Repository: juniorcl/transaction-fraud-detection
Branch: main
Commit: 401167c15672
Files: 11
Total size: 47.9 MB

Directory structure:
gitextract_ql5qvdyw/

├── .gitignore
├── LICENSE
├── README.md
├── api/
│   ├── fraud/
│   │   └── Fraud.py
│   └── handler.py
├── data/
│   └── raw/
│       └── fraud_0.1origbase.csv
├── functions/
│   ├── minmaxscaler_cycle1.joblib
│   └── onehotencoder_cycle1.joblib
├── models/
│   └── model_cycle1.joblib
├── notebooks/
│   └── transaction-fraud-detection-cycle1.ipynb
└── requirements.txt

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FILE CONTENTS
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FILE: .gitignore
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================================================
FILE: LICENSE
================================================
MIT License

Copyright (c) 2021 Clébio Oliveira Júnior

Permission is hereby granted, free of charge, to any person obtaining a copy
of this software and associated documentation files (the "Software"), to deal
in the Software without restriction, including without limitation the rights
to use, copy, modify, merge, publish, distribute, sublicense, and/or sell
copies of the Software, and to permit persons to whom the Software is
furnished to do so, subject to the following conditions:

The above copyright notice and this permission notice shall be included in all
copies or substantial portions of the Software.

THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR
IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,
FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE
AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER
LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM,
OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE
SOFTWARE.


================================================
FILE: README.md
================================================
# Transaction Fraud Detection

A data science project to predict whether a transaction is a fraud or not.

<div align="center">
    <img alt="Transaction Fraud Detection" src="./reports/figures/high-angle-credit-card-with-hook-phishing.jpg" width="100%" height="300">
</div>

<br>

## 1.0 Business Problem

The Blocker Fraud Company is a company specialized in detecting fraud in financial transactions made through mobile devices. The company has a service called "Blocker Fraud" which guarantees the blocking of fraudulent transactions.

The business model of the company is of the Service type with the monetization made by the performance of the service provided, in other words, the user pays a fixed fee on the success in detecting fraud in the customer's transactions.

However, the Blocker Fraud Company is expanding in Brazil and to acquire customers more quickly, it has adopted a very aggressive strategy. The strategy works as follows:

1. The company will receive 25% of the value of each transaction truly detected as fraud;

1. The company will receive 5% of the value of each transaction detected as fraud, but the transaction is truly legitimate.

1. The company will return 100% of the value to the customer, for each transaction detected as legitimate, however the transaction is truly a fraud.

With this aggressive strategy, the company assumes the risks of failing to detect fraud and is remunerated for assertive fraud detection.

For the client, it is an excellent business to hire the Blocker Fraud Company. Although the fee charged is very high on success, 25%, the company reduces its costs with fraudulent transactions detected correctly and even the damage caused by an error in the anti-fraud service will be covered by the Blocker Fraud Company itself.

For the company, in addition to getting many customers with this risky strategy to guarantee reimbursement in the event of a failure to detect customer fraud, it depends only on the precision and accuracy of the models built by its Data Scientists, in other words, how much the more accurate the “Blocker Fraud” model, the greater the company's revenue. However, if the model has low accuracy, the company could have a huge loss.

## 2.0 Business Assumptions

Fraud prevention is the implementation of a strategy to detect fraudulent transactions or banking actions and prevent these actions from causing financial damage and the reputation of the client and the financial institution.

There are always financial frauds and They can happen through virtual and physical ways. So the investment in security has been increasing.

The losses caused by fraud can reach R\$ 1 billion - which corresponds to half the amount that institutions invest in technology systems aimed at information security every year, according to [Febraban's 2020 Banking Technology Survey](https://blog.simply.com.br/tecnologia-bancaria-2020/).

## 3.0 Solution Strategy

My solution to solve this problem will be the development of a data science project. This project will have a machine learning model which can predict whether a transaction is fraudulent or not.

**Step 01. Data Description:** In this first section the data will be collected and studied. The missing values will be threated or removed. Finally, a initial data description will carried out to know the data. Therefore some calculations of descriptive statistics will be made, such as kurtosis, skewness, media, fashion, median and standard desviation.

**Step 02. Feature Engineering:** In this section, a mind map will be created to assist the creation of the hypothesis and the creation of new features. These assumptions will help in exploratory data analysis and may improve the model scores.

**Step 03. Data Filtering:** Data filtering is used to remove columns or rows that are not part of the business. For example, columns with customer ID, hash code or rows with age that does not consist of human age.

**Step 04. Exploratory Data Analysis:** The exploratory data analysis section consists of univariate analysis, bivariate analysis and multivariate analysis to assist in understanding of the database. The hypothesis created in step 02 will be tested in the bivariate analysis.

**Step 05. Data Preparation:** In this fifth section, the data will be prepared for machine learning modeling. Therefore, they will be transformed to improve the learning of the machine learning model, thus they can be encoded, oversampled, subsampled or rescaled.

**Step 06. Feature Selection:** After the data preparation in this section algorithms, like Boruta, will select the best columns to be used for the training of the machine learning model. This reduces the dimensionality of the database and decreases the chances of overfiting.

**Step 07. Machine Learning Modeling:** Step 07 aims to train the machine learning algorithms and how they can predict the data. For validation the model is trained, validated and applied to cross validation to know the learning capacity of the model.

**Step 08. Hyparameter Fine Tuning:** Firstly selected the best model to be applied in the project, it's important to make a fine tuning of the parameters to improve its scores. The same model performance methods apllied in the step 07 are used.

**Step 09. Conclusions:** This is a conclusion stage which the generation capacity model is tested using unseen data. In addition, some business questions are answered to show the applicability of the model in the business context.

**Step 10. Model Deploy:** This is the final step of the data science project. So, in this step the flask api is created and the model and the functions are saved to be implemented in the api.

## 4.0 Top 3 Data Insights

* #### All the fraud amount is greater than 10.000.

    **TRUE:** The values are greater than 10.000. But it's important to note that the no-fraud values is greater than 100.000 also.

    ![hypothesis2](reports/figures/hypothesis2_cycle1.png)

* #### 60% of fraud transaction occours using cash-out-type method.

    **FALSE:** The fraud transaction occours in transfer and cash-out type. However they're almost the same value.

    ![hypothesis3](reports/figures/hypothesis3_cycle1.png)

* #### Values greater than 100.000 occours using transfers-type method.

    **FALSE:** The majority transactions occours in trasnfer-type, however transactions greater than 100.000 occour in cash-out and cash-in too.

    ![hypothesis4](reports/figures/hypothesis4_cycle1.png)

## 5.0 Machine Learning Applied

Here's all cross validation results of the machine learning models with their default parameters. The cross validation method is important to show the capacity of the model to learn.

#### Dummy Model

| Balanced Accuracy |  Precision  |    Recall   |      F1     |      Kappa     |
|:-----------------:|:-----------:|:-----------:|:-----------:|:--------------:|
|   0.499 +/- 0.0   | 0.0 +/- 0.0 | 0.0 +/- 0.0 | 0.0 +/- 0.0 | -0.001 +/- 0.0 |

#### Logistic Regression

| Balanced Accuracy |  Precision  |      Recall     |        F1       |      Kappa      |
|:-----------------:|:-----------:|:---------------:|:---------------:|:---------------:|
|  0.565 +/- 0.009  | 1.0 +/- 0.0 | 0.129 +/- 0.017 | 0.229 +/- 0.027 | 0.228 +/- 0.027 |

#### K Nearest Neighbors

| Balanced Accuracy |    Precision    |      Recall     |        F1       |      Kappa      |
|:-----------------:|:---------------:|:---------------:|:---------------:|:---------------:|
|  0.705 +/- 0.037  | 0.942 +/- 0.022 | 0.409 +/- 0.074 | 0.568 +/- 0.073 | 0.567 +/- 0.073 |

#### Support Vector Machine

| Balanced Accuracy |  Precision  |     Recall     |        F1        |      Kappa      |
|:-----------------:|:-----------:|:--------------:|:----------------:|:---------------:|
|  0.595 +/- 0.013  | 1.0 +/- 0.0 | 0.19 +/- 0.026 | 0.319 +/- 0.0373 | 0.319 +/- 0.037 |

#### Random Forest

| Balanced Accuracy |    Precision    |      Recall     |        F1       |      Kappa      |
|:-----------------:|:---------------:|:---------------:|:---------------:|:---------------:|
|  0.865 +/- 0.017  | 0.972 +/- 0.014 | 0.731 +/- 0.033 | 0.834 +/- 0.022 | 0.833 +/- 0.022 |

#### XGBoost

| Balanced Accuracy |    Precision    |      Recall     |       F1       |      Kappa     |
|:-----------------:|:---------------:|:---------------:|:--------------:|:--------------:|
|   0.88 +/- 0.016  | 0.963 +/- 0.008 | 0.761 +/- 0.033 | 0.85 +/- 0.023 | 0.85 +/- 0.023 |

#### LightGBM

| Balanced Accuracy |   Precision  |      Recall     |        F1       |      Kappa      |
|:-----------------:|:------------:|:---------------:|:---------------:|:---------------:|
|  0.701 +/- 0.089  | 0.18 +/- 0.1 | 0.407 +/- 0.175 | 0.241 +/- 0.128 | 0.239 +/- 0.129 |

## 6.0 Machine Learning Performance

The chosen model was **XGBoost** and it was tuned to improve their parameters and scores. Below there's a table with the capacity of the model to learn.

| Balanced Accuracy |    Precision    |      Recall     |        F1       |      Kappa      |
|:-----------------:|:---------------:|:---------------:|:---------------:|:---------------:|
|  0.881 +/- 0.017  | 0.963 +/- 0.007 | 0.763 +/- 0.035 | 0.851 +/- 0.023 | 0.851 +/- 0.023 |

It's possible to determinize the capacity of the model to generalize using unseen data. In other words, capcity of the model to classify new data as shown.

| Balanced Accuracy | Precision | Recall |   F1  | Kappa |
|:-----------------:|:---------:|:------:|:-----:|:-----:|
|       0.915       |   0.944   |  0.829 | 0.883 | 0.883 |

## 7.0 Business Results

* #### The company receives 25% of each transaction value truly detected as fraud.

    The company can receive R\$ 60,613,782.88 detecting fraud transactions.

* #### The company receives 5% of each transaction value detected as fraud, however the transaction is legitimate.

    For wrong decisions, the company can receive R\$ 183,866.98.

* #### The company gives back 100% of the value for the customer in each transaction detected as legitimate, however the transaction is actually a fraud.

    The company must return the amount of R\$ 3,546,075.42.

* #### What is the model's Precision and Accuracy?

    For unseen data, the values of balanced accuracy is equal 91.5% and precision is equal 94.4%.

* #### How reliable is the model in classifying transactions as legitimate or fraudulent?

    The model can detect 76.3% +/- 3.5% of the fraud. However it detected 0.829 of the frauds from a unseen data.

* #### What is the revenue expected by the company classify 100% of transactions with the model?

    Using the model the company can revenue R\$ 60,797,649.86. Using the currently method to detect fraud the revenue is 0.00.

* #### What is the loss expected by the Company if it classifies 100% of the transactions with the model?

    For wrong classifications the company must return the amount of R\$ 3,546,075.42. In contrast, for wrong classifications using the currently method, the company must return the amount of R\$ 246,001,206.94.

* #### What is the profit expected by the blocker fraud company when using the model?

    The company can expect the profit of R\$ 57,251,574.44. The profit value of the currently method is R\$ -246,001,206.94.

## 8.0 Conclusions

The data is extremaly unbalanced, however it was possible to make all the data analysis and create with good scores.

The company may expect a revenue of R\$ 57,251,574.44. This result may show the capacity of a project of data science and help the company.

## 9.0 Lessons Learned

* Even when the classes are unbalanced, it's possible to create a model with good scores.

* It is possible to create a model that can classify classes with less than 1% of samples.

## 10.0 Next Steps

* Test at most more 10 hypothesis.

* Implement oversampling or subsampling techiniques to improve the model scores.

* Implement the api on the heroku plataform.

## License

This project is licensed under the MIT License - see the [LICENSE](LICENSE) file for details


================================================
FILE: api/fraud/Fraud.py
================================================
import joblib
import inflection
import pandas as pd

class Fraud:
    
    def __init__(self):
        self.minmaxscaler = joblib.load('../parameters/minmaxscaler_cycle1.joblib')
        self.onehotencoder = joblib.load('../parameters/onehotencoder_cycle1.joblib')
        
    def data_cleaning(self, df1):
        cols_old = df1.columns.tolist()
        
        snakecase = lambda i: inflection.underscore(i)
        cols_new = list(map(snakecase, cols_old))
        
        df1.columns = cols_new
        
        return df1
    
    def feature_engineering(self, df2):
        # step
        df2['step_days'] = df2['step'].apply(lambda i: i/24)
        df2['step_weeks'] = df2['step'].apply(lambda i: i/(24*7))

        # difference between initial balance before the transaction and new balance after the transaction
        df2['diff_new_old_balance'] = df2['newbalance_orig'] - df2['oldbalance_org']

        # difference between initial balance recipient before the transaction and new balance recipient after the transaction.
        df2['diff_new_old_destiny'] = df2['newbalance_dest'] - df2['oldbalance_dest']

        # name orig and name dest
        df2['name_orig'] = df2['name_orig'].apply(lambda i: i[0])
        df2['name_dest'] = df2['name_dest'].apply(lambda i: i[0])
        
        return df2.drop(columns=['name_orig', 'name_dest', 
                      'step_weeks', 'step_days'], axis=1)
    
    def data_preparation(self, df3):
        # OneHotEncoder
        df3 = self.onehotencoder.transform(df3)

        # Rescaling 
        num_columns = ['amount', 'oldbalance_org', 'newbalance_orig', 'oldbalance_dest', 
                       'newbalance_dest', 'diff_new_old_balance', 'diff_new_old_destiny']
        df3[num_columns] = self.minmaxscaler.transform(df3[num_columns])
        
        # selected columns
        final_columns_selected = ['step', 'oldbalance_org', 'newbalance_orig', 'newbalance_dest', 
                                  'diff_new_old_balance', 'diff_new_old_destiny', 'type_TRANSFER']
        return df3[final_columns_selected]
    
    def get_prediction(self, model, original_data, test_data):
        pred = model.predict(test_data)
        original_data['prediction'] = pred
        
        return original_data.to_json(orient="records", date_format="iso")

================================================
FILE: api/handler.py
================================================
import joblib
import pandas as pd
from fraud.Fraud import Fraud
from flask import Flask, request, Response

# loading model
model = joblib.load('../models/model_cycle1.joblib')

# initialize API
app = Flask(__name__)

@app.route('/fraud/predict', methods=['POST'])
def churn_predict():
    test_json = request.get_json()
   
    if test_json: # there is data
        if isinstance(test_json, dict): # unique example
            test_raw = pd.DataFrame(test_json, index=[0])
            
        else: # multiple example
            test_raw = pd.DataFrame(test_json, columns=test_json[0].keys())
            
        # Instantiate Rossmann class
        pipeline = Fraud()
        
        # data cleaning
        df1 = pipeline.data_cleaning(test_raw)
        
        # feature engineering
        df2 = pipeline.feature_engineering(df1)
        
        # data preparation
        df3 = pipeline.data_preparation(df2)
        
        # prediction
        df_response = pipeline.get_prediction(model, test_raw, df3)
        
        return df_response
        
        
    else:
        return Reponse('{}', status=200, mimetype='application/json')

if __name__ == '__main__':
    app.run('0.0.0.0') 


================================================
FILE: data/raw/fraud_0.1origbase.csv
================================================
[File too large to display: 47.0 MB]

================================================
FILE: notebooks/transaction-fraud-detection-cycle1.ipynb
================================================
{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {
    "heading_collapsed": true
   },
   "source": [
    "# i. Business Understanding"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "heading_collapsed": true,
    "hidden": true
   },
   "source": [
    "## i.i Blocker Fraud Company"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "hidden": true
   },
   "source": [
    "* The Blocker Fraud Company is a company specialized in detecting fraud in financial transactions made through mobile devices. The company has a service called “Blocker Fraud” with no guarantee of blocking fraudulent transactions.\n",
    "\n",
    "* And the business model of the company is of the Service type with the monetization made by the performance of the service provided, that is, the user pays a fixed fee on the success in detecting fraud in the customer's transactions."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "hidden": true
   },
   "source": [
    "### i.i.i Expansion Problem"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "hidden": true
   },
   "source": [
    "Blocker Fraud Company is expanding in Brazil and to acquire customers more quickly, it has adopted a very aggressive strategy. The strategy works as follows:\n",
    "1. The company will receive 25% of the value of each transaction that is truly detected as fraud.\n",
    "1. The company will receive 5% of the value of each transaction detected as fraud, but the transaction is truly legitimate.\n",
    "1. The company will return 100% of the value to the customer, for each transaction detected as legitimate, however the transaction is truly a fraud."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "heading_collapsed": true,
    "hidden": true
   },
   "source": [
    "## i.ii The Challenge"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "hidden": true
   },
   "source": [
    "You need to deliver to the CEO of Blocker Fraud Company a production model in which your access will be done via API, that is, customers will send their transactions via API so that your model classifies them as fraudulent or legitimate."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "hidden": true
   },
   "source": [
    "### i.ii.i Business Questions"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "hidden": true
   },
   "source": [
    "1. What is the model's Precision and Accuracy?\n",
    "1. How Reliable is the model in classifying transactions as legitimate or fraudulent?\n",
    "1. What is the Expected Billing by the Company if we classify 100% of transactions with the model?\n",
    "1. What is the Loss Expected by the Company in case of model failure?\n",
    "1. What is the Profit Expected by the Blocker Fraud Company when using the model?"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "heading_collapsed": true
   },
   "source": [
    "# 0.0 Imports and Helper Functions"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "hidden": true
   },
   "source": [
    "## 0.1 Imports"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 229,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2021-01-11T18:47:33.819432Z",
     "start_time": "2021-01-11T18:47:33.805991Z"
    },
    "hidden": true
   },
   "outputs": [],
   "source": [
    "import joblib\n",
    "import warnings\n",
    "import inflection\n",
    "\n",
    "import numpy             as np\n",
    "import pandas            as pd\n",
    "import seaborn           as sns\n",
    "\n",
    "import matplotlib.pyplot as plt\n",
    "\n",
    "from scipy   import stats\n",
    "from boruta  import BorutaPy\n",
    "from category_encoders import OneHotEncoder\n",
    "\n",
    "from IPython.display      import Image\n",
    "from IPython.core.display import HTML \n",
    "\n",
    "from xgboost  import XGBClassifier\n",
    "from lightgbm import LGBMClassifier\n",
    "\n",
    "from sklearn.svm          import SVC\n",
    "from sklearn.dummy        import DummyClassifier\n",
    "from sklearn.ensemble     import RandomForestClassifier\n",
    "from sklearn.neighbors    import KNeighborsClassifier\n",
    "from sklearn.linear_model import LogisticRegression\n",
    "\n",
    "from sklearn.metrics         import balanced_accuracy_score, precision_score, classification_report\n",
    "from sklearn.metrics         import recall_score, f1_score, make_scorer, cohen_kappa_score\n",
    "from sklearn.preprocessing   import MinMaxScaler\n",
    "from sklearn.model_selection import GridSearchCV, train_test_split, StratifiedKFold"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "hidden": true
   },
   "source": [
    "## 0.2 Helper Functions"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 230,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2021-01-11T18:47:34.604129Z",
     "start_time": "2021-01-11T18:47:34.599974Z"
    },
    "hidden": true
   },
   "outputs": [],
   "source": [
    "warnings.filterwarnings('ignore')\n",
    "\n",
    "seed = 42\n",
    "np.random.seed(seed)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 231,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2021-01-11T18:47:34.811059Z",
     "start_time": "2021-01-11T18:47:34.792000Z"
    },
    "hidden": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Populating the interactive namespace from numpy and matplotlib\n"
     ]
    },
    {
     "data": {
      "text/html": [
       "<style>.container { width:100% !important; }</style>"
      ],
      "text/plain": [
       "<IPython.core.display.HTML object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "def jupyter_settings():\n",
    "    %matplotlib inline\n",
    "    %pylab inline\n",
    "    \n",
    "    sns.set(font_scale=1.6)\n",
    "    \n",
    "    plt.style.use('seaborn-darkgrid')\n",
    "    plt.rcParams['figure.figsize'] = [25, 12]\n",
    "    plt.rcParams['font.size'] = 16\n",
    "    \n",
    "    display( HTML('<style>.container { width:100% !important; }</style>'))\n",
    "    pd.options.display.max_columns = None\n",
    "    pd.options.display.max_rows = None\n",
    "    pd.set_option('display.expand_frame_repr', False)\n",
    "    \n",
    "jupyter_settings()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 232,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2021-01-11T18:47:35.035622Z",
     "start_time": "2021-01-11T18:47:35.024369Z"
    },
    "hidden": true
   },
   "outputs": [],
   "source": [
    "def ml_scores(model_name, y_true, y_pred):\n",
    "    \n",
    "    accuracy = balanced_accuracy_score(y_true, y_pred)\n",
    "    precision = precision_score(y_true, y_pred)\n",
    "    recall = recall_score(y_true, y_pred)\n",
    "    f1 = f1_score(y_true, y_pred)\n",
    "    kappa = cohen_kappa_score(y_true, y_pred)\n",
    "    \n",
    "    return pd.DataFrame({'Balanced Accuracy': np.round(accuracy, 3), \n",
    "                         'Precision': np.round(precision, 3), \n",
    "                         'Recall': np.round(recall, 3),\n",
    "                         'F1': np.round(f1, 3),\n",
    "                         'Kappa': np.round(kappa, 3)}, \n",
    "                        index=[model_name])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 233,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2021-01-11T18:47:35.233972Z",
     "start_time": "2021-01-11T18:47:35.222569Z"
    },
    "hidden": true
   },
   "outputs": [],
   "source": [
    "def calcCramerV(x, y):\n",
    "    cm = pd.crosstab(x, y).values\n",
    "    n = cm.sum()\n",
    "    r, k = cm.shape\n",
    "    \n",
    "    chi2 = stats.chi2_contingency(cm)[0]\n",
    "    chi2corr = max(0, chi2 - (k-1)*(r-1)/(n-1))\n",
    "    \n",
    "    kcorr = k - (k-1)**2/(n-1)\n",
    "    rcorr = r - (r-1)**2/(n-1)\n",
    "    \n",
    "    return np.sqrt((chi2corr/n) / (min(kcorr-1, rcorr-1)))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 234,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2021-01-11T18:47:35.431569Z",
     "start_time": "2021-01-11T18:47:35.406476Z"
    },
    "hidden": true
   },
   "outputs": [],
   "source": [
    "def ml_cv_results(model_name, model, x, y, verbose=1):\n",
    "    \n",
    "    '''initial'''\n",
    "    balanced_accuracies = []\n",
    "    precisions = []\n",
    "    recalls = []\n",
    "    f1s = []\n",
    "    kappas = []\n",
    "    \n",
    "    mm = MinMaxScaler()\n",
    "    \n",
    "    x_ = x.to_numpy()\n",
    "    y_ = y.to_numpy()\n",
    "    \n",
    "    count = 0\n",
    "    \n",
    "    '''cross-validation'''\n",
    "    skf = StratifiedKFold(n_splits=5, shuffle=True)\n",
    "    \n",
    "    for index_train, index_test in skf.split(x_, y_):\n",
    "        ## Showing the Fold\n",
    "        if verbose > 0:\n",
    "            count += 1\n",
    "            print('Fold K=%i' % (count))\n",
    "    \n",
    "        ## selecting train and test\n",
    "        x_train, x_test = x.iloc[index_train], x.iloc[index_test]\n",
    "        y_train, y_test = y.iloc[index_train], y.iloc[index_test]\n",
    "        \n",
    "        ## applying the scale\n",
    "        x_train = mm.fit_transform(x_train)\n",
    "        x_test = mm.transform(x_test)\n",
    "    \n",
    "        ## training the model\n",
    "        model.fit(x_train, y_train)\n",
    "        y_pred = model.predict(x_test)\n",
    "\n",
    "        ## saving the metrics\n",
    "        balanced_accuracies.append(balanced_accuracy_score(y_test, y_pred))\n",
    "        precisions.append(precision_score(y_test, y_pred))\n",
    "        recalls.append(recall_score(y_test, y_pred))\n",
    "        f1s.append(f1_score(y_test, y_pred))\n",
    "        kappas.append(cohen_kappa_score(y_test, y_pred))\n",
    "        \n",
    "        \n",
    "    '''results'''    \n",
    "    accuracy_mean, accuracy_std = np.round(np.mean(balanced_accuracies), 3), np.round(np.std(balanced_accuracies), 3)\n",
    "    precision_mean, precision_std = np.round(np.mean(precisions), 3), np.round(np.std(precisions), 3)\n",
    "    recall_mean, recall_std = np.round(np.mean(recalls), 3), np.round(np.std(recalls), 3)\n",
    "    f1_mean, f1_std = np.round(np.mean(f1s), 3), np.round(np.std(f1s), 3)\n",
    "    kappa_mean, kappa_std = np.round(np.mean(kappas), 3), np.round(np.std(kappas), 3)\n",
    "    \n",
    "    ## saving the results in a dataframe\n",
    "    return pd.DataFrame({\"Balanced Accuracy\": \"{} +/- {}\".format(accuracy_mean, accuracy_std),\n",
    "                        \"Precision\": \"{} +/- {}\".format(precision_mean, precision_std),\n",
    "                        \"Recall\": \"{} +/- {}\".format(recall_mean, recall_std),\n",
    "                        \"F1\": \"{} +/- {}\".format(f1_mean, f1_std),\n",
    "                        \"Kappa\": \"{} +/- {}\".format(kappa_mean, kappa_std)},\n",
    "                       index=[model_name])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "heading_collapsed": true
   },
   "source": [
    "# 1.0 Data Description"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "hidden": true
   },
   "source": [
    "## 1.1 Loading Data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 235,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2021-01-11T18:47:37.450936Z",
     "start_time": "2021-01-11T18:47:36.044298Z"
    },
    "hidden": true
   },
   "outputs": [],
   "source": [
    "df1 = pd.read_csv('../database/fraud_0.1origbase.csv')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 236,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2021-01-11T18:47:37.474072Z",
     "start_time": "2021-01-11T18:47:37.453989Z"
    },
    "hidden": true
   },
   "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>step</th>\n",
       "      <th>type</th>\n",
       "      <th>amount</th>\n",
       "      <th>nameOrig</th>\n",
       "      <th>oldbalanceOrg</th>\n",
       "      <th>newbalanceOrig</th>\n",
       "      <th>nameDest</th>\n",
       "      <th>oldbalanceDest</th>\n",
       "      <th>newbalanceDest</th>\n",
       "      <th>isFraud</th>\n",
       "      <th>isFlaggedFraud</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>283</td>\n",
       "      <td>CASH_IN</td>\n",
       "      <td>210329.84</td>\n",
       "      <td>C1159819632</td>\n",
       "      <td>3778062.79</td>\n",
       "      <td>3988392.64</td>\n",
       "      <td>C1218876138</td>\n",
       "      <td>1519266.60</td>\n",
       "      <td>1308936.76</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>132</td>\n",
       "      <td>CASH_OUT</td>\n",
       "      <td>215489.19</td>\n",
       "      <td>C1372369468</td>\n",
       "      <td>21518.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>C467105520</td>\n",
       "      <td>6345756.55</td>\n",
       "      <td>6794954.89</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>355</td>\n",
       "      <td>DEBIT</td>\n",
       "      <td>4431.05</td>\n",
       "      <td>C1059822709</td>\n",
       "      <td>20674.00</td>\n",
       "      <td>16242.95</td>\n",
       "      <td>C76588246</td>\n",
       "      <td>80876.56</td>\n",
       "      <td>85307.61</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>135</td>\n",
       "      <td>CASH_OUT</td>\n",
       "      <td>214026.20</td>\n",
       "      <td>C1464960643</td>\n",
       "      <td>46909.73</td>\n",
       "      <td>0.00</td>\n",
       "      <td>C1059379810</td>\n",
       "      <td>13467450.36</td>\n",
       "      <td>13681476.56</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>381</td>\n",
       "      <td>CASH_OUT</td>\n",
       "      <td>8858.45</td>\n",
       "      <td>C831134427</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>C579876929</td>\n",
       "      <td>1667180.58</td>\n",
       "      <td>1676039.03</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   step      type     amount     nameOrig  oldbalanceOrg  newbalanceOrig     nameDest  oldbalanceDest  newbalanceDest  isFraud  isFlaggedFraud\n",
       "0   283   CASH_IN  210329.84  C1159819632     3778062.79      3988392.64  C1218876138      1519266.60      1308936.76        0               0\n",
       "1   132  CASH_OUT  215489.19  C1372369468       21518.00            0.00   C467105520      6345756.55      6794954.89        0               0\n",
       "2   355     DEBIT    4431.05  C1059822709       20674.00        16242.95    C76588246        80876.56        85307.61        0               0\n",
       "3   135  CASH_OUT  214026.20  C1464960643       46909.73            0.00  C1059379810     13467450.36     13681476.56        0               0\n",
       "4   381  CASH_OUT    8858.45   C831134427           0.00            0.00   C579876929      1667180.58      1676039.03        0               0"
      ]
     },
     "execution_count": 236,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df1.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 237,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2021-01-11T18:47:37.491715Z",
     "start_time": "2021-01-11T18:47:37.476639Z"
    },
    "hidden": true
   },
   "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>step</th>\n",
       "      <th>type</th>\n",
       "      <th>amount</th>\n",
       "      <th>nameOrig</th>\n",
       "      <th>oldbalanceOrg</th>\n",
       "      <th>newbalanceOrig</th>\n",
       "      <th>nameDest</th>\n",
       "      <th>oldbalanceDest</th>\n",
       "      <th>newbalanceDest</th>\n",
       "      <th>isFraud</th>\n",
       "      <th>isFlaggedFraud</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>636257</th>\n",
       "      <td>351</td>\n",
       "      <td>CASH_OUT</td>\n",
       "      <td>28761.10</td>\n",
       "      <td>C742050657</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.00</td>\n",
       "      <td>C568407561</td>\n",
       "      <td>328534.52</td>\n",
       "      <td>357295.62</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>636258</th>\n",
       "      <td>184</td>\n",
       "      <td>CASH_OUT</td>\n",
       "      <td>167820.71</td>\n",
       "      <td>C561181412</td>\n",
       "      <td>62265.0</td>\n",
       "      <td>0.00</td>\n",
       "      <td>C1852953580</td>\n",
       "      <td>106429.48</td>\n",
       "      <td>274250.18</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>636259</th>\n",
       "      <td>35</td>\n",
       "      <td>PAYMENT</td>\n",
       "      <td>8898.12</td>\n",
       "      <td>C1773417333</td>\n",
       "      <td>30808.0</td>\n",
       "      <td>21909.88</td>\n",
       "      <td>M445701551</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>636260</th>\n",
       "      <td>277</td>\n",
       "      <td>CASH_OUT</td>\n",
       "      <td>176147.90</td>\n",
       "      <td>C1423233247</td>\n",
       "      <td>83669.0</td>\n",
       "      <td>0.00</td>\n",
       "      <td>C1328739120</td>\n",
       "      <td>0.00</td>\n",
       "      <td>176147.90</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>636261</th>\n",
       "      <td>304</td>\n",
       "      <td>CASH_OUT</td>\n",
       "      <td>95142.89</td>\n",
       "      <td>C874575079</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.00</td>\n",
       "      <td>C666451134</td>\n",
       "      <td>431380.07</td>\n",
       "      <td>526522.96</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "        step      type     amount     nameOrig  oldbalanceOrg  newbalanceOrig     nameDest  oldbalanceDest  newbalanceDest  isFraud  isFlaggedFraud\n",
       "636257   351  CASH_OUT   28761.10   C742050657            0.0            0.00   C568407561       328534.52       357295.62        0               0\n",
       "636258   184  CASH_OUT  167820.71   C561181412        62265.0            0.00  C1852953580       106429.48       274250.18        0               0\n",
       "636259    35   PAYMENT    8898.12  C1773417333        30808.0        21909.88   M445701551            0.00            0.00        0               0\n",
       "636260   277  CASH_OUT  176147.90  C1423233247        83669.0            0.00  C1328739120            0.00       176147.90        0               0\n",
       "636261   304  CASH_OUT   95142.89   C874575079            0.0            0.00   C666451134       431380.07       526522.96        0               0"
      ]
     },
     "execution_count": 237,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df1.tail()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "hidden": true
   },
   "source": [
    "## 1.2 Columns"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "hidden": true
   },
   "source": [
    "### 1.2.1 Column Descriptions"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "hidden": true
   },
   "source": [
    "**step:** maps a unit of time in the real world. In this case 1 step is 1 hour of time. Total steps 744 (30 days simulation).\n",
    "\n",
    "**type:** CASH-IN, CASH-OUT, DEBIT, PAYMENT and TRANSFER.\n",
    "\n",
    "**amount:** amount of the transaction in local currency.\n",
    "\n",
    "**nameOrig:** customer who started the transaction\n",
    "\n",
    "**oldbalanceOrg:** initial balance before the transaction\n",
    "\n",
    "**newbalanceOrig:** new balance after the transaction\n",
    "\n",
    "**nameDest:** customer who is the recipient of the transaction\n",
    "\n",
    "**oldbalanceDest:** initial balance recipient before the transaction. Note that there is not information for customers that start with M (Merchants).\n",
    "\n",
    "**newbalanceDest:** new balance recipient after the transaction. Note that there is not information for customers that start with M (Merchants).\n",
    "\n",
    "**isFraud:** This is the transactions made by the fraudulent agents inside the simulation. In this specific dataset the fraudulent behavior of the agents aims to profit by taking control or customers accounts and try to empty the funds by transferring to another account and then cashing out of the system.\n",
    "\n",
    "**isFlaggedFraud:** The business model aims to control massive transfers from one account to another and flags illegal attempts. An illegal attempt in this dataset is an attempt to transfer more than 200.000 in a single transaction."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-12-12T21:31:42.617142Z",
     "start_time": "2020-12-12T21:31:42.609502Z"
    },
    "hidden": true
   },
   "source": [
    "### 1.2.2 Column Rename"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 238,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2021-01-11T18:47:37.498900Z",
     "start_time": "2021-01-11T18:47:37.494994Z"
    },
    "hidden": true
   },
   "outputs": [],
   "source": [
    "cols_old = df1.columns.tolist()\n",
    "\n",
    "snakecase = lambda x: inflection.underscore(x)\n",
    "cols_new = list(map(snakecase, cols_old))\n",
    "\n",
    "df1.columns = cols_new"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 239,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2021-01-11T18:47:37.505663Z",
     "start_time": "2021-01-11T18:47:37.501235Z"
    },
    "hidden": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Index(['step', 'type', 'amount', 'name_orig', 'oldbalance_org',\n",
       "       'newbalance_orig', 'name_dest', 'oldbalance_dest', 'newbalance_dest',\n",
       "       'is_fraud', 'is_flagged_fraud'],\n",
       "      dtype='object')"
      ]
     },
     "execution_count": 239,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df1.columns"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "hidden": true
   },
   "source": [
    "## 1.3 Data Dimension"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 240,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2021-01-11T18:47:37.851622Z",
     "start_time": "2021-01-11T18:47:37.847980Z"
    },
    "hidden": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Number of Rows: 636262\n",
      "Number of Cols: 11\n"
     ]
    }
   ],
   "source": [
    "print('Number of Rows: {}'.format(df1.shape[0]))\n",
    "print('Number of Cols: {}'.format(df1.shape[1]))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "hidden": true
   },
   "source": [
    "## 1.4 Data Types and Structure"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 241,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2021-01-11T18:47:38.334664Z",
     "start_time": "2021-01-11T18:47:38.201896Z"
    },
    "hidden": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 636262 entries, 0 to 636261\n",
      "Data columns (total 11 columns):\n",
      " #   Column            Non-Null Count   Dtype  \n",
      "---  ------            --------------   -----  \n",
      " 0   step              636262 non-null  int64  \n",
      " 1   type              636262 non-null  object \n",
      " 2   amount            636262 non-null  float64\n",
      " 3   name_orig         636262 non-null  object \n",
      " 4   oldbalance_org    636262 non-null  float64\n",
      " 5   newbalance_orig   636262 non-null  float64\n",
      " 6   name_dest         636262 non-null  object \n",
      " 7   oldbalance_dest   636262 non-null  float64\n",
      " 8   newbalance_dest   636262 non-null  float64\n",
      " 9   is_fraud          636262 non-null  int64  \n",
      " 10  is_flagged_fraud  636262 non-null  int64  \n",
      "dtypes: float64(5), int64(3), object(3)\n",
      "memory usage: 53.4+ MB\n"
     ]
    }
   ],
   "source": [
    "df1.info()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "hidden": true
   },
   "source": [
    "## 1.5 Check NA"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 242,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2021-01-11T18:47:38.655459Z",
     "start_time": "2021-01-11T18:47:38.542451Z"
    },
    "hidden": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "step                0.0\n",
       "type                0.0\n",
       "amount              0.0\n",
       "name_orig           0.0\n",
       "oldbalance_org      0.0\n",
       "newbalance_orig     0.0\n",
       "name_dest           0.0\n",
       "oldbalance_dest     0.0\n",
       "newbalance_dest     0.0\n",
       "is_fraud            0.0\n",
       "is_flagged_fraud    0.0\n",
       "dtype: float64"
      ]
     },
     "execution_count": 242,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df1.isna().mean()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "hidden": true
   },
   "source": [
    "## 1.6 Fill Out NA"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "hidden": true
   },
   "source": [
    "There's no NaN values to fill."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "hidden": true
   },
   "source": [
    "## 1.7 Change Data Type"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "hidden": true
   },
   "source": [
    " I will change the values 0 and 1 to 'yes' and 'no'. It'll help on the data description and analysis sections."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 243,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2021-01-11T18:47:39.517461Z",
     "start_time": "2021-01-11T18:47:39.450119Z"
    },
    "hidden": true
   },
   "outputs": [],
   "source": [
    "df1['is_fraud'] = df1['is_fraud'].map({1: 'yes', 0: 'no'})\n",
    "df1['is_flagged_fraud'] = df1['is_flagged_fraud'].map({1: 'yes', 0: 'no'})"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "hidden": true
   },
   "source": [
    "## 1.8 Description Statistics"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 244,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2021-01-11T18:47:39.985703Z",
     "start_time": "2021-01-11T18:47:39.840317Z"
    },
    "hidden": true
   },
   "outputs": [],
   "source": [
    "num_attributes = df1.select_dtypes(exclude='object')\n",
    "cat_attributes = df1.select_dtypes(include='object')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "hidden": true
   },
   "source": [
    "### 1.8.1 Numerical Attributes"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 245,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2021-01-11T18:47:40.716577Z",
     "start_time": "2021-01-11T18:47:40.217657Z"
    },
    "hidden": true
   },
   "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>count</th>\n",
       "      <th>mean</th>\n",
       "      <th>std</th>\n",
       "      <th>min</th>\n",
       "      <th>25%</th>\n",
       "      <th>50%</th>\n",
       "      <th>75%</th>\n",
       "      <th>max</th>\n",
       "      <th>range</th>\n",
       "      <th>variation coefficient</th>\n",
       "      <th>skew</th>\n",
       "      <th>kurtosis</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>step</th>\n",
       "      <td>636262.0</td>\n",
       "      <td>2.429319e+02</td>\n",
       "      <td>1.423309e+02</td>\n",
       "      <td>1.0</td>\n",
       "      <td>155.000</td>\n",
       "      <td>238.000</td>\n",
       "      <td>3.340000e+02</td>\n",
       "      <td>7.420000e+02</td>\n",
       "      <td>7.410000e+02</td>\n",
       "      <td>0.585888</td>\n",
       "      <td>0.375052</td>\n",
       "      <td>0.324437</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>amount</th>\n",
       "      <td>636262.0</td>\n",
       "      <td>1.800585e+05</td>\n",
       "      <td>6.069714e+05</td>\n",
       "      <td>0.0</td>\n",
       "      <td>13407.425</td>\n",
       "      <td>74815.770</td>\n",
       "      <td>2.084988e+05</td>\n",
       "      <td>5.722962e+07</td>\n",
       "      <td>5.722962e+07</td>\n",
       "      <td>3.370967</td>\n",
       "      <td>29.577107</td>\n",
       "      <td>1546.661043</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>oldbalance_org</th>\n",
       "      <td>636262.0</td>\n",
       "      <td>8.317937e+05</td>\n",
       "      <td>2.885636e+06</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.000</td>\n",
       "      <td>14239.000</td>\n",
       "      <td>1.072998e+05</td>\n",
       "      <td>4.368662e+07</td>\n",
       "      <td>4.368662e+07</td>\n",
       "      <td>3.469173</td>\n",
       "      <td>5.243428</td>\n",
       "      <td>32.766036</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>newbalance_orig</th>\n",
       "      <td>636262.0</td>\n",
       "      <td>8.528354e+05</td>\n",
       "      <td>2.921296e+06</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.000</td>\n",
       "      <td>0.000</td>\n",
       "      <td>1.435652e+05</td>\n",
       "      <td>4.367380e+07</td>\n",
       "      <td>4.367380e+07</td>\n",
       "      <td>3.425393</td>\n",
       "      <td>5.173614</td>\n",
       "      <td>31.932547</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>oldbalance_dest</th>\n",
       "      <td>636262.0</td>\n",
       "      <td>1.096212e+06</td>\n",
       "      <td>3.375389e+06</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.000</td>\n",
       "      <td>131539.745</td>\n",
       "      <td>9.384820e+05</td>\n",
       "      <td>3.551855e+08</td>\n",
       "      <td>3.551855e+08</td>\n",
       "      <td>3.079138</td>\n",
       "      <td>20.622388</td>\n",
       "      <td>1074.335293</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>newbalance_dest</th>\n",
       "      <td>636262.0</td>\n",
       "      <td>1.221809e+06</td>\n",
       "      <td>3.656213e+06</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.000</td>\n",
       "      <td>214712.725</td>\n",
       "      <td>1.109230e+06</td>\n",
       "      <td>3.555534e+08</td>\n",
       "      <td>3.555534e+08</td>\n",
       "      <td>2.992460</td>\n",
       "      <td>19.623903</td>\n",
       "      <td>928.694204</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                    count          mean           std  min        25%         50%           75%           max         range  variation coefficient       skew     kurtosis\n",
       "step             636262.0  2.429319e+02  1.423309e+02  1.0    155.000     238.000  3.340000e+02  7.420000e+02  7.410000e+02               0.585888   0.375052     0.324437\n",
       "amount           636262.0  1.800585e+05  6.069714e+05  0.0  13407.425   74815.770  2.084988e+05  5.722962e+07  5.722962e+07               3.370967  29.577107  1546.661043\n",
       "oldbalance_org   636262.0  8.317937e+05  2.885636e+06  0.0      0.000   14239.000  1.072998e+05  4.368662e+07  4.368662e+07               3.469173   5.243428    32.766036\n",
       "newbalance_orig  636262.0  8.528354e+05  2.921296e+06  0.0      0.000       0.000  1.435652e+05  4.367380e+07  4.367380e+07               3.425393   5.173614    31.932547\n",
       "oldbalance_dest  636262.0  1.096212e+06  3.375389e+06  0.0      0.000  131539.745  9.384820e+05  3.551855e+08  3.551855e+08               3.079138  20.622388  1074.335293\n",
       "newbalance_dest  636262.0  1.221809e+06  3.656213e+06  0.0      0.000  214712.725  1.109230e+06  3.555534e+08  3.555534e+08               2.992460  19.623903   928.694204"
      ]
     },
     "execution_count": 245,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "describe = num_attributes.describe().T\n",
    "\n",
    "describe['range'] = (num_attributes.max() - num_attributes.min()).tolist()\n",
    "describe['variation coefficient'] = (num_attributes.std() / num_attributes.mean()).tolist()\n",
    "describe['skew'] = num_attributes.skew().tolist()\n",
    "describe['kurtosis'] = num_attributes.kurtosis().tolist()\n",
    "\n",
    "describe"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "hidden": true
   },
   "source": [
    "* All the data has a coeficient of variation greater than 25%, therefore they aren't homogeneous.\n",
    "\n",
    "* The step variable starts from 1 hour to 742 hour (30 days).\n",
    "\n",
    "* Some variables are higher shap and right skewed.\n",
    "\n",
    "* 50% of the newbalance_orig is 0. Maybe there are some transfers that don't go to the destination.\n",
    "\n",
    "* The skew is higher positive, therefore the values may be in less values."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "hidden": true
   },
   "source": [
    "### 1.8.2 Categorical Attributes"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 246,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2021-01-11T18:47:42.821513Z",
     "start_time": "2021-01-11T18:47:40.809829Z"
    },
    "hidden": true
   },
   "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>type</th>\n",
       "      <th>name_orig</th>\n",
       "      <th>name_dest</th>\n",
       "      <th>is_fraud</th>\n",
       "      <th>is_flagged_fraud</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>636262</td>\n",
       "      <td>636262</td>\n",
       "      <td>636262</td>\n",
       "      <td>636262</td>\n",
       "      <td>636262</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>unique</th>\n",
       "      <td>5</td>\n",
       "      <td>636171</td>\n",
       "      <td>457224</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>top</th>\n",
       "      <td>CASH_OUT</td>\n",
       "      <td>C241218843</td>\n",
       "      <td>C2083562754</td>\n",
       "      <td>no</td>\n",
       "      <td>no</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>freq</th>\n",
       "      <td>224216</td>\n",
       "      <td>2</td>\n",
       "      <td>14</td>\n",
       "      <td>635441</td>\n",
       "      <td>636260</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "            type   name_orig    name_dest is_fraud is_flagged_fraud\n",
       "count     636262      636262       636262   636262           636262\n",
       "unique         5      636171       457224        2                2\n",
       "top     CASH_OUT  C241218843  C2083562754       no               no\n",
       "freq      224216           2           14   635441           636260"
      ]
     },
     "execution_count": 246,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "cat_attributes.describe()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "hidden": true
   },
   "source": [
    "* The majority type is cash_out with 2237500.\n",
    "\n",
    "* There's a lot of variability in name_orig, so it could be hard to use one hot encoding.\n",
    "\n",
    "* There's less name_orig than name_dest. There's more users sending than receiving, however use one hot encoding will not help.\n",
    "\n",
    "* There's more fraud than the flagged fraud, it shows that the current method can't recognize fraud efficiently."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "heading_collapsed": true
   },
   "source": [
    "# 2.0 Feature Engineering"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 247,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2021-01-11T18:47:42.876765Z",
     "start_time": "2021-01-11T18:47:42.823469Z"
    },
    "hidden": true
   },
   "outputs": [],
   "source": [
    "df2 = df1.copy()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "hidden": true
   },
   "source": [
    "## 2.1 Mind Map"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 248,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2021-01-11T18:47:42.884372Z",
     "start_time": "2021-01-11T18:47:42.878669Z"
    },
    "hidden": true
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<IPython.core.display.Image object>"
      ]
     },
     "execution_count": 248,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "Image('../images/Fraud-mindmap.png')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "hidden": true
   },
   "source": [
    "## 2.2 Hypothesis Creation"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "hidden": true
   },
   "source": [
    "### 2.2.1 User"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "hidden": true
   },
   "source": [
    "* 90% of the twentyone-year-old users did a fraud transiction.\n",
    "\n",
    "* The majority fraud transiction occours for the same initial letter user.\n",
    "\n",
    "* The fraud amount is greater than 10.000.\n",
    "\n",
    "* The 60% of the age is greater than 30 year old."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "hidden": true
   },
   "source": [
    "### 2.2.2 Type"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "hidden": true
   },
   "source": [
    "* 60% of fraud transaction occours using cash-out-type method.\n",
    "\n",
    "* The majority transfers occours using tranfers-type method.\n",
    "\n",
    "* Values greater than 100.000 occours using transfers-type method.\n",
    "\n",
    "* Payment type occurs with values lower than 100.000"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "hidden": true
   },
   "source": [
    "### 2.2.3 Origin and Destiny Transactions"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "hidden": true
   },
   "source": [
    "* 60% of the difference between origin destiny transactions is equal 0 for frauds.\n",
    "\n",
    "* Origin values are greater than destiny values for fraud transaction."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "hidden": true
   },
   "source": [
    "### 2.2.4 Time"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "hidden": true
   },
   "source": [
    "* Fraud transactions occours at least in 3 days.\n",
    "\n",
    "* 40% of the cash-out transactions occours less than 1 day.\n",
    "\n",
    "* 60% of the transaction less than 100.000 occours at least 10 days.\n",
    "\n",
    "* The transactions greater than 10.000 occours at most in 2 weeks. "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "hidden": true
   },
   "source": [
    "## 2.3 Hipothesys List"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "hidden": true
   },
   "source": [
    "1. The majority fraud transiction occours for the same initial letter user.\n",
    "\n",
    "1. All the fraud amount is greater than 10.000.\n",
    "\n",
    "1. 60% of fraud transaction occours using cash-out-type method.\n",
    "\n",
    "1. The majority transfers occours using tranfers-type method.\n",
    "\n",
    "1. Fraud transactions occours at least in 3 days."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "hidden": true
   },
   "source": [
    "## 2.4 Feature Engineering"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 249,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2021-01-11T18:47:44.922596Z",
     "start_time": "2021-01-11T18:47:43.999043Z"
    },
    "hidden": true
   },
   "outputs": [],
   "source": [
    "# step\n",
    "df2['step_days'] = df2['step'].apply(lambda i: i/24)\n",
    "df2['step_weeks'] = df2['step'].apply(lambda i: i/(24*7))\n",
    "\n",
    "# difference between initial balance before the transaction and new balance after the transaction\n",
    "df2['diff_new_old_balance'] = df2['newbalance_orig'] - df2['oldbalance_org']\n",
    "\n",
    "# difference between initial balance recipient before the transaction and new balance recipient after the transaction.\n",
    "df2['diff_new_old_destiny'] = df2['newbalance_dest'] - df2['oldbalance_dest']\n",
    "\n",
    "# name orig and name dest\n",
    "df2['name_orig'] = df2['name_orig'].apply(lambda i: i[0])\n",
    "df2['name_dest'] = df2['name_dest'].apply(lambda i: i[0])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "heading_collapsed": true
   },
   "source": [
    "# 3.0 Selecting Columns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 250,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2021-01-11T18:47:44.957038Z",
     "start_time": "2021-01-11T18:47:44.924834Z"
    },
    "hidden": true
   },
   "outputs": [],
   "source": [
    "df3 = df2.copy()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "hidden": true
   },
   "source": [
    "## 3.1 Selecting Columns"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "hidden": true
   },
   "source": [
    "I'll use all the columns for data analysis"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "hidden": true
   },
   "source": [
    "## 3.2 Selecting Lines"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "hidden": true
   },
   "source": [
    "I'll use all the lines."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "heading_collapsed": true
   },
   "source": [
    "# 4.0 Exploratory Data Analisys"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 251,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2021-01-11T18:47:45.536329Z",
     "start_time": "2021-01-11T18:47:45.479614Z"
    },
    "hidden": true
   },
   "outputs": [],
   "source": [
    "df4 = df3.copy()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "hidden": true
   },
   "source": [
    "## 4.1 Univariate Analysis"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "hidden": true
   },
   "source": [
    "### 4.1.1 Response Variable"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 252,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2021-01-11T18:47:46.652434Z",
     "start_time": "2021-01-11T18:47:46.010096Z"
    },
    "hidden": true
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1800x864 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "ax = sns.countplot(y='is_fraud', data=df4);\n",
    "\n",
    "total = df4['is_fraud'].size\n",
    "for p in ax.patches:\n",
    "        percentage = ' {:.1f}%'.format(100 * p.get_width()/total)\n",
    "        x = p.get_x() + p.get_width() + 0.02\n",
    "        y = p.get_y() + p.get_height()/2\n",
    "        ax.annotate(percentage, (x, y))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "hidden": true
   },
   "source": [
    "### 4.1.2 Numerical Variables"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 253,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2021-01-11T18:48:13.254494Z",
     "start_time": "2021-01-11T18:47:46.656150Z"
    },
    "hidden": true
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1800x864 with 10 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "num_attributes = df4.select_dtypes(exclude='object')\n",
    "columns = num_attributes.columns.tolist()\n",
    "j = 1\n",
    "\n",
    "for column in columns:\n",
    "    plt.subplot(2, 5, j)\n",
    "    sns.distplot(num_attributes[column]);\n",
    "    \n",
    "    j += 1"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "hidden": true
   },
   "source": [
    "### 4.1.3 Categorical Variables"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 254,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2021-01-11T18:48:15.988749Z",
     "start_time": "2021-01-11T18:48:13.257365Z"
    },
    "hidden": true
   },
   "outputs": [
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<Figure size 1800x864 with 5 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "cat_attributes = df4.select_dtypes(include='object')\n",
    "columns = cat_attributes.columns.tolist()\n",
    "j = 1\n",
    "\n",
    "for column in columns:\n",
    "    plt.subplot(3, 2, j)\n",
    "    ax = sns.countplot(y=column, data=cat_attributes)\n",
    "    \n",
    "    total = cat_attributes[column].size\n",
    "    for p in ax.patches:\n",
    "        percentage = ' {:.1f}%'.format(100 * p.get_width()/total)\n",
    "        x = p.get_x() + p.get_width() + 0.02\n",
    "        y = p.get_y() + p.get_height()/2\n",
    "        ax.annotate(percentage, (x, y))\n",
    "    \n",
    "    j += 1"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "hidden": true
   },
   "source": [
    "## 4.2 Bivariate Analysis"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "hidden": true
   },
   "source": [
    "### H1 The majority fraud transiction occours for the same user.\n",
    "**TRUE:** The same user origem and destiny has got the same inital letter."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 255,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2021-01-11T18:48:16.333520Z",
     "start_time": "2021-01-11T18:48:15.991520Z"
    },
    "hidden": true
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1800x864 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "aux1 = df4[df4['is_fraud'] == 'yes']\n",
    "sns.countplot(y='name_orig', data=aux1);"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 256,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2021-01-11T18:48:16.577943Z",
     "start_time": "2021-01-11T18:48:16.336607Z"
    },
    "hidden": true
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1800x864 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.countplot(y='name_dest', data=aux1);"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "hidden": true
   },
   "source": [
    "### H2 All the fraud amount is greater than 10.000.\n",
    "\n",
    "**TRUE:** The values are greater than 10.000. But it's important to note that the no-fraud values is greater than 100.000 also."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 257,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2021-01-11T18:48:38.633178Z",
     "start_time": "2021-01-11T18:48:16.580551Z"
    },
    "hidden": true
   },
   "outputs": [
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<Figure size 1800x864 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.barplot(y='amount', x='is_fraud', data=df4);"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "hidden": true
   },
   "source": [
    "### H3 60% of fraud transaction occours using cash-out-type method.\n",
    "\n",
    "**FALSE:** The fraud transaction occours in transfer and cash-out type. However they're almost the same value."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 258,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2021-01-11T18:48:38.986355Z",
     "start_time": "2021-01-11T18:48:38.637113Z"
    },
    "hidden": true
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1800x864 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "aux1 = df4[df4['is_fraud'] == 'yes']\n",
    "ax = sns.countplot(y='type', data=aux1)\n",
    "\n",
    "total = aux1['type'].size\n",
    "for p in ax.patches:\n",
    "        percentage = ' {:.1f}%'.format(100 * p.get_width()/total)\n",
    "        x = p.get_x() + p.get_width() + 0.02\n",
    "        y = p.get_y() + p.get_height()/2\n",
    "        ax.annotate(percentage, (x, y))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "hidden": true
   },
   "source": [
    "To see the complete transiction-type and I'll plot them here."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 259,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2021-01-11T18:48:39.986516Z",
     "start_time": "2021-01-11T18:48:38.989749Z"
    },
    "hidden": true
   },
   "outputs": [
    {
     "data": {
      "image/png": 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AAAAAoMAE/QAAAAAAUGC1qrsAAAAAAAAWrVdeeSl33nlbPvnkk5SUlGTdddfPsceekDXXbFMx5sknh+TPf74nw4d/kTp1lknHjp1y/PE906LFCvNcd+zYMbn55uvzxhtDM378uLRsuXJ69Dg8O+64c8WYW265IY88Mih169bLiSeemq222qbSGo8++nDuu++u/OlP96d27doLfe9LA0/0AwAAAAAswd599+2cddYp2XDDjrn99rtz1VXXZ/LkSTn99JMyZcrkJMlf//pkLr20d7p02T133vnnXHLJ5fnkk09y1lmnZs6cOXNdd86cOTnzzFPy0Ucf5oILLslddz2QnXbaJb17n5sXX3w+SfLee+9m0KAH06dP3xxxxNHp0+fCTJ8+vWKNiRMn5uab+6dnz9OF/D+DoB8AAAAAYAn27LNPZ5111s3RRx+XlVdeJe3arZsTTuiZMWNG55//fC9J8re//TXbb//r7L//QVlppZbp0KFTDj/8qHzyyb8zcuTwua77+eef5cMP388RRxyd9dffMC1brpyDDz40a67ZJk899WSSZOjQV7P55ltmvfU2SJcuu6Vu3br54IN/Vaxxyy03ZMMNO2SjjTov+l/EEszRPQAAAAAAS7CTTz59nn01a9ZMkvTp0/cHxsw9Ri4pKam0xn/Vrl0n/9OVMWPGZPnlW1T0tWjRIqNHj0qSfPTRh3nqqSdy551//vFN8IM80Q8AAAAAsBT56qsvc/PN16ddu/XSvn3HuY4ZNuyT3HXX7dlmm+2z0kot5zpmtdVap337jrnnnj9VhPfPP/9sPvrog+y2217/M6q84guBJCkpqZHy8qS8vDz9+l2WQw45NC1atKi6OD+JJ/oBAAAAAJYCL73095x33lmZMWN6tttux5x55jlVnsYfNOjBXHddv8yaNSt77901J5546g+uecklV+TMM0/O3nvvklq1aqWkpCRnnnluNtpokyTJsssuV/ElQJKMHj0qzZs3z+OPP5oJEybkwAMPzgMP3JdBgx5IeXl59tvvwHTteuDC3/wSTtAPAAAAALAU6NChU/74x3sycuTIDBhwU04++Xe59tqbU69evYoxv/51l3TsuFGGDfskN910Xb755pv84Q99U6NG1cNhysvL07v3ufnuu2m58spr06zZsnnttZdz1VWXp1mzZtl00y3SoUOn9O59bsaNG5tPPx2WCRMmZJVVVs355/fKuef2zvDhX+SOO27NHXfck/Ly8hx2WLd06rRJWrVa7Zf81RSeoB8AAAAAYClQr169rLJKq6yySquss8662WefXfPII4NywAHdK8Y0bNgwDRs2zKqrtkqrVq3So8eB+fvfn8/WW29bZb2XX34xr732cu68889p3Xr1JEnbtmvl888/y80335BNN90iHTtulE022TR7771LatasmVNPPSt33XVH1l9/g2yyyaYZOPDP2WCDDdOixQpJkg037JC33npD0P8TOaMfAAAAAGAJ9sorL+WDD/5Vqa1p06ZZdtllM3z4F5k9e3aef/7ZfPbZp5XGtGrVOjVq1Mjw4V/Mdd0vvvgsSbLqqq0qta+0UsuMHDm84vM551yQxx57Ok888WzatFkrTzwxJCec8P2RQJMmTUr9+g0qxtav3yATJ05Y4L0urQT9AAAAAABLsDvvvD39+19dqW3ixAkZM2Z0Skt/lZo1a+bqq6/I3Xf/sdKYzz77NHPmzElpaelc1y0t/VWSZMSI4ZXahw//vKLvvxo1apQ6deqkX7/L0r17j4oX8DZo0DDjx4+rGDdu3Ng0atR4Qba5VBP0AwAAAAAswbp375F33307119/TT7//LN8+OEHueCCc1K7dp3stNMuSZJu3Xrkr399Mvfee2dGjBied999J5de2jvLLdc8W221TZLk/ff/mW7d9s1HH32YJNlii62z/PIt0qfPhXnvvXfz5ZcjM2jQg3nuub9ll112r1LHk08Oyfjx49OtW4+KtvbtO+bdd9/Jm2++ntdffzX/+Mc76dCh06L/pSxhnNEPAAAAALAE22KLrXPBBZfk3nvvysCBf06DBg2z9trr5MYbb8sKK6yYJOna9YDUqFGSsrKHcuutN6ZJk6bZYIP2ufjiyyqO1pk2bVqGD/8i06ZNS/L9mf/9+9+S66+/JqefflKmTZuWFVZYMccee0Klc/+TZPLkybnxxuvSq9f5qV27dkX7GmusmWOOOS4XXHBOSkpKcsIJp2S11Vr/Qr+ZJUdJeXl5eXUXQfXqdsY91V0CAAAAALAQ3Ht594wePam6y2ARKC1tNM8+R/cAAAAAAECBCfoBAAAAAKDABP0AAAAAAFBggn4AAAAAACgwQT8AAAAAABSYoB8AAAAAAApM0A8AAAAAAAUm6AcAAAAAgAIT9AMAAAAAQIEJ+gEAAAAAoMAE/QAAAAAAUGCCfgAAAAAAKDBBPwAAAAAAFJigHwAAAAAACkzQDwAAAAAABSboBwAAAACAAhP0AwAAAABAgQn6AQAAAACgwAoV9F933XVp27ZtpZ+11lorm266aU466aR8/PHHVeYcddRRadu2ba688spK7Y8++mjatm2bP/7xj3O9Vnl5ebp27ZpNNtkk48aNS5Iccsghadu2bS677LJ51vjWW29V1DZy5MiK9u22265K7f/35+mnn06SjBw5Mm3btk2HDh0yatSouV7nrLPOyiGHHJIkGTRo0I+u3bZt25x11lnz/uUCAAAAAFBItaq7gAVx4403prS0NEkyc+bMDBs2LDfddFP233//DBw4MK1bt06S/Oc//8mLL76Yxo0bZ/DgwenZs2dq1qyZJNl9993z0EMPpX///tltt93SvHnzStd46KGH8t577+Xiiy/OsssuW9Feu3btPPbYYznttNMq1vrfBg8enNq1a2fmzJlV+tq1a5fevXvPc1+rrrpqpc9TpkzJ5ZdfXuVLiv9r2223zUMPPVTx+V//+lfOP//8HH/88dlmm20q2ps1a/aD6wAAAAAAUDyFeqL/v9q0aZP11lsv6623Xjp06JD99tsvt912W2bOnJlbb721YtzgwYMzZ86c9OzZM6NGjcpLL71UaZ3zzz8/06ZNyxVXXFGpfcKECenXr186duyYrl27Vurr2LFjRo0alZdffrlKXTNnzsyTTz6Z9u3bz7XuBg0aVNQ9t5/GjRtXGt+lS5c8+uijeeONN37w99GsWbNK66y22mpJkpVWWqlSe8uWLX9wHQAAAAAAiqeQQf/ctGrVKq1atcp7771X0TZw4MB06NAhXbt2TePGjVNWVlZpTuvWrXPEEUdk8ODBeeuttyrar7nmmkycODEXXHBBSkpKKs1ZYYUVstZaa+Xhhx+uUsPzzz+fiRMnZuutt14oezrggAOyzjrr5KKLLsrs2bMXypoAAAAAACxZlpigP0lq1qyZOXPmJEnefPPNfP7559lrr71Sp06ddOnSJc8880wmTpxYac6xxx6blVZaKRdddFHmzJmTDz/8MPfff39+85vfpE2bNnO9zk477ZSnn346kydPrtT+yCOPpFOnTpWO+vk5atSokfPOOy8fffRR7rvvvoWyJgAAAAAAS5YlJugfN25cPv/886yzzjpJvn+av27duunSpUuSZK+99sr06dMzZMiQSvPq1q2b8847L++//37uu+++XHTRRVl++eVz/PHHz/Nau+++e6ZPn54nn3yyom3SpEl57rnnsttuuy3UfXXo0CF77LFHrrnmmoqXAgMAAAAAwH8VPuifOXNmPvroo5xyyimZPXt2DjvssEydOjVPPPFEdthhhzRq1CjJ94F5q1atqhzfkyTbbLNNdtxxx/Tp0ydvvPFGzj333NSvX3+e11x55ZXTvn37Ssf3PPnkk5kzZ0523nnnec4bOnRo2rZtO9efffbZZ57zTj/99MyZM+dHX8oLAAAAAMDSp1Z1F7Agtt9++yptrVq1yo033ph27dpl0KBBmTp1arbddttKT8Fvv/32ue222zJs2LCsvvrqleafc845+etf/5rOnTvPdf3/a4899kjv3r0zcuTItGzZMo888ki23HLLNGnSZJ5z2rVrl0suuWSufXXr1p3nvNLS0hx//PG57LLLcuCBB2b99df/0frm14yZs3Pv5d0X2noAAAAAQPWZNn1mdZdANShk0H/LLbfkV7/6VZKkpKQkTZs2TYsWLSr6Bw0alCQ59dRT5zq/rKwsp512WqW2FVZYodI/f0yXLl1yySWXZPDgwdl3333z+uuvp1+/fj84p0GDBll77bXna/3/6+CDD86DDz6Y3r1758EHH1ygNeamTu2aGT160kJbD2BhKi1t5B4FLJbcn4DFmXsUsDhzj4JFo5BB/+qrr56WLVvOtW/48OF54403sueee2avvfaq0n/11Vdn8ODB6dmzZ2rWrLnANTRt2jRbb711nnrqqdStWzf169fPdtttt8Dr/ZjatWvnvPPOy6GHHrpQg34AAAAAAIqtkEH/Dxk4cGDKy8tz5JFHpk2bNlX6v/rqq5xzzjl56aWXstVWW/2sa+2xxx458cQTkyQ77rjjDx6/szBsuumm2WmnnXLVVVelffv2i/RaAAAAAAAUQ+Ffxvu/zZkzJw8//HDatWs315A/SXbeeefUq1dvri/l/am23XbbNG7cOB9++GF22223Hx0/ZcqUvPfee/P8GTFixI+ucdZZZ2XatGl59tlnf3b9AAAAAAAU3xL1RP9LL72Ub775Jr/97W/nOaZhw4bZYYcd8tRTT2XixIlp3LjxAl+vTp062XnnnfPMM89ks802+9Hx//rXv9K1a9d59u+yyy656qqrfnCNFVdcMUcddVSuueaan1wvAAAAAABLnpLy8vLy6i6C6uUFKMDiykuagMWV+xOwOHOPAhZn7lGw4EpLG82zb4k6ugcAAAAAAJY2gn4AAAAAACgwQT8AAAAAABSYoB8AAAAAAApM0A8AAAAAAAUm6AcAAAAAgAIT9AMAAAAAQIEJ+gEAAAAAoMAE/QAAAAAAUGCCfgAAAAAAKDBBPwAAAAAAFJigHwAAAAAACkzQDwAAAAAABSboBwAAAACAAhP0AwAAAABAgQn6AQAAAACgwAT9AAAAAABQYIJ+AAAAAAAoMEE/AAAAAAAUmKAfAAAAAAAKTNAPAAAAAAAFJugHAAAAAIACE/QDAAAAAECBCfoBAAAAAKDABP0AAAAAAFBggn4AAAAAACgwQT8AAAAAABSYoB8AAAAAAApM0A8AAAAAAAUm6AcAAAAAgAIT9AMAAAAAQIEJ+gEAAAAAoMAE/QAAAAAAUGCCfgAAAAAAKDBBPwAAAAAAFJigHwAAAAAACkzQDwAAAAAABSboBwAAAACAAhP0AwAAAABAgQn6AQAAAACgwAT9AAAAAABQYIJ+AAAAAAAoMEE/AAAAAAAUmKAfAAAAAAAKTNAPAAAAAAAFJugHAAAAAIACE/QDAAAAAECBCfoBAAAAAKDABP0AAAAAAFBggn4AAAAAACgwQT8AAAAAABSYoB8AAAAAAApM0A8AAAAAAAUm6AcAAAAAgAIT9AMAAAAAQIEJ+gEAAAAAoMAE/QAAAAAAUGCCfgAAAAAAKDBBPwAAAAAAFJigHwAAAAAACkzQDwAAAAAABSboBwAAAACAAhP0AwAAAABAgQn6AQAAAACgwAT9AAAAAABQYIJ+AAAAAAAoMEE/AAAAAAAUmKAfAAAAAAAKTNAPAAAAAAAFJugHAAAAAIACE/QDAAAAAECB1aruAqheM2bOTmlpo+ouA2Cefu49atr0mZk0cdpCqgYAAABg8SPoX8rVqV0z3c64p7rLAFhk7r28eyZF0A8AAAAsuRzdAwAAAAAABSboBwAAAACAAhP0AwAAAABAgQn6AQAAAACgwAT9AAAAAABQYIJ+AAAAAAAoMEE/AAAAAAAUmKAfAAAAAAAKTNAPAAAAAAAFJugHAAAAAIACE/QDAAAAAECBCfoBAAAAAKDABP0ALJU+/PCDnHLK8dl11+2zww5b5KSTjs2HH77/g3MmTZqUq6++Ivvss2u22aZz9t9/z/zxjwMyZ86cijGDBj2YPffcOXvssVMefvihKmu8/vpr2XXX7TNx4oSFvicAAABg6VSrugsAgF/af/7zTU4++dhsssmmue66W5Ikf/rTgJx00rG5664H8qtfLT/Xeeeff3a+/vqr9Op1flZYYcW8+upLufrqK7PMMnVz0EEH55tvvkn//lfn4osvS61atXL22adms822rFhv1qxZufrqK3L00cenceMmv9h+AQAAgCWbJ/oBWOq8+OILmTx5ck47rVdat149rVuvntNPPydTpkzJSy/9fa5z/vOfb/L++//KiSeemk6dNs5KK7XMvvsekE6dNs5zzz2TJHnzzaFZe+11stlmW2TjjTunTZu18uabr1es8ec/35P69etnt932/EX2CQAAACwdPNEPwFKnpKQkSVKz5v//vrtWrVopKSnJ/3RVsfzyLfLkk8/Ota9mzZpJkrFjx2T55VtUtLdosULGjBmdJBk9elTuvPP2XH31DalRw/fsAAAAwMIjaQBgqbPttjukadNmueWWGzN16pTMnDkzt99+c5o0aZKtt95+vtaYNWtWhgx5JO+++04OOujgJEl5eXmlLwpq1ChJeXl5kqR//6uy/fa/ztprt1vo+wEAAACWbp7oB2Cp06xZs/Trd13OOKNnBg78c2rUqJGmTZvlyiuvS7NmzX50/jHHHJ733/9nmjRpmt69L8mWW26TJFluueUqHdUzatSodOy4cd5664288cbQ3HvvwDzzzFO5445bM3369Oyww045+ujjFtU2AQAAgKWEJ/oBWOqMGTMm55xzRjbYYMPccMOAXHfdzenUaeOcffap+eabb350fu/el2bAgDuz11775vzze+Wpp55IkrRv3yn/+td7GT7883z++Wd5//1/Zv31N0y/fpfnt7/9XcrLkz59Lsx5512UAQPuzNNP/yWvvvryot4uAAAAsITzRD8AS5377787M2bMyHnnXZRatb7/v8J27dbL/vvvmT//+Z6cdNKpPzh/+eVbZPnlW6RNm7Xy3Xff5aqrrsgOO+yUlVZqmYMOOiSHHHJAkuSww36bv//9+dStWzd77LF3Xnnlpay00spp23atJMnmm2+Vt956PZ07b7ZoNwwAAAAs0QT9ACx1vvjis6y0UsuKkD/5/oW6K6ywYkaOHD7XOd9883X+8Y93st12O1aa17r16pk0aWLGjx+X5ZZrniOPPCbduvVIkkydOjUHH9w1/fr1T40aNTJp0sTUr1+/Ym79+vUzbtzYRbRLAAAAYGnh6B4Aljqlpb/KV199mVmzZlW0zZ49OyNHjkhp6a/mOmfEiOG58MLz8u67b1dqHzbskyyzzDJp3LhJRVv9+vVTv379XH/91dl22x2yzjrrJkkaNmyY8ePHV4wbP35cGjVqvDC3BgAAACyFBP0ALHX23rtrxo8flyuv7JNhwz7Jp58Oy1VXXZ5x48Zmp512rRjXrdu+efjhh5IkHTp0ylprrZPLL78kr776cr78cmQee+zhPPzwQ9l11z1Su3btStd455238tprr+SYY46vaGvXbv2MGvVNnnnmqbz//j/z978/lw4dOv0SWwYAAACWYI7uAWCps+aabXPNNTfm5pv756ijfpMkWWONNrnssn7ZYIMNK8YNH/5Fvv322yTfH+1z+eVX5aab+ueSSy7IlClTsuKKK+aww36bAw88uNL6s2bNSr9+l+Woo45NkyZNK9qbNWuWc865INdff02mTZuWvffumk033XxRbxcAAABYwpWUl5eXV3cRVK9uZ9xT3SUALDL3Xt49o0dPqu4ygCVMaWkj9xZgseUeBSzO3KNgwZWWNppnn6N7AAAAAACgwAT9AAAAAABQYIJ+AAAAAAAoMEE/AAAAAAAUmKAfAAAAAAAKTNAPAAAAAAAFJugHAAAAAGChOP74o7LFFp0W2fpjxozJKaccn+222yzbb795Ro36zyK71k/1+OOPZostOuXhhx/6xa9d6xe/IgAAAAAAS6QzzuiVqVOnLrL1H3zwvgwd+mp+85sj0rnzZmnSpOkiu1aRCPoBAAAAAFgoVlml1SJdf/z4cUmSXXfdIyuuuNIivVaROLoHAAAAAICF4v8e3fPoow/nyCN7pEuX7bL99pvnoIP2yU039c/06dN+8tpbbNEpjz/+aJJk//33zBZbdMrXX3+VSy65IFts0Sn//veH+e1ve2S77TbLN998kySZOnVq+ve/OgccsFe23XbT7LLL9jnmmMPz/PPPVlr7tttuzhZbdMqrr75cqf3rr7/KFlt0yvnnn12p/d5778x+++2ZbbfdNPvtt0fuvPP2zJkz5yfvaWHxRD8AAAAAAAvdI4+U5fLLL8l++x2U3/3uxNSsWTPvvPNW7rrrjowY8UUuueSKn7TegAF35vbbb83LL/89f/hDvzRv3jzNm5dW9F97bb/suuseWWONtmnWrGmS5IILzslrr72cI444Ohts0CGTJ0/Kn/50W8455/RcccXV2XTTLX7yvv7853tyww3XZosttsopp5ye2bNn54knHsvw4V/85LUWFkE/AAAAAAAL3csv/z0NGzbMSSedWtG2wQbt07r16hk16j8pLy9PSUnJfK+31lrrpEmTJkmS1VdfIyussGKl/rXXbpe99upa8Xn69GlZZpll0q1bj/TocXhF+4orrpRDDtk/Tz75+E8O+svLy3PvvXelefPSXHzx5alV6/uIfbPNtsxhh3X/SWstTIJ+AAAAAAAWutLS5TN58uTceuuN2Wef/bLccs2TJFtuuc0iuV7nzptV+rzMMnVz0UV/qDJulVVWTZL85z/f/ORrfPXVlxk7dkx23HHnipA/SWrUqJGtttomw4Z9/JPXXBgE/QAAAAAALHTHHntCxowZnT/96bb86U+3pVWr1unYsVN23LFL1l13vYV+vWbNmlVp+/DD9/Pgg/fl7bffyvjx4zJz5syKvgU5U3/cuLFJUvGlxf9WWvqrn7zewiLoBwAAAABgoatfv3769LkyI0YMz2uvvZw333w9Q4Y8koEDH8iBBx6c448/eaFer2bNynH3J598nN/97sjUr98ghx56RNZcc63Ur18vSeb7mJ3y8vL5vr6X8QIAAAAAsERaeeVVsvLKq6Rr1wMzZcrk9Op1Ru6//+7su+/+Vc7ZX5iefHJIZsyYkd///qJss832Fe3ffFP1yJ4aNWokSWbNmlWpffTo0ZU+N2u2bJJk/PhxVdb4+uuvfnbNC6pGtV0ZAAAAAIAl0owZM3Lddf3y1FNPVmpv0KBhNtlk0yTJhAkTFmkNs2fPTvL9uwL+t/vuuzNJ5SfwGzf+/iW/33xTOax/4YW/Vfq84oorpWnTZhk69NVKXwrMnj07f//7cwup8p/OE/0AAAAAACxUderUyciRI/LwwwPz9ddfZr31Nkjt2rXzxRef5c9/vjutWrXOmmu2WaQ1dOy4UR588L7ceOO1Oeyw32bWrFl57LHBqV+/flZeeZV88cVnee21V7L22u2y2WZbpH//q3LvvXelWbPl0qxZs7z66sv55JNPKq1Zo0aNdO16QAYMuCm9ep2WvffeL7Nnz8pDD/05yyyzzCLdzw8R9AMAAAAAsNBdeOEfctddd+Svf30yd9/9x5SXl6e09Ff59a93SbduPVKzZs1Fev0tttgqJ5zQMwMHPpDTTz8pzZuXpkuX3dKjx+F5+um/5Jpr+ub888/OlVdem3XXXT8XXtgnt912Sy699ILUq1c/W2+9bS6++LJ06bJtpXV79Dg85eXleeyxwTn77FPTvHlpdtttz6yxxpo566xTF+me5qWk/Ke8TWAhOuuss1JWVvaj4/r06ZOysrIMHTq0Sl/dunWz2mqrZa+99srBBx+cWrXm/r3FUUcdleeffz6//e1vc9ppp1Xpf+2119KjR4+suOKKefzxx1OvXr0qYw455JCstNJK+cMf/lDRNnHixNxzzz154okn8vXXX2fq1Klp2rRp1l9//fzud7/Leuv9/zdH/9h+69Spk/feey9Jst122+XLL7+c9y8lyfXXX58ddtghI0eOzPbbb1+lv6SkJE2aNEn79u1z5JFHplOnTvNcq9sZ9/zgtQCK7N7Lu2f06EnVXQawhCktbeTeAiy23KOAxZl7FCy40tJG8+yrtif6jz/++HTv/v/fbPzAAw/kgQceyI033pjS0tKK9pYtW6asrCylpaW58cYbK60xbty4PP300+nTp08++uij9OnTp8p1/vOf/+TFF19M48aNM3jw4PTs2XOe3xR99dVXuemmm9KzZ88frf+7777LQQcdlDFjxuSwww5Lhw4dkiQff/xxbrvttvTo0SN33nlnpbA/SR566KG5rvfflz38V7t27dK7d+95Xn/VVVet9Hn//ffP/vvvX/F51qxZGT58eO6444785je/yYABA7Lpppv+6L4AAAAAACiWagv6W7ZsmZYtW1Z8fu6555Ikbdq0qdT+X3Xq1KkSmifJ1ltvnalTp2bQoEE5+eSTs/zylV+sMHjw4MyZMyc9e/ZM796989JLL2Wrrbaaa01dunTJ7bffnn333TerrLLKD9b/l7/8JZ988kluuOGGSk/Ub7zxxtluu+3y61//Ovfee2+VLx/mtoe5adCgwXyPTZJf/epXVca3b98+W2+9dbbddltBPwAAAACwWCkvL694Ye78mNeJLiwhZ/S3b98+jz32WL7++usqQf/AgQPToUOHdO3aNVdddVXKysrmGfT/7ne/y1tvvZVLLrkkN9988w9ec9SoUUmSlVZaqUrfCiuskFdeeSUNGzZcwB0tPE2bNs1qq632o0cBAQAAAAD8kt5++82ceOIx8z3+xRffWITVFNsSEfR/9NFHKSkpqfKXAG+++WY+//zzHHHEEalTp066dOmShx9+OBMnTkzjxo2rrFO/fv2cccYZOfXUU/Pss89m2223rTLmv9q2bZskueSSS3LFFVekRYsWlfoXh5A/+f6IoREjRqR9+/bVXQoAAAAAQIW11lond9zh/aELQ6GD/jFjxuTJJ5/MoEGDsttuu6V58+aV+gcOHJi6deumS5cuSZK99torf/7znzNkyJAcdNBBc11zt912y/33359LL700m2++eerUqTPXcVtttVV23HHH/PWvf812222Xjh07ZuONN06nTp3SqVOn1K5de+Fu9ieaNWtWPv/88/Tr1y+TJk1Kjx49qrUeAAAAAID/rX79+llzzbbVXcYSoTBB/5dfflnxFP3/1rRp0/To0aPKC3SnTp2aJ554IjvssEMaNfr+bcQdOnRIq1atUlZWNs+gP0nOO++87L333rn11ltz3HHHzXVMSUlJrrvuujz++ON56KGH8vrrr2fo0KGVajrmmGPm+eLfHzN06NC57jf5/kW9gwYNqtTWv3//9O/fv8rYtddeO/37988WW2wx17Vmz5yRey/vnjmzZqZGrer9cgJgUZg2fWZ1lwAAAACwSBUm6C8tLc2tt95a8XnMmDE5+uijs/vuu+fMM8+sMv7JJ5/M1KlTs+2222bcuHEV7dtvv31uu+22DBs2LKuvvvpcr9W2bdt069Ytt9xyS/bee++suOKKcx1XUlKSXXfdNbvuumumTZuWd955Jy+//HLKyspy7bXXZuzYsfn9739fZe256dChQ+67776Kz+3atcsll1wy17F169at0nbAAQdU+vJi4MCBufvuu3PRRRf94Et9a9aukzcvPzIdzxiQ0aMnzXMcQHUoLW3k3gQAAADwIwoT9NepUydrr712pbauXbvmvvvuy0EHHVQltP/vE++nnnrqXNcrKyvLaaedNs/rnXjiiRkyZEj69OmT66677kfrq1u3bjp37pzOnTvniCOOyCGHHJIHHnggZ599dqVjfB5++OG5zq9fv36lzw0aNKiy3x9SWlpaafzJJ5+cIUOG5NJLL630BQIAAAAAAEuWwgT9c3PCCSfk0UcfzaWXXprbbruton348OF54403sueee2avvfaqMu/qq6/O4MGD07Nnz3kerdO4ceOccsopOffcc/PSSy9V6isvL8/bb7+dmTNnZpNNNqkyt0mTJtl6663z0UcfZcKECZXeHfBTwvufo2HDhvnd736Xiy++OEOGDMmuu+76i1wXAAAAAIBfVo3qLuDnKC0tzaGHHpoXX3wxzz33XEX7wIEDU15eniOPPDKbbbZZlZ/9998/o0aNqhLg/19du3bN+uuvn4suuigzZ/7/M55LSkpy8cUX55RTTsnYsWOrzJs1a1beeOONlJaWZtlll11o+/2pDjzwwKyyyiq58sorM23atGqrAwAAAACARafQQX+SHHnkkVluueXyhz/8ITNnzsycOXPy8MMPp127dmnTps1c5+y8886pV69eysrKfnDtkpKSnHfeefn888/zzjvvVOrr1atXpk6dmv322y9//OMf89JLL+WNN97Iww8/nEMPPTTvvPNOevXqlRo1FuxXPGXKlLz33nvz/BkxYsSPrlG7du307NkzX331VQYMGLBAdQAAAAAAsHgr9NE9yfdn2R933HG58MILc/fdd2eNNdbIN998k9/+9rfznNOwYcPssMMOeeqppzJx4sQfXH/99dfPvvvum4ceeqhSe6dOnfLggw9mwIABufPOOzNq1KgkSfPmzdOxY8c88MADP/gS3B/zr3/9K127dp1n/y677JKrrrrqR9fZZZddcscdd2TAgAHp2rVrWrRoscA1AQAAAACw+CkpLy8vr+4iqF5vXn5kOp4xIKNHT6ruUgAqKS1t5N4ELJbcn4DFmXsUsDhzj1pyNGpcN3WXqV3dZVSYNn1mJk1cso8vLy1tNM++wj/RDwAAAADAL6vuMrXT7Yx7qruMCvde3j2TsmQH/T+k8Gf0AwAAAADA0swT/QAAAAAALLWOP/6ozJgxI6ef3ivXXts3H374fho1apwddtgpRx31u9Sq9X2M/vDDD2XQoAczcuSI1K5dO+uss26OOOKYrLvugr+rdWHxRD8AAAAAAEu1iRMn5uKLf5+dd941l112Vdq375h7770zgwcPSpLceeftufLKP6RDh41y2WVX5dxze2fGjBk58cRjMmzYJ9VcvaAfAAAAAICl3MiRw3PccSdn1133SIcOnXLaaWendu3aeeut1zNt2rTcddcfs+WW2+Tkk0/LRhttki233CZ9+lyZkpLk3nvvrO7yBf0AAAAAACzd6tSpk4022qTic7169dKkSdNMmjQpH3zwr3z33dRsueXWleY0btwk7dqtl3/8451fuNqqBP38oJEjR+T000/KjjtumZ122jrnn98r48eP/9F5M2bMyGWXXZIttuiUxx9/tEr/LbfckN122yFdu+6eF154rkr/o48+nG7d9s3MmTMXxjYAAAAAAOapWbNlU1JSUqmtVq1amTNnTsaMGZ0kKS0trTJvueWaV/RXJ0E/8zRt2rT07Hlc5swpz3XX3Zy+fa/LV1+NTK9ep6W8vHye87766sscc8zh+cc/3p5r/3vvvZtBgx5Mnz59c8QRR6dPnwszffr0iv6JEyfm5pv7p2fP01O7du2Fvi8AAAAAgP/t/4b889tXXl6ekpLqj9mrvwIWW3/5y+MZPXpUzj33gqy11jpZd931c8YZ5+a9997N22+/Oc95Dz54f9ZYY8307XvdXPuHDn01m2++ZdZbb4N06bJb6tatmw8++FdF/y233JANN+yQjTbqvND3BAAAAADwU5SWLp8kGTVqVJW+MWNGz/VJ/1+aoJ95evPN17Pmmm3SrNmyFW3//fz666/Nc17XrgekV6/z5/k0/pgxY7L88i0qPrdo0SKjR3//P5KPPvowTz31RI4//pSFtAsAAAAAgAW31lprp2HDhnnhhWcrtY8fPz7vv//PdOy4UTVV9v/Vqu4CWHyNHDkiK63Uskr7CiusmBEjhs9z3tzmVFZe6c9dSkpqpLz8+z9z6dfvshxyyKFp0aLFD8wHAAAAAPhlLLPMMjn00CPTv//Vufbavtlii60zceKE3HvvXalTp066d/9NdZco6Gfepk6dmnr16lVpr1evfqZOnbLA6y677HIVT/AnyejRo9K8efM8/vijmTBhQg488OA88MB9GTTogZSXl2e//Q5M164HLvD1AAAAAAB+jgMPPDj16tXPQw/dn7Kyh7LMMstkww075KyzzpuPB58XPUE/v7gOHTqld+9zM27c2Hz66bBMmDAhq6yyas4/v1fOPbd3hg//InfccWvuuOOelJeX57DDuqVTp03SqtVq1V06AAAAAJBk2vSZuffy7tVdRoVp02cu8Nz+/W+Za/tDDz1a6fOee+6TPffcZ4GvsygJ+pmnhg0bZsqUyVXap0yZnBVWWHGB1+3YcaNsssmm2XvvXVKzZs2ceupZueuuO7L++htkk002zcCBf84GG2yYFi1WSJJsuGGHvPXWG4J+AAAAAFhMTJo4LZMyrbrL4H94GS/z1LLlyvnyyy8rtZWXl2fkyBE/O3Q/55wL8thjT+eJJ55NmzZr5YknhuSEE05NkkyaNCn16zeoGFu/foNMnDjhZ10PAAAAAGBJJehnnjbZZNN89tmwSufpv/feu5k8eVI6d978Z6/fqFGj1KlTJ/36XZbu3XtUvIC3QYOGGT9+XMW4cePGplGjxj/7egAAAAAASyJBP/O0ww47ZaWVWubiiy/Ip58Oy4cfvp8rr+yTzp03S7t26yb5/kW63brtmxdffKFi3vjx4zN27Jh8++23SZLJkydn7NgxGTt2TJVrPPnkkIwfP/7/tXfnUVnWeR/HP6xiguKCG6mEyC0qKIGW5oqPiMvIaHMY13yOOqYhTKMjaJZP9ZyMEZcUc2k8RxvNDczKJUWjwdSxUTInnUlsMTd0IlAoF7jhev7wcM/w4JYNXVzc79c5/nH/ft/r4nf5x+fI19/9uzR69FOOsfDwCB0//qlyco7oyJHD+tvfPtWjj0ZW78MCAAAAAAAAgEVxRj/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      "text/plain": [
       "<Figure size 1800x864 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "ax = sns.countplot(y='type', hue='is_fraud', data=df4)\n",
    "\n",
    "total = df4['type'].size\n",
    "for p in ax.patches:\n",
    "        percentage = ' {:.1f}%'.format(100 * p.get_width()/total)\n",
    "        x = p.get_x() + p.get_width() + 0.02\n",
    "        y = p.get_y() + p.get_height()/2\n",
    "        ax.annotate(percentage, (x, y))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "hidden": true
   },
   "source": [
    "### H4 Values greater than 100.000 occours using transfers-type method.\n",
    "\n",
    "**FALSE:** The majority transactions occours in trasnfer-type, however transactions greater than 100.000 occour in cash-out and cash-in too."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 260,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2021-01-11T18:48:50.994068Z",
     "start_time": "2021-01-11T18:48:39.989305Z"
    },
    "hidden": true
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1800x864 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "ax = sns.barplot(y='type', x='amount', data=df4);\n",
    "\n",
    "total = df4['type'].size\n",
    "for p in ax.patches:\n",
    "        percentage = ' {:.1f}%'.format(100 * p.get_width()/total)\n",
    "        x = p.get_x() + p.get_width() + 0.02\n",
    "        y = p.get_y() + p.get_height()/2\n",
    "        ax.annotate(percentage, (x, y))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "hidden": true
   },
   "source": [
    "### H5 Fraud transactions occours at least in 3 days.\n",
    "\n",
    "**TRUE:** The values for transactions and days in fraud aren't similar."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 261,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2021-01-11T18:48:51.755961Z",
     "start_time": "2021-01-11T18:48:50.996915Z"
    },
    "hidden": true
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1800x864 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "aux1 = df4[df4['is_fraud'] == 'yes']\n",
    "sns.regplot(x='step_days', y='amount', data=aux1);"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "hidden": true
   },
   "source": [
    "## 4.3 Multivariaty Analysis"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "hidden": true
   },
   "source": [
    "### 4.3.1 Numerical Analysis"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 262,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2021-01-11T18:48:52.874628Z",
     "start_time": "2021-01-11T18:48:51.759338Z"
    },
    "hidden": true
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1800x864 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "corr = num_attributes.corr()\n",
    "\n",
    "mask = np.zeros_like(corr)\n",
    "mask[np.triu_indices_from(mask)] = True\n",
    "\n",
    "with sns.axes_style(\"white\"):\n",
    "    ax = sns.heatmap(corr, annot=True, mask=mask, vmin=-1, center=0, vmax=1, square=True)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "hidden": true
   },
   "source": [
    "### 4.3.2 Categorical Variables"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 263,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2021-01-11T18:48:56.147550Z",
     "start_time": "2021-01-11T18:48:52.876985Z"
    },
    "hidden": true
   },
   "outputs": [],
   "source": [
    "dict_corr = {}\n",
    "columns = cat_attributes.columns.tolist()\n",
    "\n",
    "for column in columns:\n",
    "    dict_corr[column] = {}\n",
    "    \n",
    "    for column2 in columns:\n",
    "        dict_corr[column][column2] = calcCramerV(cat_attributes[column], cat_attributes[column2])\n",
    "        \n",
    "corr = pd.DataFrame(dict_corr)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 264,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2021-01-11T18:48:56.593007Z",
     "start_time": "2021-01-11T18:48:56.149515Z"
    },
    "hidden": true
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1800x864 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "mask = np.zeros_like(corr)\n",
    "mask[np.triu_indices_from(mask)] = True\n",
    "\n",
    "with sns.axes_style(\"white\"):\n",
    "    ax = sns.heatmap(corr, annot=True, mask=mask, vmin=0, vmax=1, square=True)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "heading_collapsed": true
   },
   "source": [
    "# 5.0 Data Preparation"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 347,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2021-01-11T21:08:18.459835Z",
     "start_time": "2021-01-11T21:08:18.344976Z"
    },
    "hidden": true
   },
   "outputs": [],
   "source": [
    "df5 = df4.copy()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "hidden": true
   },
   "source": [
    "## 5.1 Spliting into Train, Valid and Test"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 348,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2021-01-11T21:08:28.207352Z",
     "start_time": "2021-01-11T21:08:28.110364Z"
    },
    "hidden": true
   },
   "outputs": [],
   "source": [
    "X = df5.drop(columns=['is_fraud', 'is_flagged_fraud', 'name_orig', 'name_dest', \n",
    "                      'step_weeks', 'step_days'], axis=1)\n",
    "y = df5['is_fraud'].map({'yes': 1, 'no': 0})"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 349,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2021-01-11T21:08:29.045814Z",
     "start_time": "2021-01-11T21:08:28.582692Z"
    },
    "hidden": true
   },
   "outputs": [],
   "source": [
    "# spliting into temp and test\n",
    "X_temp, X_test, y_temp, y_test = train_test_split(X, y, test_size=.2, stratify=y)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 350,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2021-01-11T21:08:29.458292Z",
     "start_time": "2021-01-11T21:08:29.049935Z"
    },
    "hidden": true
   },
   "outputs": [],
   "source": [
    "# spliting into train and valid\n",
    "X_train, X_valid, y_train, y_valid = train_test_split(X_temp, y_temp, test_size=.2, stratify=y_temp)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "hidden": true
   },
   "source": [
    "## 5.2 One Hot Encoder"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 351,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2021-01-11T21:08:32.964462Z",
     "start_time": "2021-01-11T21:08:31.876822Z"
    },
    "hidden": true
   },
   "outputs": [],
   "source": [
    "ohe = OneHotEncoder(cols=['type'], use_cat_names=True)\n",
    "\n",
    "X_train = ohe.fit_transform(X_train)\n",
    "X_valid = ohe.transform(X_valid)\n",
    "\n",
    "X_temp = ohe.fit_transform(X_temp)\n",
    "X_test = ohe.transform(X_test)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "hidden": true
   },
   "source": [
    "## 5.3 Rescaling"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 271,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2021-01-11T18:49:00.603027Z",
     "start_time": "2021-01-11T18:48:59.044877Z"
    },
    "hidden": true
   },
   "outputs": [],
   "source": [
    "num_columns = ['amount', 'oldbalance_org', 'newbalance_orig', 'oldbalance_dest', 'newbalance_dest',\n",
    "               'diff_new_old_balance', 'diff_new_old_destiny']\n",
    "mm = MinMaxScaler()\n",
    "X_params = X_temp.copy()\n",
    "\n",
    "X_train[num_columns] = mm.fit_transform(X_train[num_columns])\n",
    "X_valid[num_columns] = mm.transform(X_valid[num_columns])\n",
    "\n",
    "X_params[num_columns] = mm.fit_transform(X_temp[num_columns])\n",
    "X_test[num_columns] = mm.transform(X_test[num_columns])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "heading_collapsed": true
   },
   "source": [
    "# 6.0 Feature Selection"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "hidden": true
   },
   "source": [
    "## 6.1 Boruta"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 272,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2021-01-11T18:49:09.114850Z",
     "start_time": "2021-01-11T18:49:09.111669Z"
    },
    "hidden": true
   },
   "outputs": [],
   "source": [
    "# X_boruta = X_params.values\n",
    "# y_boruta = y_temp.values.ravel()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 273,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2021-01-11T18:49:09.529190Z",
     "start_time": "2021-01-11T18:49:09.520638Z"
    },
    "hidden": true
   },
   "outputs": [],
   "source": [
    "# boruta = BorutaPy(RandomForestClassifier(), n_estimators='auto')\n",
    "# boruta.fit(X_boruta, y_boruta)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "hidden": true
   },
   "source": [
    "### 6.1.1 Best Features"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 274,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2021-01-11T18:49:10.256466Z",
     "start_time": "2021-01-11T18:49:10.250350Z"
    },
    "hidden": true
   },
   "outputs": [],
   "source": [
    "# cols_selected_boruta = boruta.support_.tolist()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 275,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2021-01-11T18:49:10.575397Z",
     "start_time": "2021-01-11T18:49:10.570081Z"
    },
    "hidden": true
   },
   "outputs": [],
   "source": [
    "# columns_selected = X_params.loc[:, cols_selected_boruta].columns.tolist()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 276,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2021-01-11T18:49:10.883715Z",
     "start_time": "2021-01-11T18:49:10.878218Z"
    },
    "hidden": true
   },
   "outputs": [],
   "source": [
    "# columns_selected"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 277,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2021-01-11T18:49:11.303967Z",
     "start_time": "2021-01-11T18:49:11.301425Z"
    },
    "hidden": true
   },
   "outputs": [],
   "source": [
    "# ['step',\n",
    "#  'amount',\n",
    "#  'oldbalance_org',\n",
    "#  'newbalance_orig',\n",
    "#  'oldbalance_dest',\n",
    "#  'newbalance_dest',\n",
    "#  'diff_new_old_balance',\n",
    "#  'diff_new_old_destiny',\n",
    "#  'type_TRANSFER']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 278,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2021-01-11T18:49:11.686754Z",
     "start_time": "2021-01-11T18:49:11.682897Z"
    },
    "hidden": true
   },
   "outputs": [],
   "source": [
    "final_columns_selected = ['step', 'oldbalance_org', \n",
    "                          'newbalance_orig', 'newbalance_dest', \n",
    "                          'diff_new_old_balance', 'diff_new_old_destiny', \n",
    "                          'type_TRANSFER']"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "heading_collapsed": true
   },
   "source": [
    "# 7.0 Machine Learning Modeling"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 279,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2021-01-11T18:49:15.589482Z",
     "start_time": "2021-01-11T18:49:15.542574Z"
    },
    "hidden": true
   },
   "outputs": [],
   "source": [
    "X_train_cs = X_train[final_columns_selected]\n",
    "X_valid_cs = X_valid[final_columns_selected]\n",
    "\n",
    "X_temp_cs = X_temp[final_columns_selected]\n",
    "X_test_cs = X_test[final_columns_selected]\n",
    "\n",
    "X_params_cs = X_params[final_columns_selected]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "hidden": true
   },
   "source": [
    "## 7.1 Baseline"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 280,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2021-01-11T18:49:16.207942Z",
     "start_time": "2021-01-11T18:49:16.165042Z"
    },
    "hidden": true
   },
   "outputs": [],
   "source": [
    "dummy = DummyClassifier()\n",
    "dummy.fit(X_train_cs, y_train)\n",
    "\n",
    "y_pred = dummy.predict(X_valid_cs)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 281,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2021-01-11T18:49:16.806621Z",
     "start_time": "2021-01-11T18:49:16.392671Z"
    },
    "hidden": true
   },
   "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>Balanced Accuracy</th>\n",
       "      <th>Precision</th>\n",
       "      <th>Recall</th>\n",
       "      <th>F1</th>\n",
       "      <th>Kappa</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>dummy</th>\n",
       "      <td>0.499</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>-0.001</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "       Balanced Accuracy  Precision  Recall   F1  Kappa\n",
       "dummy              0.499        0.0     0.0  0.0 -0.001"
      ]
     },
     "execution_count": 281,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "dummy_results = ml_scores('dummy', y_valid, y_pred)\n",
    "dummy_results"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "hidden": true
   },
   "source": [
    "### 7.1.1 Classification Report"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 282,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2021-01-11T18:49:17.080288Z",
     "start_time": "2021-01-11T18:49:16.873841Z"
    },
    "hidden": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "              precision    recall  f1-score   support\n",
      "\n",
      "           0       1.00      1.00      1.00    101671\n",
      "           1       0.00      0.00      0.00       131\n",
      "\n",
      "    accuracy                           1.00    101802\n",
      "   macro avg       0.50      0.50      0.50    101802\n",
      "weighted avg       1.00      1.00      1.00    101802\n",
      "\n"
     ]
    }
   ],
   "source": [
    "print(classification_report(y_valid, y_pred))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "hidden": true
   },
   "source": [
    "### 7.1.2 Cross Validation"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 283,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2021-01-11T18:49:20.042373Z",
     "start_time": "2021-01-11T18:49:17.255171Z"
    },
    "hidden": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Fold K=1\n",
      "Fold K=2\n",
      "Fold K=3\n",
      "Fold K=4\n",
      "Fold K=5\n"
     ]
    },
    {
     "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>Balanced Accuracy</th>\n",
       "      <th>Precision</th>\n",
       "      <th>Recall</th>\n",
       "      <th>F1</th>\n",
       "      <th>Kappa</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>Dummy</th>\n",
       "      <td>0.499 +/- 0.0</td>\n",
       "      <td>0.0 +/- 0.0</td>\n",
       "      <td>0.0 +/- 0.0</td>\n",
       "      <td>0.0 +/- 0.0</td>\n",
       "      <td>-0.001 +/- 0.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "      Balanced Accuracy    Precision       Recall           F1           Kappa\n",
       "Dummy     0.499 +/- 0.0  0.0 +/- 0.0  0.0 +/- 0.0  0.0 +/- 0.0  -0.001 +/- 0.0"
      ]
     },
     "execution_count": 283,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "dummy_cv = ml_cv_results('Dummy', DummyClassifier(), X_temp, y_temp)\n",
    "dummy_cv"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "hidden": true
   },
   "source": [
    "## 7.2 Logistic Regression"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 284,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2021-01-11T18:49:26.692331Z",
     "start_time": "2021-01-11T18:49:20.044652Z"
    },
    "hidden": true
   },
   "outputs": [],
   "source": [
    "lg = LogisticRegression()\n",
    "lg.fit(X_train_cs, y_train)\n",
    "\n",
    "y_pred = lg.predict(X_valid_cs)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 285,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2021-01-11T18:49:27.135619Z",
     "start_time": "2021-01-11T18:49:26.696473Z"
    },
    "hidden": true
   },
   "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>Balanced Accuracy</th>\n",
       "      <th>Precision</th>\n",
       "      <th>Recall</th>\n",
       "      <th>F1</th>\n",
       "      <th>Kappa</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>Logistic Regression</th>\n",
       "      <td>0.584</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.168</td>\n",
       "      <td>0.288</td>\n",
       "      <td>0.287</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                     Balanced Accuracy  Precision  Recall     F1  Kappa\n",
       "Logistic Regression              0.584        1.0   0.168  0.288  0.287"
      ]
     },
     "execution_count": 285,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "lg_results = ml_scores('Logistic Regression', y_valid, y_pred)\n",
    "lg_results"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "hidden": true
   },
   "source": [
    "### 7.2.1 Classification Report"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 286,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2021-01-11T18:49:27.359204Z",
     "start_time": "2021-01-11T18:49:27.138782Z"
    },
    "hidden": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "              precision    recall  f1-score   support\n",
      "\n",
      "           0       1.00      1.00      1.00    101671\n",
      "           1       1.00      0.17      0.29       131\n",
      "\n",
      "    accuracy                           1.00    101802\n",
      "   macro avg       1.00      0.58      0.64    101802\n",
      "weighted avg       1.00      1.00      1.00    101802\n",
      "\n"
     ]
    }
   ],
   "source": [
    "print(classification_report(y_valid, y_pred))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "hidden": true
   },
   "source": [
    "### 7.2.2 Cross Validation"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 287,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2021-01-11T18:49:45.056149Z",
     "start_time": "2021-01-11T18:49:27.362727Z"
    },
    "hidden": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Fold K=1\n",
      "Fold K=2\n",
      "Fold K=3\n",
      "Fold K=4\n",
      "Fold K=5\n"
     ]
    },
    {
     "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>Balanced Accuracy</th>\n",
       "      <th>Precision</th>\n",
       "      <th>Recall</th>\n",
       "      <th>F1</th>\n",
       "      <th>Kappa</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>Logistic Regression</th>\n",
       "      <td>0.565 +/- 0.009</td>\n",
       "      <td>1.0 +/- 0.0</td>\n",
       "      <td>0.129 +/- 0.017</td>\n",
       "      <td>0.229 +/- 0.027</td>\n",
       "      <td>0.228 +/- 0.027</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                    Balanced Accuracy    Precision           Recall               F1            Kappa\n",
       "Logistic Regression   0.565 +/- 0.009  1.0 +/- 0.0  0.129 +/- 0.017  0.229 +/- 0.027  0.228 +/- 0.027"
      ]
     },
     "execution_count": 287,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "lg_cv = ml_cv_results('Logistic Regression',\n",
    "                      LogisticRegression(), \n",
    "                      X_temp_cs, y_temp)\n",
    "lg_cv"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "hidden": true
   },
   "source": [
    "## 7.3 K Nearest Neighbors"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 288,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2021-01-11T18:49:57.229314Z",
     "start_time": "2021-01-11T18:49:45.059296Z"
    },
    "hidden": true
   },
   "outputs": [],
   "source": [
    "knn = KNeighborsClassifier()\n",
    "knn.fit(X_train_cs, y_train)\n",
    "\n",
    "y_pred = knn.predict(X_valid_cs)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 289,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2021-01-11T18:49:57.583127Z",
     "start_time": "2021-01-11T18:49:57.231046Z"
    },
    "hidden": true
   },
   "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>Balanced Accuracy</th>\n",
       "      <th>Precision</th>\n",
       "      <th>Recall</th>\n",
       "      <th>F1</th>\n",
       "      <th>Kappa</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>K Nearest Neighbors</th>\n",
       "      <td>0.565</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.13</td>\n",
       "      <td>0.23</td>\n",
       "      <td>0.23</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                     Balanced Accuracy  Precision  Recall    F1  Kappa\n",
       "K Nearest Neighbors              0.565        1.0    0.13  0.23   0.23"
      ]
     },
     "execution_count": 289,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "knn_results = ml_scores('K Nearest Neighbors', y_valid, y_pred)\n",
    "knn_results"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "hidden": true
   },
   "source": [
    "### 7.3.1 Classification Report"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 290,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2021-01-11T18:49:57.762156Z",
     "start_time": "2021-01-11T18:49:57.586973Z"
    },
    "hidden": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "              precision    recall  f1-score   support\n",
      "\n",
      "           0       1.00      1.00      1.00    101671\n",
      "           1       1.00      0.13      0.23       131\n",
      "\n",
      "    accuracy                           1.00    101802\n",
      "   macro avg       1.00      0.56      0.61    101802\n",
      "weighted avg       1.00      1.00      1.00    101802\n",
      "\n"
     ]
    }
   ],
   "source": [
    "print(classification_report(y_valid, y_pred))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "hidden": true
   },
   "source": [
    "### 7.3.2 Cross Validation"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 291,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2021-01-11T19:07:47.919357Z",
     "start_time": "2021-01-11T18:49:57.764539Z"
    },
    "hidden": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Fold K=1\n",
      "Fold K=2\n",
      "Fold K=3\n",
      "Fold K=4\n",
      "Fold K=5\n"
     ]
    },
    {
     "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>Balanced Accuracy</th>\n",
       "      <th>Precision</th>\n",
       "      <th>Recall</th>\n",
       "      <th>F1</th>\n",
       "      <th>Kappa</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>K Nearest Neighbors</th>\n",
       "      <td>0.705 +/- 0.037</td>\n",
       "      <td>0.942 +/- 0.022</td>\n",
       "      <td>0.409 +/- 0.074</td>\n",
       "      <td>0.568 +/- 0.073</td>\n",
       "      <td>0.567 +/- 0.073</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                    Balanced Accuracy        Precision           Recall               F1            Kappa\n",
       "K Nearest Neighbors   0.705 +/- 0.037  0.942 +/- 0.022  0.409 +/- 0.074  0.568 +/- 0.073  0.567 +/- 0.073"
      ]
     },
     "execution_count": 291,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "knn_cv = ml_cv_results('K Nearest Neighbors', KNeighborsClassifier(), \n",
    "                       X_temp_cs, y_temp)\n",
    "knn_cv"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "hidden": true
   },
   "source": [
    "## 7.4 Support Vector Machine"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 292,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2021-01-11T19:08:11.602901Z",
     "start_time": "2021-01-11T19:07:47.928024Z"
    },
    "hidden": true
   },
   "outputs": [],
   "source": [
    "svm = SVC()\n",
    "svm.fit(X_train_cs, y_train)\n",
    "\n",
    "y_pred = svm.predict(X_valid_cs)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 293,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2021-01-11T19:08:12.006181Z",
     "start_time": "2021-01-11T19:08:11.604735Z"
    },
    "hidden": true
   },
   "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>Balanced Accuracy</th>\n",
       "      <th>Precision</th>\n",
       "      <th>Recall</th>\n",
       "      <th>F1</th>\n",
       "      <th>Kappa</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>SVM</th>\n",
       "      <td>0.5</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "     Balanced Accuracy  Precision  Recall   F1  Kappa\n",
       "SVM                0.5        0.0     0.0  0.0    0.0"
      ]
     },
     "execution_count": 293,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "svm_results = ml_scores('SVM', y_valid, y_pred)\n",
    "svm_results"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "hidden": true
   },
   "source": [
    "### 7.4.1 Classification Report"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 294,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2021-01-11T19:08:12.197097Z",
     "start_time": "2021-01-11T19:08:12.008910Z"
    },
    "hidden": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "              precision    recall  f1-score   support\n",
      "\n",
      "           0       1.00      1.00      1.00    101671\n",
      "           1       0.00      0.00      0.00       131\n",
      "\n",
      "    accuracy                           1.00    101802\n",
      "   macro avg       0.50      0.50      0.50    101802\n",
      "weighted avg       1.00      1.00      1.00    101802\n",
      "\n"
     ]
    }
   ],
   "source": [
    "print(classification_report(y_valid, y_pred))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "hidden": true
   },
   "source": [
    "### 7.4.2 Cross Validation"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 295,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2021-01-11T19:09:56.802520Z",
     "start_time": "2021-01-11T19:08:12.200264Z"
    },
    "hidden": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Fold K=1\n",
      "Fold K=2\n",
      "Fold K=3\n",
      "Fold K=4\n",
      "Fold K=5\n"
     ]
    },
    {
     "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>Balanced Accuracy</th>\n",
       "      <th>Precision</th>\n",
       "      <th>Recall</th>\n",
       "      <th>F1</th>\n",
       "      <th>Kappa</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>SVM</th>\n",
       "      <td>0.595 +/- 0.013</td>\n",
       "      <td>1.0 +/- 0.0</td>\n",
       "      <td>0.19 +/- 0.026</td>\n",
       "      <td>0.319 +/- 0.037</td>\n",
       "      <td>0.319 +/- 0.037</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "    Balanced Accuracy    Precision          Recall               F1            Kappa\n",
       "SVM   0.595 +/- 0.013  1.0 +/- 0.0  0.19 +/- 0.026  0.319 +/- 0.037  0.319 +/- 0.037"
      ]
     },
     "execution_count": 295,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "svm_cv = ml_cv_results('SVM', SVC(), X_temp_cs, y_temp)\n",
    "svm_cv"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "hidden": true
   },
   "source": [
    "## 7.5 Random Forest"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 296,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2021-01-11T19:10:42.703370Z",
     "start_time": "2021-01-11T19:09:56.805739Z"
    },
    "hidden": true
   },
   "outputs": [],
   "source": [
    "rf = RandomForestClassifier(class_weight='balanced')\n",
    "rf.fit(X_train_cs, y_train)\n",
    "\n",
    "y_pred = rf.predict(X_valid_cs)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 297,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2021-01-11T19:10:43.040609Z",
     "start_time": "2021-01-11T19:10:42.705221Z"
    },
    "hidden": true
   },
   "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>Balanced Accuracy</th>\n",
       "      <th>Precision</th>\n",
       "      <th>Recall</th>\n",
       "      <th>F1</th>\n",
       "      <th>Kappa</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>Random Forest</th>\n",
       "      <td>0.844</td>\n",
       "      <td>0.978</td>\n",
       "      <td>0.687</td>\n",
       "      <td>0.807</td>\n",
       "      <td>0.807</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "               Balanced Accuracy  Precision  Recall     F1  Kappa\n",
       "Random Forest              0.844      0.978   0.687  0.807  0.807"
      ]
     },
     "execution_count": 297,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "rf_results = ml_scores('Random Forest', y_valid, y_pred)\n",
    "rf_results"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "hidden": true
   },
   "source": [
    "### 7.5.1 Classification Report"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 298,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2021-01-11T19:10:43.220005Z",
     "start_time": "2021-01-11T19:10:43.043253Z"
    },
    "hidden": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "              precision    recall  f1-score   support\n",
      "\n",
      "           0       1.00      1.00      1.00    101671\n",
      "           1       0.98      0.69      0.81       131\n",
      "\n",
      "    accuracy                           1.00    101802\n",
      "   macro avg       0.99      0.84      0.90    101802\n",
      "weighted avg       1.00      1.00      1.00    101802\n",
      "\n"
     ]
    }
   ],
   "source": [
    "print(classification_report(y_valid, y_pred))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "hidden": true
   },
   "source": [
    "### 7.5.2 Cross Validation"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 299,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2021-01-11T19:15:54.845672Z",
     "start_time": "2021-01-11T19:10:43.223572Z"
    },
    "hidden": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Fold K=1\n",
      "Fold K=2\n",
      "Fold K=3\n",
      "Fold K=4\n",
      "Fold K=5\n"
     ]
    },
    {
     "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>Balanced Accuracy</th>\n",
       "      <th>Precision</th>\n",
       "      <th>Recall</th>\n",
       "      <th>F1</th>\n",
       "      <th>Kappa</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>Random Forest</th>\n",
       "      <td>0.865 +/- 0.017</td>\n",
       "      <td>0.972 +/- 0.014</td>\n",
       "      <td>0.731 +/- 0.033</td>\n",
       "      <td>0.834 +/- 0.022</td>\n",
       "      <td>0.833 +/- 0.022</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "              Balanced Accuracy        Precision           Recall               F1            Kappa\n",
       "Random Forest   0.865 +/- 0.017  0.972 +/- 0.014  0.731 +/- 0.033  0.834 +/- 0.022  0.833 +/- 0.022"
      ]
     },
     "execution_count": 299,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "rf_cv = ml_cv_results('Random Forest', \n",
    "                      RandomForestClassifier(), \n",
    "                      X_temp_cs, y_temp)\n",
    "rf_cv"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "hidden": true
   },
   "source": [
    "## 7.6 XGBoost"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 301,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2021-01-11T19:17:29.668827Z",
     "start_time": "2021-01-11T19:17:03.475475Z"
    },
    "hidden": true
   },
   "outputs": [],
   "source": [
    "xgb = XGBClassifier()\n",
    "xgb.fit(X_train_cs, y_train)\n",
    "\n",
    "y_pred = xgb.predict(X_valid_cs)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 302,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2021-01-11T19:17:30.046338Z",
     "start_time": "2021-01-11T19:17:29.672182Z"
    },
    "hidden": true
   },
   "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>Balanced Accuracy</th>\n",
       "      <th>Precision</th>\n",
       "      <th>Recall</th>\n",
       "      <th>F1</th>\n",
       "      <th>Kappa</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>XGBoost</th>\n",
       "      <td>0.863</td>\n",
       "      <td>0.969</td>\n",
       "      <td>0.725</td>\n",
       "      <td>0.83</td>\n",
       "      <td>0.83</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "         Balanced Accuracy  Precision  Recall    F1  Kappa\n",
       "XGBoost              0.863      0.969   0.725  0.83   0.83"
      ]
     },
     "execution_count": 302,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "xgb_results = ml_scores('XGBoost', y_valid, y_pred)\n",
    "xgb_results"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "hidden": true
   },
   "source": [
    "### 7.6.1 Classification Report"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 303,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2021-01-11T19:17:30.279678Z",
     "start_time": "2021-01-11T19:17:30.052006Z"
    },
    "hidden": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "              precision    recall  f1-score   support\n",
      "\n",
      "           0       1.00      1.00      1.00    101671\n",
      "           1       0.97      0.73      0.83       131\n",
      "\n",
      "    accuracy                           1.00    101802\n",
      "   macro avg       0.98      0.86      0.91    101802\n",
      "weighted avg       1.00      1.00      1.00    101802\n",
      "\n"
     ]
    }
   ],
   "source": [
    "print(classification_report(y_valid, y_pred))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "hidden": true
   },
   "source": [
    "### 7.6.2 Cross Validation"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 305,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2021-01-11T19:21:43.707004Z",
     "start_time": "2021-01-11T19:19:26.657905Z"
    },
    "hidden": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Fold K=1\n",
      "Fold K=2\n",
      "Fold K=3\n",
      "Fold K=4\n",
      "Fold K=5\n"
     ]
    },
    {
     "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>Balanced Accuracy</th>\n",
       "      <th>Precision</th>\n",
       "      <th>Recall</th>\n",
       "      <th>F1</th>\n",
       "      <th>Kappa</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>XGBoost</th>\n",
       "      <td>0.88 +/- 0.016</td>\n",
       "      <td>0.963 +/- 0.008</td>\n",
       "      <td>0.761 +/- 0.033</td>\n",
       "      <td>0.85 +/- 0.023</td>\n",
       "      <td>0.85 +/- 0.023</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "        Balanced Accuracy        Precision           Recall              F1           Kappa\n",
       "XGBoost    0.88 +/- 0.016  0.963 +/- 0.008  0.761 +/- 0.033  0.85 +/- 0.023  0.85 +/- 0.023"
      ]
     },
     "execution_count": 305,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "xgb_cv = ml_cv_results('XGBoost', XGBClassifier(),\n",
    "                       X_temp_cs, y_temp)\n",
    "xgb_cv"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "hidden": true
   },
   "source": [
    "## 7.7 LightGBM"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 306,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2021-01-11T19:21:47.264120Z",
     "start_time": "2021-01-11T19:21:43.709212Z"
    },
    "hidden": true
   },
   "outputs": [],
   "source": [
    "lightgbm = LGBMClassifier()\n",
    "lightgbm.fit(X_train_cs, y_train)\n",
    "\n",
    "y_pred = lightgbm.predict(X_valid_cs)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 307,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2021-01-11T19:21:47.620836Z",
     "start_time": "2021-01-11T19:21:47.266776Z"
    },
    "hidden": true
   },
   "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>Balanced Accuracy</th>\n",
       "      <th>Precision</th>\n",
       "      <th>Recall</th>\n",
       "      <th>F1</th>\n",
       "      <th>Kappa</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>LightGBM</th>\n",
       "      <td>0.69</td>\n",
       "      <td>0.327</td>\n",
       "      <td>0.382</td>\n",
       "      <td>0.352</td>\n",
       "      <td>0.351</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "          Balanced Accuracy  Precision  Recall     F1  Kappa\n",
       "LightGBM               0.69      0.327   0.382  0.352  0.351"
      ]
     },
     "execution_count": 307,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "lightgbm_results = ml_scores('LightGBM', y_valid, y_pred)\n",
    "lightgbm_results"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "hidden": true
   },
   "source": [
    "### 7.7.1 Classification Report"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 308,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2021-01-11T19:21:47.822441Z",
     "start_time": "2021-01-11T19:21:47.624065Z"
    },
    "hidden": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "              precision    recall  f1-score   support\n",
      "\n",
      "           0       1.00      1.00      1.00    101671\n",
      "           1       0.33      0.38      0.35       131\n",
      "\n",
      "    accuracy                           1.00    101802\n",
      "   macro avg       0.66      0.69      0.68    101802\n",
      "weighted avg       1.00      1.00      1.00    101802\n",
      "\n"
     ]
    }
   ],
   "source": [
    "print(classification_report(y_valid, y_pred))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "hidden": true
   },
   "source": [
    "### 7.7.2 Cross Validation"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 309,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2021-01-11T19:22:05.573208Z",
     "start_time": "2021-01-11T19:21:47.824854Z"
    },
    "hidden": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Fold K=1\n",
      "Fold K=2\n",
      "Fold K=3\n",
      "Fold K=4\n",
      "Fold K=5\n"
     ]
    },
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
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       "\n",
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       "    }\n",
       "\n",
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       "        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>Balanced Accuracy</th>\n",
       "      <th>Precision</th>\n",
       "      <th>Recall</th>\n",
       "      <th>F1</th>\n",
       "      <th>Kappa</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>LightGDM</th>\n",
       "      <td>0.701 +/- 0.089</td>\n",
       "      <td>0.18 +/- 0.1</td>\n",
       "      <td>0.407 +/- 0.175</td>\n",
       "      <td>0.241 +/- 0.128</td>\n",
       "      <td>0.239 +/- 0.129</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "         Balanced Accuracy     Precision           Recall               F1            Kappa\n",
       "LightGDM   0.701 +/- 0.089  0.18 +/- 0.1  0.407 +/- 0.175  0.241 +/- 0.128  0.239 +/- 0.129"
      ]
     },
     "execution_count": 309,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "lightgbm_cv = ml_cv_results('LightGDM', LGBMClassifier(), \n",
    "                            X_temp_cs, y_temp)\n",
    "lightgbm_cv"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "hidden": true
   },
   "source": [
    "## 7.8 Comparing Model's Performance"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "hidden": true
   },
   "source": [
    "### 7.8.1 Single Performance"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 310,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2021-01-11T19:22:05.598542Z",
     "start_time": "2021-01-11T19:22:05.575164Z"
    },
    "hidden": true
   },
   "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>Balanced Accuracy</th>\n",
       "      <th>Precision</th>\n",
       "      <th>Recall</th>\n",
       "      <th>F1</th>\n",
       "      <th>Kappa</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>dummy</th>\n",
       "      <td>0.499</td>\n",
       "      <td>0.000</td>\n",
       "      <td>0.000</td>\n",
       "      <td>0.000</td>\n",
       "      <td>-0.001</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>SVM</th>\n",
       "      <td>0.500</td>\n",
       "      <td>0.000</td>\n",
       "      <td>0.000</td>\n",
       "      <td>0.000</td>\n",
       "      <td>0.000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>K Nearest Neighbors</th>\n",
       "      <td>0.565</td>\n",
       "      <td>1.000</td>\n",
       "      <td>0.130</td>\n",
       "      <td>0.230</td>\n",
       "      <td>0.230</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Logistic Regression</th>\n",
       "      <td>0.584</td>\n",
       "      <td>1.000</td>\n",
       "      <td>0.168</td>\n",
       "      <td>0.288</td>\n",
       "      <td>0.287</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>LightGBM</th>\n",
       "      <td>0.690</td>\n",
       "      <td>0.327</td>\n",
       "      <td>0.382</td>\n",
       "      <td>0.352</td>\n",
       "      <td>0.351</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Random Forest</th>\n",
       "      <td>0.844</td>\n",
       "      <td>0.978</td>\n",
       "      <td>0.687</td>\n",
       "      <td>0.807</td>\n",
       "      <td>0.807</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>XGBoost</th>\n",
       "      <td>0.863</td>\n",
       "      <td>0.969</td>\n",
       "      <td>0.725</td>\n",
       "      <td>0.830</td>\n",
       "      <td>0.830</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                     Balanced Accuracy  Precision  Recall     F1  Kappa\n",
       "dummy                            0.499      0.000   0.000  0.000 -0.001\n",
       "SVM                              0.500      0.000   0.000  0.000  0.000\n",
       "K Nearest Neighbors              0.565      1.000   0.130  0.230  0.230\n",
       "Logistic Regression              0.584      1.000   0.168  0.288  0.287\n",
       "LightGBM                         0.690      0.327   0.382  0.352  0.351\n",
       "Random Forest                    0.844      0.978   0.687  0.807  0.807\n",
       "XGBoost                          0.863      0.969   0.725  0.830  0.830"
      ]
     },
     "execution_count": 310,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "modeling_performance = pd.concat([dummy_results, lg_results, knn_results, \n",
    "                                  rf_results, xgb_results, lightgbm_results,\n",
    "                                 svm_results])\n",
    "modeling_performance.sort_values(by=\"F1\", ascending=True)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "hidden": true
   },
   "source": [
    "### 7.8.2 Cross Validation Performance"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 311,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2021-01-11T19:22:05.627838Z",
     "start_time": "2021-01-11T19:22:05.601384Z"
    },
    "hidden": true
   },
   "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>Balanced Accuracy</th>\n",
       "      <th>Precision</th>\n",
       "      <th>Recall</th>\n",
       "      <th>F1</th>\n",
       "      <th>Kappa</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>Dummy</th>\n",
       "      <td>0.499 +/- 0.0</td>\n",
       "      <td>0.0 +/- 0.0</td>\n",
       "      <td>0.0 +/- 0.0</td>\n",
       "      <td>0.0 +/- 0.0</td>\n",
       "      <td>-0.001 +/- 0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Logistic Regression</th>\n",
       "      <td>0.565 +/- 0.009</td>\n",
       "      <td>1.0 +/- 0.0</td>\n",
       "      <td>0.129 +/- 0.017</td>\n",
       "      <td>0.229 +/- 0.027</td>\n",
       "      <td>0.228 +/- 0.027</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>LightGDM</th>\n",
       "      <td>0.701 +/- 0.089</td>\n",
       "      <td>0.18 +/- 0.1</td>\n",
       "      <td>0.407 +/- 0.175</td>\n",
       "      <td>0.241 +/- 0.128</td>\n",
       "      <td>0.239 +/- 0.129</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>SVM</th>\n",
       "      <td>0.595 +/- 0.013</td>\n",
       "      <td>1.0 +/- 0.0</td>\n",
       "      <td>0.19 +/- 0.026</td>\n",
       "      <td>0.319 +/- 0.037</td>\n",
       "      <td>0.319 +/- 0.037</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>K Nearest Neighbors</th>\n",
       "      <td>0.705 +/- 0.037</td>\n",
       "      <td>0.942 +/- 0.022</td>\n",
       "      <td>0.409 +/- 0.074</td>\n",
       "      <td>0.568 +/- 0.073</td>\n",
       "      <td>0.567 +/- 0.073</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Random Forest</th>\n",
       "      <td>0.865 +/- 0.017</td>\n",
       "      <td>0.972 +/- 0.014</td>\n",
       "      <td>0.731 +/- 0.033</td>\n",
       "      <td>0.834 +/- 0.022</td>\n",
       "      <td>0.833 +/- 0.022</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>XGBoost</th>\n",
       "      <td>0.88 +/- 0.016</td>\n",
       "      <td>0.963 +/- 0.008</td>\n",
       "      <td>0.761 +/- 0.033</td>\n",
       "      <td>0.85 +/- 0.023</td>\n",
       "      <td>0.85 +/- 0.023</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                    Balanced Accuracy        Precision           Recall               F1            Kappa\n",
       "Dummy                   0.499 +/- 0.0      0.0 +/- 0.0      0.0 +/- 0.0      0.0 +/- 0.0   -0.001 +/- 0.0\n",
       "Logistic Regression   0.565 +/- 0.009      1.0 +/- 0.0  0.129 +/- 0.017  0.229 +/- 0.027  0.228 +/- 0.027\n",
       "LightGDM              0.701 +/- 0.089     0.18 +/- 0.1  0.407 +/- 0.175  0.241 +/- 0.128  0.239 +/- 0.129\n",
       "SVM                   0.595 +/- 0.013      1.0 +/- 0.0   0.19 +/- 0.026  0.319 +/- 0.037  0.319 +/- 0.037\n",
       "K Nearest Neighbors   0.705 +/- 0.037  0.942 +/- 0.022  0.409 +/- 0.074  0.568 +/- 0.073  0.567 +/- 0.073\n",
       "Random Forest         0.865 +/- 0.017  0.972 +/- 0.014  0.731 +/- 0.033  0.834 +/- 0.022  0.833 +/- 0.022\n",
       "XGBoost                0.88 +/- 0.016  0.963 +/- 0.008  0.761 +/- 0.033   0.85 +/- 0.023   0.85 +/- 0.023"
      ]
     },
     "execution_count": 311,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "modeling_performance_cv = pd.concat([dummy_cv, lg_cv, knn_cv, rf_cv, \n",
    "                                     xgb_cv, lightgbm_cv, svm_cv])\n",
    "\n",
    "modeling_performance_cv.sort_values(by=\"F1\", ascending=True)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "heading_collapsed": true
   },
   "source": [
    "# 8.0 Hyperparameter Fine Tuning"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 312,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2021-01-11T19:22:05.640133Z",
     "start_time": "2021-01-11T19:22:05.633309Z"
    },
    "hidden": true
   },
   "outputs": [],
   "source": [
    "f1 = make_scorer(f1_score)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 313,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2021-01-11T19:22:05.653525Z",
     "start_time": "2021-01-11T19:22:05.644570Z"
    },
    "hidden": true
   },
   "outputs": [],
   "source": [
    "params = {\n",
    "    'booster': ['gbtree', 'gblinear', 'dart'],\n",
    "    'eta': [0.3, 0.1, 0.01],\n",
    "    'scale_pos_weight': [1, 774, 508, 99]\n",
    "}"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 315,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2021-01-11T20:55:31.693518Z",
     "start_time": "2021-01-11T19:25:06.941400Z"
    },
    "hidden": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "GridSearchCV(cv=StratifiedKFold(n_splits=5, random_state=None, shuffle=False),\n",
       "             estimator=XGBClassifier(base_score=None, booster=None,\n",
       "                                     colsample_bylevel=None,\n",
       "                                     colsample_bynode=None,\n",
       "                                     colsample_bytree=None, gamma=None,\n",
       "                                     gpu_id=None, importance_type='gain',\n",
       "                                     interaction_constraints=None,\n",
       "                                     learning_rate=None, max_delta_step=None,\n",
       "                                     max_depth=None, min_child_weight=None,\n",
       "                                     mis...constraints=None,\n",
       "                                     n_estimators=100, n_jobs=None,\n",
       "                                     num_parallel_tree=None, random_state=None,\n",
       "                                     reg_alpha=None, reg_lambda=None,\n",
       "                                     scale_pos_weight=None, subsample=None,\n",
       "                                     tree_method=None, validate_parameters=None,\n",
       "                                     verbosity=None),\n",
       "             param_grid={'booster': ['gbtree', 'gblinear', 'dart'],\n",
       "                         'eta': [0.3, 0.1, 0.01],\n",
       "                         'scale_pos_weight': [1, 774, 508, 99]},\n",
       "             scoring=make_scorer(f1_score))"
      ]
     },
     "execution_count": 315,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "gs = GridSearchCV(XGBClassifier(), \n",
    "                  param_grid=params, \n",
    "                  scoring=f1, \n",
    "                  cv=StratifiedKFold(n_splits=5))\n",
    "\n",
    "gs.fit(X_params_cs, y_temp)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 316,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2021-01-11T20:55:31.706511Z",
     "start_time": "2021-01-11T20:55:31.695941Z"
    },
    "hidden": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'booster': 'gbtree', 'eta': 0.3, 'scale_pos_weight': 1}"
      ]
     },
     "execution_count": 316,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "best_params = gs.best_params_\n",
    "best_params"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 317,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2021-01-11T20:55:31.712716Z",
     "start_time": "2021-01-11T20:55:31.709317Z"
    },
    "hidden": true
   },
   "outputs": [],
   "source": [
    "best_params = {'booster': 'gbtree', 'eta': 0.3, 'scale_pos_weight': 1}"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 318,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2021-01-11T20:55:31.720495Z",
     "start_time": "2021-01-11T20:55:31.715238Z"
    },
    "hidden": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.864172533896524"
      ]
     },
     "execution_count": 318,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "gs.best_score_"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "hidden": true
   },
   "source": [
    "## 8.1 Results"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 319,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2021-01-11T20:55:31.724999Z",
     "start_time": "2021-01-11T20:55:31.722348Z"
    },
    "hidden": true
   },
   "outputs": [],
   "source": [
    "xgb_gs = XGBClassifier(\n",
    "    booster=best_params['booster'],\n",
    "    eta=best_params['eta'],\n",
    "    scale_pos_weight=best_params['scale_pos_weight']\n",
    ")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 320,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2021-01-11T20:55:52.745584Z",
     "start_time": "2021-01-11T20:55:31.726949Z"
    },
    "hidden": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "XGBClassifier(base_score=0.5, booster='gbtree', colsample_bylevel=1,\n",
       "              colsample_bynode=1, colsample_bytree=1, eta=0.3, gamma=0,\n",
       "              gpu_id=-1, importance_type='gain', interaction_constraints='',\n",
       "              learning_rate=0.300000012, max_delta_step=0, max_depth=6,\n",
       "              min_child_weight=1, missing=nan, monotone_constraints='()',\n",
       "              n_estimators=100, n_jobs=0, num_parallel_tree=1, random_state=0,\n",
       "              reg_alpha=0, reg_lambda=1, scale_pos_weight=1, subsample=1,\n",
       "              tree_method='exact', validate_parameters=1, verbosity=None)"
      ]
     },
     "execution_count": 320,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "xgb_gs.fit(X_train_cs, y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 321,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2021-01-11T20:55:53.028663Z",
     "start_time": "2021-01-11T20:55:52.747809Z"
    },
    "hidden": true
   },
   "outputs": [],
   "source": [
    "y_pred = xgb_gs.predict(X_valid_cs)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "hidden": true
   },
   "source": [
    "### 8.1.2 Single Results"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 322,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2021-01-11T20:56:13.792056Z",
     "start_time": "2021-01-11T20:56:13.507732Z"
    },
    "hidden": true
   },
   "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>Balanced Accuracy</th>\n",
       "      <th>Precision</th>\n",
       "      <th>Recall</th>\n",
       "      <th>F1</th>\n",
       "      <th>Kappa</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>XGBoost GS</th>\n",
       "      <td>0.863</td>\n",
       "      <td>0.969</td>\n",
       "      <td>0.725</td>\n",
       "      <td>0.83</td>\n",
       "      <td>0.83</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "            Balanced Accuracy  Precision  Recall    F1  Kappa\n",
       "XGBoost GS              0.863      0.969   0.725  0.83   0.83"
      ]
     },
     "execution_count": 322,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "xgb_gs_results = ml_scores('XGBoost GS', y_valid, y_pred)\n",
    "xgb_gs_results"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "hidden": true
   },
   "source": [
    "### 8.1.3 Cross Validation"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 323,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2021-01-11T20:58:41.339028Z",
     "start_time": "2021-01-11T20:56:15.338989Z"
    },
    "hidden": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Fold K=1\n",
      "Fold K=2\n",
      "Fold K=3\n",
      "Fold K=4\n",
      "Fold K=5\n"
     ]
    },
    {
     "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>Balanced Accuracy</th>\n",
       "      <th>Precision</th>\n",
       "      <th>Recall</th>\n",
       "      <th>F1</th>\n",
       "      <th>Kappa</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>XGBoost GS</th>\n",
       "      <td>0.881 +/- 0.017</td>\n",
       "      <td>0.963 +/- 0.007</td>\n",
       "      <td>0.763 +/- 0.035</td>\n",
       "      <td>0.851 +/- 0.023</td>\n",
       "      <td>0.851 +/- 0.023</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "           Balanced Accuracy        Precision           Recall               F1            Kappa\n",
       "XGBoost GS   0.881 +/- 0.017  0.963 +/- 0.007  0.763 +/- 0.035  0.851 +/- 0.023  0.851 +/- 0.023"
      ]
     },
     "execution_count": 323,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "xgb_gs_cv = ml_cv_results('XGBoost GS', xgb_gs, X_temp_cs, y_temp)\n",
    "xgb_gs_cv"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "heading_collapsed": true
   },
   "source": [
    "# 9.0 Conclusions "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "hidden": true
   },
   "source": [
    "## 9.1 Final Model"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 324,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2021-01-11T21:04:19.379185Z",
     "start_time": "2021-01-11T21:03:46.423881Z"
    },
    "hidden": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "XGBClassifier(base_score=0.5, booster='gbtree', colsample_bylevel=1,\n",
       "              colsample_bynode=1, colsample_bytree=1, eta=0.3, gamma=0,\n",
       "              gpu_id=-1, importance_type='gain', interaction_constraints='',\n",
       "              learning_rate=0.300000012, max_delta_step=0, max_depth=6,\n",
       "              min_child_weight=1, missing=nan, monotone_constraints='()',\n",
       "              n_estimators=100, n_jobs=0, num_parallel_tree=1, random_state=0,\n",
       "              reg_alpha=0, reg_lambda=1, scale_pos_weight=1, subsample=1,\n",
       "              tree_method='exact', validate_parameters=1, verbosity=None)"
      ]
     },
     "execution_count": 324,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "final_model = XGBClassifier(\n",
    "    booster=best_params['booster'],\n",
    "    eta=best_params['eta'],\n",
    "    scale_pos_weight=best_params['scale_pos_weight']\n",
    ")\n",
    "\n",
    "final_model.fit(X_params_cs, y_temp)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "hidden": true
   },
   "source": [
    "### 9.1.1 Unseen Data Score"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 325,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2021-01-11T21:04:19.721539Z",
     "start_time": "2021-01-11T21:04:19.391316Z"
    },
    "hidden": true
   },
   "outputs": [],
   "source": [
    "y_pred = final_model.predict(X_test_cs)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 326,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2021-01-11T21:04:20.158624Z",
     "start_time": "2021-01-11T21:04:19.723764Z"
    },
    "hidden": true
   },
   "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>Balanced Accuracy</th>\n",
       "      <th>Precision</th>\n",
       "      <th>Recall</th>\n",
       "      <th>F1</th>\n",
       "      <th>Kappa</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>unseen</th>\n",
       "      <td>0.915</td>\n",
       "      <td>0.944</td>\n",
       "      <td>0.829</td>\n",
       "      <td>0.883</td>\n",
       "      <td>0.883</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "        Balanced Accuracy  Precision  Recall     F1  Kappa\n",
       "unseen              0.915      0.944   0.829  0.883  0.883"
      ]
     },
     "execution_count": 326,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "unseen_scores = ml_scores('unseen', y_test, y_pred)\n",
    "unseen_scores"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "hidden": true
   },
   "source": [
    "## 9.2 Blocker Fraud Company Expasion"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "hidden": true
   },
   "source": [
    "### 9.2.1 The company receives 25% of each transaction value truly detected as fraud."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 327,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2021-01-11T21:04:20.217495Z",
     "start_time": "2021-01-11T21:04:20.160741Z"
    },
    "hidden": true
   },
   "outputs": [],
   "source": [
    "df_test = df5.loc[X_test.index, :]\n",
    "df_test['predictions'] = y_pred"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 328,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2021-01-11T21:04:20.250683Z",
     "start_time": "2021-01-11T21:04:20.219574Z"
    },
    "hidden": true
   },
   "outputs": [],
   "source": [
    "aux1 = df_test[(df_test['is_fraud'] == 'yes') & (df_test['predictions'] == 1)]\n",
    "receives = aux1['amount'].sum() * 0.25"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 329,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2021-01-11T21:04:20.258680Z",
     "start_time": "2021-01-11T21:04:20.252971Z"
    },
    "hidden": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "The company can receive 60613782.88 detecting fraud transactions.\n"
     ]
    }
   ],
   "source": [
    "print('The company can receive %.2f detecting fraud transactions.' % (receives))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "hidden": true
   },
   "source": [
    "### 9.2.2 The company receives 5% of each transaction value detected as fraud, however the transaction is legitimate."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 330,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2021-01-11T21:04:20.278116Z",
     "start_time": "2021-01-11T21:04:20.261386Z"
    },
    "hidden": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "For wrong decisions, the company can receive 183866.98.\n"
     ]
    }
   ],
   "source": [
    "aux1 = df_test[(df_test['is_fraud'] == 'no') & (df_test['predictions'] == 1)]\n",
    "receives = aux1['amount'].sum() * 0.05\n",
    "\n",
    "print('For wrong decisions, the company can receive %.2f.' % (receives))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "hidden": true
   },
   "source": [
    "### 9.2.3 The company gives back 100% of the value for the customer in each transaction detected as legitimate, however the transaction is actually a fraud."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 331,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2021-01-11T21:04:20.297204Z",
     "start_time": "2021-01-11T21:04:20.281709Z"
    },
    "hidden": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "However, the company must return the amount of 3546075.42.\n"
     ]
    }
   ],
   "source": [
    "aux1 = df_test[(df_test['is_fraud'] == 'yes') & (df_test['predictions'] == 0)]\n",
    "receives = aux1['amount'].sum()\n",
    "\n",
    "print('However, the company must return the amount of %.2f.' % (receives))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "hidden": true
   },
   "source": [
    "## 9.3 Model's Performance"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "hidden": true
   },
   "source": [
    "### 9.3.1 What is the model's Precision and Accuracy?"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 332,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2021-01-11T21:04:20.305075Z",
     "start_time": "2021-01-11T21:04:20.300099Z"
    },
    "hidden": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "For unseen data, the values of balanced accuracy is equal 0.92 and precision is equal 0.94.\n"
     ]
    }
   ],
   "source": [
    "print('For unseen data, the values of balanced accuracy is equal %.2f and precision is equal %.2f.' % (unseen_scores['Balanced Accuracy'], unseen_scores['Precision']))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "hidden": true
   },
   "source": [
    "### 9.3.2 How reliable is the model in classifying transactions as legitimate or fraudulent?"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 340,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2021-01-11T21:05:16.886043Z",
     "start_time": "2021-01-11T21:05:16.881350Z"
    },
    "hidden": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "The model can detect 0.851 +/- 0.023 of the fraud. However it detected 0.84 of the frauds from a unseen data.\n"
     ]
    }
   ],
   "source": [
    "print('The model can detect 0.851 +/- 0.023 of the fraud. However it detected 0.84 of the frauds from a unseen data.')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "hidden": true
   },
   "source": [
    "### 9.3.3 What is the revenue expected by the company  classify 100% of transactions with the model?"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 334,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2021-01-11T21:04:20.372277Z",
     "start_time": "2021-01-11T21:04:20.316349Z"
    },
    "hidden": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Using the model the company can revenue 60797649.86.\n"
     ]
    }
   ],
   "source": [
    "aux1 = df_test[(df_test['is_fraud'] == 'yes') & (df_test['predictions'] == 1)]\n",
    "receives = aux1['amount'].sum() * 0.25\n",
    "\n",
    "aux2 = df_test[(df_test['is_fraud'] == 'no') & (df_test['predictions'] == 1)]\n",
    "receives2 = aux2['amount'].sum() * 0.05\n",
    "\n",
    "print('Using the model the company can revenue %.2f.' % (receives + receives2))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 335,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2021-01-11T21:04:20.423598Z",
     "start_time": "2021-01-11T21:04:20.376776Z"
    },
    "hidden": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "However the currently method the revenue is 0.00.\n"
     ]
    }
   ],
   "source": [
    "aux3 = df_test[(df_test['is_fraud'] == 'yes') & (df_test['is_flagged_fraud'] == 'yes')]\n",
    "curr_receives = aux3['amount'].sum() * 0.25\n",
    "\n",
    "aux4 = df_test[(df_test['is_fraud'] == 'no') & (df_test['is_flagged_fraud'] == 'yes')]\n",
    "curr_receives2 = aux4['amount'].sum() * 0.05\n",
    "\n",
    "print('However the currently method the revenue is %.2f.' % (curr_receives + curr_receives2))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "hidden": true
   },
   "source": [
    "### 9.3.4 What is the loss expected by the Company if it classifies 100% of the transactions with the model?"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 336,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2021-01-11T21:04:20.460738Z",
     "start_time": "2021-01-11T21:04:20.428461Z"
    },
    "hidden": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "For wrong classifications the company must return the amount of 3546075.42.\n"
     ]
    }
   ],
   "source": [
    "aux1 = df_test[(df_test['is_fraud'] == 'yes') & (df_test['predictions'] == 0)]\n",
    "loss = aux1['amount'].sum()\n",
    "\n",
    "print('For wrong classifications the company must return the amount of %.2f.' % (loss))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 337,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2021-01-11T21:04:20.524063Z",
     "start_time": "2021-01-11T21:04:20.463672Z"
    },
    "hidden": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "For wrong classifications using the currently method, the company must return the amount of 246001206.94.\n"
     ]
    }
   ],
   "source": [
    "aux1 = df_test[(df_test['is_fraud'] == 'yes') & (df_test['is_flagged_fraud'] == 'no')]\n",
    "curr_loss = aux1['amount'].sum()\n",
    "\n",
    "print('For wrong classifications using the currently method, the company must return the amount of %.2f.' % (curr_loss))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "hidden": true
   },
   "source": [
    "### 9.3.5 What is the profit expected by the blocker fraud company when using the model?"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 338,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2021-01-11T21:04:20.539074Z",
     "start_time": "2021-01-11T21:04:20.528161Z"
    },
    "hidden": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "The company can expect the profit of 57251574.44.\n"
     ]
    }
   ],
   "source": [
    "print('The company can expect the profit of %.2f.' % (receives + receives2 - loss))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 339,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2021-01-11T21:04:20.550429Z",
     "start_time": "2021-01-11T21:04:20.543382Z"
    },
    "hidden": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Using the currently method, the profit is -246001206.94.\n"
     ]
    }
   ],
   "source": [
    "print('Using the currently method, the profit is %.2f.' % (curr_receives + curr_receives - curr_loss))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "heading_collapsed": true
   },
   "source": [
    "# 10.0 Model Deploy"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "hidden": true
   },
   "source": [
    "## 10.1 Saving"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 341,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2021-01-11T21:05:56.929387Z",
     "start_time": "2021-01-11T21:05:24.082949Z"
    },
    "hidden": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "['../models/model_cycle1.joblib']"
      ]
     },
     "execution_count": 341,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "final_model = XGBClassifier(\n",
    "    booster=best_params['booster'],\n",
    "    eta=best_params['eta'],\n",
    "    scale_pos_weight=best_params['scale_pos_weight']\n",
    ")\n",
    "\n",
    "final_model.fit(X_params_cs, y_temp)\n",
    "\n",
    "joblib.dump(final_model, '../models/model_cycle1.joblib')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 342,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2021-01-11T21:05:56.967807Z",
     "start_time": "2021-01-11T21:05:56.931583Z"
    },
    "hidden": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "['../parameters/minmaxscaler_cycle1.joblib']"
      ]
     },
     "execution_count": 342,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "mm = MinMaxScaler()\n",
    "mm.fit(X_params_cs, y_temp)\n",
    "\n",
    "joblib.dump(mm, '../parameters/minmaxscaler_cycle1.joblib')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 352,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2021-01-11T21:08:49.293823Z",
     "start_time": "2021-01-11T21:08:49.286026Z"
    },
    "hidden": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "['../parameters/onehotencoder_cycle1.joblib']"
      ]
     },
     "execution_count": 352,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "joblib.dump(ohe, '../parameters/onehotencoder_cycle1.joblib')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "hidden": true
   },
   "source": [
    "## 10.2 Fraud Class"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 353,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2021-01-11T21:08:52.576698Z",
     "start_time": "2021-01-11T21:08:52.566166Z"
    },
    "hidden": true
   },
   "outputs": [],
   "source": [
    "import joblib\n",
    "import inflection\n",
    "import pandas as pd\n",
    "\n",
    "class Fraud:\n",
    "    \n",
    "    def __init__(self):\n",
    "        self.minmaxscaler = joblib.load('../parameters/minmaxscaler_cycle1.joblib')\n",
    "        self.onehotencoder = joblib.load('../parameters/onehotencoder_cycle1.joblib')\n",
    "        \n",
    "    def data_cleaning(self, df1):\n",
    "        cols_old = df1.columns.tolist()\n",
    "        \n",
    "        snakecase = lambda i: inflection.underscore(i)\n",
    "        cols_new = list(map(snakecase, cols_old))\n",
    "        \n",
    "        df1.columns = cols_new\n",
    "        \n",
    "        return df1\n",
    "    \n",
    "    def feature_engineering(self, df2):\n",
    "        # step\n",
    "        df2['step_days'] = df2['step'].apply(lambda i: i/24)\n",
    "        df2['step_weeks'] = df2['step'].apply(lambda i: i/(24*7))\n",
    "\n",
    "        # difference between initial balance before the transaction and new balance after the transaction\n",
    "        df2['diff_new_old_balance'] = df2['newbalance_orig'] - df2['oldbalance_org']\n",
    "\n",
    "        # difference between initial balance recipient before the transaction and new balance recipient after the transaction.\n",
    "        df2['diff_new_old_destiny'] = df2['newbalance_dest'] - df2['oldbalance_dest']\n",
    "\n",
    "        # name orig and name dest\n",
    "        df2['name_orig'] = df2['name_orig'].apply(lambda i: i[0])\n",
    "        df2['name_dest'] = df2['name_dest'].apply(lambda i: i[0])\n",
    "        \n",
    "        return df2.drop(columns=['name_orig', 'name_dest', 'step_weeks', 'step_days'], axis=1)\n",
    "    \n",
    "    def data_preparation(self, df3):\n",
    "        # One Hot Encoding\n",
    "        df3 = pd.get_dummies(df3)\n",
    "        \n",
    "        # Rescaling \n",
    "        num_columns = ['amount', 'oldbalance_org', 'newbalance_orig', 'oldbalance_dest', \n",
    "                       'newbalance_dest', 'diff_new_old_balance', 'diff_new_old_destiny']\n",
    "        df3[num_columns] = self.minmaxscaler.transform(df3[num_columns])\n",
    "        \n",
    "        # OneHotEncoder\n",
    "        df3 = self.onehotencoder.transform(df3)\n",
    "        \n",
    "        # selected columns\n",
    "        final_columns_selected = ['step', 'oldbalance_org', 'newbalance_orig', 'newbalance_dest', \n",
    "                                  'diff_new_old_balance', 'diff_new_old_destiny', 'type_TRANSFER']\n",
    "        return df3[final_columns_selected]\n",
    "    \n",
    "    def get_prediction(self, model, original_data, test_data):\n",
    "        pred = model.predict(test_data)\n",
    "        original_data['prediction'] = pred\n",
    "        \n",
    "        return original_data.to_json(orient=\"records\", date_format=\"iso\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "hidden": true
   },
   "source": [
    "## 10.3 API Handler"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 354,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2021-01-11T21:08:53.901515Z",
     "start_time": "2021-01-11T21:08:53.881407Z"
    },
    "hidden": true
   },
   "outputs": [
    {
     "ename": "ModuleNotFoundError",
     "evalue": "No module named 'churn'",
     "output_type": "error",
     "traceback": [
      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[0;31mModuleNotFoundError\u001b[0m                       Traceback (most recent call last)",
      "\u001b[0;32m<ipython-input-354-b54483709a9f>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m\u001b[0m\n\u001b[1;32m      1\u001b[0m \u001b[0;32mimport\u001b[0m \u001b[0mjoblib\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m      2\u001b[0m \u001b[0;32mimport\u001b[0m \u001b[0mpandas\u001b[0m \u001b[0;32mas\u001b[0m \u001b[0mpd\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m----> 3\u001b[0;31m \u001b[0;32mfrom\u001b[0m \u001b[0mchurn\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mChurn\u001b[0m \u001b[0;32mimport\u001b[0m \u001b[0mChurn\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m      4\u001b[0m \u001b[0;32mfrom\u001b[0m \u001b[0mflask\u001b[0m \u001b[0;32mimport\u001b[0m \u001b[0mFlask\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mrequest\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mResponse\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m      5\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;31mModuleNotFoundError\u001b[0m: No module named 'churn'"
     ]
    }
   ],
   "source": [
    "import joblib\n",
    "import pandas as pd\n",
    "from churn.Churn import Churn\n",
    "from flask import Flask, request, Response\n",
    "\n",
    "# loading model\n",
    "model = joblib.load('../models/model_cycle1.joblib')\n",
    "\n",
    "# initialize API\n",
    "app = Flask(__name__)\n",
    "\n",
    "@app.route('/fraud/predict', methods=['POST'])\n",
    "def churn_predict():\n",
    "    test_json = request.get_json()\n",
    "   \n",
    "    if test_json: # there is data\n",
    "        if isinstance(test_json, dict): # unique example\n",
    "            test_raw = pd.DataFrame(test_json, index=[0])\n",
    "            \n",
    "        else: # multiple example\n",
    "            test_raw = pd.DataFrame(test_json, columns=test_json[0].keys())\n",
    "            \n",
    "        # Instantiate Rossmann class\n",
    "        pipeline = Fraud()\n",
    "        \n",
    "        # data cleaning\n",
    "        df1 = pipeline.data_cleaning(test_raw)\n",
    "        \n",
    "        # feature engineering\n",
    "        df2 = pipeline.feature_engineering(df1)\n",
    "        \n",
    "        # data preparation\n",
    "        df3 = pipeline.data_preparation(df2)\n",
    "        \n",
    "        # prediction\n",
    "        df_response = pipeline.get_prediction(model, test_raw, df3)\n",
    "        \n",
    "        return df_response\n",
    "        \n",
    "        \n",
    "    else:\n",
    "        return Reponse('{}', status=200, mimetype='application/json')\n",
    "\n",
    "if __name__ == '__main__':\n",
    "    app.run('0.0.0.0')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "hidden": true
   },
   "source": [
    "## 10.4 API Tester"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 166,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2021-01-11T18:25:36.827345Z",
     "start_time": "2021-01-11T18:25:35.417258Z"
    },
    "hidden": true
   },
   "outputs": [],
   "source": [
    "# loading test dataset\n",
    "df10 = pd.read_csv(\"../database/fraud_0.1origbase.csv\").iloc[5, :-2]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 167,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2021-01-11T18:25:36.840932Z",
     "start_time": "2021-01-11T18:25:36.829367Z"
    },
    "hidden": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "step                     208\n",
       "type                 CASH_IN\n",
       "amount                256441\n",
       "nameOrig         C1001269496\n",
       "oldbalanceOrg            554\n",
       "Name: 5, dtype: object"
      ]
     },
     "execution_count": 167,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df10.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 168,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2021-01-11T18:25:41.840172Z",
     "start_time": "2021-01-11T18:25:41.837309Z"
    },
    "hidden": true
   },
   "outputs": [],
   "source": [
    "# convert Dataframe to json\n",
    "data = df10.to_json()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 169,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2021-01-11T18:25:42.316636Z",
     "start_time": "2021-01-11T18:25:42.306247Z"
    },
    "hidden": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'{\"step\":208,\"type\":\"CASH_IN\",\"amount\":256440.86,\"nameOrig\":\"C1001269496\",\"oldbalanceOrg\":554.0,\"newbalanceOrig\":256994.86,\"nameDest\":\"C1503528288\",\"oldbalanceDest\":0.0,\"newbalanceDest\":0.0}'"
      ]
     },
     "execution_count": 169,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 170,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2021-01-11T18:25:44.978148Z",
     "start_time": "2021-01-11T18:25:44.974930Z"
    },
    "hidden": true
   },
   "outputs": [],
   "source": [
    "import requests"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 224,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2021-01-11T18:37:23.766410Z",
     "start_time": "2021-01-11T18:37:23.618922Z"
    },
    "hidden": true
   },
   "outputs": [],
   "source": [
    "url = 'http://0.0.0.0:5000/fraud/predict'\n",
    "header = {'Content-type': 'application/json'}\n",
    "\n",
    "r = requests.post(url=url, data=data, headers=header)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 225,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2021-01-11T18:37:24.356996Z",
     "start_time": "2021-01-11T18:37:24.339403Z"
    },
    "hidden": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "200"
      ]
     },
     "execution_count": 225,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "r.status_code"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 226,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2021-01-11T18:37:40.889263Z",
     "start_time": "2021-01-11T18:37:40.876879Z"
    },
    "hidden": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[{'step': 208,\n",
       "  'type': 'CASH_IN',\n",
       "  'amount': 256440.86,\n",
       "  'name_orig': 'C',\n",
       "  'oldbalance_org': 554.0,\n",
       "  'newbalance_orig': 256994.86,\n",
       "  'name_dest': 'C',\n",
       "  'oldbalance_dest': 0.0,\n",
       "  'newbalance_dest': 0.0,\n",
       "  'step_days': 8.6666666667,\n",
       "  'step_weeks': 1.2380952381,\n",
       "  'diff_new_old_balance': 256440.86,\n",
       "  'diff_new_old_destiny': 0.0,\n",
       "  'prediction': 0}]"
      ]
     },
     "execution_count": 226,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "r.json()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 227,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2021-01-11T18:38:07.428531Z",
     "start_time": "2021-01-11T18:38:07.417682Z"
    },
    "hidden": true
   },
   "outputs": [],
   "source": [
    "d = pd.DataFrame(r.json(), columns=r.json()[0].keys())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 228,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2021-01-11T18:38:08.754022Z",
     "start_time": "2021-01-11T18:38:08.730062Z"
    },
    "hidden": true
   },
   "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>step</th>\n",
       "      <th>type</th>\n",
       "      <th>amount</th>\n",
       "      <th>name_orig</th>\n",
       "      <th>oldbalance_org</th>\n",
       "      <th>newbalance_orig</th>\n",
       "      <th>name_dest</th>\n",
       "      <th>oldbalance_dest</th>\n",
       "      <th>newbalance_dest</th>\n",
       "      <th>step_days</th>\n",
       "      <th>step_weeks</th>\n",
       "      <th>diff_new_old_balance</th>\n",
       "      <th>diff_new_old_destiny</th>\n",
       "      <th>prediction</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>208</td>\n",
       "      <td>CASH_IN</td>\n",
       "      <td>256440.86</td>\n",
       "      <td>C</td>\n",
       "      <td>554.0</td>\n",
       "      <td>256994.86</td>\n",
       "      <td>C</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>8.666667</td>\n",
       "      <td>1.238095</td>\n",
       "      <td>256440.86</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   step     type     amount name_orig  oldbalance_org  newbalance_orig name_dest  oldbalance_dest  newbalance_dest  step_days  step_weeks  diff_new_old_balance  diff_new_old_destiny  prediction\n",
       "0   208  CASH_IN  256440.86         C           554.0        256994.86         C              0.0              0.0   8.666667    1.238095             256440.86                   0.0           0"
      ]
     },
     "execution_count": 228,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "d"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "hidden": true
   },
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.8.5"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 4
}


================================================
FILE: requirements.txt
================================================
argon2-cffi==20.1.0
async-generator==1.10
attrs==20.3.0
backcall==0.2.0
bleach==3.2.1
Boruta==0.3
category-encoders==2.2.2
cffi==1.14.4
click==7.1.2
cycler==0.10.0
decorator==4.4.2
defusedxml==0.6.0
entrypoints==0.3
Flask==1.1.2
imbalanced-learn==0.7.0
imblearn==0.0
inflection==0.5.1
ipykernel==5.4.2
ipython==7.19.0
ipython-genutils==0.2.0
ipywidgets==7.5.1
itsdangerous==1.1.0
jedi==0.17.2
Jinja2==2.11.2
joblib==0.17.0
jsonschema==3.2.0
jupyter==1.0.0
jupyter-client==6.1.7
jupyter-console==6.2.0
jupyter-core==4.7.0
jupyterlab-pygments==0.1.2
kiwisolver==1.3.1
lightgbm==3.1.1
MarkupSafe==1.1.1
matplotlib==3.3.3
mistune==0.8.4
nbclient==0.5.1
nbconvert==6.0.7
nbformat==5.0.8
nest-asyncio==1.4.3
notebook==6.1.5
numpy==1.19.4
packaging==20.8
pandas==1.1.5
pandocfilters==1.4.3
parso==0.7.1
patsy==0.5.1
pexpect==4.8.0
pickleshare==0.7.5
Pillow==8.0.1
prometheus-client==0.9.0
prompt-toolkit==3.0.8
ptyprocess==0.6.0
pycparser==2.20
Pygments==2.7.3
pyparsing==2.4.7
pyrsistent==0.17.3
python-dateutil==2.8.1
pytz==2020.4
pyzmq==20.0.0
qtconsole==5.0.1
QtPy==1.9.0
scikit-learn==0.23.2
scipy==1.5.4
seaborn==0.11.0
Send2Trash==1.5.0
six==1.15.0
sklearn==0.0
statsmodels==0.12.1
terminado==0.9.1
testpath==0.4.4
threadpoolctl==2.1.0
tornado==6.1
traitlets==5.0.5
wcwidth==0.2.5
webencodings==0.5.1
Werkzeug==1.0.1
widgetsnbextension==3.5.1
xgboost==1.3.0.post0