing activities such as promotional pricing or product placements. In addition to market basket analysis, association rules are employed today in application areas including Web usage mining, intrusion detection, continuous production, and bioinformatics. In contrast with sequence mining, association rule learning typically does not consider the order of items either within a transaction or across transactions. Learning classifier systems (LCS) are a family of rule-based machine learning algorithms that combine a discovery component, typically a genetic algorithm, with a learning component, performing either supervised learning, reinforcement learning, or unsupervised learning. They seek to identify a set of context-dependent rules that collectively store and apply knowledge in a piecewise manner in order to make predictions. Inductive logic programming (ILP) is an approach to rule-learning using logic programming as a uniform representation for input examples, background knowledge, and hypotheses. Given an encoding of the known background knowledge and a set of examples represented as a logical database of facts, an ILP system will derive a hypothesized logic program that entails all positive and no negative examples. Inductive programming is a related field that considers any kind of programming languages for representing hypotheses (and not only logic programming), such as functional programs. Inductive logic programming is particularly useful in bioinformatics and natural language processing. Gordon Plotkin and Ehud Shapiro laid the initial theoretical foundation for inductive machine learning in a logical setting. Shapiro built their first implementation (Model Inference System) in 1981: a Prolog program that inductively inferred logic programs from positive and negative examples. The term inductive here refers to philosophical induction, suggesting a theory to explain observed facts, rather than mathematical induction, proving a property for all members of a well-ordered set. === Models === Performing machine learning involves creating a model, which is trained on some training data and then can process additional data to make predictions. Various types of models have been used and researched for machine learning systems. ==== Artificial neural networks ==== Artificial neural networks (ANNs), or connectionist systems, are computing systems vaguely inspired by the biological neural networks that constitute animal brains. Such systems "learn" to perform tasks by considering examples, generally without being programmed with any task-specific rules. An ANN is a model based on a collection of connected units or nodes called "artificial neurons", which loosely model the neurons in a biological brain. Each connection, like the synapses in a biological brain, can transmit information, a "signal", from one artificial neuron to another. An artificial neuron that receives a signal can process it and then signal additional artificial neurons connected to it. In common ANN implementations, the signal at a connection between artificial neurons is a real number, and the output of each artificial neuron is computed by some non-linear function of the sum of its inputs. The connections between artificial neurons are called "edges". Artificial neurons and edges typically have a weight that adjusts as learning proceeds. The weight increases or decreases the strength of the signal at a connection. Artificial neurons may have a threshold such that the signal is only sent if the aggregate signal crosses that threshold. Typically, artificial neurons are aggregated into layers. Different layers may perform different kinds of transformations on their inputs. Signals travel from the first layer (the input layer) to the last layer (the output layer), possibly after traversing the layers multiple times. The original goal of the ANN approach was to solve problems in the same way that a human brain would. However, over time, attention moved to performing specific tasks, leading to deviations from biology. Artificial neural networks have been used on a variety of tasks, including computer vision, speech recognition, machine translation, social network filtering, playing board and video games and medical diagnosis. Deep learning consists of multiple hidden layers in an artificial neural network. This approach tries to model the way the human brain processes light and sound into vision and hearing. Some successful applications of deep learning are computer vision and speech recognition. ==== Decision trees ==== Decision tree learning uses a decision tree as a predictive model to go from observations about an item (represented in the branches) to conclusions about the item's target value (represented in the leaves). It is one of the predictive modeling approaches used in statistics, data mining and machine learning. Tree models where the target variable can take a discrete set of values are called classification trees; in these tree structures, leaves represent class labels and branches represent conjunctions of features that lead to those class labels. Decision trees where the target variable can take continuous values (typically real numbers) are called regression trees. In decision analysis, a decision tree can be used to visually and explicitly represent decisions and decision making. In data mining, a decision tree describes data, but the resulting classification tree can be an input for decision making. ==== Support vector machines ==== Support vector machines (SVMs), also known as support vector networks, are a set of related supervised learning methods used for classification and regression. Given a set of training examples, each marked as belonging to one of two categories, an SVM training algorithm builds a model that predicts whether a new example falls into one category or the other. An SVM training algorithm is a non-probabilistic, binary, linear classifier, although methods such as Platt scaling exist to use SVM in a probabilistic classification setting. In addition to performing linear classification, SVMs can efficiently perform a non-linear classification using what is called the kernel trick, implicitly mapping their inputs into high-dimensional feature spaces. ==== Regression analysis ==== Regression analysis encompasses a large variety of statistical methods to estimate the relationship between input variables and their associated features. Its most common form is linear regression, where a single line is drawn to best fit the given data according to a mathematical criterion such as ordinary least squares. The latter is often extended by regularization (mathematics) methods to mitigate overfitting and bias, as in ridge regression. When dealing with non-linear problems, go-to models include polynomial regression (for example, used for trendline fitting in Microsoft Excel ), Logistic regression (often used in statistical classification) or even kernel regression, which introduces non-linearity by taking advantage of the kernel trick to implicitly map input variables to higher dimensional space. ==== Bayesian networks ==== A Bayesian network, belief network or directed acyclic graphical model is a probabilistic graphical model that represents a set of random variables and their conditional independence with a directed acyclic graph (DAG). For example, a Bayesian network could represent the probabilistic relationships between diseases and symptoms. Given symptoms, the network can be used to compute the probabilities of the presence of various diseases. Efficient algorithms exist that perform inference and learning. Bayesian networks that model sequences of variables, like speech signals or protein sequences, are called dynamic Bayesian networks. Generalizations of Bayesian networks that can represent and solve decision problems under uncertainty are called influence diagrams. ==== Genetic algorithms ==== A genetic algorithm (GA) is a search algorithm and heuristic technique that mimics the process of natural selection, using methods such as mutation and crossover to generate new genotypes in the hope of finding good solutions to a given problem. In machine learning, genetic algorithms were used in the 1980s and 1990s. Conversely, machine learning techniques have been used to improve the performance of genetic and evolutionary algorithms. === Training models === Usually, machine learning models require a lot of data in order for them to perform well. Usually, when training a machine learning model, one needs to collect a large, representative sample of data from a training set. Data from the training set can be as varied as a corpus of text, a collection of images, and data collected from individual users of a service. Overfitting is something to watch out for when training a machine learning model. ==== Federated learning ==== Federated learning is a new approach to training machine learning models that decentralizes the training process, allowing for users' privacy to be maintained by not needing to send their data to a centralized server. This also increases efficiency by decentralizing the training process to many devices. For example, Gboard uses federated machine learning to train search query prediction models on users' mobile phones without having to send individual searches back to Google. == Applications == There are many applications for machine learning, including: In 2006, the media-services provider Netflix held the first "Netflix Prize" competition to find a program to better predict user preferences and improve the accuracy on its existing Cinematch movie recommendation algorithm by at least 10%. A joint team made up of researchers from AT&T Labs-Research in collaboration with the teams Big Chaos and Pragmatic Theory built an ensemble model to win the Grand Prize in 2009 for $1 million. Shortly after the prize was awarded, Netflix realized that viewers' ratings were not the best indicators of their viewing patterns ("everything is a recommendation") and they changed their recommendation engine accordingly. In 2010 The Wall Street Journal wrote about the firm Rebellion Research and their use of machine learning to predict the financial crisis. In 2012, co-founder of Sun Microsystems, Vinod Khosla, predicted that 80% of medical doctors' jobs would be lost in the next two decades to automated machine learning medical diagnostic software. In 2014, it was reported that a machine learning algorithm had been applied in the field of art history to study fine art paintings, and that it may have revealed previously unrecognized influences among artists. In 2019 Springer Nature published the first research book created using machine learning. == Limitations == Although machine learning has been transformative in some fields, machine-learning programs often fail to deliver expected results. Reasons for this are numerous: lack of (suitable) data, lack of access to the data, data bias, privacy problems, badly chosen tasks and algorithms, wrong tools and people, lack of resources, and evaluation problems. In 2018, a self-driving car from Uber failed to detect a pedestrian, who was killed after a collision. Attempts to use machine learning in healthcare with the IBM Watson system failed to deliver even after years of time and billions of investment. === Bias === Machine learning approaches in particular can suffer from different data biases. A machine learning system trained on current customers only may not be able to predict the needs of new customer groups that are not represented in the training data. When trained on man-made data, machine learning is likely to pick up the same constitutional and unconscious biases already present in society. Language models learned from data have been shown to contain human-like biases. Machine learning systems used for criminal risk assessment have been found to be biased against black people. In 2015, Google photos would often tag black people as gorillas, and in 2018 this still was not well resolved, but Google reportedly was still using the workaround to remove all gorillas from the training data, and thus was not able to recognize real gorillas at all. Similar issues with recognizing non-white people have been found in many other systems. In 2016, Microsoft tested a chatbot that learned from Twitter, and it quickly picked up racist and sexist language. Because of such challenges, the effective use of machine learning may take longer to be adopted in other domains. Concern for fairness in machine learning, that is, reducing bias in machine learning and propelling its use for human good is increasingly expressed by artificial intelligence scientists, including Fei-Fei Li, who reminds engineers that "There’s nothing artificial about AI...It’s inspired by people, it’s created by people, and—most importantly—it impacts people. It is a powerful tool we are only just beginning to understand, and that is a profound responsibility.” == Model assessments == Classification machine learning models can be validated by accuracy estimation techniques like the Holdout method, which splits the data in a training and test set (conventionally 2/3 training set and 1/3 test set designation) and evaluates the performance of the training model on the test set. In comparison, the K-fold-cross-validation method randomly partitions the data into K subsets and then K experiments are performed each respectively considering 1 subset for evaluation and the remaining K-1 subsets for training the model. In addition to the holdout and cross-validation methods, bootstrap, which samples n instances with replacement from the dataset, can be used to assess model accuracy. In addition to overall accuracy, investigators frequently report sensitivity and specificity meaning True Positive Rate (TPR) and True Negative Rate (TNR) respectively. Similarly, investigators sometimes report the False Positive Rate (FPR) as well as the False Negative Rate (FNR). However, these rates are ratios that fail to reveal their numerators and denominators. The Total Operating Characteristic (TOC) is an effective method to express a model's diagnostic ability. TOC shows the numerators and denominators of the previously mentioned rates, thus TOC provides more information than the commonly used Receiver Operating Characteristic (ROC) and ROC's associated Area Under the Curve (AUC). == Ethics == Machine learning poses a host of ethical questions. Systems which are trained on datasets collected with biases may exhibit these biases upon use (algorithmic bias), thus digitizing cultural prejudices. For example, using job hiring data from a firm with racist hiring policies may lead to a machine learning system duplicating the bias by scoring job applicants against similarity to previous successful applicants. Responsible collection of data and documentation of algorithmic rules used by a system thus is a critical part of machine learning. Because human languages contain biases, machines trained on language corpora will necessarily also learn these biases. Other forms of ethical challenges, not related to personal biases, are more seen in health care. There are concerns among health care professionals that these systems might not be designed in the public's interest but as income-generating machines. This is especially true in the United States where there is a long-standing ethical dilemma of improving health care, but also increasing profits. For example, the algorithms could be designed to provide patients with unnecessary tests or medication in which the algorithm's proprietary owners hold stakes. There is huge potential for machine learning in health care to provide professionals a great tool to diagnose, medicate, and even plan recovery paths for patients, but this will not happen until the personal biases mentioned previously, and these "greed" biases are addressed. == Software == Software suites containing a variety of machine learning algorithms include the following: === Free and open-source software === === Proprietary software with free and open-source editions === === Proprietary software === == Journals == Journal of Machine Learning Research Machine Learning Nature Machine Intelligence Neural Computation == Conferences == Conference on Neural Information Processing Systems International Conference on Machine Learning == See also == == References == == Further reading == == External links == International Machine Learning Society mloss is an academic database of open-source machine learning software. Machine Learning Crash Course by Google. This is a free course on machine learning through the use of TensorFlow. ================================================ FILE: week6/questions/corpus/natural_language_processing.txt ================================================ https://en.wikipedia.org/wiki/Natural_language_processing Natural language processing (NLP) is a subfield of linguistics, computer science, information engineering, and artificial intelligence concerned with the interactions between computers and human (natural) languages, in particular how to program computers to process and analyze large amounts of natural language data. Challenges in natural language processing frequently involve speech recognition, natural language understanding, and natural language generation. == History == The history of natural language processing (NLP) generally started in the 1950s, although work can be found from earlier periods. In 1950, Alan Turing published an article titled "Computing Machinery and Intelligence" which proposed what is now called the Turing test as a criterion of intelligence. The Georgetown experiment in 1954 involved fully automatic translation of more than sixty Russian sentences into English. The authors claimed that within three or five years, machine translation would be a solved problem. However, real progress was much slower, and after the ALPAC report in 1966, which found that ten-year-long research had failed to fulfill the expectations, funding for machine translation was dramatically reduced. Little further research in machine translation was conducted until the late 1980s when the first statistical machine translation systems were developed. Some notably successful natural language processing systems developed in the 1960s were SHRDLU, a natural language system working in restricted "blocks worlds" with restricted vocabularies, and ELIZA, a simulation of a Rogerian psychotherapist, written by Joseph Weizenbaum between 1964 and 1966. Using almost no information about human thought or emotion, ELIZA sometimes provided a startlingly human-like interaction. When the "patient" exceeded the very small knowledge base, ELIZA might provide a generic response, for example, responding to "My head hurts" with "Why do you say your head hurts?". During the 1970s, many programmers began to write "conceptual ontologies", which structured real-world information into computer-understandable data. Examples are MARGIE (Schank, 1975), SAM (Cullingford, 1978), PAM (Wilensky, 1978), TaleSpin (Meehan, 1976), QUALM (Lehnert, 1977), Politics (Carbonell, 1979), and Plot Units (Lehnert 1981). During this time, many chatterbots were written including PARRY, Racter, and Jabberwacky. Up to the 1980s, most natural language processing systems were based on complex sets of hand-written rules. Starting in the late 1980s, however, there was a revolution in natural language processing with the introduction of machine learning algorithms for language processing. This was due to both the steady increase in computational power (see Moore's law) and the gradual lessening of the dominance of Chomskyan theories of linguistics (e.g. transformational grammar), whose theoretical underpinnings discouraged the sort of corpus linguistics that underlies the machine-learning approach to language processing. Some of the earliest-used machine learning algorithms, such as decision trees, produced systems of hard if-then rules similar to existing hand-written rules. However, part-of-speech tagging introduced the use of hidden Markov models to natural language processing, and increasingly, research has focused on statistical models, which make soft, probabilistic decisions based on attaching real-valued weights to the features making up the input data. The cache language models upon which many speech recognition systems now rely are examples of such statistical models. Such models are generally more robust when given unfamiliar input, especially input that contains errors (as is very common for real-world data), and produce more reliable results when integrated into a larger system comprising multiple subtasks. Many of the notable early successes occurred in the field of machine translation, due especially to work at IBM Research, where successively more complicated statistical models were developed. These systems were able to take advantage of existing multilingual textual corpora that had been produced by the Parliament of Canada and the European Union as a result of laws calling for the translation of all governmental proceedings into all official languages of the corresponding systems of government. However, most other systems depended on corpora specifically developed for the tasks implemented by these systems, which was (and often continues to be) a major limitation in the success of these systems. As a result, a great deal of research has gone into methods of more effectively learning from limited amounts of data. Recent research has increasingly focused on unsupervised and semi-supervised learning algorithms. Such algorithms can learn from data that has not been hand-annotated with the desired answers or using a combination of annotated and non-annotated data. Generally, this task is much more difficult than supervised learning, and typically produces less accurate results for a given amount of input data. However, there is an enormous amount of non-annotated data available (including, among other things, the entire content of the World Wide Web), which can often make up for the inferior results if the algorithm used has a low enough time complexity to be practical. In the 2010s, representation learning and deep neural network-style machine learning methods became widespread in natural language processing, due in part to a flurry of results showing that such techniques can achieve state-of-the-art results in many natural language tasks, for example in language modeling, parsing, and many others. Popular techniques include the use of word embeddings to capture semantic properties of words, and an increase in end-to-end learning of a higher-level task (e.g., question answering) instead of relying on a pipeline of separate intermediate tasks (e.g., part-of-speech tagging and dependency parsing). In some areas, this shift has entailed substantial changes in how NLP systems are designed, such that deep neural network-based approaches may be viewed as a new paradigm distinct from statistical natural language processing. For instance, the term neural machine translation (NMT) emphasizes the fact that deep learning-based approaches to machine translation directly learn sequence-to-sequence transformations, obviating the need for intermediate steps such as word alignment and language modeling that was used in statistical machine translation (SMT). == Rule-based vs. statistical NLP == In the early days, many language-processing systems were designed by hand-coding a set of rules: such as by writing grammars or devising heuristic rules for stemming. Since the so-called "statistical revolution" in the late 1980s and mid-1990s, much natural language processing research has relied heavily on machine learning. The machine-learning paradigm calls instead for using statistical inference to automatically learn such rules through the analysis of large corpora (the plural form of corpus, is a set of documents, possibly with human or computer annotations) of typical real-world examples. Many different classes of machine-learning algorithms have been applied to natural-language-processing tasks. These algorithms take as input a large set of "features" that are generated from the input data. Some of the earliest-used algorithms, such as decision trees, produced systems of hard if-then rules similar to the systems of handwritten rules that were then common. Increasingly, however, research has focused on statistical models, which make soft, probabilistic decisions based on attaching real-valued weights to each input feature. Such models have the advantage that they can express the relative certainty of many different possible answers rather than only one, producing more reliable results when such a model is included as a component of a larger system. Systems based on machine-learning algorithms have many advantages over hand-produced rules: The learning procedures used during machine learning automatically focus on the most common cases, whereas when writing rules by hand it is often not at all obvious where the effort should be directed. Automatic learning procedures can make use of statistical inference algorithms to produce models that are robust to unfamiliar input (e.g. containing words or structures that have not been seen before) and to erroneous input (e.g. with misspelled words or words accidentally omitted). Generally, handling such input gracefully with handwritten rules, or, more generally, creating systems of handwritten rules that make soft decisions, is extremely difficult, error-prone and time-consuming. Systems based on automatically learning the rules can be made more accurate simply by supplying more input data. However, systems based on handwritten rules can only be made more accurate by increasing the complexity of the rules, which is a much more difficult task. In particular, there is a limit to the complexity of systems based on handcrafted rules, beyond which the systems become more and more unmanageable. However, creating more data to input to machine-learning systems simply requires a corresponding increase in the number of man-hours worked, generally without significant increases in the complexity of the annotation process. == Major evaluations and tasks == The following is a list of some of the most commonly researched tasks in natural language processing. Some of these tasks have direct real-world applications, while others more commonly serve as subtasks that are used to aid in solving larger tasks. Though natural language processing tasks are closely intertwined, they are frequently subdivided into categories for convenience. A coarse division is given below. === Syntax === Grammar induction Generate a formal grammar that describes a language's syntax. Lemmatization The task of removing inflectional endings only and to return the base dictionary form of a word which is also known as a lemma. Morphological segmentation Separate words into individual morphemes and identify the class of the morphemes. The difficulty of this task depends greatly on the complexity of the morphology (i.e. the structure of words) of the language being considered. English has fairly simple morphology, especially inflectional morphology, and thus it is often possible to ignore this task entirely and simply model all possible forms of a word (e.g. "open, opens, opened, opening") as separate words. In languages such as Turkish or Meitei, a highly agglutinated Indian language, however, such an approach is not possible, as each dictionary entry has thousands of possible word forms. Part-of-speech tagging Given a sentence, determine the part of speech (POS) for each word. Many words, especially common ones, can serve as multiple parts of speech. For example, "book" can be a noun ("the book on the table") or verb ("to book a flight"); "set" can be a noun, verb or adjective; and "out" can be any of at least five different parts of speech. Some languages have more such ambiguity than others. Languages with little inflectional morphology, such as English, are particularly prone to such ambiguity. Chinese is prone to such ambiguity because it is a tonal language during verbalization. Such inflection is not readily conveyed via the entities employed within the orthography to convey the intended meaning. Parsing Determine the parse tree (grammatical analysis) of a given sentence. The grammar for natural languages is ambiguous and typical sentences have multiple possible analyses. Perhaps surprisingly, for a typical sentence, there may be thousands of potential parses (most of which will seem completely nonsensical to a human). There are two primary types of parsing, Dependency Parsing, and Constituency Parsing. Dependency Parsing focuses on the relationships between words in a sentence (marking things like Primary Objects and predicates), whereas Constituency Parsing focuses on building out the Parse Tree using a Probabilistic Context-Free Grammar (PCFG). See also: Stochastic grammar. Sentence breaking (also known as sentence boundary disambiguation) Given a chunk of text, find the sentence boundaries. Sentence boundaries are often marked by periods or other punctuation marks, but these same characters can serve other purposes (e.g. marking abbreviations). Stemming The process of reducing inflected (or sometimes derived) words to their root form. (e.g. "close" will be the root for "closed", "closing", "close", "closer" etc.). Word segmentation Separate a chunk of continuous text into separate words. For a language like English, this is fairly trivial, since words are usually separated by spaces. However, some written languages like Chinese, Japanese and Thai do not mark word boundaries in such a fashion, and in those languages text segmentation is a significant task requiring knowledge of the vocabulary and morphology of words in the language. Sometimes this process is also used in cases like Bag of Words (BOW) creation in data mining. Terminology extraction The goal of terminology extraction is to automatically extract relevant terms from a given corpus. === Semantics === Lexical semantics What is the computational meaning of individual words in context? Distributional semantics How can we learn semantic representations from data? Machine translation Automatically translate text from one human language to another. This is one of the most difficult problems, and is a member of a class of problems colloquially termed "AI-complete", i.e. requiring all of the different types of knowledge that humans possess (grammar, semantics, facts about the real world, etc.) to solve properly. Named entity recognition (NER) Given a stream of text, determine which items in the text map to proper names, such as people or places, and what the type of each such name is (e.g. person, location, organization). Although capitalization can aid in recognizing named entities in languages such as English, this information cannot aid in determining the type of named entity, and in any case, is often inaccurate or insufficient. For example, the first letter of a sentence is also capitalized, and named entities often span several words, only some of which are capitalized. Furthermore, many other languages in non-Western scripts (e.g. Chinese or Arabic) do not have any capitalization at all, and even languages with capitalization may not consistently use it to distinguish names. For example, German capitalizes all nouns, regardless of whether they are names, and French and Spanish do not capitalize names that serve as adjectives. Natural language generation Convert information from computer databases or semantic intents into readable human language. Natural language understanding Convert chunks of text into more formal representations such as first-order logic structures that are easier for computer programs to manipulate. Natural language understanding involves the identification of the intended semantic from the multiple possible semantics which can be derived from a natural language expression which usually takes the form of organized notations of natural language concepts. Introduction and creation of language metamodel and ontology are efficient however empirical solutions. An explicit formalization of natural language semantics without confusions with implicit assumptions such as closed-world assumption (CWA) vs. open-world assumption, or subjective Yes/No vs. objective True/False is expected for the construction of a basis of semantics formalization. Optical character recognition (OCR) Given an image representing printed text, determine the corresponding text. Question answering Given a human-language question, determine its answer. Typical questions have a specific right answer (such as "What is the capital of Canada?"), but sometimes open-ended questions are also considered (such as "What is the meaning of life?"). Recent works have looked at even more complex questions. Recognizing Textual entailment Given two text fragments, determine if one being true entails the other, entails the other's negation, or allows the other to be either true or false. Relationship extraction Given a chunk of text, identify the relationships among named entities (e.g. who is married to whom). Sentiment analysis (see also multimodal sentiment analysis) Extract subjective information usually from a set of documents, often using online reviews to determine "polarity" about specific objects. It is especially useful for identifying trends of public opinion in social media, for marketing. Topic segmentation and recognition Given a chunk of text, separate it into segments each of which is devoted to a topic, and identify the topic of the segment. Word sense disambiguation Many words have more than one meaning; we have to select the meaning which makes the most sense in context. For this problem, we are typically given a list of words and associated word senses, e.g. from a dictionary or an online resource such as WordNet. === Discourse === Automatic summarization Produce a readable summary of a chunk of text. Often used to provide summaries of the text of a known type, such as research papers, articles in the financial section of a newspaper. Coreference resolution Given a sentence or larger chunk of text, determine which words ("mentions") refer to the same objects ("entities"). Anaphora resolution is a specific example of this task, and is specifically concerned with matching up pronouns with the nouns or names to which they refer. The more general task of coreference resolution also includes identifying so-called "bridging relationships" involving referring expressions. For example, in a sentence such as "He entered John's house through the front door", "the front door" is a referring expression and the bridging relationship to be identified is the fact that the door being referred to is the front door of John's house (rather than of some other structure that might also be referred to). Discourse analysis This rubric includes several related tasks. One task is identifying the discourse structure of a connected text, i.e. the nature of the discourse relationships between sentences (e.g. elaboration, explanation, contrast). Another possible task is recognizing and classifying the speech acts in a chunk of text (e.g. yes-no question, content question, statement, assertion, etc.). === Speech === Speech recognition Given a sound clip of a person or people speaking, determine the textual representation of the speech. This is the opposite of text to speech and is one of the extremely difficult problems colloquially termed "AI-complete" (see above). In natural speech there are hardly any pauses between successive words, and thus speech segmentation is a necessary subtask of speech recognition (see below). In most spoken languages, the sounds representing successive letters blend into each other in a process termed coarticulation, so the conversion of the analog signal to discrete characters can be a very difficult process. Also, given that words in the same language are spoken by people with different accents, the speech recognition software must be able to recognize the wide variety of input as being identical to each other in terms of its textual equivalent. Speech segmentation Given a sound clip of a person or people speaking, separate it into words. A subtask of speech recognition and typically grouped with it. Text-to-speech Given a text, transform those units and produce a spoken representation. Text-to-speech can be used to aid the visually impaired. === Dialogue === The first published work by an artificial intelligence was published in 2018, 1 the Road, marketed as a novel, contains sixty million words. == See also == == References == == Further reading == ================================================ FILE: week6/questions/corpus/neural_network.txt ================================================ https://en.wikipedia.org/wiki/Neural_network Artificial neural networks (ANN) or connectionist systems are computing systems vaguely inspired by the biological neural networks that constitute animal brains. Such systems "learn" to perform tasks by considering examples, generally without being programmed with task-specific rules. For example, in image recognition, they might learn to identify images that contain cats by analyzing example images that have been manually labeled as "cat" or "no cat" and using the results to identify cats in other images. They do this without any prior knowledge of cats, for example, that they have fur, tails, whiskers and cat-like faces. Instead, they automatically generate identifying characteristics from the examples that they process. An ANN is based on a collection of connected units or nodes called artificial neurons, which loosely model the neurons in a biological brain. Each connection, like the synapses in a biological brain, can transmit a signal to other neurons. An artificial neuron that receives a signal then processes it and can signal neurons connected to it. In ANN implementations, the "signal" at a connection is a real number, and the output of each neuron is computed by some non-linear function of the sum of its inputs. The connections are called edges. Neurons and edges typically have a weight that adjusts as learning proceeds. The weight increases or decreases the strength of the signal at a connection. Neurons may have a threshold such that a signal is sent only if the aggregate signal crosses that threshold. Typically, neurons are aggregated into layers. Different layers may perform different transformations on their inputs. Signals travel from the first layer (the input layer), to the last layer (the output layer), possibly after traversing the layers multiple times. The original goal of the ANN approach was to solve problems in the same way that a human brain would. But over time, attention moved to performing specific tasks, leading to deviations from biology. ANNs have been used on a variety of tasks, including computer vision, speech recognition, machine translation, social network filtering, playing board and video games, medical diagnosis, and even in activities that have traditionally been considered as reserved to humans, like painting. == History == Warren McCulloch and Walter Pitts (1943) opened the subject by creating a computational model for neural networks. In the late 1940s, D. O. Hebb created a learning hypothesis based on the mechanism of neural plasticity that became known as Hebbian learning. Farley and Wesley A. Clark (1954) first used computational machines, then called "calculators", to simulate a Hebbian network. Rosenblatt (1958) created the perceptron. The first functional networks with many layers were published by Ivakhnenko and Lapa in 1965, as the Group Method of Data Handling. The basics of continuous backpropagation were derived in the context of control theory by Kelley in 1960 and by Bryson in 1961, using principles of dynamic programming. In 1970, Seppo Linnainmaa published the general method for automatic differentiation (AD) of discrete connected networks of nested differentiable functions. In 1973, Dreyfus used backpropagation to adapt parameters of controllers in proportion to error gradients. Werbos's (1975) backpropagation algorithm enabled practical training of multi-layer networks. In 1982, he applied Linnainmaa's AD method to neural networks in the way that became widely used. Thereafter research stagnated following Minsky and Papert (1969), who discovered that basic perceptrons were incapable of processing the exclusive-or circuit and that computers lacked sufficient power to process useful neural networks. In 1992, max-pooling was introduced to help with least-shift invariance and tolerance to deformation to aid 3D object recognition. Schmidhuber adopted a multi-level hierarchy of networks (1992) pre-trained one level at a time by unsupervised learning and fine-tuned by backpropagation. The development of metal–oxide–semiconductor (MOS) very-large-scale integration (VLSI), in the form of complementary MOS (CMOS) technology, enabled the development of practical artificial neural networks in the 1980s. A landmark publication in the field was the 1989 book Analog VLSI Implementation of Neural Systems by Carver A. Mead and Mohammed Ismail. Geoffrey Hinton et al. (2006) proposed learning a high-level representation using successive layers of binary or real-valued latent variables with a restricted Boltzmann machine to model each layer. In 2012, Ng and Dean created a network that learned to recognize higher-level concepts, such as cats, only from watching unlabeled images. Unsupervised pre-training and increased computing power from GPUs and distributed computing allowed the use of larger networks, particularly in image and visual recognition problems, which became known as "deep learning". Ciresan and colleagues (2010) showed that despite the vanishing gradient problem, GPUs make backpropagation feasible for many-layered feedforward neural networks. Between 2009 and 2012, ANNs began winning prizes in ANN contests, approaching human level performance on various tasks, initially in pattern recognition and machine learning. For example, the bi-directional and multi-dimensional long short-term memory (LSTM) of Graves et al. won three competitions in connected handwriting recognition in 2009 without any prior knowledge about the three languages to be learned. Ciresan and colleagues built the first pattern recognizers to achieve human-competitive/superhuman performance on benchmarks such as traffic sign recognition (IJCNN 2012). == Models == ANNs began as an attempt to exploit the architecture of the human brain to perform tasks that conventional algorithms had little success with. They soon reoriented towards improving empirical results, mostly abandoning attempts to remain true to their biological precursors. Neurons are connected to each other in various patterns, to allow the output of some neurons to become the input of others. The network forms a directed, weighted graph. An artificial neural network consists of a collection of simulated neurons. Each neuron is a node which is connected to other nodes via links that correspond to biological axon-synapse-dendrite connections. Each link has a weight, which determines the strength of one node's influence on another. === Components of ANNs === ==== Neurons ==== ANNs are composed of artificial neurons which retain the biological concept of neurons, which receive input, combine the input with their internal state (activation) and an optional threshold using an activation function, and produce output using an output function. The initial inputs are external data, such as images and documents. The ultimate outputs accomplish the task, such as recognizing an object in an image. The important characteristic of the activation function is that it provides a smooth, differentiable transition as input values change, i.e. a small change in input produces a small change in output. ==== Connections and weights ==== The network consists of connections, each connection providing the output of one neuron as an input to another neuron. Each connection is assigned a weight that represents its relative importance. A given neuron can have multiple input and output connections. ==== Propagation function ==== The propagation function computes the input to a neuron from the outputs of its predecessor neurons and their connections as a weighted sum. A bias term can be added to the result of the propagation. === Organization === The neurons are typically organized into multiple layers, especially in deep learning. Neurons of one layer connect only to neurons of the immediately preceding and immediately following layers. The layer that receives external data is the input layer. The layer that produces the ultimate result is the output layer. In between them are zero or more hidden layers. Single layer and unlayered networks are also used. Between two layers, multiple connection patterns are possible. They can be fully connected, with every neuron in one layer connecting to every neuron in the next layer. They can be pooling, where a group of neurons in one layer connect to a single neuron in the next layer, thereby reducing the number of neurons in that layer. Neurons with only such connections form a directed acyclic graph and are known as feedforward networks. Alternatively, networks that allow connections between neurons in the same or previous layers are known as recurrent networks. === Hyperparameter === A hyperparameter is a constant parameter whose value is set before the learning process begins. The values of parameters are derived via learning. Examples of hyperparameters include learning rate, the number of hidden layers and batch size. The values of some hyperparameters can be dependent on those of other hyperparameters. For example, the size of some layers can depend on the overall number of layers. === Learning === Learning is the adaptation of the network to better handle a task by considering sample observations. Learning involves adjusting the weights (and optional thresholds) of the network to improve the accuracy of the result. This is done by minimizing the observed errors. Learning is complete when examining additional observations does not usefully reduce the error rate. Even after learning, the error rate typically does not reach 0. If after learning, the error rate is too high, the network typically must be redesigned. Practically this is done by defining a cost function that is evaluated periodically during learning. As long as its output continues to decline, learning continues. The cost is frequently defined as a statistic whose value can only be approximated. The outputs are actually numbers, so when the error is low, the difference between the output (almost certainly a cat) and the correct answer (cat) is small. Learning attempts to reduce the total of the differences across the observations. Most learning models can be viewed as a straightforward application of optimization theory and statistical estimation. ==== Learning rate ==== The learning rate defines the size of the corrective steps that the model takes to adjust for errors in each observation. A high learning rate shortens the training time, but with lower ultimate accuracy, while a lower learning rate takes longer, but with the potential for greater accuracy. Optimizations such as Quickprop are primarily aimed at speeding up error minimization, while other improvements mainly try to increase reliability. In order to avoid oscillation inside the network such as alternating connection weights, and to improve the rate of convergence, refinements use an adaptive learning rate that increases or decreases as appropriate. The concept of momentum allows the balance between the gradient and the previous change to be weighted such that the weight adjustment depends to some degree on the previous change. A momentum close to 0 emphasizes the gradient, while a value close to 1 emphasizes the last change. ==== Cost function ==== While it is possible to define a cost function ad hoc, frequently the choice is determined by the functions desirable properties (such as convexity) or because it arises from the model (e.g., in a probabilistic model the model's posterior probability can be used as an inverse cost). ==== Backpropagation ==== Backpropagation is a method to adjust the connection weights to compensate for each error found during learning. The error amount is effectively divided among the connections. Technically, backprop calculates the gradient (the derivative) of the cost function associated with a given state with respect to the weights. The weight updates can be done via stochastic gradient descent or other methods, such as Extreme Learning Machines, "No-prop" networks, training without backtracking, "weightless" networks, and non-connectionist neural networks. === Learning paradigms === The three major learning paradigms are supervised learning, unsupervised learning and reinforcement learning. They each correspond to a particular learning task ==== Supervised learning ==== Supervised learning uses a set of paired inputs and desired outputs. The learning task is to produce the desired output for each input. In this case the cost function is related to eliminating incorrect deductions. A commonly used cost is the mean-squared error, which tries to minimize the average squared error between the network's output and the desired output. Tasks suited for supervised learning are pattern recognition (also known as classification) and regression (also known as function approximation). Supervised learning is also applicable to sequential data (e.g., for hand writing, speech and gesture recognition). This can be thought of as learning with a "teacher", in the form of a function that provides continuous feedback on the quality of solutions obtained thus far. ==== Unsupervised learning ==== In unsupervised learning, input data is given along with the cost function, some function of the data x {\displaystyle \textstyle x} and the network's output. The cost function is dependent on the task (the model domain) and any a priori assumptions (the implicit properties of the model, its parameters and the observed variables). As a trivial example, consider the model f ( x ) = a {\displaystyle \textstyle f(x)=a} where a {\displaystyle \textstyle a} is a constant and the cost C = E [ ( x − f ( x ) ) 2 ] {\displaystyle \textstyle C=E[(x-f(x))^{2}]} . Minimizing this cost produces a value of a {\displaystyle \textstyle a} that is equal to the mean of the data. The cost function can be much more complicated. Its form depends on the application: for example, in compression it could be related to the mutual information between x {\displaystyle \textstyle x} and f ( x ) {\displaystyle \textstyle f(x)} , whereas in statistical modeling, it could be related to the posterior probability of the model given the data (note that in both of those examples those quantities would be maximized rather than minimized). Tasks that fall within the paradigm of unsupervised learning are in general estimation problems; the applications include clustering, the estimation of statistical distributions, compression and filtering. ==== Reinforcement learning ==== In applications such as playing video games, an actor takes a string of actions, receiving a generally unpredictable response from the environment after each one. The goal is to win the game, i.e., generate the most positive (lowest cost) responses. In reinforcement learning, the aim is to weight the network (devise a policy) to perform actions that minimize long-term (expected cumulative) cost. At each point in time the agent performs an action and the environment generates an observation and an instantaneous cost, according to some (usually unknown) rules. The rules and the long-term cost usually only can be estimated. At any juncture, the agent decides whether to explore new actions to uncover their costs or to exploit prior learning to proceed more quickly. Formally the environment is modeled as a Markov decision process (MDP) with states s 1 , . . . , s n ∈ S {\displaystyle \textstyle {s_{1},...,s_{n}}\in S} and actions a 1 , . . . , a m ∈ A {\displaystyle \textstyle {a_{1},...,a_{m}}\in A} . Because the state transitions are not known, probability distributions are used instead: the instantaneous cost distribution P ( c t | s t ) {\displaystyle \textstyle P(c_{t}|s_{t})} , the observation distribution P ( x t | s t ) {\displaystyle \textstyle P(x_{t}|s_{t})} and the transition distribution P ( s t + 1 | s t , a t ) {\displaystyle \textstyle P(s_{t+1}|s_{t},a_{t})} , while a policy is defined as the conditional distribution over actions given the observations. Taken together, the two define a Markov chain (MC). The aim is to discover the lowest-cost MC. ANNs serve as the learning component in such applications. Dynamic programming coupled with ANNs (giving neurodynamic programming) has been applied to problems such as those involved in vehicle routing, video games, natural resource management and medicine because of ANNs ability to mitigate losses of accuracy even when reducing the discretization grid density for numerically approximating the solution of control problems. Tasks that fall within the paradigm of reinforcement learning are control problems, games and other sequential decision making tasks. ==== Self learning ==== Self learning in neural networks was introduced in 1982 along with a neural network capable of self-learning named Crossbar Adaptive Array (CAA). It is a system with only one input, situation s, and only one output, action (or behavior) a. It has neither external advice input nor external reinforcement input from the environment. The CAA computes, in a crossbar fashion, both decisions about actions and emotions (feelings) about encountered situations. The system is driven by the interaction between cognition and emotion. Given memory matrix W =||w(a,s)||, the crossbar self learning algorithm in each iteration performs the following computation: In situation s perform action a; Receive consequence situation s’; Compute emotion of being in consequence situation v(s’); Update crossbar memory w’(a,s) = w(a,s) + v(s’). The backpropagated value (secondary reinforcement) is the emotion toward the consequence situation. The CAA exists in two environments, one is behavioral environment where it behaves, and the other is genetic environment, where from it initially and only once receives initial emotions about to be encountered situations in the behavioral environment. Having received the genome vector (species vector) from the genetic environment, the CAA will learn a goal-seeking behavior, in the behavioral environment that contains both desirable and undesirable situations. === Other === In a Bayesian framework, a distribution over the set of allowed models is chosen to minimize the cost. Evolutionary methods, gene expression programming, simulated annealing, expectation-maximization, non-parametric methods and particle swarm optimization are other learning algorithms. Convergent recursion is a learning algorithm for cerebellar model articulation controller (CMAC) neural networks. ==== Modes ==== Two modes of learning are available: stochastic and batch. In stochastic learning, each input creates a weight adjustment. In batch learning weights are adjusted based on a batch of inputs, accumulating errors over the batch. Stochastic learning introduces "noise" into the process, using the local gradient calculated from one data point; this reduces the chance of the network getting stuck in local minima. However, batch learning typically yields a faster, more stable descent to a local minimum, since each update is performed in the direction of the batch's average error. A common compromise is to use "mini-batches", small batches with samples in each batch selected stochastically from the entire data set. == Types == ANNs have evolved into a broad family of techniques that have advanced the state of the art across multiple domains. The simplest types have one or more static components, including number of units, number of layers, unit weights and topology. Dynamic types allow one or more of these to evolve via learning. The latter are much more complicated, but can shorten learning periods and produce better results. Some types allow/require learning to be "supervised" by the operator, while others operate independently. Some types operate purely in hardware, while others are purely software and run on general purpose computers. Some of the main breakthroughs include: convolutional neural networks that have proven particularly successful in processing visual and other two-dimensional data; long short-term memory avoid the vanishing gradient problem and can handle signals that have a mix of low and high frequency components aiding large-vocabulary speech recognition, text-to-speech synthesis, and photo-real talking heads; competitive networks such as generative adversarial networks in which multiple networks (of varying structure) compete with each other, on tasks such as winning a game or on deceiving the opponent about the authenticity of an input. == Network design == Neural architecture search (NAS) uses machine learning to automate ANN design. Various approaches to NAS have designed networks that compare well with hand-designed systems. The basic search algorithm is to propose a candidate model, evaluate it against a dataset and use the results as feedback to teach the NAS network. Available systems include AutoML and AutoKeras. Design issues include deciding the number, type and connectedness of network layers, as well as the size of each and the connection type (full, pooling, ...). Hyperparameters must also be defined as part of the design (they are not learned), governing matters such as how many neurons are in each layer, learning rate, step, stride, depth, receptive field and padding (for CNNs), etc. == Use == Using Artificial neural networks requires an understanding of their characteristics. Choice of model: This depends on the data representation and the application. Overly complex models slow learning. Learning algorithm: Numerous trade-offs exist between learning algorithms. Almost any algorithm will work well with the correct hyperparameters for training on a particular data set. However, selecting and tuning an algorithm for training on unseen data requires significant experimentation. Robustness: If the model, cost function and learning algorithm are selected appropriately, the resulting ANN can become robust. ANN capabilities fall within the following broad categories: Function approximation, or regression analysis, including time series prediction, fitness approximation and modeling. Classification, including pattern and sequence recognition, novelty detection and sequential decision making. Data processing, including filtering, clustering, blind source separation and compression. Robotics, including directing manipulators and prostheses. Control, including computer numerical control. == Applications == Because of their ability to reproduce and model nonlinear processes, Artificial neural networks have found applications in many disciplines. Application areas include system identification and control (vehicle control, trajectory prediction, process control, natural resource management), quantum chemistry, general game playing, pattern recognition (radar systems, face identification, signal classification, 3D reconstruction, object recognition and more), sequence recognition (gesture, speech, handwritten and printed text recognition), medical diagnosis, finance (e.g. automated trading systems), data mining, visualization, machine translation, social network filtering and e-mail spam filtering. ANNs have been used to diagnose cancers, including lung cancer, prostate cancer, colorectal cancer and to distinguish highly invasive cancer cell lines from less invasive lines using only cell shape information. ANNs have been used to accelerate reliability analysis of infrastructures subject to natural disasters and to predict foundation settlements. ANNs have also been used for building black-box models in geoscience: hydrology, ocean modelling and coastal engineering, and geomorphology. ANNs have been employed in cybersecurity, with the objective to discriminate between legitimate activities and malicious ones. For example, machine learning has been used for classifying Android malware, for identifying domains belonging to threat actors and for detecting URLs posing a security risk. Research is underway on ANN systems designed for penetration testing, for detecting botnets, credit cards frauds and network intrusions. ANNs have been proposed as a tool to simulate the properties of many-body open quantum systems. In brain research ANNs have studied short-term behavior of individual neurons, the dynamics of neural circuitry arise from interactions between individual neurons and how behavior can arise from abstract neural modules that represent complete subsystems. Studies considered long-and short-term plasticity of neural systems and their relation to learning and memory from the individual neuron to the system level. == Theoretical properties == === Computational power === The multilayer perceptron is a universal function approximator, as proven by the universal approximation theorem. However, the proof is not constructive regarding the number of neurons required, the network topology, the weights and the learning parameters. A specific recurrent architecture with rational-valued weights (as opposed to full precision real number-valued weights) has the power of a universal Turing machine, using a finite number of neurons and standard linear connections. Further, the use of irrational values for weights results in a machine with super-Turing power. === Capacity === A model's "capacity" property corresponds to its ability to model any given function. It is related to the amount of information that can be stored in the network and to the notion of complexity. Two notions of capacity are known by the community. The information capacity and the VC Dimension. The information capacity of a perceptron is intensively discussed in Sir David MacKay's book which summarizes work by Thomas Cover . The capacity of a network of standard neurons (not convolutional) can be derived by four rules that derive from understanding a neuron as an electrical element. The information capacity captures the functions modelable by the network given any data as input. The second notion, is the VC dimension. VC Dimension uses the principles of measure theory and finds the maximum capacity under the best possible circumstances. This is, given input data in a specific form. As noted in , the VC Dimension for arbitrary inputs is half the information capacity of a Perceptron. The VC Dimension for arbitrary points is sometimes referred to as Memory Capacity . === Convergence === Models may not consistently converge on a single solution, firstly because local minima may exist, depending on the cost function and the model. Secondly, the optimization method used might not guarantee to converge when it begins far from any local minimum. Thirdly, for sufficiently large data or parameters, some methods become impractical. Convergence behavior of certain types of ANN architectures are more understood than others. Such as when the width of network approaches to infinity, the ANN resembles linear model, thus such ANN follows the convergence behavior of linear model also. Another example is when parameters are small, it is observed that ANN often fits target functions from low to high frequencies. Such phenomenon is in opposite to the behavior of some well studied iterative numerical schemes such as Jacobi method. === Generalization and statistics === Applications whose goal is to create a system that generalizes well to unseen examples, face the possibility of over-training. This arises in convoluted or over-specified systems when the network capacity significantly exceeds the needed free parameters. Two approaches address over-training. The first is to use cross-validation and similar techniques to check for the presence of over-training and to select hyperparameters to minimize the generalization error. The second is to use some form of regularization. This concept emerges in a probabilistic (Bayesian) framework, where regularization can be performed by selecting a larger prior probability over simpler models; but also in statistical learning theory, where the goal is to minimize over two quantities: the 'empirical risk' and the 'structural risk', which roughly corresponds to the error over the training set and the predicted error in unseen data due to overfitting. Supervised neural networks that use a mean squared error (MSE) cost function can use formal statistical methods to determine the confidence of the trained model. The MSE on a validation set can be used as an estimate for variance. This value can then be used to calculate the confidence interval of network output, assuming a normal distribution. A confidence analysis made this way is statistically valid as long as the output probability distribution stays the same and the network is not modified. By assigning a softmax activation function, a generalization of the logistic function, on the output layer of the neural network (or a softmax component in a component-based network) for categorical target variables, the outputs can be interpreted as posterior probabilities. This is useful in classification as it gives a certainty measure on classifications. The softmax activation function is: y i = e x i ∑ j = 1 c e x j {\displaystyle y_{i}={\frac {e^{x_{i}}}{\sum _{j=1}^{c}e^{x_{j}}}}} == Criticism == === Training === A common criticism of neural networks, particularly in robotics, is that they require too much training for real-world operation. Potential solutions include randomly shuffling training examples, by using a numerical optimization algorithm that does not take too large steps when changing the network connections following an example, grouping examples in so-called mini-batches and/or introducing a recursive least squares algorithm for CMAC. === Theory === A fundamental objection is that ANNs do not sufficiently reflect neuronal function. Backpropagation is a critical step, although no such mechanism exists in biological neural networks. How information is coded by real neurons is not known. Sensor neurons fire action potentials more frequently with sensor activation and muscle cells pull more strongly when their associated motor neurons receive action potentials more frequently. Other than the case of relaying information from a sensor neuron to a motor neuron, almost nothing of the principles of how information is handled by biological neural networks is known. A central claim of ANNs is that they embody new and powerful general principles for processing information. Unfortunately, these principles are ill-defined. It is often claimed that they are emergent from the network itself. This allows simple statistical association (the basic function of artificial neural networks) to be described as learning or recognition. Alexander Dewdney commented that, as a result, artificial neural networks have a "something-for-nothing quality, one that imparts a peculiar aura of laziness and a distinct lack of curiosity about just how good these computing systems are. No human hand (or mind) intervenes; solutions are found as if by magic; and no one, it seems, has learned anything". One response to Dewdney is that neural networks handle many complex and diverse tasks, ranging from autonomously flying aircraft to detecting credit card fraud to mastering the game of Go. Technology writer Roger Bridgman commented: Neural networks, for instance, are in the dock not only because they have been hyped to high heaven, (what hasn't?) but also because you could create a successful net without understanding how it worked: the bunch of numbers that captures its behaviour would in all probability be "an opaque, unreadable table...valueless as a scientific resource". In spite of his emphatic declaration that science is not technology, Dewdney seems here to pillory neural nets as bad science when most of those devising them are just trying to be good engineers. An unreadable table that a useful machine could read would still be well worth having. Biological brains use both shallow and deep circuits as reported by brain anatomy, displaying a wide variety of invariance. Weng argued that the brain self-wires largely according to signal statistics and therefore, a serial cascade cannot catch all major statistical dependencies. === Hardware === Large and effective neural networks require considerable computing resources. While the brain has hardware tailored to the task of processing signals through a graph of neurons, simulating even a simplified neuron on von Neumann architecture may consume vast amounts of memory and storage. Furthermore, the designer often needs to transmit signals through many of these connections and their associated neurons – which require enormous CPU power and time. Schmidhuber noted that the resurgence of neural networks in the twenty-first century is largely attributable to advances in hardware: from 1991 to 2015, computing power, especially as delivered by GPGPUs (on GPUs), has increased around a million-fold, making the standard backpropagation algorithm feasible for training networks that are several layers deeper than before. The use of accelerators such as FPGAs and GPUs can reduce training times from months to days. Neuromorphic engineering addresses the hardware difficulty directly, by constructing non-von-Neumann chips to directly implement neural networks in circuitry. Another type of chip optimized for neural network processing is called a Tensor Processing Unit, or TPU. === Practical counterexamples === Analyzing what has been learned by an ANN, is much easier than to analyze what has been learned by a biological neural network. Furthermore, researchers involved in exploring learning algorithms for neural networks are gradually uncovering general principles that allow a learning machine to be successful. For example, local vs. non-local learning and shallow vs. deep architecture. === Hybrid approaches === Advocates of hybrid models (combining neural networks and symbolic approaches), claim that such a mixture can better capture the mechanisms of the human mind. == Gallery == == See also == == References == == Bibliography == == External links == The Neural Network Zoo – a compilation of neural network types The Stilwell Brain – a Mind Field episode featuring an experiment in which humans act as individual neurons in a neural network that classifies handwritten digits ================================================ FILE: week6/questions/corpus/probability.txt ================================================ https://en.wikipedia.org/wiki/Probability Probability is a numerical description of how likely an event is to occur or how likely it is that a proposition is true. Probability is a number between 0 and 1, where, roughly speaking, 0 indicates impossibility and 1 indicates certainty. The higher the probability of an event, the more likely it is that the event will occur. A simple example is the tossing of a fair (unbiased) coin. Since the coin is fair, the two outcomes ("heads" and "tails") are both equally probable; the probability of "heads" equals the probability of "tails"; and since no other outcomes are possible, the probability of either "heads" or "tails" is 1/2 (which could also be written as 0.5 or 50%). These concepts have been given an axiomatic mathematical formalization in probability theory, which is used widely in such areas of study as mathematics, statistics, finance, gambling, science (in particular physics), artificial intelligence/machine learning, computer science, game theory, and philosophy to, for example, draw inferences about the expected frequency of events. Probability theory is also used to describe the underlying mechanics and regularities of complex systems. == Interpretations == When dealing with experiments that are random and well-defined in a purely theoretical setting (like tossing a fair coin), probabilities can be numerically described by the number of desired outcomes divided by the total number of all outcomes. For example, tossing a fair coin twice will yield "head-head", "head-tail", "tail-head", and "tail-tail" outcomes. The probability of getting an outcome of "head-head" is 1 out of 4 outcomes, or, in numerical terms, 1/4, 0.25 or 25%. However, when it comes to practical application, there are two major competing categories of probability interpretations, whose adherents possess different views about the fundamental nature of probability: Objectivists assign numbers to describe some objective or physical state of affairs. The most popular version of objective probability is frequentist probability, which claims that the probability of a random event denotes the relative frequency of occurrence of an experiment's outcome, when repeating the experiment. This interpretation considers probability to be the relative frequency "in the long run" of outcomes. A modification of this is propensity probability, which interprets probability as the tendency of some experiment to yield a certain outcome, even if it is performed only once. Subjectivists assign numbers per subjective probability, i.e., as a degree of belief. The degree of belief has been interpreted as, "the price at which you would buy or sell a bet that pays 1 unit of utility if E, 0 if not E." The most popular version of subjective probability is Bayesian probability, which includes expert knowledge as well as experimental data to produce probabilities. The expert knowledge is represented by some (subjective) prior probability distribution. These data are incorporated in a likelihood function. The product of the prior and the likelihood, normalized, results in a posterior probability distribution that incorporates all the information known to date. By Aumann's agreement theorem, Bayesian agents whose prior beliefs are similar will end up with similar posterior beliefs. However, sufficiently different priors can lead to different conclusions regardless of how much information the agents share. == Etymology == The word probability derives from the Latin probabilitas, which can also mean "probity", a measure of the authority of a witness in a legal case in Europe, and often correlated with the witness's nobility. In a sense, this differs much from the modern meaning of probability, which, in contrast, is a measure of the weight of empirical evidence, and is arrived at from inductive reasoning and statistical inference. == History == The scientific study of probability is a modern development of mathematics. Gambling shows that there has been an interest in quantifying the ideas of probability for millennia, but exact mathematical descriptions arose much later. There are reasons for the slow development of the mathematics of probability. Whereas games of chance provided the impetus for the mathematical study of probability, fundamental issues are still obscured by the superstitions of gamblers. According to Richard Jeffrey, "Before the middle of the seventeenth century, the term 'probable' (Latin probabilis) meant approvable, and was applied in that sense, univocally, to opinion and to action. A probable action or opinion was one such as sensible people would undertake or hold, in the circumstances." However, in legal contexts especially, 'probable' could also apply to propositions for which there was good evidence. The earliest known forms of probability and statistics were developed by Middle Eastern mathematicians studying cryptography between the 8th and 13th centuries. Al-Khalil (717–786) wrote the Book of Cryptographic Messages which contains the first use of permutations and combinations to list all possible Arabic words with and without vowels. Al-Kindi (801–873) made the earliest known use of statistical inference in his work on cryptanalysis and frequency analysis. An important contribution of Ibn Adlan (1187–1268) was on sample size for use of frequency analysis. The sixteenth century Italian polymath Gerolamo Cardano demonstrated the efficacy of defining odds as the ratio of favourable to unfavourable outcomes (which implies that the probability of an event is given by the ratio of favourable outcomes to the total number of possible outcomes). Aside from the elementary work by Cardano, the doctrine of probabilities dates to the correspondence of Pierre de Fermat and Blaise Pascal (1654). Christiaan Huygens (1657) gave the earliest known scientific treatment of the subject. Jakob Bernoulli's Ars Conjectandi (posthumous, 1713) and Abraham de Moivre's Doctrine of Chances (1718) treated the subject as a branch of mathematics. See Ian Hacking's The Emergence of Probability and James Franklin's The Science of Conjecture for histories of the early development of the very concept of mathematical probability. The theory of errors may be traced back to Roger Cotes's Opera Miscellanea (posthumous, 1722), but a memoir prepared by Thomas Simpson in 1755 (printed 1756) first applied the theory to the discussion of errors of observation. The reprint (1757) of this memoir lays down the axioms that positive and negative errors are equally probable, and that certain assignable limits define the range of all errors. Simpson also discusses continuous errors and describes a probability curve. The first two laws of error that were proposed both originated with Pierre-Simon Laplace. The first law was published in 1774 and stated that the frequency of an error could be expressed as an exponential function of the numerical magnitude of the error, disregarding sign. The second law of error was proposed in 1778 by Laplace and stated that the frequency of the error is an exponential function of the square of the error. The second law of error is called the normal distribution or the Gauss law. "It is difficult historically to attribute that law to Gauss, who in spite of his well-known precocity had probably not made this discovery before he was two years old."Daniel Bernoulli (1778) introduced the principle of the maximum product of the probabilities of a system of concurrent errors. Adrien-Marie Legendre (1805) developed the method of least squares, and introduced it in his Nouvelles méthodes pour la détermination des orbites des comètes (New Methods for Determining the Orbits of Comets). In ignorance of Legendre's contribution, an Irish-American writer, Robert Adrain, editor of "The Analyst" (1808), first deduced the law of facility of error, ϕ ( x ) = c e − h 2 x 2 , {\displaystyle \phi (x)=ce^{-h^{2}x^{2}},} where h {\displaystyle h} is a constant depending on precision of observation, and c {\displaystyle c} is a scale factor ensuring that the area under the curve equals 1. He gave two proofs, the second being essentially the same as John Herschel's (1850). Gauss gave the first proof that seems to have been known in Europe (the third after Adrain's) in 1809. Further proofs were given by Laplace (1810, 1812), Gauss (1823), James Ivory (1825, 1826), Hagen (1837), Friedrich Bessel (1838), W.F. Donkin (1844, 1856), and Morgan Crofton (1870). Other contributors were Ellis (1844), De Morgan (1864), Glaisher (1872), and Giovanni Schiaparelli (1875). Peters's (1856) formula for r, the probable error of a single observation, is well known. In the nineteenth century authors on the general theory included Laplace, Sylvestre Lacroix (1816), Littrow (1833), Adolphe Quetelet (1853), Richard Dedekind (1860), Helmert (1872), Hermann Laurent (1873), Liagre, Didion, and Karl Pearson. Augustus De Morgan and George Boole improved the exposition of the theory. Andrey Markov introduced the notion of Markov chains (1906), which played an important role in stochastic processes theory and its applications. The modern theory of probability based on the measure theory was developed by Andrey Kolmogorov (1931).On the geometric side (see integral geometry) contributors to The Educational Times were influential (Miller, Crofton, McColl, Wolstenholme, Watson, and Artemas Martin). == Theory == Like other theories, the theory of probability is a representation of its concepts in formal terms—that is, in terms that can be considered separately from their meaning. These formal terms are manipulated by the rules of mathematics and logic, and any results are interpreted or translated back into the problem domain. There have been at least two successful attempts to formalize probability, namely the Kolmogorov formulation and the Cox formulation. In Kolmogorov's formulation (see probability space), sets are interpreted as events and probability itself as a measure on a class of sets. In Cox's theorem, probability is taken as a primitive (that is, not further analyzed) and the emphasis is on constructing a consistent assignment of probability values to propositions. In both cases, the laws of probability are the same, except for technical details. There are other methods for quantifying uncertainty, such as the Dempster–Shafer theory or possibility theory, but those are essentially different and not compatible with the laws of probability as usually understood. == Applications == Probability theory is applied in everyday life in risk assessment and modeling. The insurance industry and markets use actuarial science to determine pricing and make trading decisions. Governments apply probabilistic methods in environmental regulation, entitlement analysis (Reliability theory of aging and longevity), and financial regulation. A good example of the use of probability theory in equity trading is the effect of the perceived probability of any widespread Middle East conflict on oil prices, which have ripple effects in the economy as a whole. An assessment by a commodity trader that a war is more likely can send that commodity's prices up or down, and signals other traders of that opinion. Accordingly, the probabilities are neither assessed independently nor necessarily very rationally. The theory of behavioral finance emerged to describe the effect of such groupthink on pricing, on policy, and on peace and conflict. In addition to financial assessment, probability can be used to analyze trends in biology (e.g. disease spread) as well as ecology (e.g. biological Punnett squares). As with finance, risk assessment can be used as a statistical tool to calculate the likelihood of undesirable events occurring and can assist with implementing protocols to avoid encountering such circumstances. Probability is used to design games of chance so that casinos can make a guaranteed profit, yet provide payouts to players that are frequent enough to encourage continued play. The discovery of rigorous methods to assess and combine probability assessments has changed society. Another significant application of probability theory in everyday life is reliability. Many consumer products, such as automobiles and consumer electronics, use reliability theory in product design to reduce the probability of failure. Failure probability may influence a manufacturer's decisions on a product's warranty. The cache language model and other statistical language models that are used in natural language processing are also examples of applications of probability theory. == Mathematical treatment == Consider an experiment that can produce a number of results. The collection of all possible results is called the sample space of the experiment. The power set of the sample space is formed by considering all different collections of possible results. For example, rolling a die can produce six possible results. One collection of possible results gives an odd number on the die. Thus, the subset {1,3,5} is an element of the power set of the sample space of dice rolls. These collections are called "events". In this case, {1,3,5} is the event that the die falls on some odd number. If the results that actually occur fall in a given event, the event is said to have occurred. A probability is a way of assigning every event a value between zero and one, with the requirement that the event made up of all possible results (in our example, the event {1,2,3,4,5,6}) is assigned a value of one. To qualify as a probability, the assignment of values must satisfy the requirement that if you look at a collection of mutually exclusive events (events with no common results, e.g., the events {1,6}, {3}, and {2,4} are all mutually exclusive), the probability that at least one of the events will occur is given by the sum of the probabilities of all the individual events. The probability of an event A is written as P ( A ) {\displaystyle P(A)} , p ( A ) {\displaystyle p(A)} , or Pr ( A ) {\displaystyle {\text{Pr}}(A)} . This mathematical definition of probability can extend to infinite sample spaces, and even uncountable sample spaces, using the concept of a measure. The opposite or complement of an event A is the event [not A] (that is, the event of A not occurring), often denoted as A ¯ , A ∁ , ¬ A {\displaystyle {\overline {A}},A^{\complement },\neg A} , or ∼ A {\displaystyle {\sim }A} ; its probability is given by P(not A) = 1 − P(A). As an example, the chance of not rolling a six on a six-sided die is 1 – (chance of rolling a six) = 1 − 1 6 = 5 6 {\displaystyle =1-{\tfrac {1}{6}}={\tfrac {5}{6}}} . See Complementary event for a more complete treatment. If two events A and B occur on a single performance of an experiment, this is called the intersection or joint probability of A and B, denoted as P ( A ∩ B ) {\displaystyle P(A\cap B)} . === Independent events === If two events, A and B are independent then the joint probability is P ( A and B ) = P ( A ∩ B ) = P ( A ) P ( B ) , {\displaystyle P(A{\mbox{ and }}B)=P(A\cap B)=P(A)P(B),\,} for example, if two coins are flipped the chance of both being heads is 1 2 × 1 2 = 1 4 {\displaystyle {\tfrac {1}{2}}\times {\tfrac {1}{2}}={\tfrac {1}{4}}} . === Mutually exclusive events === If either event A or event B but never both occurs on a single performance of an experiment, then they are called mutually exclusive events. If two events are mutually exclusive then the probability of both occurring is denoted as P ( A ∩ B ) {\displaystyle P(A\cap B)} . P ( A and B ) = P ( A ∩ B ) = 0 {\displaystyle P(A{\mbox{ and }}B)=P(A\cap B)=0} If two events are mutually exclusive then the probability of either occurring is denoted as P ( A ∪ B ) {\displaystyle P(A\cup B)} . P ( A or B ) = P ( A ∪ B ) = P ( A ) + P ( B ) − P ( A ∩ B ) = P ( A ) + P ( B ) − 0 = P ( A ) + P ( B ) {\displaystyle P(A{\mbox{ or }}B)=P(A\cup B)=P(A)+P(B)-P(A\cap B)=P(A)+P(B)-0=P(A)+P(B)} For example, the chance of rolling a 1 or 2 on a six-sided die is P ( 1 or 2 ) = P ( 1 ) + P ( 2 ) = 1 6 + 1 6 = 1 3 . {\displaystyle P(1{\mbox{ or }}2)=P(1)+P(2)={\tfrac {1}{6}}+{\tfrac {1}{6}}={\tfrac {1}{3}}.} === Not mutually exclusive events === If the events are not mutually exclusive then P ( A or B ) = P ( A ∪ B ) = P ( A ) + P ( B ) − P ( A and B ) . {\displaystyle P\left(A{\hbox{ or }}B\right)=P(A\cup B)=P\left(A\right)+P\left(B\right)-P\left(A{\mbox{ and }}B\right).} For example, when drawing a single card at random from a regular deck of cards, the chance of getting a heart or a face card (J,Q,K) (or one that is both) is 13 52 + 12 52 − 3 52 = 11 26 {\displaystyle {\tfrac {13}{52}}+{\tfrac {12}{52}}-{\tfrac {3}{52}}={\tfrac {11}{26}}} , because of the 52 cards of a deck 13 are hearts, 12 are face cards, and 3 are both: here the possibilities included in the "3 that are both" are included in each of the "13 hearts" and the "12 face cards" but should only be counted once. === Conditional probability === Conditional probability is the probability of some event A, given the occurrence of some other event B. Conditional probability is written P ( A ∣ B ) {\displaystyle P(A\mid B)} , and is read "the probability of A, given B". It is defined by P ( A ∣ B ) = P ( A ∩ B ) P ( B ) . {\displaystyle P(A\mid B)={\frac {P(A\cap B)}{P(B)}}.\,} If P ( B ) = 0 {\displaystyle P(B)=0} then P ( A ∣ B ) {\displaystyle P(A\mid B)} is formally undefined by this expression. However, it is possible to define a conditional probability for some zero-probability events using a σ-algebra of such events (such as those arising from a continuous random variable).For example, in a bag of 2 red balls and 2 blue balls (4 balls in total), the probability of taking a red ball is 1 / 2 {\displaystyle 1/2} ; however, when taking a second ball, the probability of it being either a red ball or a blue ball depends on the ball previously taken, such as, if a red ball was taken, the probability of picking a red ball again would be 1 / 3 {\displaystyle 1/3} since only 1 red and 2 blue balls would have been remaining. === Inverse probability === In probability theory and applications, Bayes' rule relates the odds of event A 1 {\displaystyle A_{1}} to event A 2 {\displaystyle A_{2}} , before (prior to) and after (posterior to) conditioning on another event B {\displaystyle B} . The odds on A 1 {\displaystyle A_{1}} to event A 2 {\displaystyle A_{2}} is simply the ratio of the probabilities of the two events. When arbitrarily many events A {\displaystyle A} are of interest, not just two, the rule can be rephrased as posterior is proportional to prior times likelihood, P ( A | B ) ∝ P ( A ) P ( B | A ) {\displaystyle P(A|B)\propto P(A)P(B|A)} where the proportionality symbol means that the left hand side is proportional to (i.e., equals a constant times) the right hand side as A {\displaystyle A} varies, for fixed or given B {\displaystyle B} (Lee, 2012; Bertsch McGrayne, 2012). In this form it goes back to Laplace (1774) and to Cournot (1843); see Fienberg (2005). See Inverse probability and Bayes' rule. === Summary of probabilities === == Relation to randomness and probability in quantum mechanics == In a deterministic universe, based on Newtonian concepts, there would be no probability if all conditions were known (Laplace's demon), (but there are situations in which sensitivity to initial conditions exceeds our ability to measure them, i.e. know them). In the case of a roulette wheel, if the force of the hand and the period of that force are known, the number on which the ball will stop would be a certainty (though as a practical matter, this would likely be true only of a roulette wheel that had not been exactly levelled – as Thomas A. Bass' Newtonian Casino revealed). This also assumes knowledge of inertia and friction of the wheel, weight, smoothness and roundness of the ball, variations in hand speed during the turning and so forth. A probabilistic description can thus be more useful than Newtonian mechanics for analyzing the pattern of outcomes of repeated rolls of a roulette wheel. Physicists face the same situation in kinetic theory of gases, where the system, while deterministic in principle, is so complex (with the number of molecules typically the order of magnitude of the Avogadro constant 6.02×1023) that only a statistical description of its properties is feasible. Probability theory is required to describe quantum phenomena. A revolutionary discovery of early 20th century physics was the random character of all physical processes that occur at sub-atomic scales and are governed by the laws of quantum mechanics. The objective wave function evolves deterministically but, according to the Copenhagen interpretation, it deals with probabilities of observing, the outcome being explained by a wave function collapse when an observation is made. However, the loss of determinism for the sake of instrumentalism did not meet with universal approval. Albert Einstein famously remarked in a letter to Max Born: "I am convinced that God does not play dice". Like Einstein, Erwin Schrödinger, who discovered the wave function, believed quantum mechanics is a statistical approximation of an underlying deterministic reality. In some modern interpretations of the statistical mechanics of measurement, quantum decoherence is invoked to account for the appearance of subjectively probabilistic experimental outcomes. == See also == Chance (disambiguation) Class membership probabilities Contingency Equiprobability Heuristics in judgment and decision-making Probability theory Randomness Statistics Estimators Estimation Theory Probability density functionIn LawBalance of probabilities == Notes == == References == == Bibliography == Kallenberg, O. (2005) Probabilistic Symmetries and Invariance Principles. Springer-Verlag, New York. 510 pp. ISBN 0-387-25115-4 Kallenberg, O. (2002) Foundations of Modern Probability, 2nd ed. Springer Series in Statistics. 650 pp. ISBN 0-387-95313-2 Olofsson, Peter (2005) Probability, Statistics, and Stochastic Processes, Wiley-Interscience. 504 pp ISBN 0-471-67969-0. == External links == Virtual Laboratories in Probability and Statistics (Univ. of Ala.-Huntsville) Probability on In Our Time at the BBC Probability and Statistics EBook Edwin Thompson Jaynes. Probability Theory: The Logic of Science. Preprint: Washington University, (1996). — HTML index with links to PostScript files and PDF (first three chapters) People from the History of Probability and Statistics (Univ. of Southampton) Probability and Statistics on the Earliest Uses Pages (Univ. of Southampton) Earliest Uses of Symbols in Probability and Statistics on Earliest Uses of Various Mathematical Symbols A tutorial on probability and Bayes' theorem devised for first-year Oxford University students [1] pdf file of An Anthology of Chance Operations (1963) at UbuWeb Introduction to Probability – eBook, by Charles Grinstead, Laurie Snell Source (GNU Free Documentation License) (in English and Italian) Bruno de Finetti, Probabilità e induzione, Bologna, CLUEB, 1993. ISBN 88-8091-176-7 (digital version) Richard P. Feynman's Lecture on probability. ================================================ FILE: week6/questions/corpus/python.txt ================================================ https://en.wikipedia.org/wiki/Python_(programming_language) evel, general-purpose programming language. Created by Guido van Rossum and first released in 1991, Python's design philosophy emphasizes code readability with its notable use of significant whitespace. Its language constructs and object-oriented approach aim to help programmers write clear, logical code for small and large-scale projects. Python is dynamically typed and garbage-collected. It supports multiple programming paradigms, including structured (particularly, procedural), object-oriented, and functional programming. Python is often described as a "batteries included" language due to its comprehensive standard library. Python was conceived in the late 1980s as a successor to the ABC language. Python 2.0, released in 2000, introduced features like list comprehensions and a garbage collection system capable of collecting reference cycles. Python 3.0, released in 2008, was a major revision of the language that is not completely backward-compatible, and much Python 2 code does not run unmodified on Python 3. The Python 2 language, i.e. Python 2.7.x, was officially discontinued on 1 January 2020 (first planned for 2015) after which security patches and other improvements will not be released for it. With Python 2's end-of-life, only Python 3.5.x and later are supported. Python interpreters are available for many operating systems. A global community of programmers develops and maintains CPython, an open source reference implementation. A non-profit organization, the Python Software Foundation, manages and directs resources for Python and CPython development. == History == Python was conceived in the late 1980s by Guido van Rossum at Centrum Wiskunde & Informatica (CWI) in the Netherlands as a successor to the ABC language (itself inspired by SETL), capable of exception handling and interfacing with the Amoeba operating system. Its implementation began in December 1989. Van Rossum shouldered sole responsibility for the project, as the lead developer, until 12 July 2018, when he announced his "permanent vacation" from his responsibilities as Python's Benevolent Dictator For Life, a title the Python community bestowed upon him to reflect his long-term commitment as the project's chief decision-maker. He now shares his leadership as a member of a five-person steering council. In January 2019, active Python core developers elected Brett Cannon, Nick Coghlan, Barry Warsaw, Carol Willing and Van Rossum to a five-member "Steering Council" to lead the project. Python 2.0 was released on 16 October 2000 with many major new features, including a cycle-detecting garbage collector and support for Unicode. Python 3.0 was released on 3 December 2008. It was a major revision of the language that is not completely backward-compatible. Many of its major features were backported to Python 2.6.x and 2.7.x version series. Releases of Python 3 include the 2to3 utility, which automates (at least partially) the translation of Python 2 code to Python 3.Python 2.7's end-of-life date was initially set at 2015 then postponed to 2020 out of concern that a large body of existing code could not easily be forward-ported to Python 3. == Features and philosophy == Python is a multi-paradigm programming language. Object-oriented programming and structured programming are fully supported, and many of its features support functional programming and aspect-oriented programming (including by metaprogramming and metaobjects (magic methods)). Many other paradigms are supported via extensions, including design by contract and logic programming. Python uses dynamic typing and a combination of reference counting and a cycle-detecting garbage collector for memory management. It also features dynamic name resolution (late binding), which binds method and variable names during program execution. Python's design offers some support for functional programming in the Lisp tradition. It has filter, map, and reduce functions; list comprehensions, dictionaries, sets, and generator expressions. The standard library has two modules (itertools and functools) that implement functional tools borrowed from Haskell and Standard ML. The language's core philosophy is summarized in the document The Zen of Python (PEP 20), which includes aphorisms such as: Beautiful is better than ugly. Explicit is better than implicit. Simple is better than complex. Complex is better than complicated. Readability counts. Rather than having all of its functionality built into its core, Python was designed to be highly extensible. This compact modularity has made it particularly popular as a means of adding programmable interfaces to existing applications. Van Rossum's vision of a small core language with a large standard library and easily extensible interpreter stemmed from his frustrations with ABC, which espoused the opposite approach. Python strives for a simpler, less-cluttered syntax and grammar while giving developers a choice in their coding methodology. In contrast to Perl's "there is more than one way to do it" motto, Python embraces a "there should be one—and preferably only one—obvious way to do it" design philosophy. Alex Martelli, a Fellow at the Python Software Foundation and Python book author, writes that "To describe something as 'clever' is not considered a compliment in the Python culture."Python's developers strive to avoid premature optimization, and reject patches to non-critical parts of the CPython reference implementation that would offer marginal increases in speed at the cost of clarity. When speed is important, a Python programmer can move time-critical functions to extension modules written in languages such as C, or use PyPy, a just-in-time compiler. Cython is also available, which translates a Python script into C and makes direct C-level API calls into the Python interpreter. An important goal of Python's developers is keeping it fun to use. This is reflected in the language's name—a tribute to the British comedy group Monty Python—and in occasionally playful approaches to tutorials and reference materials, such as examples that refer to spam and eggs (from a famous Monty Python sketch) instead of the standard foo and bar. A common neologism in the Python community is pythonic, which can have a wide range of meanings related to program style. To say that code is pythonic is to say that it uses Python idioms well, that it is natural or shows fluency in the language, that it conforms with Python's minimalist philosophy and emphasis on readability. In contrast, code that is difficult to understand or reads like a rough transcription from another programming language is called unpythonic. Users and admirers of Python, especially those considered knowledgeable or experienced, are often referred to as Pythonistas. == Syntax and semantics == Python is meant to be an easily readable language. Its formatting is visually uncluttered, and it often uses English keywords where other languages use punctuation. Unlike many other languages, it does not use curly brackets to delimit blocks, and semicolons after statements are optional. It has fewer syntactic exceptions and special cases than C or Pascal. === Indentation === Python uses whitespace indentation, rather than curly brackets or keywords, to delimit blocks. An increase in indentation comes after certain statements; a decrease in indentation signifies the end of the current block. Thus, the program's visual structure accurately represents the program's semantic structure. This feature is sometimes termed the off-side rule, which some other languages share, but in most languages indentation doesn't have any semantic meaning. === Statements and control flow === Python's statements include (among others): The assignment statement (token '=', the equals sign). This operates differently than in traditional imperative programming languages, and this fundamental mechanism (including the nature of Python's version of variables) illuminates many other features of the language. Assignment in C, e.g., x = 2, translates to "typed variable name x receives a copy of numeric value 2". The (right-hand) value is copied into an allocated storage location for which the (left-hand) variable name is the symbolic address. The memory allocated to the variable is large enough (potentially quite large) for the declared type. In the simplest case of Python assignment, using the same example, x = 2, translates to "(generic) name x receives a reference to a separate, dynamically allocated object of numeric (int) type of value 2." This is termed binding the name to the object. Since the name's storage location doesn't contain the indicated value, it is improper to call it a variable. Names may be subsequently rebound at any time to objects of greatly varying types, including strings, procedures, complex objects with data and methods, etc. Successive assignments of a common value to multiple names, e.g., x = 2; y = 2; z = 2 result in allocating storage to (at most) three names and one numeric object, to which all three names are bound. Since a name is a generic reference holder it is unreasonable to associate a fixed data type with it. However at a given time a name will be bound to some object, which will have a type; thus there is dynamic typing. The if statement, which conditionally executes a block of code, along with else and elif (a contraction of else-if). The for statement, which iterates over an iterable object, capturing each element to a local variable for use by the attached block. The while statement, which executes a block of code as long as its condition is true. The try statement, which allows exceptions raised in its attached code block to be caught and handled by except clauses; it also ensures that clean-up code in a finally block will always be run regardless of how the block exits. The raise statement, used to raise a specified exception or re-raise a caught exception. The class statement, which executes a block of code and attaches its local namespace to a class, for use in object-oriented programming. The def statement, which defines a function or method. The with statement, from Python 2.5 released in September 2006, which encloses a code block within a context manager (for example, acquiring a lock before the block of code is run and releasing the lock afterwards, or opening a file and then closing it), allowing Resource Acquisition Is Initialization (RAII)-like behavior and replaces a common try/finally idiom. The break statement, exits from the loop. The continue statement, skips this iteration and continues with the next item. The pass statement, which serves as a NOP. It is syntactically needed to create an empty code block. The assert statement, used during debugging to check for conditions that ought to apply. The yield statement, which returns a value from a generator function. From Python 2.5, yield is also an operator. This form is used to implement coroutines. The import statement, which is used to import modules whose functions or variables can be used in the current program. There are three ways of using import: import [as ] or from import * or from import [as ], [as ], .... The print statement was changed to the print() function in Python 3.Python does not support tail call optimization or first-class continuations, and, according to Guido van Rossum, it never will. However, better support for coroutine-like functionality is provided in 2.5, by extending Python's generators. Before 2.5, generators were lazy iterators; information was passed unidirectionally out of the generator. From Python 2.5, it is possible to pass information back into a generator function, and from Python 3.3, the information can be passed through multiple stack levels. === Expressions === Some Python expressions are similar to languages such as C and Java, while some are not: Addition, subtraction, and multiplication are the same, but the behavior of division differs. There are two types of divisions in Python. They are floor division (or integer division) // and floating point/division. Python also added the ** operator for exponentiation. From Python 3.5, the new @ infix operator was introduced. It is intended to be used by libraries such as NumPy for matrix multiplication. From Python 3.8, the syntax :=, called the 'walrus operator' was introduced. It assigns values to variables as part of a larger expression. In Python, == compares by value, versus Java, which compares numerics by value and objects by reference. (Value comparisons in Java on objects can be performed with the equals() method.) Python's is operator may be used to compare object identities (comparison by reference). In Python, comparisons may be chained, for example a <= b <= c. Python uses the words and, or, not for its boolean operators rather than the symbolic &&, ||, ! used in Java and C. Python has a type of expression termed a list comprehension. Python 2.4 extended list comprehensions into a more general expression termed a generator expression. Anonymous functions are implemented using lambda expressions; however, these are limited in that the body can only be one expression. Conditional expressions in Python are written as x if c else y (different in order of operands from the c ? x : y operator common to many other languages). Python makes a distinction between lists and tuples. Lists are written as [1, 2, 3], are mutable, and cannot be used as the keys of dictionaries (dictionary keys must be immutable in Python). Tuples are written as (1, 2, 3), are immutable and thus can be used as the keys of dictionaries, provided all elements of the tuple are immutable. The + operator can be used to concatenate two tuples, which does not directly modify their contents, but rather produces a new tuple containing the elements of both provided tuples. Thus, given the variable t initially equal to (1, 2, 3), executing t = t + (4, 5) first evaluates t + (4, 5), which yields (1, 2, 3, 4, 5), which is then assigned back to t, thereby effectively "modifying the contents" of t, while conforming to the immutable nature of tuple objects. Parentheses are optional for tuples in unambiguous contexts. Python features sequence unpacking wherein multiple expressions, each evaluating to anything that can be assigned to (a variable, a writable property, etc.), are associated in the identical manner to that forming tuple literals and, as a whole, are put on the left hand side of the equal sign in an assignment statement. The statement expects an iterable object on the right hand side of the equal sign that produces the same number of values as the provided writable expressions when iterated through, and will iterate through it, assigning each of the produced values to the corresponding expression on the left. Python has a "string format" operator %. This functions analogous to printf format strings in C, e.g. "spam=%s eggs=%d" % ("blah", 2) evaluates to "spam=blah eggs=2". In Python 3 and 2.6+, this was supplemented by the format() method of the str class, e.g. "spam={0} eggs={1}".format("blah", 2). Python 3.6 added "f-strings": blah = "blah"; eggs = 2; f'spam={blah} eggs={eggs}'. Python has various kinds of string literals: Strings delimited by single or double quote marks. Unlike in Unix shells, Perl and Perl-influenced languages, single quote marks and double quote marks function identically. Both kinds of string use the backslash (\) as an escape character. String interpolation became available in Python 3.6 as "formatted string literals". Triple-quoted strings, which begin and end with a series of three single or double quote marks. They may span multiple lines and function like here documents in shells, Perl and Ruby. Raw string varieties, denoted by prefixing the string literal with an r. Escape sequences are not interpreted; hence raw strings are useful where literal backslashes are common, such as regular expressions and Windows-style paths. Compare "@-quoting" in C#. Python has array index and array slicing expressions on lists, denoted as a[key], a[start:stop] or a[start:stop:step]. Indexes are zero-based, and negative indexes are relative to the end. Slices take elements from the start index up to, but not including, the stop index. The third slice parameter, called step or stride, allows elements to be skipped and reversed. Slice indexes may be omitted, for example a[:] returns a copy of the entire list. Each element of a slice is a shallow copy. In Python, a distinction between expressions and statements is rigidly enforced, in contrast to languages such as Common Lisp, Scheme, or Ruby. This leads to duplicating some functionality. For example: List comprehensions vs. for-loops Conditional expressions vs. if blocks The eval() vs. exec() built-in functions (in Python 2, exec is a statement); the former is for expressions, the latter is for statements. Statements cannot be a part of an expression, so list and other comprehensions or lambda expressions, all being expressions, cannot contain statements. A particular case of this is that an assignment statement such as a = 1 cannot form part of the conditional expression of a conditional statement. This has the advantage of avoiding a classic C error of mistaking an assignment operator = for an equality operator == in conditions: if (c = 1) { ... } is syntactically valid (but probably unintended) C code but if c = 1: ... causes a syntax error in Python. === Methods === Methods on objects are functions attached to the object's class; the syntax instance.method(argument) is, for normal methods and functions, syntactic sugar for Class.method(instance, argument). Python methods have an explicit self parameter to access instance data, in contrast to the implicit self (or this) in some other object-oriented programming languages (e.g., C++, Java, Objective-C, or Ruby). === Typing === Python uses duck typing and has typed objects but untyped variable names. Type constraints are not checked at compile time; rather, operations on an object may fail, signifying that the given object is not of a suitable type. Despite being dynamically typed, Python is strongly typed, forbidding operations that are not well-defined (for example, adding a number to a string) rather than silently attempting to make sense of them. Python allows programmers to define their own types using classes, which are most often used for object-oriented programming. New instances of classes are constructed by calling the class (for example, SpamClass() or EggsClass()), and the classes are instances of the metaclass type (itself an instance of itself), allowing metaprogramming and reflection. Before version 3.0, Python had two kinds of classes: old-style and new-style. The syntax of both styles is the same, the difference being whether the class object is inherited from, directly or indirectly (all new-style classes inherit from object and are instances of type). In versions of Python 2 from Python 2.2 onwards, both kinds of classes can be used. Old-style classes were eliminated in Python 3.0. The long term plan is to support gradual typing and from Python 3.5, the syntax of the language allows specifying static types but they are not checked in the default implementation, CPython. An experimental optional static type checker named mypy supports compile-time type checking. ^a Not directly accessible by name === Mathematics === Python has the usual symbols for arithmetic operators (+, -, *, /), the floor division operator // and the remainder operator % (where the remainder can be negative, e.g. 4 % -3 == -2). It also has ** for exponentiation, e.g. 5**3 == 125 and 9**0.5 == 3.0, and a matrix multiply operator @ . These operators work like in traditional math; with the same precedence rules, the operators infix ( + and - can also be unary to represent positive and negative numbers respectively). Division between integers produces floating point results. The behavior of division has changed significantly over time: Python 2.1 and earlier used C's division behavior. The / operator is integer division if both operands are integers, and floating-point division otherwise. Integer division rounds towards 0, e.g. 7/3 == 2 and -7/3 == -2. Python 2.2 changed integer division to round towards negative infinity, e.g. 7/3 == 2 and -7/3 == -3. The floor division // operator was introduced. So 7//3 == 2, -7//3 == -3, 7.5//3 == 2.0 and -7.5//3 == -3.0. Adding from __future__ import division causes a module to use Python 3.0 rules for division (see next). Python 3.0 changed / to always be floating-point division, e.g. 5/2 == 2.5.In Python terms, / is true division (or simply division), and // is floor division. / before version 3.0 is classic division. Rounding towards negative infinity, though different from most languages, adds consistency. For instance, it means that the equation (a + b)//b == a//b + 1 is always true. It also means that the equation b*(a//b) + a%b == a is valid for both positive and negative values of a. However, maintaining the validity of this equation means that while the result of a%b is, as expected, in the half-open interval [0, b), where b is a positive integer, it has to lie in the interval (b, 0] when b is negative. Python provides a round function for rounding a float to the nearest integer. For tie-breaking, Python 3 uses round to even: round(1.5) and round(2.5) both produce 2. Versions before 3 used round-away-from-zero: round(0.5) is 1.0, round(-0.5) is −1.0.Python allows boolean expressions with multiple equality relations in a manner that is consistent with general use in mathematics. For example, the expression a < b < c tests whether a is less than b and b is less than c. C-derived languages interpret this expression differently: in C, the expression would first evaluate a < b, resulting in 0 or 1, and that result would then be compared with c. Python uses arbitrary-precision arithmetic for all integer operations. The Decimal type/class in the decimal module provides decimal floating point numbers to a pre-defined arbitrary precision and several rounding modes. The Fraction class in the fractions module provides arbitrary precision for rational numbers. Due to Python's extensive mathematics library, and the third-party library NumPy that further extends the native capabilities, it is frequently used as a scientific scripting language to aid in problems such as numerical data processing and manipulation. == Python programming examples == Hello world program: Program to calculate the factorial of a positive integer: == Libraries == Python's large standard library, commonly cited as one of its greatest strengths, provides tools suited to many tasks. For Internet-facing applications, many standard formats and protocols such as MIME and HTTP are supported. It includes modules for creating graphical user interfaces, connecting to relational databases, generating pseudorandom numbers, arithmetic with arbitrary-precision decimals, manipulating regular expressions, and unit testing. Some parts of the standard library are covered by specifications (for example, the Web Server Gateway Interface (WSGI) implementation wsgiref follows PEP 333), but most modules are not. They are specified by their code, internal documentation, and test suites. However, because most of the standard library is cross-platform Python code, only a few modules need altering or rewriting for variant implementations. As of November 2019, the Python Package Index (PyPI), the official repository for third-party Python software, contains over 200,000 packages with a wide range of functionality, including: Graphical user interfaces Web frameworks Multimedia Databases Networking Test frameworks Automation Web scraping Documentation System administration Scientific computing Text processing Image processing Machine learning Data analytics == Development environments == Most Python implementations (including CPython) include a read–eval–print loop (REPL), permitting them to function as a command line interpreter for which the user enters statements sequentially and receives results immediately. Other shells, including IDLE and IPython, add further abilities such as improved auto-completion, session state retention and syntax highlighting. As well as standard desktop integrated development environments, there are Web browser-based IDEs; SageMath (intended for developing science and math-related Python programs); PythonAnywhere, a browser-based IDE and hosting environment; and Canopy IDE, a commercial Python IDE emphasizing scientific computing. == Implementations == === Reference implementation === CPython is the reference implementation of Python. It is written in C, meeting the C89 standard with several select C99 features. It compiles Python programs into an intermediate bytecode which is then executed by its virtual machine. CPython is distributed with a large standard library written in a mixture of C and native Python. It is available for many platforms, including Windows and most modern Unix-like systems. Platform portability was one of its earliest priorities. === Other implementations === PyPy is a fast, compliant interpreter of Python 2.7 and 3.6. Its just-in-time compiler brings a significant speed improvement over CPython but several libraries written in C cannot be used with it. Stackless Python is a significant fork of CPython that implements microthreads; it does not use the C memory stack, thus allowing massively concurrent programs. PyPy also has a stackless version. MicroPython and CircuitPython are Python 3 variants optimized for microcontrollers. This includes Lego Mindstorms EV3. === Unsupported implementations === Other just-in-time Python compilers have been developed, but are now unsupported: Google began a project named Unladen Swallow in 2009, with the aim of speeding up the Python interpreter five-fold by using the LLVM, and of improving its multithreading ability to scale to thousands of cores, while ordinary implementations suffer from the global interpreter lock. Psyco was a just-in-time specializing compiler that integrates with CPython and transforms bytecode to machine code at runtime. The emitted code is specialized for certain data types and is faster than standard Python code. In 2005, Nokia released a Python interpreter for the Series 60 mobile phones named PyS60. It includes many of the modules from the CPython implementations and some additional modules to integrate with the Symbian operating system. The project has been kept up-to-date to run on all variants of the S60 platform, and several third-party modules are available. The Nokia N900 also supports Python with GTK widget libraries, enabling programs to be written and run on the target device. Cross-compilers to other languages === There are several compilers to high-level object languages, with either unrestricted Python, a restricted subset of Python, or a language similar to Python as the source language: Jython enables the use of the Java class library from a Python program. IronPython follows a similar approach in order to run Python programs on the .NET Common Language Runtime. The RPython language can be compiled to C, and is used to build the PyPy interpreter of Python. Pyjs compiles Python to JavaScript. Cython compiles Python to C and C++. Numba uses LLVM to compile Python to machine code. Pythran compiles Python to C++. Somewhat dated Pyrex (latest release in 2010) and Shed Skin (latest release in 2013) compile to C and C++ respectively. Google's Grumpy compiles Python to Go. MyHDL compiles Python to VHDL. Nuitka compiles Python into C++. === Performance === A performance comparison of various Python implementations on a non-numerical (combinatorial) workload was presented at EuroSciPy '13. == Development == Python's development is conducted largely through the Python Enhancement Proposal (PEP) process, the primary mechanism for proposing major new features, collecting community input on issues and documenting Python design decisions. Python coding style is covered in PEP 8. Outstanding PEPs are reviewed and commented on by the Python community and the steering council. Enhancement of the language corresponds with development of the CPython reference implementation. The mailing list python-dev is the primary forum for the language's development. Specific issues are discussed in the Roundup bug tracker hosted at bugs.python.org. Development originally took place on a self-hosted source-code repository running Mercurial, until Python moved to GitHub in January 2017.CPython's public releases come in three types, distinguished by which part of the version number is incremented: Backward-incompatible versions, where code is expected to break and need to be manually ported. The first part of the version number is incremented. These releases happen infrequently—for example, version 3.0 was released 8 years after 2.0. Major or "feature" releases, about every 18 months, are largely compatible but introduce new features. The second part of the version number is incremented. Each major version is supported by bugfixes for several years after its release. Bugfix releases, which introduce no new features, occur about every 3 months and are made when a sufficient number of bugs have been fixed upstream since the last release. Security vulnerabilities are also patched in these releases. The third and final part of the version number is incremented. Python 3.9 alpha1 was announced in November 2019 and with the adoption of a new yearly release cadence, the first release of 3.9 is slated for November 2020.Many alpha, beta, and release-candidates are also released as previews and for testing before final releases. Although there is a rough schedule for each release, they are often delayed if the code is not ready. Python's development team monitors the state of the code by running the large unit test suite during development, and using the BuildBot continuous integration system. The major academic conference on Python is PyCon. There are also special Python mentoring programmes, such as Pyladies. == Naming == Python's name is derived from the British comedy group Monty Python, whom Python creator Guido van Rossum enjoyed while developing the language. Monty Python references appear frequently in Python code and culture; for example, the metasyntactic variables often used in Python literature are spam and eggs instead of the traditional foo and bar. The official Python documentation also contains various references to Monty Python routines. The prefix Py- is used to show that something is related to Python. Examples of the use of this prefix in names of Python applications or libraries include Pygame, a binding of SDL to Python (commonly used to create games); PyQt and PyGTK, which bind Qt and GTK to Python respectively; and PyPy, a Python implementation originally written in Python. == API documentation generators == Python API documentation generators include: Sphinx Epydoc HeaderDoc pydoc == Uses == Since 2003, Python has consistently ranked in the top ten most popular programming languages in the TIOBE Programming Community Index where, as of February 2020, it is the third most popular language (behind Java, and C). It was selected Programming Language of the Year in 2007, 2010, and 2018.An empirical study found that scripting languages, such as Python, are more productive than conventional languages, such as C and Java, for programming problems involving string manipulation and search in a dictionary, and determined that memory consumption was often "better than Java and not much worse than C or C++".Large organizations that use Python include Wikipedia, Google, Yahoo!, CERN, NASA, Facebook, Amazon, Instagram, Spotify and some smaller entities like ILM and ITA. The social news networking site Reddit is written entirely in Python. Python can serve as a scripting language for web applications, e.g., via mod_wsgi for the Apache web server. With Web Server Gateway Interface, a standard API has evolved to facilitate these applications. Web frameworks like Django, Pylons, Pyramid, TurboGears, web2py, Tornado, Flask, Bottle and Zope support developers in the design and maintenance of complex applications. Pyjs and IronPython can be used to develop the client-side of Ajax-based applications. SQLAlchemy can be used as data mapper to a relational database. Twisted is a framework to program communications between computers, and is used (for example) by Dropbox. Libraries such as NumPy, SciPy and Matplotlib allow the effective use of Python in scientific computing, with specialized libraries such as Biopython and Astropy providing domain-specific functionality. SageMath is a mathematical software with a notebook interface programmable in Python: its library covers many aspects of mathematics, including algebra, combinatorics, numerical mathematics, number theory, and calculus. Python has been successfully embedded in many software products as a scripting language, including in finite element method software such as Abaqus, 3D parametric modeler like FreeCAD, 3D animation packages such as 3ds Max, Blender, Cinema 4D, Lightwave, Houdini, Maya, modo, MotionBuilder, Softimage, the visual effects compositor Nuke, 2D imaging programs like GIMP, Inkscape, Scribus and Paint Shop Pro, and musical notation programs like scorewriter and capella. GNU Debugger uses Python as a pretty printer to show complex structures such as C++ containers. Esri promotes Python as the best choice for writing scripts in ArcGIS. It has also been used in several video games, and has been adopted as first of the three available programming languages in Google App Engine, the other two being Java and Go. Python is commonly used in artificial intelligence projects with the help of libraries like TensorFlow, Keras, Pytorch and Scikit-learn. As a scripting language with modular architecture, simple syntax and rich text processing tools, Python is often used for natural language processing. Many operating systems include Python as a standard component. It ships with most Linux distributions, AmigaOS 4, FreeBSD (as a package), NetBSD, OpenBSD (as a package) and macOS and can be used from the command line (terminal). Many Linux distributions use installers written in Python: Ubuntu uses the Ubiquity installer, while Red Hat Linux and Fedora use the Anaconda installer. Gentoo Linux uses Python in its package management system, Portage. Python is used extensively in the information security industry, including in exploit development. Most of the Sugar software for the One Laptop per Child XO, now developed at Sugar Labs, is written in Python. The Raspberry Pi single-board computer project has adopted Python as its main user-programming language. Due to Python's user-friendly conventions and easy-to-understand language, it is commonly used as an intro language into computing sciences with students. This allows students to easily learn computing theories and concepts and then apply them to other programming languages. LibreOffice includes Python, and intends to replace Java with Python. Its Python Scripting Provider is a core feature since Version 4.0 from 7 February 2013. == Languages influenced by Python == Python's design and philosophy have influenced many other programming languages: Boo uses indentation, a similar syntax, and a similar object model. Cobra uses indentation and a similar syntax, and its "Acknowledgements" document lists Python first among languages that influenced it. However, Cobra directly supports design-by-contract, unit tests, and optional static typing. CoffeeScript, a programming language that cross-compiles to JavaScript, has Python-inspired syntax. ECMAScript borrowed iterators and generators from Python. Go is designed for the "speed of working in a dynamic language like Python" and shares the same syntax for slicing arrays. Groovy was motivated by the desire to bring the Python design philosophy to Java. Julia was designed "with true macros [.. and to be] as usable for general programming as Python [and] should be as fast as C". Calling to or from Julia is possible; to with PyCall.jl and a Python package pyjulia allows calling, in the other direction, from Python. Kotlin is a functional programming language with an interactive shell similar to Python. However, Kotlin is statically typed with access to standard Java libraries. Nim uses indentation and a similar syntax, however it is statically typed, and offers powerful macros. Ruby's creator, Yukihiro Matsumoto, has said: "I wanted a scripting language that was more powerful than Perl, and more object-oriented than Python. That's why I decided to design my own language." Swift, a programming language developed by Apple, has some Python-inspired syntax. GDScript, dynamically typed programming language used to create video-games. It is extremely similar to Python with a few minor differences. Python's development practices have also been emulated by other languages. For example, the practice of requiring a document describing the rationale for, and issues surrounding, a change to the language (in Python, a PEP) is also used in Tcl and Erlang. == See also == Python syntax and semantics pip (package manager) IPython == References == === Sources === "Python for Artificial Intelligence". Wiki.python.org. 19 July 2012. Archived from the original on 1 November 2012. Retrieved 3 December 2012. Paine, Jocelyn, ed. (August 2005). "AI in Python". AI Expert Newsletter. Amzi!. Retrieved 11 February 2012. "PyAIML 0.8.5 : Python Package Index". Pypi.python.org. Retrieved 17 July 2013. Russell, Stuart J. & Norvig, Peter (2009). Artificial Intelligence: A Modern Approach (3rd ed.). Upper Saddle River, NJ: Prentice Hall. ISBN 978-0-13-604259-4. == Further reading == Downey, Allen B. (May 2012). Think Python: How to Think Like a Computer Scientist (Version 1.6.6 ed.). ISBN 978-0-521-72596-5. Hamilton, Naomi (5 August 2008). "The A-Z of Programming Languages: Python". Computerworld. Archived from the original on 29 December 2008. Retrieved 31 March 2010. Lutz, Mark (2013). Learning Python (5th ed.). O'Reilly Media. ISBN 978-0-596-15806-4. Pilgrim, Mark (2004). Dive Into Python. Apress. ISBN 978-1-59059-356-1. Pilgrim, Mark (2009). Dive Into Python 3. Apress. ISBN 978-1-4302-2415-0. Summerfield, Mark (2009). Programming in Python 3 (2nd ed.). Addison-Wesley Professional. ISBN 978-0-321-68056-3. == External links == Official website Python (programming language) at Curlie ================================================ FILE: week6/questions/questions.py ================================================ import nltk import sys import os import string import math FILE_MATCHES = 1 SENTENCE_MATCHES = 1 def main(): # Check command-line arguments if len(sys.argv) != 2: sys.exit("Usage: python questions.py corpus") # Calculate IDF values across files files = load_files(sys.argv[1]) file_words = { filename: tokenize(files[filename]) for filename in files } file_idfs = compute_idfs(file_words) # Prompt user for query query = set(tokenize(input("Query: "))) # Determine top file matches according to TF-IDF filenames = top_files(query, file_words, file_idfs, n=FILE_MATCHES) # Extract sentences from top files sentences = dict() for filename in filenames: for passage in files[filename].split("\n"): for sentence in nltk.sent_tokenize(passage): tokens = tokenize(sentence) if tokens: sentences[sentence] = tokens # Compute IDF values across sentences idfs = compute_idfs(sentences) # Determine top sentence matches matches = top_sentences(query, sentences, idfs, n=SENTENCE_MATCHES) for match in matches: print(match) def load_files(directory): """ Given a directory name, return a dictionary mapping the filename of each `.txt` file inside that directory to the file's contents as a string. """ dictionary = {} for file in os.listdir(directory): with open(os.path.join(directory, file), encoding="utf-8") as ofile: dictionary[file] = ofile.read() return dictionary def tokenize(document): """ Given a document (represented as a string), return a list of all of the words in that document, in order. Process document by coverting all words to lowercase, and removing any punctuation or English stopwords. """ tokenized = nltk.tokenize.word_tokenize(document.lower()) final_list = [x for x in tokenized if x not in string.punctuation and x not in nltk.corpus.stopwords.words("english")] return final_list def compute_idfs(documents): """ Given a dictionary of `documents` that maps names of documents to a list of words, return a dictionary that maps words to their IDF values. Any word that appears in at least one of the documents should be in the resulting dictionary. """ idf_dictio = {} doc_len = len(documents) unique_words = set(sum(documents.values(), [])) for word in unique_words: count = 0 for doc in documents.values(): if word in doc: count += 1 idf_dictio[word] = math.log(doc_len/count) return idf_dictio def top_files(query, files, idfs, n): """ Given a `query` (a set of words), `files` (a dictionary mapping names of files to a list of their words), and `idfs` (a dictionary mapping words to their IDF values), return a list of the filenames of the the `n` top files that match the query, ranked according to tf-idf. """ scores = {} for filename, filecontent in files.items(): file_score = 0 for word in query: if word in filecontent: file_score += filecontent.count(word) * idfs[word] if file_score != 0: scores[filename] = file_score sorted_by_score = [k for k, v in sorted(scores.items(), key=lambda x: x[1], reverse=True)] return sorted_by_score[:n] def top_sentences(query, sentences, idfs, n): """ Given a `query` (a set of words), `sentences` (a dictionary mapping sentences to a list of their words), and `idfs` (a dictionary mapping words to their IDF values), return a list of the `n` top sentences that match the query, ranked according to idf. If there are ties, preference should be given to sentences that have a higher query term density. """ scores = {} for sentence, sentwords in sentences.items(): score = 0 for word in query: if word in sentwords: score += idfs[word] if score != 0: density = sum([sentwords.count(x) for x in query]) / len(sentwords) scores[sentence] = (score, density) sorted_by_score = [k for k, v in sorted(scores.items(), key=lambda x: (x[1][0], x[1][1]), reverse=True)] return sorted_by_score[:n] if __name__ == "__main__": main()