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Repository: sachinnpraburaj/Intelligent-Travel-Recommendation-System
Branch: master
Commit: d0f64997c626
Files: 112
Total size: 79.0 MB
Directory structure:
gitextract_pzr7p8zx/
├── README.md
├── Restaurants (Yelp) Dataset-EDA.ipynb
├── TripAdvisor_Crawler_Parser.ipynb
├── attraction_etl.ipynb
├── attractions_crawler.ipynb
├── attractions_details_crawler.ipynb
├── attractions_recc.py
├── combine_batches.ipynb
├── etl/
│ ├── attraction_reviews/
│ │ ├── part-00000-49e3efc3-a4b7-41e8-b45e-c3f57c21a6a5-c000.snappy.parquet
│ │ ├── part-00001-49e3efc3-a4b7-41e8-b45e-c3f57c21a6a5-c000.snappy.parquet
│ │ ├── part-00002-49e3efc3-a4b7-41e8-b45e-c3f57c21a6a5-c000.snappy.parquet
│ │ └── part-00003-49e3efc3-a4b7-41e8-b45e-c3f57c21a6a5-c000.snappy.parquet
│ ├── attraction_reviews.json
│ ├── attractions.json
│ ├── del_dup/
│ │ ├── part-00000-c453ac1f-d0ec-49e3-8984-030e500feb5f-c000.json
│ │ ├── part-00001-c453ac1f-d0ec-49e3-8984-030e500feb5f-c000.json
│ │ ├── part-00002-c453ac1f-d0ec-49e3-8984-030e500feb5f-c000.json
│ │ └── part-00003-c453ac1f-d0ec-49e3-8984-030e500feb5f-c000.json
│ ├── newh_df/
│ │ ├── part-00000-8b7be6b6-c958-4a62-847d-550064dd5a78-c000.json
│ │ ├── part-00001-8b7be6b6-c958-4a62-847d-550064dd5a78-c000.json
│ │ ├── part-00002-8b7be6b6-c958-4a62-847d-550064dd5a78-c000.json
│ │ └── part-00003-8b7be6b6-c958-4a62-847d-550064dd5a78-c000.json
│ └── u_id_df/
│ ├── part-00000-c0814be7-b91e-4586-9388-7a44d9ed338e-c000.json
│ ├── part-00001-c0814be7-b91e-4586-9388-7a44d9ed338e-c000.json
│ ├── part-00002-c0814be7-b91e-4586-9388-7a44d9ed338e-c000.json
│ └── part-00003-c0814be7-b91e-4586-9388-7a44d9ed338e-c000.json
├── final_hotel_recc.ipynb
├── get_att_recc.ipynb
├── hotel_etl.ipynb
├── hotel_recc.py
├── mf_models/
│ └── model_file/
│ ├── itemFactors/
│ │ ├── part-00000-daa146bf-1542-404b-b7b1-a4e3cff69a72-c000.snappy.parquet
│ │ ├── part-00001-daa146bf-1542-404b-b7b1-a4e3cff69a72-c000.snappy.parquet
│ │ ├── part-00002-daa146bf-1542-404b-b7b1-a4e3cff69a72-c000.snappy.parquet
│ │ ├── part-00003-daa146bf-1542-404b-b7b1-a4e3cff69a72-c000.snappy.parquet
│ │ ├── part-00004-daa146bf-1542-404b-b7b1-a4e3cff69a72-c000.snappy.parquet
│ │ ├── part-00005-daa146bf-1542-404b-b7b1-a4e3cff69a72-c000.snappy.parquet
│ │ ├── part-00006-daa146bf-1542-404b-b7b1-a4e3cff69a72-c000.snappy.parquet
│ │ ├── part-00007-daa146bf-1542-404b-b7b1-a4e3cff69a72-c000.snappy.parquet
│ │ ├── part-00008-daa146bf-1542-404b-b7b1-a4e3cff69a72-c000.snappy.parquet
│ │ └── part-00009-daa146bf-1542-404b-b7b1-a4e3cff69a72-c000.snappy.parquet
│ ├── metadata/
│ │ └── part-00000
│ └── userFactors/
│ ├── part-00000-1ce6925f-8f15-4e37-b696-1731c4499927-c000.snappy.parquet
│ ├── part-00001-1ce6925f-8f15-4e37-b696-1731c4499927-c000.snappy.parquet
│ ├── part-00002-1ce6925f-8f15-4e37-b696-1731c4499927-c000.snappy.parquet
│ ├── part-00003-1ce6925f-8f15-4e37-b696-1731c4499927-c000.snappy.parquet
│ ├── part-00004-1ce6925f-8f15-4e37-b696-1731c4499927-c000.snappy.parquet
│ ├── part-00005-1ce6925f-8f15-4e37-b696-1731c4499927-c000.snappy.parquet
│ ├── part-00006-1ce6925f-8f15-4e37-b696-1731c4499927-c000.snappy.parquet
│ ├── part-00007-1ce6925f-8f15-4e37-b696-1731c4499927-c000.snappy.parquet
│ ├── part-00008-1ce6925f-8f15-4e37-b696-1731c4499927-c000.snappy.parquet
│ └── part-00009-1ce6925f-8f15-4e37-b696-1731c4499927-c000.snappy.parquet
├── outputs/
│ ├── attraction_details/
│ │ ├── part-00000-24d79c41-ab21-4f52-bb5e-fed77bf823f8-c000.snappy.parquet
│ │ ├── part-00000-8b76e71d-55f4-47ef-a621-b32a2f3e1179-c000.snappy.parquet
│ │ ├── part-00001-24d79c41-ab21-4f52-bb5e-fed77bf823f8-c000.snappy.parquet
│ │ ├── part-00001-8b76e71d-55f4-47ef-a621-b32a2f3e1179-c000.snappy.parquet
│ │ ├── part-00002-8b76e71d-55f4-47ef-a621-b32a2f3e1179-c000.snappy.parquet
│ │ └── part-00003-8b76e71d-55f4-47ef-a621-b32a2f3e1179-c000.snappy.parquet
│ ├── attraction_reviews/
│ │ ├── part-00000-01b23ac3-b793-40dc-b90a-93a239db5d07-c000.snappy.parquet
│ │ ├── part-00000-603b562f-5b1d-4e02-9a5d-f66acc3232a1-c000.snappy.parquet
│ │ ├── part-00002-01b23ac3-b793-40dc-b90a-93a239db5d07-c000.snappy.parquet
│ │ └── part-00002-603b562f-5b1d-4e02-9a5d-f66acc3232a1-c000.snappy.parquet
│ ├── attractions_cat.json
│ ├── attractions_details_batch1.json
│ ├── attractions_details_batch2.json
│ ├── attractions_reviews_batch1.json
│ └── attractions_reviews_batch2.json
├── rbm.py
├── rbm_training.ipynb
├── recommendations/
│ ├── e10_r5000_lr0.01_hu64_bs8/
│ │ ├── hb.npy
│ │ ├── user1_seen.csv
│ │ ├── user1_unseen.csv
│ │ ├── vb.npy
│ │ └── w.npy
│ ├── e20_r20000_lr0.01_hu128_bs16/
│ │ ├── hb.npy
│ │ ├── user1_seen.csv
│ │ ├── user1_unseen.csv
│ │ ├── vb.npy
│ │ └── w.npy
│ ├── e20_r20000_lr0.01_hu64_bs16/
│ │ ├── hb.npy
│ │ ├── user1_seen.csv
│ │ ├── user1_unseen.csv
│ │ ├── vb.npy
│ │ └── w.npy
│ ├── e20_r40000_lr0.01_hu128_bs8/
│ │ ├── hb.npy
│ │ ├── user1_seen.csv
│ │ ├── user1_unseen.csv
│ │ ├── vb.npy
│ │ └── w.npy
│ ├── e20_r5000_lr0.01_hu128_bs8/
│ │ ├── hb.npy
│ │ ├── user1_seen.csv
│ │ ├── user1_unseen.csv
│ │ ├── vb.npy
│ │ └── w.npy
│ ├── e50_r40000_lr0.01_hu128_bs16/
│ │ ├── hb.npy
│ │ ├── user1_seen.csv
│ │ ├── user1_unseen.csv
│ │ ├── vb.npy
│ │ └── w.npy
│ └── e50_r40000_lr0.01_hu256_bs16/
│ ├── hb.npy
│ ├── user1_seen.csv
│ ├── user1_unseen.csv
│ └── vb.npy
├── requirements.txt
├── sample_table_widget.ipynb
├── tripadvisor_hotel_output/
│ ├── file_mainpage_url.txt
│ ├── hotel_info.csv
│ ├── hotel_info.json
│ ├── hotel_info_dedup.csv
│ ├── hotel_info_dedup.json
│ ├── reviews_dedup.csv
│ └── reviews_dedup.json
└── utils.py
================================================
FILE CONTENTS
================================================
================================================
FILE: README.md
================================================
# Intelligent Travel Recommendation System <br />
<br />
Video demo: https://youtu.be/V635gdcw1h0 <br />
Project Report: report.pdf <br />
Poster: poster.pdf
# Objective:
To provide a tailor made plan consisting of possible places to stay, attractions to visit and restaurants to eat at for the entire duration of travel. We recommend restaurants separately for each meal of the day (breakfast, lunch and dinner) and provide two recommendations per meal per day. We also recommend five possible stay options (hotels) for your travel alongside possible attractions to view. Attractions are recommended based on timing, (i.e) which ones to view during the day and which ones are better off at night. Again we provide two attraction recommendations per timing per day for the entire duration of travel.
# Project Overview:
We have used three different recommender systems (one each for attractions, hotels and restaurants).
1. RBM, a Deep learning technique for Attractions.
2. Matrix Factorization with ALS, a highly scalable and distributed Collaborative Filtering technique for hotels.
3. Hybrid- A combination of K-Means algorithm for Content Based Filtering and K-Nearest Neighbors for Memory based Collaborative Filtering for restaurants.
Few visualizations for the project were done using python libraries and others have been done using Tableau software. They can be accessed from [EDA](/EDA) folder.
# Steps for execution:
For restaurants- Dataset for the project should be downloaded from [Yelp dataset challenge](https://www.yelp.ca/dataset/download) and stored in yelp_dataset folder.
For hotels- We scraped TripAdvisor to obtain the dataset. Dataset can be read from [tripadvisor_hotel_output](/tripadvisor_hotel_output) folder.
For attractions- We scraped TripAdvisor to obtain the dataset. Dataset can be read from [outputs](/outputs) folder.
# Files: <br />
###### [attractions_crawler.ipynb](/attractions_crawler.ipynb)
-- To collect urls of attractions from tripadvisor.
###### [attractions_details_crawler.ipynb](/attractions_details_crawler.ipynb)
-- To extract attraction details and reviews on each attraction from collected urls in batches
###### [combine_batches.ipynb](/combine_batches.ipynb)
-- To combine the data collected in batches
###### [attraction_etl.ipynb](/attraction_etl.ipynb)
-- To perform ETL on attraction details and attractions reviews datasets.
###### [attractions_recc.py](/attractions_recc.py)
-- The core code to provide attraction recommendation using the trained RBM model.
###### [final_hotel_recc.ipynb](/final_hotel_recc.ipynb)
-- The final code that integrates ETL on hotels dataset and MF-ALS model output to display hotel recommendations.
###### [get_att_recc.ipynb](/get_att_recc.ipynb)
-- The final code that integrates ETL on attractions dataset and RBM model output to display attraction recommendations.
###### [hotel_etl.ipynb](/hotel_etl.ipynb)
-- To perform 'Extract Transform Load (ETL)' on hotels dataset that has been scraped from TripAdvisor.
###### [hotel_recc.py](/hotel_recc.py)
-- The core code that models MF-ALS and outputs recommendations.
###### [rbm_training.ipynb](/rbm_training.ipynb)
-- The code to perform training and tuning of the RBM, deep learning model.
###### [rbm.py](/rbm.py)
-- The code that loads the best model and outputs recommendations for users.
###### [requirements.txt](/requirements.txt)
-- File to handle dependencies for thus project.
###### [Restaurants (Yelp) Dataset-EDA.ipynb]
-- The notebook that has the code and shows EDA visualizations for Yelp (restaurants) dataset.
###### [TripAdvisor_Crawler_Parser.ipynb](/TripAdvisor_Crawler_Parser.ipynb)
-- The notebook performs collection, extraction, cleaning, parsing and obtaining hotel urls, hotel related information, user reviews, user ratings and user related information.
###### [utils.py](/utils.py)
-- Consists of helper functions for the RBM model.
###### [Hybrid_Recommder.ipynb](/Hybrid_Recommder.ipynb)
-- The core code for ETL on yelp dataset and hybrid recommender model.
# Folders:
###### [etl](/etl)
-- Saved model parameters and model outputs from MF-ALS.
###### [input-output](/input-output)
-- contains screenshots of input and output images of ITRS application on the whole.
###### [outputs](/outputs)
-- Contains dataset for attractions.
###### [tripadvisor_hotel_output](/tripadvisor_hotel_output)
-- Contains dataset for hotels.
###### [downloads](/downloads)
-- Contains attraction images downloaded using google_download_images API.
###### [mf_models](/mf_models)
-- Contains the saved best obtained Matrix Factorization- ALS (MF-ALS) model.
###### [rbm_models](/rbm_models)
-- Contains saved RBM models, tried out for different parameters.
###### [EDA](/analysis)
-- all results (visualizations) of Exploratory Data Analysis (EDA) are be stored here.
================================================
FILE: Restaurants (Yelp) Dataset-EDA.ipynb
================================================
{
"cells": [
{
"cell_type": "code",
"execution_count": 92,
"metadata": {},
"outputs": [
{
"data": {
"text/html": [
"<script type=\"text/javascript\">window.PlotlyConfig = {MathJaxConfig: 'local'};</script><script type=\"text/javascript\">if (window.MathJax) {MathJax.Hub.Config({SVG: {font: \"STIX-Web\"}});}</script><script>requirejs.config({paths: { 'plotly': ['https://cdn.plot.ly/plotly-latest.min']},});if(!window._Plotly) {require(['plotly'],function(plotly) {window._Plotly=plotly;});}</script>"
],
"text/vnd.plotly.v1+html": [
"<script type=\"text/javascript\">window.PlotlyConfig = {MathJaxConfig: 'local'};</script><script type=\"text/javascript\">if (window.MathJax) {MathJax.Hub.Config({SVG: {font: \"STIX-Web\"}});}</script><script>requirejs.config({paths: { 'plotly': ['https://cdn.plot.ly/plotly-latest.min']},});if(!window._Plotly) {require(['plotly'],function(plotly) {window._Plotly=plotly;});}</script>"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"name": "stdout",
"output_type": "stream",
"text": [
"headers loaded.. plotly plotting set..\n"
]
}
],
"source": [
"# import libraries\n",
"import os\n",
"import json\n",
"import re\n",
"import sys\n",
"import csv\n",
"import pandas as pd\n",
"import numpy as np\n",
"import plotly.plotly as py\n",
"import plotly.graph_objs as go\n",
"from plotly.offline import download_plotlyjs, init_notebook_mode, plot, iplot\n",
"import seaborn as seab\n",
"# Plot the data using plotly offline mode\n",
"init_notebook_mode(connected=True)\n",
"print('headers loaded.. plotly plotting set..')"
]
},
{
"cell_type": "code",
"execution_count": 22,
"metadata": {},
"outputs": [],
"source": [
"# Normalize function- to normalize values for review score and tip score//\n",
"def normalize(x, new_min = 0, new_max = 100):\n",
" output = []\n",
" old_min, old_max = min(x), max(x)\n",
"\n",
" for iter_val in x:\n",
" val = (new_max - new_min) / (old_max - old_min) * (iter_val - old_min) + new_min\n",
" output.append(val)\n",
"\n",
" return output"
]
},
{
"cell_type": "code",
"execution_count": 33,
"metadata": {},
"outputs": [
{
"data": {
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"</div>"
],
"text/plain": [
" business_id cool date funny \\\n",
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"1 I cannot believe how things have changed in 3 ... 1 \n",
"2 Good selection of classes of beers and mains. ... 0 \n",
"3 Came here on a Thursday night at 6:30 p.m. My ... 0 \n",
"4 Met a friend for dinner there tonight. The ser... 6 \n",
"\n",
" user_id sentiment_score_reviews \n",
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"3 DbccYu3OppWKl21OanZnTg 0.9806 \n",
"4 tAcB1ZohRtjozsymvqqHzQ 0.6124 "
]
},
"execution_count": 33,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"# Read reviews dataframe\n",
"reviews_df = pd.read_csv('reviews.csv')\n",
"reviews_df.head()"
]
},
{
"cell_type": "code",
"execution_count": 6,
"metadata": {},
"outputs": [
{
"data": {
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"</style>\n",
"<table border=\"1\" class=\"dataframe\">\n",
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" <th>business_id</th>\n",
" <th>categories</th>\n",
" <th>city</th>\n",
" <th>hours</th>\n",
" <th>is_open</th>\n",
" <th>latitude</th>\n",
" <th>longitude</th>\n",
" <th>name</th>\n",
" <th>postal_code</th>\n",
" <th>review_count</th>\n",
" <th>stars</th>\n",
" <th>state</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>0</th>\n",
" <td>30 Eglinton Avenue W</td>\n",
" <td>{'RestaurantsReservations': 'True', 'GoodForMe...</td>\n",
" <td>QXAEGFB4oINsVuTFxEYKFQ</td>\n",
" <td>Specialty Food, Restaurants, Dim Sum, Imported...</td>\n",
" <td>Mississauga</td>\n",
" <td>{'Monday': '9:0-0:0', 'Tuesday': '9:0-0:0', 'W...</td>\n",
" <td>1</td>\n",
" <td>43.605499</td>\n",
" <td>-79.652289</td>\n",
" <td>Emerald Chinese Restaurant</td>\n",
" <td>L5R 3E7</td>\n",
" <td>128</td>\n",
" <td>2.5</td>\n",
" <td>ON</td>\n",
" </tr>\n",
" <tr>\n",
" <th>1</th>\n",
" <td>Credit Valley Town Plaza, F2 - 6045 Creditview Rd</td>\n",
" <td>{'BusinessParking': \"{'garage': False, 'street...</td>\n",
" <td>68dUKd8_8liJ7in4aWOSEA</td>\n",
" <td>Shipping Centers, Couriers & Delivery Services...</td>\n",
" <td>Mississauga</td>\n",
" <td>{'Monday': '9:0-19:0', 'Tuesday': '9:0-20:0', ...</td>\n",
" <td>1</td>\n",
" <td>43.599475</td>\n",
" <td>-79.711584</td>\n",
" <td>The UPS Store</td>\n",
" <td>L5V 0B1</td>\n",
" <td>3</td>\n",
" <td>2.5</td>\n",
" <td>ON</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2</th>\n",
" <td>1210 8th Street SW, Unit 220</td>\n",
" <td>{'BikeParking': 'False', 'ByAppointmentOnly': ...</td>\n",
" <td>fcXOEZdXYeZqnQ3lGlOXmg</td>\n",
" <td>Local Services, Professional Services, Compute...</td>\n",
" <td>Calgary</td>\n",
" <td>{'Monday': '9:0-17:0', 'Tuesday': '9:0-17:0', ...</td>\n",
" <td>1</td>\n",
" <td>51.041771</td>\n",
" <td>-114.081109</td>\n",
" <td>Nucleus Information Service</td>\n",
" <td>T2R 1L3</td>\n",
" <td>5</td>\n",
" <td>2.0</td>\n",
" <td>AB</td>\n",
" </tr>\n",
" <tr>\n",
" <th>3</th>\n",
" <td>1455 16th Avenue</td>\n",
" <td>{'ByAppointmentOnly': 'False', 'BikeParking': ...</td>\n",
" <td>A98xW4qb7vOTguggHFs7Ng</td>\n",
" <td>Fitness & Instruction, Active Life, Yoga</td>\n",
" <td>Richmond Hill</td>\n",
" <td>{'Monday': '16:0-23:0', 'Tuesday': '16:0-23:0'...</td>\n",
" <td>1</td>\n",
" <td>43.861503</td>\n",
" <td>-79.388499</td>\n",
" <td>Hot Yoga Wellness</td>\n",
" <td>L4B 3G6</td>\n",
" <td>4</td>\n",
" <td>4.0</td>\n",
" <td>ON</td>\n",
" </tr>\n",
" <tr>\n",
" <th>4</th>\n",
" <td>445 King Street W, Suite 101</td>\n",
" <td>{'AcceptsInsurance': 'True', 'ByAppointmentOnl...</td>\n",
" <td>zSpQmEBvRe3IhTUlMSA6HQ</td>\n",
" <td>Trainers, Health & Medical, Active Life, Physi...</td>\n",
" <td>Toronto</td>\n",
" <td>{'Monday': '6:0-22:0', 'Tuesday': '6:0-22:0', ...</td>\n",
" <td>1</td>\n",
" <td>43.645041</td>\n",
" <td>-79.395799</td>\n",
" <td>Totum Life Science</td>\n",
" <td>M5V 1K4</td>\n",
" <td>23</td>\n",
" <td>4.0</td>\n",
" <td>ON</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div>"
],
"text/plain": [
" address \\\n",
"0 30 Eglinton Avenue W \n",
"1 Credit Valley Town Plaza, F2 - 6045 Creditview Rd \n",
"2 1210 8th Street SW, Unit 220 \n",
"3 1455 16th Avenue \n",
"4 445 King Street W, Suite 101 \n",
"\n",
" attributes business_id \\\n",
"0 {'RestaurantsReservations': 'True', 'GoodForMe... QXAEGFB4oINsVuTFxEYKFQ \n",
"1 {'BusinessParking': \"{'garage': False, 'street... 68dUKd8_8liJ7in4aWOSEA \n",
"2 {'BikeParking': 'False', 'ByAppointmentOnly': ... fcXOEZdXYeZqnQ3lGlOXmg \n",
"3 {'ByAppointmentOnly': 'False', 'BikeParking': ... A98xW4qb7vOTguggHFs7Ng \n",
"4 {'AcceptsInsurance': 'True', 'ByAppointmentOnl... zSpQmEBvRe3IhTUlMSA6HQ \n",
"\n",
" categories city \\\n",
"0 Specialty Food, Restaurants, Dim Sum, Imported... Mississauga \n",
"1 Shipping Centers, Couriers & Delivery Services... Mississauga \n",
"2 Local Services, Professional Services, Compute... Calgary \n",
"3 Fitness & Instruction, Active Life, Yoga Richmond Hill \n",
"4 Trainers, Health & Medical, Active Life, Physi... Toronto \n",
"\n",
" hours is_open latitude \\\n",
"0 {'Monday': '9:0-0:0', 'Tuesday': '9:0-0:0', 'W... 1 43.605499 \n",
"1 {'Monday': '9:0-19:0', 'Tuesday': '9:0-20:0', ... 1 43.599475 \n",
"2 {'Monday': '9:0-17:0', 'Tuesday': '9:0-17:0', ... 1 51.041771 \n",
"3 {'Monday': '16:0-23:0', 'Tuesday': '16:0-23:0'... 1 43.861503 \n",
"4 {'Monday': '6:0-22:0', 'Tuesday': '6:0-22:0', ... 1 43.645041 \n",
"\n",
" longitude name postal_code review_count stars \\\n",
"0 -79.652289 Emerald Chinese Restaurant L5R 3E7 128 2.5 \n",
"1 -79.711584 The UPS Store L5V 0B1 3 2.5 \n",
"2 -114.081109 Nucleus Information Service T2R 1L3 5 2.0 \n",
"3 -79.388499 Hot Yoga Wellness L4B 3G6 4 4.0 \n",
"4 -79.395799 Totum Life Science M5V 1K4 23 4.0 \n",
"\n",
" state \n",
"0 ON \n",
"1 ON \n",
"2 AB \n",
"3 ON \n",
"4 ON "
]
},
"execution_count": 6,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"# Read business (restaurants) dataframe\n",
"business_df = pd.read_csv('business.csv')\n",
"business_df.head()"
]
},
{
"cell_type": "code",
"execution_count": 9,
"metadata": {},
"outputs": [
{
"data": {
"text/html": [
"<div>\n",
"<style scoped>\n",
" .dataframe tbody tr th:only-of-type {\n",
" vertical-align: middle;\n",
" }\n",
"\n",
" .dataframe tbody tr th {\n",
" vertical-align: top;\n",
" }\n",
"\n",
" .dataframe thead th {\n",
" text-align: right;\n",
" }\n",
"</style>\n",
"<table border=\"1\" class=\"dataframe\">\n",
" <thead>\n",
" <tr style=\"text-align: right;\">\n",
" <th></th>\n",
" <th>business_id</th>\n",
" <th>compliment_count</th>\n",
" <th>date</th>\n",
" <th>text</th>\n",
" <th>user_id</th>\n",
" <th>sentiment_score_tips</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>0</th>\n",
" <td>0hudPyuCBlKg79OwKBw-eQ</td>\n",
" <td>0</td>\n",
" <td>2012-10-11 23:06:21</td>\n",
" <td>Only worth a visit in the summer time, to take...</td>\n",
" <td>w8p4JW6-ERsbg8aB5wjEAg</td>\n",
" <td>0.6369</td>\n",
" </tr>\n",
" <tr>\n",
" <th>1</th>\n",
" <td>r49iBfbnfoK7yt4rdsL_7g</td>\n",
" <td>0</td>\n",
" <td>2016-01-05 06:39:33</td>\n",
" <td>Hands down best AYCE in town! Love it!</td>\n",
" <td>CaFbX7topxdLpBJ_SNDfGQ</td>\n",
" <td>0.8745</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2</th>\n",
" <td>wZhB4U003eg2GQ-F7D2D4A</td>\n",
" <td>0</td>\n",
" <td>2016-08-12 22:18:07</td>\n",
" <td>When you receive the bag of seafood tie it up ...</td>\n",
" <td>oIHhfloe5VQamDFDB2dr1Q</td>\n",
" <td>0.9379</td>\n",
" </tr>\n",
" <tr>\n",
" <th>3</th>\n",
" <td>j9I2YkFCOKyAEb_3FbFqUA</td>\n",
" <td>0</td>\n",
" <td>2011-04-09 18:58:36</td>\n",
" <td>#poutinecrawl #yyc stop #1</td>\n",
" <td>JnHaPHkRO2_8V-DysmIBVw</td>\n",
" <td>-0.2960</td>\n",
" </tr>\n",
" <tr>\n",
" <th>4</th>\n",
" <td>XXGzCIcxzAZ6H98mxZbdqA</td>\n",
" <td>0</td>\n",
" <td>2010-04-26 15:34:54</td>\n",
" <td>Um. All kinds of awesome! Big clean and all t...</td>\n",
" <td>qvvpeBLkV4Shx0Teh87lPA</td>\n",
" <td>0.7959</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div>"
],
"text/plain": [
" business_id compliment_count date \\\n",
"0 0hudPyuCBlKg79OwKBw-eQ 0 2012-10-11 23:06:21 \n",
"1 r49iBfbnfoK7yt4rdsL_7g 0 2016-01-05 06:39:33 \n",
"2 wZhB4U003eg2GQ-F7D2D4A 0 2016-08-12 22:18:07 \n",
"3 j9I2YkFCOKyAEb_3FbFqUA 0 2011-04-09 18:58:36 \n",
"4 XXGzCIcxzAZ6H98mxZbdqA 0 2010-04-26 15:34:54 \n",
"\n",
" text user_id \\\n",
"0 Only worth a visit in the summer time, to take... w8p4JW6-ERsbg8aB5wjEAg \n",
"1 Hands down best AYCE in town! Love it! CaFbX7topxdLpBJ_SNDfGQ \n",
"2 When you receive the bag of seafood tie it up ... oIHhfloe5VQamDFDB2dr1Q \n",
"3 #poutinecrawl #yyc stop #1 JnHaPHkRO2_8V-DysmIBVw \n",
"4 Um. All kinds of awesome! Big clean and all t... qvvpeBLkV4Shx0Teh87lPA \n",
"\n",
" sentiment_score_tips \n",
"0 0.6369 \n",
"1 0.8745 \n",
"2 0.9379 \n",
"3 -0.2960 \n",
"4 0.7959 "
]
},
"execution_count": 9,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"# Read tip dataframe\n",
"tip_df = pd.read_csv('tip.csv')\n",
"tip_df.head()"
]
},
{
"cell_type": "code",
"execution_count": 16,
"metadata": {},
"outputs": [
{
"data": {
"text/html": [
"<div>\n",
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" .dataframe tbody tr th:only-of-type {\n",
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"\n",
" .dataframe thead th {\n",
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"</style>\n",
"<table border=\"1\" class=\"dataframe\">\n",
" <thead>\n",
" <tr style=\"text-align: right;\">\n",
" <th></th>\n",
" <th>business_id</th>\n",
" <th>date</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>0</th>\n",
" <td>--1UhMGODdWsrMastO9DZw</td>\n",
" <td>2016-04-26 19:49:16, 2016-08-30 18:36:57, 2016...</td>\n",
" </tr>\n",
" <tr>\n",
" <th>1</th>\n",
" <td>--6MefnULPED_I942VcFNA</td>\n",
" <td>2011-06-04 18:22:23, 2011-07-23 23:51:33, 2012...</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2</th>\n",
" <td>--DaPTJW3-tB1vP-PfdTEg</td>\n",
" <td>2012-06-03 17:46:09, 2012-08-04 16:19:52, 2012...</td>\n",
" </tr>\n",
" <tr>\n",
" <th>3</th>\n",
" <td>--Gc998IMjLn8yr-HTzGUg</td>\n",
" <td>2014-07-01 01:20:47</td>\n",
" </tr>\n",
" <tr>\n",
" <th>4</th>\n",
" <td>--SrzpvFLwP_YFwB_Cetow</td>\n",
" <td>2011-02-10 03:51:06, 2011-02-28 20:35:49, 2011...</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div>"
],
"text/plain": [
" business_id date\n",
"0 --1UhMGODdWsrMastO9DZw 2016-04-26 19:49:16, 2016-08-30 18:36:57, 2016...\n",
"1 --6MefnULPED_I942VcFNA 2011-06-04 18:22:23, 2011-07-23 23:51:33, 2012...\n",
"2 --DaPTJW3-tB1vP-PfdTEg 2012-06-03 17:46:09, 2012-08-04 16:19:52, 2012...\n",
"3 --Gc998IMjLn8yr-HTzGUg 2014-07-01 01:20:47\n",
"4 --SrzpvFLwP_YFwB_Cetow 2011-02-10 03:51:06, 2011-02-28 20:35:49, 2011..."
]
},
"execution_count": 16,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"# Read checkin dataframe\n",
"checkin_df = pd.read_csv('checkin.csv')\n",
"checkin_df.head()"
]
},
{
"cell_type": "code",
"execution_count": 20,
"metadata": {},
"outputs": [
{
"data": {
"text/html": [
"<div>\n",
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"</style>\n",
"<table border=\"1\" class=\"dataframe\">\n",
" <thead>\n",
" <tr style=\"text-align: right;\">\n",
" <th></th>\n",
" <th>average_stars</th>\n",
" <th>compliment_cool</th>\n",
" <th>compliment_cute</th>\n",
" <th>compliment_funny</th>\n",
" <th>compliment_hot</th>\n",
" <th>compliment_list</th>\n",
" <th>compliment_more</th>\n",
" <th>compliment_note</th>\n",
" <th>compliment_photos</th>\n",
" <th>compliment_plain</th>\n",
" <th>...</th>\n",
" <th>cool</th>\n",
" <th>elite</th>\n",
" <th>fans</th>\n",
" <th>friends</th>\n",
" <th>funny</th>\n",
" <th>name</th>\n",
" <th>review_count</th>\n",
" <th>useful</th>\n",
" <th>user_id</th>\n",
" <th>yelping_since</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>0</th>\n",
" <td>3.60</td>\n",
" <td>5</td>\n",
" <td>0</td>\n",
" <td>5</td>\n",
" <td>1</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>4</td>\n",
" <td>3</td>\n",
" <td>11</td>\n",
" <td>...</td>\n",
" <td>34</td>\n",
" <td>2017,2018</td>\n",
" <td>9</td>\n",
" <td>CfGCj80EdA-xS-mTWlAn4Q, JgD2Rk9K07MkZgG7Nb9YzA...</td>\n",
" <td>26</td>\n",
" <td>Peter</td>\n",
" <td>47</td>\n",
" <td>57</td>\n",
" <td>gvXtMj3XuPr0xHjgmlmtng</td>\n",
" <td>2014-01-05 20:45:54</td>\n",
" </tr>\n",
" <tr>\n",
" <th>1</th>\n",
" <td>3.45</td>\n",
" <td>64</td>\n",
" <td>0</td>\n",
" <td>64</td>\n",
" <td>16</td>\n",
" <td>0</td>\n",
" <td>8</td>\n",
" <td>42</td>\n",
" <td>10</td>\n",
" <td>32</td>\n",
" <td>...</td>\n",
" <td>974</td>\n",
" <td>2015,2016,2017,2018</td>\n",
" <td>71</td>\n",
" <td>tRC9YLo4LHquMVXZ9VO4Ag, -YpfTgz88rsPwsOvlfKn7w...</td>\n",
" <td>1007</td>\n",
" <td>Javier</td>\n",
" <td>470</td>\n",
" <td>1655</td>\n",
" <td>pU6GoRTcl1rIOi6zMynjog</td>\n",
" <td>2011-12-16 16:13:01</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2</th>\n",
" <td>4.31</td>\n",
" <td>23</td>\n",
" <td>5</td>\n",
" <td>23</td>\n",
" <td>10</td>\n",
" <td>4</td>\n",
" <td>6</td>\n",
" <td>21</td>\n",
" <td>1</td>\n",
" <td>39</td>\n",
" <td>...</td>\n",
" <td>399</td>\n",
" <td>2008,2009</td>\n",
" <td>13</td>\n",
" <td>o8H8CioXzL4ZR_zxZYcv3Q, yJB1hHDIq18Qvmo3yo9_KA...</td>\n",
" <td>316</td>\n",
" <td>allison</td>\n",
" <td>391</td>\n",
" <td>708</td>\n",
" <td>_ICqwy7SW8J6OJBab1HkwA</td>\n",
" <td>2007-10-06 02:42:28</td>\n",
" </tr>\n",
" <tr>\n",
" <th>3</th>\n",
" <td>2.64</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>1</td>\n",
" <td>...</td>\n",
" <td>3</td>\n",
" <td>NaN</td>\n",
" <td>1</td>\n",
" <td>5fvybUMH9N9Gx1zKw8UbBQ, I0HBD2I7uT_LU_gA9Gb9HA...</td>\n",
" <td>4</td>\n",
" <td>A</td>\n",
" <td>19</td>\n",
" <td>33</td>\n",
" <td>z5y4ejvC8uaUg5fGEJHbRA</td>\n",
" <td>2013-05-28 17:49:42</td>\n",
" </tr>\n",
" <tr>\n",
" <th>4</th>\n",
" <td>3.95</td>\n",
" <td>201</td>\n",
" <td>1</td>\n",
" <td>201</td>\n",
" <td>146</td>\n",
" <td>0</td>\n",
" <td>22</td>\n",
" <td>163</td>\n",
" <td>112</td>\n",
" <td>118</td>\n",
" <td>...</td>\n",
" <td>2158</td>\n",
" <td>2014,2015,2016,2017,2018</td>\n",
" <td>58</td>\n",
" <td>HHmX2L4Gg6L0WJNcxs4Ptw, 8DEyKVyplnOcSKx39vatbg...</td>\n",
" <td>1338</td>\n",
" <td>Fred</td>\n",
" <td>431</td>\n",
" <td>3157</td>\n",
" <td>rnU1EFMSnVJGrQxrnjaODg</td>\n",
" <td>2010-12-28 01:37:20</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"<p>5 rows × 22 columns</p>\n",
"</div>"
],
"text/plain": [
" average_stars compliment_cool compliment_cute compliment_funny \\\n",
"0 3.60 5 0 5 \n",
"1 3.45 64 0 64 \n",
"2 4.31 23 5 23 \n",
"3 2.64 0 0 0 \n",
"4 3.95 201 1 201 \n",
"\n",
" compliment_hot compliment_list compliment_more compliment_note \\\n",
"0 1 0 0 4 \n",
"1 16 0 8 42 \n",
"2 10 4 6 21 \n",
"3 0 0 0 0 \n",
"4 146 0 22 163 \n",
"\n",
" compliment_photos compliment_plain ... cool \\\n",
"0 3 11 ... 34 \n",
"1 10 32 ... 974 \n",
"2 1 39 ... 399 \n",
"3 0 1 ... 3 \n",
"4 112 118 ... 2158 \n",
"\n",
" elite fans \\\n",
"0 2017,2018 9 \n",
"1 2015,2016,2017,2018 71 \n",
"2 2008,2009 13 \n",
"3 NaN 1 \n",
"4 2014,2015,2016,2017,2018 58 \n",
"\n",
" friends funny name \\\n",
"0 CfGCj80EdA-xS-mTWlAn4Q, JgD2Rk9K07MkZgG7Nb9YzA... 26 Peter \n",
"1 tRC9YLo4LHquMVXZ9VO4Ag, -YpfTgz88rsPwsOvlfKn7w... 1007 Javier \n",
"2 o8H8CioXzL4ZR_zxZYcv3Q, yJB1hHDIq18Qvmo3yo9_KA... 316 allison \n",
"3 5fvybUMH9N9Gx1zKw8UbBQ, I0HBD2I7uT_LU_gA9Gb9HA... 4 A \n",
"4 HHmX2L4Gg6L0WJNcxs4Ptw, 8DEyKVyplnOcSKx39vatbg... 1338 Fred \n",
"\n",
" review_count useful user_id yelping_since \n",
"0 47 57 gvXtMj3XuPr0xHjgmlmtng 2014-01-05 20:45:54 \n",
"1 470 1655 pU6GoRTcl1rIOi6zMynjog 2011-12-16 16:13:01 \n",
"2 391 708 _ICqwy7SW8J6OJBab1HkwA 2007-10-06 02:42:28 \n",
"3 19 33 z5y4ejvC8uaUg5fGEJHbRA 2013-05-28 17:49:42 \n",
"4 431 3157 rnU1EFMSnVJGrQxrnjaODg 2010-12-28 01:37:20 \n",
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},
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"source": [
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"user_df.head()"
]
},
{
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"# Hotel rating distribution plot//\n",
"\n",
"# Preparing data//\n",
"ratings_count = business_df.groupby(['stars']).size()\n",
"# print(ratings_count)\n",
"\n",
"# Plot data//\n",
"trace0 = go.Bar(\n",
" y = ratings_count.values,\n",
" x = ratings_count.index,\n",
" name = 'Number of restaurants',\n",
" text = 'Count per star value',\n",
" marker=dict(\n",
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" xaxis=go.layout.XAxis(title='Star rating',\n",
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" ticks='outside',\n",
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" linecolor='#636363',\n",
" linewidth=6\n",
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" showlegend=True,\n",
" legend=dict(bgcolor='lightgray',\n",
" bordercolor='gray',\n",
" borderwidth=2\n",
" )\n",
" )\n",
"\n",
"# Add our data to the plot\n",
"data = [trace0]\n",
"\n",
"# Combine data and layout into a single figure\n",
"fig = dict(data=data, layout=layout)\n",
"\n",
"# IPython notebook- plot\n",
"iplot(fig, filename='bar-plot-starsdistrib')\n"
]
},
{
"cell_type": "code",
"execution_count": 71,
"metadata": {},
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" Unnamed: 0 address \\\n",
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"3 5 865 York Mills Road, Unit 1 \n",
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"1 {'BusinessParking': \"{'garage': False, 'street... 68dUKd8_8liJ7in4aWOSEA \n",
"2 {'BikeParking': 'False', 'ByAppointmentOnly': ... fcXOEZdXYeZqnQ3lGlOXmg \n",
"3 {'RestaurantsDelivery': 'False', 'RestaurantsP... C9oCPomVP0mtKa8z99E3gg \n",
"4 {'RestaurantsPriceRange2': '3', 'BusinessParki... C9keC4mWuXdl2mYFHZXudQ \n",
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" categories city \\\n",
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"1 Shipping Centers, Couriers & Delivery Services... Mississauga \n",
"2 Local Services, Professional Services, Compute... Calgary \n",
"3 Bakeries, Food Toronto \n",
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"\n",
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"1 {'Monday': '9:0-19:0', 'Tuesday': '9:0-20:0', ... 1 43.599475 \n",
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},
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],
"source": [
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"business_df_new = pd.read_csv('business_featurized.csv')\n",
"business_df_new.head()"
]
},
{
"cell_type": "code",
"execution_count": 100,
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gitextract_pzr7p8zx/ ├── README.md ├── Restaurants (Yelp) Dataset-EDA.ipynb ├── TripAdvisor_Crawler_Parser.ipynb ├── attraction_etl.ipynb ├── attractions_crawler.ipynb ├── attractions_details_crawler.ipynb ├── attractions_recc.py ├── combine_batches.ipynb ├── etl/ │ ├── attraction_reviews/ │ │ ├── part-00000-49e3efc3-a4b7-41e8-b45e-c3f57c21a6a5-c000.snappy.parquet │ │ ├── part-00001-49e3efc3-a4b7-41e8-b45e-c3f57c21a6a5-c000.snappy.parquet │ │ ├── part-00002-49e3efc3-a4b7-41e8-b45e-c3f57c21a6a5-c000.snappy.parquet │ │ └── part-00003-49e3efc3-a4b7-41e8-b45e-c3f57c21a6a5-c000.snappy.parquet │ ├── attraction_reviews.json │ ├── attractions.json │ ├── del_dup/ │ │ ├── part-00000-c453ac1f-d0ec-49e3-8984-030e500feb5f-c000.json │ │ ├── part-00001-c453ac1f-d0ec-49e3-8984-030e500feb5f-c000.json │ │ ├── part-00002-c453ac1f-d0ec-49e3-8984-030e500feb5f-c000.json │ │ └── part-00003-c453ac1f-d0ec-49e3-8984-030e500feb5f-c000.json │ ├── newh_df/ │ │ ├── part-00000-8b7be6b6-c958-4a62-847d-550064dd5a78-c000.json │ │ ├── part-00001-8b7be6b6-c958-4a62-847d-550064dd5a78-c000.json │ │ ├── part-00002-8b7be6b6-c958-4a62-847d-550064dd5a78-c000.json │ │ └── part-00003-8b7be6b6-c958-4a62-847d-550064dd5a78-c000.json │ └── u_id_df/ │ ├── part-00000-c0814be7-b91e-4586-9388-7a44d9ed338e-c000.json │ ├── part-00001-c0814be7-b91e-4586-9388-7a44d9ed338e-c000.json │ ├── part-00002-c0814be7-b91e-4586-9388-7a44d9ed338e-c000.json │ └── part-00003-c0814be7-b91e-4586-9388-7a44d9ed338e-c000.json ├── final_hotel_recc.ipynb ├── get_att_recc.ipynb ├── hotel_etl.ipynb ├── hotel_recc.py ├── mf_models/ │ └── model_file/ │ ├── itemFactors/ │ │ ├── part-00000-daa146bf-1542-404b-b7b1-a4e3cff69a72-c000.snappy.parquet │ │ ├── part-00001-daa146bf-1542-404b-b7b1-a4e3cff69a72-c000.snappy.parquet │ │ ├── part-00002-daa146bf-1542-404b-b7b1-a4e3cff69a72-c000.snappy.parquet │ │ ├── part-00003-daa146bf-1542-404b-b7b1-a4e3cff69a72-c000.snappy.parquet │ │ ├── part-00004-daa146bf-1542-404b-b7b1-a4e3cff69a72-c000.snappy.parquet │ │ ├── part-00005-daa146bf-1542-404b-b7b1-a4e3cff69a72-c000.snappy.parquet │ │ ├── part-00006-daa146bf-1542-404b-b7b1-a4e3cff69a72-c000.snappy.parquet │ │ ├── part-00007-daa146bf-1542-404b-b7b1-a4e3cff69a72-c000.snappy.parquet │ │ ├── part-00008-daa146bf-1542-404b-b7b1-a4e3cff69a72-c000.snappy.parquet │ │ └── part-00009-daa146bf-1542-404b-b7b1-a4e3cff69a72-c000.snappy.parquet │ ├── metadata/ │ │ └── part-00000 │ └── userFactors/ │ ├── part-00000-1ce6925f-8f15-4e37-b696-1731c4499927-c000.snappy.parquet │ ├── part-00001-1ce6925f-8f15-4e37-b696-1731c4499927-c000.snappy.parquet │ ├── part-00002-1ce6925f-8f15-4e37-b696-1731c4499927-c000.snappy.parquet │ ├── part-00003-1ce6925f-8f15-4e37-b696-1731c4499927-c000.snappy.parquet │ ├── part-00004-1ce6925f-8f15-4e37-b696-1731c4499927-c000.snappy.parquet │ ├── part-00005-1ce6925f-8f15-4e37-b696-1731c4499927-c000.snappy.parquet │ ├── part-00006-1ce6925f-8f15-4e37-b696-1731c4499927-c000.snappy.parquet │ ├── part-00007-1ce6925f-8f15-4e37-b696-1731c4499927-c000.snappy.parquet │ ├── part-00008-1ce6925f-8f15-4e37-b696-1731c4499927-c000.snappy.parquet │ └── part-00009-1ce6925f-8f15-4e37-b696-1731c4499927-c000.snappy.parquet ├── outputs/ │ ├── attraction_details/ │ │ ├── part-00000-24d79c41-ab21-4f52-bb5e-fed77bf823f8-c000.snappy.parquet │ │ ├── part-00000-8b76e71d-55f4-47ef-a621-b32a2f3e1179-c000.snappy.parquet │ │ ├── part-00001-24d79c41-ab21-4f52-bb5e-fed77bf823f8-c000.snappy.parquet │ │ ├── part-00001-8b76e71d-55f4-47ef-a621-b32a2f3e1179-c000.snappy.parquet │ │ ├── part-00002-8b76e71d-55f4-47ef-a621-b32a2f3e1179-c000.snappy.parquet │ │ └── part-00003-8b76e71d-55f4-47ef-a621-b32a2f3e1179-c000.snappy.parquet │ ├── attraction_reviews/ │ │ ├── part-00000-01b23ac3-b793-40dc-b90a-93a239db5d07-c000.snappy.parquet │ │ ├── part-00000-603b562f-5b1d-4e02-9a5d-f66acc3232a1-c000.snappy.parquet │ │ ├── part-00002-01b23ac3-b793-40dc-b90a-93a239db5d07-c000.snappy.parquet │ │ └── part-00002-603b562f-5b1d-4e02-9a5d-f66acc3232a1-c000.snappy.parquet │ ├── attractions_cat.json │ ├── attractions_details_batch1.json │ ├── attractions_details_batch2.json │ ├── attractions_reviews_batch1.json │ └── attractions_reviews_batch2.json ├── rbm.py ├── rbm_training.ipynb ├── recommendations/ │ ├── e10_r5000_lr0.01_hu64_bs8/ │ │ ├── hb.npy │ │ ├── user1_seen.csv │ │ ├── user1_unseen.csv │ │ ├── vb.npy │ │ └── w.npy │ ├── e20_r20000_lr0.01_hu128_bs16/ │ │ ├── hb.npy │ │ ├── user1_seen.csv │ │ ├── user1_unseen.csv │ │ ├── vb.npy │ │ └── w.npy │ ├── e20_r20000_lr0.01_hu64_bs16/ │ │ ├── hb.npy │ │ ├── user1_seen.csv │ │ ├── user1_unseen.csv │ │ ├── vb.npy │ │ └── w.npy │ ├── e20_r40000_lr0.01_hu128_bs8/ │ │ ├── hb.npy │ │ ├── user1_seen.csv │ │ ├── user1_unseen.csv │ │ ├── vb.npy │ │ └── w.npy │ ├── e20_r5000_lr0.01_hu128_bs8/ │ │ ├── hb.npy │ │ ├── user1_seen.csv │ │ ├── user1_unseen.csv │ │ ├── vb.npy │ │ └── w.npy │ ├── e50_r40000_lr0.01_hu128_bs16/ │ │ ├── hb.npy │ │ ├── user1_seen.csv │ │ ├── user1_unseen.csv │ │ ├── vb.npy │ │ └── w.npy │ └── e50_r40000_lr0.01_hu256_bs16/ │ ├── hb.npy │ ├── user1_seen.csv │ ├── user1_unseen.csv │ └── vb.npy ├── requirements.txt ├── sample_table_widget.ipynb ├── tripadvisor_hotel_output/ │ ├── file_mainpage_url.txt │ ├── hotel_info.csv │ ├── hotel_info.json │ ├── hotel_info_dedup.csv │ ├── hotel_info_dedup.json │ ├── reviews_dedup.csv │ └── reviews_dedup.json └── utils.py
SYMBOL INDEX (28 symbols across 4 files)
FILE: attractions_recc.py
function f (line 16) | def f(row):
function sim_score (line 27) | def sim_score(row):
function get_recc (line 41) | def get_recc(att_df, cat_rating):
function filter_df (line 74) | def filter_df(filename, user, low, high, province, att_df):
function get_image (line 85) | def get_image(name):
function top_recc (line 111) | def top_recc(with_url, final):
function find_closest (line 126) | def find_closest(with_url, loc, tod, final):
function final_output (line 143) | def final_output(days, final):
FILE: hotel_recc.py
function get_rating (line 15) | def get_rating(x):
function amenities_rating (line 29) | def amenities_rating(spark, amenities_pref, newh_df):
function model_train (line 49) | def model_train(spark, usr_rating):
function get_hotel_recc (line 86) | def get_hotel_recc(spark, usrid_s2):
function get_image (line 104) | def get_image(name):
function get_hotel_output (line 129) | def get_hotel_output(days, final):
FILE: rbm.py
class RBM (line 11) | class RBM(object):
method __init__ (line 15) | def __init__(self, alpha, H, num_vis):
method training (line 24) | def training(self, train, valid, user, epochs, batchsize, free_energy,...
method load_predict (line 131) | def load_predict(self, filename, train, user):
method calculate_scores (line 157) | def calculate_scores(self, ratings, attractions, rec, user):
method export (line 233) | def export(self, unseen, seen, filename, user):
method export_errors_plot (line 256) | def export_errors_plot(self, filename):
method export_free_energy_plot (line 262) | def export_free_energy_plot(self, filename):
FILE: utils.py
class Util (line 10) | class Util(object):
method read_data (line 12) | def read_data(self, folder):
method clean_subset (line 22) | def clean_subset(self, ratings, num_rows):
method preprocess (line 32) | def preprocess(self, ratings):
method split_data (line 55) | def split_data(self, total_data):
method free_energy (line 70) | def free_energy(self, v_sample, W, vb, hb):
Copy disabled (too large)
Download .json
Condensed preview — 112 files, each showing path, character count, and a content snippet. Download the .json file for the full structured content (38,308K chars).
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},
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"preview": "{\n \"cells\": [\n {\n \"cell_type\": \"code\",\n \"execution_count\": 92,\n \"metadata\": {},\n \"outputs\": [\n {\n \"data\""
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"preview": "import pandas as pd\nimport numpy as np\nimport ipywidgets as w\nfrom ipywidgets import HBox, VBox\nfrom ipywidgets import L"
},
{
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"preview": "{\n \"cells\": [\n {\n \"cell_type\": \"code\",\n \"execution_count\": null,\n \"metadata\": {},\n \"outputs\": [],\n \"source\": "
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"preview": "[{\"province\":\"british_columbia\",\"category\":\"featured_tours_and_tickets\",\"attraction_id\":0,\"city\":\"vancouver\",\"country\":\""
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"path": "etl/del_dup/part-00000-c453ac1f-d0ec-49e3-8984-030e500feb5f-c000.json",
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"preview": "{\"address\":\"17 Marjorie Cres Charlottetown Prince Edward Island\",\"amenities\":[\"['Free parking'\",\" 'Free High Speed Inter"
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"preview": "{\"address\":\"400 Pyramid Lake Road Alberta\",\"amenities\":[\"['Free High Speed Internet (WiFi)'\",\" 'Pool'\",\" 'Free parking'\""
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"preview": "{\"address\":\"51 Fitzroy St Charlottetown Prince Edward Island\",\"amenities\":[\"['Free High Speed Internet (WiFi)'\",\" 'Free "
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"chars": 493865,
"preview": "{\"id\":26,\"amenities\":\"\"}\n{\"id\":29,\"amenities\":\"Free High Speed Internet WiFi\"}\n{\"id\":29,\"amenities\":\" Breakfast included"
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"chars": 513107,
"preview": "{\"id\":243,\"amenities\":\"Free parking\"}\n{\"id\":243,\"amenities\":\" Free High Speed Internet WiFi\"}\n{\"id\":243,\"amenities\":\" Go"
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"preview": "{\"id\":2570,\"user_name\":\"MikeBCalgary\",\"user_profile\":\"https://www.tripadvisor.ca/Profile/MikeBCalgary\",\"user_rating\":3,\""
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"preview": "{\n \"cells\": [\n {\n \"cell_type\": \"code\",\n \"execution_count\": 1,\n \"metadata\": {},\n \"outputs\": [],\n \"source\": [\n "
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"preview": "{\n \"cells\": [\n {\n \"cell_type\": \"code\",\n \"execution_count\": 1,\n \"metadata\": {},\n \"outputs\": [],\n \"source\": [\n "
},
{
"path": "hotel_recc.py",
"chars": 7516,
"preview": "import pandas as pd\nimport ipywidgets as w\nfrom IPython.display import display, IFrame\nimport math, re, numpy as np, pys"
},
{
"path": "mf_models/model_file/metadata/part-00000",
"chars": 256,
"preview": "{\"class\":\"org.apache.spark.ml.recommendation.ALSModel\",\"timestamp\":1554954320224,\"sparkVersion\":\"2.3.1\",\"uid\":\"ALS_45dda"
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"chars": 728605,
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"chars": 356038,
"preview": "[{\"attraction_id\":2000,\"name\":\"the_pork_and_bacon_walking_food_tour_of_toronto's_hogtown_history\",\"country\":\"canada\",\"pr"
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{
"path": "outputs/attractions_reviews_batch1.json",
"chars": 6781513,
"preview": "[{\"attraction_id\":0,\"rating\":5.0,\"review\":\"Another 'Dave' Guides us Around Vancouver. Landsea\\u2019s Vancouver City Tour"
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"path": "outputs/attractions_reviews_batch2.json",
"chars": 5363815,
"preview": "[{\"attraction_id\":2000,\"user\":\"dominicemery\",\"rating\":5.0,\"review\":\"Interesting and Delicious . This is a great tour, no"
},
{
"path": "rbm.py",
"chars": 11590,
"preview": "import pandas as pd\nimport numpy as np\nimport tensorflow as tf\nfrom utils import Util\nimport matplotlib\nmatplotlib.use('"
},
{
"path": "rbm_training.ipynb",
"chars": 5399,
"preview": "{\n \"cells\": [\n {\n \"cell_type\": \"code\",\n \"execution_count\": 1,\n \"metadata\": {\n \"colab\": {},\n \"colab_type\": \""
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"chars": 2207,
"preview": ",att_cat,att_name,att_price\n0,\"cruises,_sailing_&_water_tours\",niagara_falls_american-side_tour_with_maid_of_the_mist_bo"
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{
"path": "recommendations/e10_r5000_lr0.01_hu64_bs8/user1_unseen.csv",
"chars": 80564,
"preview": ",att_cat,att_name,att_price,score\n277,outdoor_activities,nordic_backcountry_skiing_tour_in_jacques-cartier_national_park"
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"path": "recommendations/e20_r20000_lr0.01_hu128_bs16/user1_seen.csv",
"chars": 2207,
"preview": ",att_cat,att_name,att_price\n0,\"cruises,_sailing_&_water_tours\",niagara_falls_american-side_tour_with_maid_of_the_mist_bo"
},
{
"path": "recommendations/e20_r20000_lr0.01_hu128_bs16/user1_unseen.csv",
"chars": 115812,
"preview": ",att_cat,att_name,att_price,score\n498,tours_&_sightseeing,45-minute_beacon_hill_park_carriage_tour,165.0,5.0\n449,tours_&"
},
{
"path": "recommendations/e20_r20000_lr0.01_hu64_bs16/user1_seen.csv",
"chars": 2207,
"preview": ",att_cat,att_name,att_price\n0,\"cruises,_sailing_&_water_tours\",niagara_falls_american-side_tour_with_maid_of_the_mist_bo"
},
{
"path": "recommendations/e20_r20000_lr0.01_hu64_bs16/user1_unseen.csv",
"chars": 115395,
"preview": ",att_cat,att_name,att_price,score\n1074,recommended_experiences,laurentian_mountains_day_trip_from_montreal,100.0,5.0\n498"
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{
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"chars": 158259,
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},
{
"path": "recommendations/e20_r5000_lr0.01_hu128_bs8/user1_seen.csv",
"chars": 2237,
"preview": ",att_name,att_cat,att_price\r\n0,niagara_falls_american-side_tour_with_maid_of_the_mist_boat_ride,\"cruises,_sailing_&_wate"
},
{
"path": "recommendations/e20_r5000_lr0.01_hu128_bs8/user1_unseen.csv",
"chars": 81733,
"preview": ",att_name,att_cat,att_price,score\r\n277,nordic_backcountry_skiing_tour_in_jacques-cartier_national_park,outdoor_activitie"
},
{
"path": "recommendations/e50_r40000_lr0.01_hu128_bs16/user1_seen.csv",
"chars": 2207,
"preview": ",att_cat,att_name,att_price\n0,\"cruises,_sailing_&_water_tours\",niagara_falls_american-side_tour_with_maid_of_the_mist_bo"
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{
"path": "recommendations/e50_r40000_lr0.01_hu128_bs16/user1_unseen.csv",
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"preview": ",att_cat,att_name,att_price,score\n1493,recommended_experiences,maligne_lake_cruise,82.95,5.0\n793,tours_&_sightseeing,ful"
},
{
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"chars": 2207,
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},
{
"path": "recommendations/e50_r40000_lr0.01_hu256_bs16/user1_unseen.csv",
"chars": 158320,
"preview": ",att_cat,att_name,att_price,score\n1493,recommended_experiences,maligne_lake_cruise,82.95,4.999999999999999\n1372,recommen"
},
{
"path": "requirements.txt",
"chars": 4139,
"preview": "absl-py==0.7.1\nalabaster==0.7.12\nanaconda-client==1.7.2\nanaconda-navigator==1.9.7\nanaconda-project==0.8.2\nappnope==0.1.0"
},
{
"path": "sample_table_widget.ipynb",
"chars": 14877,
"preview": "{\n \"cells\": [\n {\n \"cell_type\": \"code\",\n \"execution_count\": 59,\n \"metadata\": {},\n \"outputs\": [],\n \"source\": [\n"
},
{
"path": "tripadvisor_hotel_output/file_mainpage_url.txt",
"chars": 1130349,
"preview": "0\nhttps://www.tripadvisor.ca/Hotel_Review-g154998-d182928-Reviews-Niagara_Falls_Marriott_Fallsview_Hotel_Spa-Niagara_Fal"
},
{
"path": "tripadvisor_hotel_output/hotel_info.csv",
"chars": 1771147,
"preview": "id,hotel_name,hotel_rating,hotel_experience,amenities,address,country,location,price\n1,Niagara Falls Marriott Fallsview "
},
{
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"preview": "id,user_rating,user_name,user_profile,user_review,user_reviewdate,user_staydate\n9003,4.0,Michelle W,https://www.tripadvi"
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// ... and 55 more files (download for full content)
About this extraction
This page contains the full source code of the sachinnpraburaj/Intelligent-Travel-Recommendation-System GitHub repository, extracted and formatted as plain text for AI agents and large language models (LLMs). The extraction includes 112 files (79.0 MB), approximately 8.9M tokens, and a symbol index with 28 extracted functions, classes, methods, constants, and types. Use this with OpenClaw, Claude, ChatGPT, Cursor, Windsurf, or any other AI tool that accepts text input. You can copy the full output to your clipboard or download it as a .txt file.
Extracted by GitExtract — free GitHub repo to text converter for AI. Built by Nikandr Surkov.