blocker_fraud_company_notebook.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Challenge description**\n",
    "\n",
    "\n",
    "The purpose of this project is creating a model of high precision and accuracy in detecting fraud of transactions made through mobile devices.\n",
    "\n",
    "At the end of the project the CEO of Blocker Fraud Company will have a model in production capable of send transactions by API and it will classifies them as fraudulent or legitimate.\n",
    "\n",
    "In addition, there is going to be a  report reporting  model's performance and results in relation to the profit and loss that the company will have when using the model you produced. The main subjects will contain the answers to the following questions:\n",
    "\n",
    "    1- What is the Precision and Accuracy of the model?\n",
    "    2- How Reliable is the model in classifying transactions as legitimate or fraudulent?\n",
    "    3- What is the Expected Billing by the Company if we classify 100% of the transactions with the model?\n",
    "    4- What is the Loss Expected by the Company in case of model failure?\n",
    "    5- What is the Profit Expected by the Blocker Fraud Company when using the model?"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#  1. INTRODUCTION"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "heading_collapsed": true
   },
   "source": [
    "## 1.1 Data Dictionary"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-11-10T14:31:25.597341Z",
     "start_time": "2020-11-10T14:31:25.587433Z"
    },
    "hidden": true
   },
   "source": [
    "Source data set: https://www.kaggle.com/ntnu-testimon/paysim1\n",
    "\n",
    "| Feature        | Description                                                                                                                                                                                                                                                                                           |\n",
    "|:----------------|:-------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|\n",
    "| 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",
    "| type           | Transaction type \\(CASH\\-IN, CASH\\-OUT, DEBIT, PAYMENT and TRANSFER\\)                                                                                                                                                                                                                                 |\n",
    "| amount         | amount of the transaction in local currency                                                                                                                                                                                                                                                           |\n",
    "| nameOrig       | customer who started the transaction                                                                                                                                                                                                                                                                  |\n",
    "| oldbalanceOrg  | initial balance before the transaction                                                                                                                                                                                                                                                                |\n",
    "| newbalanceOrig | new balance after the transaction                                                                                                                                                                                                                                                                     |\n",
    "| nameDest       | customer who is the recipient of the transaction                                                                                                                                                                                                                                                      |\n",
    "| oldbalanceDest | initial balance recipient before the transaction\\. Note that there is not information for customers that start with M \\(Merchants\\)                                                                                                                                                                   |\n",
    "| newbalanceDest | new balance recipient after the transaction\\. Note that there is not information for customers that start with M \\(Merchants\\)                                                                                                                                                                        |\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",
    "| 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 200K in a single transaction                                                                                 |\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 1.2 IMPORTS"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-11-15T15:23:24.968535Z",
     "start_time": "2020-11-15T15:23:24.959346Z"
    }
   },
   "outputs": [],
   "source": [
    "# general libraries \n",
    "import math\n",
    "import numpy  as np\n",
    "import pandas as pd\n",
    "import random as rd\n",
    "\n",
    "\n",
    "\n",
    "#altering texts\n",
    "import inflection\n",
    "\n",
    "#creating and configuring graphics\n",
    "import seaborn as sns\n",
    "from matplotlib            import pyplot as plt\n",
    "from IPython.display       import Image\n",
    "from IPython.core.display  import HTML\n",
    "\n",
    "\n",
    "#data selection and algorithm  \n",
    "import xgboost as xgb\n",
    "from scipy                   import stats  as ss\n",
    "from boruta                  import BorutaPy\n",
    "from sklearn.metrics         import mean_absolute_error, mean_squared_error\n",
    "from sklearn.ensemble        import RandomForestRegressor\n",
    "from sklearn.linear_model    import LinearRegression, Lasso\n",
    "from sklearn.preprocessing   import RobustScaler, MinMaxScaler, LabelEncoder\n",
    "from sklearn                 import datasets, linear_model\n",
    "from sklearn.model_selection import train_test_split, KFold\n",
    "from sklearn.neighbors       import KNeighborsClassifier\n",
    "\n",
    "#others\n",
    "import warnings\n",
    "warnings.filterwarnings( 'ignore' )\n",
    "\n",
    "#Handling Oversampling\n",
    "from imblearn.over_sampling import SMOTE \n",
    "\n",
    "#API \n",
    "from flask import Flask, request, Response"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "##  1.3 Helper Functions"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-11-15T15:23:25.069481Z",
     "start_time": "2020-11-15T15:23:24.977693Z"
    }
   },
   "outputs": [],
   "source": [
    "def jupyter_settings():\n",
    "    %matplotlib inline\n",
    "    %pylab inline\n",
    "    \n",
    "    plt.style.use( 'bmh' )\n",
    "    plt.rcParams['figure.figsize'] = [25, 12]\n",
    "    plt.rcParams['font.size'] = 24\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",
    "    sns.set()\n",
    "    \n",
    "\n",
    "\n",
    "def ml_error(model_name,y,yhat):\n",
    "    \n",
    "    mae = mean_absolute_error(y,yhat)\n",
    "    mape = mean_absolute_percentage_error(y,yhat)\n",
    "    rmse = np.sqrt(mean_squared_error(y,yhat))\n",
    "    return pd.DataFrame({'Model Name': model_name,\n",
    "                         'MAE': mae,\n",
    "                         'MAPE': mape,\n",
    "                          'RMSE':rmse},index=[0])\n",
    "\n",
    "def mean_absolute_percentage_error(y, yhat):\n",
    "   return np.mean( np.abs((y-yhat)/y))\n",
    "\n",
    "def mean_percentage_error( y, yhat ):\n",
    "    return np.mean( ( y - yhat ) / y )\n",
    "\n",
    "\n",
    "\n",
    "\n",
    "    return pd.DataFrame( {'Model Name': model_name,\n",
    "                          'MAE CV': np.round( np.mean( mae_list ), 2 ).astype( str ) + ' +/- ' + np.round( np.std( mae_list ), 2 ).astype( str ),\n",
    "                          'MAPE CV': np.round( np.mean( mape_list ), 2 ).astype( str ) + ' +/- ' + np.round( np.std( mape_list ), 2 ).astype( str ),\n",
    "                          'RMSE CV': np.round( np.mean( rmse_list ), 2 ).astype( str ) + ' +/- ' + np.round( np.std( rmse_list ), 2 ).astype( str ) }, index=[0] )\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 1.4 Loading Data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-11-15T15:23:41.544615Z",
     "start_time": "2020-11-15T15:23:25.072427Z"
    }
   },
   "outputs": [],
   "source": [
    "df_raw = pd.read_csv('data/train.csv')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "heading_collapsed": true
   },
   "source": [
    "# 2.0 Data Description"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-11-15T15:23:42.075869Z",
     "start_time": "2020-11-15T15:23:41.548396Z"
    },
    "hidden": true
   },
   "outputs": [],
   "source": [
    "df1 = df_raw.copy()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "hidden": true
   },
   "source": [
    "##  2.1 Rename Columns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-11-15T15:23:42.083620Z",
     "start_time": "2020-11-15T15:23:42.079232Z"
    },
    "hidden": true
   },
   "outputs": [],
   "source": [
    "cols_old = ['step', 'type', 'amount', 'nameOrig', 'oldbalanceOrg', 'newbalanceOrig',\n",
    "       'nameDest', 'oldbalanceDest', 'newbalanceDest', 'isFraud',\n",
    "       'isFlaggedFraud']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-11-15T15:23:42.150474Z",
     "start_time": "2020-11-15T15:23:42.086510Z"
    },
    "hidden": true
   },
   "outputs": [],
   "source": [
    "snakecase = lambda x: inflection.underscore(x)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-11-15T15:23:42.218936Z",
     "start_time": "2020-11-15T15:23:42.154339Z"
    },
    "hidden": true
   },
   "outputs": [],
   "source": [
    "cols_new = list(map(snakecase,cols_old))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-11-15T15:23:42.301976Z",
     "start_time": "2020-11-15T15:23:42.223294Z"
    },
    "hidden": true
   },
   "outputs": [],
   "source": [
    "df1.columns = cols_new"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "hidden": true
   },
   "source": [
    "## 2.2 Data Dimension "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-11-15T15:23:42.370183Z",
     "start_time": "2020-11-15T15:23:42.310459Z"
    },
    "hidden": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Number of rows 6362620\n",
      "Number of columns 11\n"
     ]
    }
   ],
   "source": [
    "print(f'Number of rows {df1.shape[0]}')\n",
    "print(f'Number of columns {df1.shape[1]}')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "hidden": true
   },
   "source": [
    "The data Dimension is **6.362.620,00** rows and 11 columns, due hardware limitations, we will need to get a smaller sample from this data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-11-15T15:23:42.527383Z",
     "start_time": "2020-11-15T15:23:42.375530Z"
    },
    "hidden": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "The fraud percentual is 0.12908204481801522%\n"
     ]
    }
   ],
   "source": [
    "perc = df1['is_fraud'][(df1['is_fraud']==1)].sum() /  df1['is_fraud'].count()\n",
    "print('The fraud percentual is {}%'.format(perc*100))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "hidden": true
   },
   "source": [
    "## 2.3 Data Type"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-11-15T15:23:43.094486Z",
     "start_time": "2020-11-15T15:23:42.530369Z"
    },
    "hidden": true
   },
   "outputs": [],
   "source": [
    "df2 = df1.copy()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-11-15T15:23:43.109284Z",
     "start_time": "2020-11-15T15:23:43.097532Z"
    },
    "hidden": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "step                  int64\n",
       "type                 object\n",
       "amount              float64\n",
       "name_orig            object\n",
       "oldbalance_org      float64\n",
       "newbalance_orig     float64\n",
       "name_dest            object\n",
       "oldbalance_dest     float64\n",
       "newbalance_dest     float64\n",
       "is_fraud              int64\n",
       "is_flagged_fraud      int64\n",
       "dtype: object"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df1.dtypes"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "hidden": true
   },
   "source": [
    "## 2.4 Check NA"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-11-15T15:23:43.167743Z",
     "start_time": "2020-11-15T15:23:43.112323Z"
    },
    "hidden": true
   },
   "outputs": [],
   "source": [
    "#there isn't NA values"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-11-15T15:23:44.538727Z",
     "start_time": "2020-11-15T15:23:43.171737Z"
    },
    "hidden": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "step                0\n",
       "type                0\n",
       "amount              0\n",
       "name_orig           0\n",
       "oldbalance_org      0\n",
       "newbalance_orig     0\n",
       "name_dest           0\n",
       "oldbalance_dest     0\n",
       "newbalance_dest     0\n",
       "is_fraud            0\n",
       "is_flagged_fraud    0\n",
       "dtype: int64"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df2.isna().sum()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "hidden": true
   },
   "source": [
    "## 2.5 Descriptive Statistics\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-11-15T15:23:44.697941Z",
     "start_time": "2020-11-15T15:23:44.541696Z"
    },
    "hidden": true
   },
   "outputs": [],
   "source": [
    "num_attributes = df1.select_dtypes(include=['int64','float64'])\n",
    "cat_attributes = df1['type']"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "hidden": true
   },
   "source": [
    "###  Attributes"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-11-15T15:24:01.891036Z",
     "start_time": "2020-11-15T15:23:44.700635Z"
    },
    "hidden": true
   },
   "outputs": [],
   "source": [
    "# Central Tendency - mean, meadina \n",
    "ct1 = pd.DataFrame( num_attributes.apply( np.mean ) ).T\n",
    "ct2 = pd.DataFrame( num_attributes.apply( np.median ) ).T\n",
    "\n",
    "# dispersion - std, min, max, range, skew, kurtosis\n",
    "d1 = pd.DataFrame( num_attributes.apply( np.std ) ).T \n",
    "d2 = pd.DataFrame( num_attributes.apply( min ) ).T \n",
    "d3 = pd.DataFrame( num_attributes.apply( max ) ).T \n",
    "d4 = pd.DataFrame( num_attributes.apply( lambda x: x.max() - x.min() ) ).T \n",
    "d5 = pd.DataFrame( num_attributes.apply( lambda x: x.skew() ) ).T \n",
    "d6 = pd.DataFrame( num_attributes.apply( lambda x: x.kurtosis() ) ).T \n",
    "\n",
    "# concatenar\n",
    "metrics = pd.concat( [d2, d3, d4, ct1, ct2, d1, d5, d6] ).T.reset_index()\n",
    "metrics.columns = ['attributes', 'min', 'max', 'range', 'mean', 'median', 'std', 'skew', 'kurtosis']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-11-15T15:24:01.926921Z",
     "start_time": "2020-11-15T15:24:01.893689Z"
    },
    "hidden": true
   },
   "outputs": [
    {
     "data": {
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       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>attributes</th>\n",
       "      <th>min</th>\n",
       "      <th>max</th>\n",
       "      <th>range</th>\n",
       "      <th>mean</th>\n",
       "      <th>median</th>\n",
       "      <th>std</th>\n",
       "      <th>skew</th>\n",
       "      <th>kurtosis</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>step</td>\n",
       "      <td>1.0</td>\n",
       "      <td>7.430000e+02</td>\n",
       "      <td>7.420000e+02</td>\n",
       "      <td>2.433972e+02</td>\n",
       "      <td>239.000</td>\n",
       "      <td>1.423320e+02</td>\n",
       "      <td>0.375177</td>\n",
       "      <td>0.329071</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>amount</td>\n",
       "      <td>0.0</td>\n",
       "      <td>9.244552e+07</td>\n",
       "      <td>9.244552e+07</td>\n",
       "      <td>1.798619e+05</td>\n",
       "      <td>74871.940</td>\n",
       "      <td>6.038582e+05</td>\n",
       "      <td>30.993949</td>\n",
       "      <td>1797.956705</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>oldbalance_org</td>\n",
       "      <td>0.0</td>\n",
       "      <td>5.958504e+07</td>\n",
       "      <td>5.958504e+07</td>\n",
       "      <td>8.338831e+05</td>\n",
       "      <td>14208.000</td>\n",
       "      <td>2.888242e+06</td>\n",
       "      <td>5.249136</td>\n",
       "      <td>32.964879</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>newbalance_orig</td>\n",
       "      <td>0.0</td>\n",
       "      <td>4.958504e+07</td>\n",
       "      <td>4.958504e+07</td>\n",
       "      <td>8.551137e+05</td>\n",
       "      <td>0.000</td>\n",
       "      <td>2.924048e+06</td>\n",
       "      <td>5.176884</td>\n",
       "      <td>32.066985</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>oldbalance_dest</td>\n",
       "      <td>0.0</td>\n",
       "      <td>3.560159e+08</td>\n",
       "      <td>3.560159e+08</td>\n",
       "      <td>1.100702e+06</td>\n",
       "      <td>132705.665</td>\n",
       "      <td>3.399180e+06</td>\n",
       "      <td>19.921758</td>\n",
       "      <td>948.674125</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>newbalance_dest</td>\n",
       "      <td>0.0</td>\n",
       "      <td>3.561793e+08</td>\n",
       "      <td>3.561793e+08</td>\n",
       "      <td>1.224996e+06</td>\n",
       "      <td>214661.440</td>\n",
       "      <td>3.674129e+06</td>\n",
       "      <td>19.352302</td>\n",
       "      <td>862.156508</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>is_fraud</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.000000e+00</td>\n",
       "      <td>1.000000e+00</td>\n",
       "      <td>1.290820e-03</td>\n",
       "      <td>0.000</td>\n",
       "      <td>3.590479e-02</td>\n",
       "      <td>27.779538</td>\n",
       "      <td>769.702982</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>is_flagged_fraud</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.000000e+00</td>\n",
       "      <td>1.000000e+00</td>\n",
       "      <td>2.514687e-06</td>\n",
       "      <td>0.000</td>\n",
       "      <td>1.585775e-03</td>\n",
       "      <td>630.603629</td>\n",
       "      <td>397659.062500</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "         attributes  min           max         range          mean  \\\n",
       "0              step  1.0  7.430000e+02  7.420000e+02  2.433972e+02   \n",
       "1            amount  0.0  9.244552e+07  9.244552e+07  1.798619e+05   \n",
       "2    oldbalance_org  0.0  5.958504e+07  5.958504e+07  8.338831e+05   \n",
       "3   newbalance_orig  0.0  4.958504e+07  4.958504e+07  8.551137e+05   \n",
       "4   oldbalance_dest  0.0  3.560159e+08  3.560159e+08  1.100702e+06   \n",
       "5   newbalance_dest  0.0  3.561793e+08  3.561793e+08  1.224996e+06   \n",
       "6          is_fraud  0.0  1.000000e+00  1.000000e+00  1.290820e-03   \n",
       "7  is_flagged_fraud  0.0  1.000000e+00  1.000000e+00  2.514687e-06   \n",
       "\n",
       "       median           std        skew       kurtosis  \n",
       "0     239.000  1.423320e+02    0.375177       0.329071  \n",
       "1   74871.940  6.038582e+05   30.993949    1797.956705  \n",
       "2   14208.000  2.888242e+06    5.249136      32.964879  \n",
       "3       0.000  2.924048e+06    5.176884      32.066985  \n",
       "4  132705.665  3.399180e+06   19.921758     948.674125  \n",
       "5  214661.440  3.674129e+06   19.352302     862.156508  \n",
       "6       0.000  3.590479e-02   27.779538     769.702982  \n",
       "7       0.000  1.585775e-03  630.603629  397659.062500  "
      ]
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "metrics"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "hidden": true
   },
   "source": [
    "### Categorical attributes and variable filter "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-11-15T15:24:02.262823Z",
     "start_time": "2020-11-15T15:24:01.929088Z"
    },
    "hidden": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0       PAYMENT\n",
       "2      TRANSFER\n",
       "3      CASH_OUT\n",
       "9         DEBIT\n",
       "389     CASH_IN\n",
       "Name: type, dtype: object"
      ]
     },
     "execution_count": 20,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "cat_attributes = cat_attributes.drop_duplicates()\n",
    "cat_attributes.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-11-15T15:24:04.978521Z",
     "start_time": "2020-11-15T15:24:02.265269Z"
    },
    "hidden": true
   },
   "outputs": [],
   "source": [
    "a1 = df2['type'][(df2['type']=='PAYMENT') & (df2['is_fraud']==1)].count()\n",
    "a2 = df2['type'][(df2['type']=='TRANSFER') & (df2['is_fraud']==1)].count()\n",
    "a3 = df2['type'][(df2['type']=='CASH_OUT') & (df2['is_fraud']==1)].count()\n",
    "a4 = df2['type'][(df2['type']=='DEBIT') & (df2['is_fraud']==1)].count()\n",
    "a5 = df2['type'][(df2['type']=='CASH_IN') & (df2['is_fraud']==1)].count()\n",
    "a6 = df2['type'][(df2['is_fraud']==1)].count()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-11-15T15:24:04.985866Z",
     "start_time": "2020-11-15T15:24:04.980864Z"
    },
    "hidden": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Payment 0. Transfer 4097. Cash_out 4116 . Debit 0 . Cash_in 0. Total 8213\n"
     ]
    }
   ],
   "source": [
    "print('Payment {}. Transfer {}. Cash_out {} . Debit {} . Cash_in {}. Total {}'.format(a1,a2,a3,a4,a5,a6) )"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "hidden": true
   },
   "source": [
    "### Deleting not useful types"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "hidden": true
   },
   "source": [
    "There is not any fraud example in Payments, Cash_in and Debit, based on that, we will drop these types of transactions from the data frame"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-11-15T15:24:05.042515Z",
     "start_time": "2020-11-15T15:24:04.988937Z"
    },
    "hidden": true
   },
   "outputs": [],
   "source": [
    "#df2 = df2[(df2.type != 'PAYMENT') & (df2.type != 'DEBIT') & (df2.type != 'CASH_IN') ]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "heading_collapsed": true
   },
   "source": [
    "# 3. Feature Engineering"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-11-15T15:24:05.098495Z",
     "start_time": "2020-11-15T15:24:05.046308Z"
    },
    "hidden": true
   },
   "outputs": [],
   "source": [
    "#turning the sample into a csv file, so we can always use the same sample\n",
    "#df2.to_csv(r'/home/leonardo/blocker_fraud_company/data/df_sample.csv')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-11-15T15:24:06.357755Z",
     "start_time": "2020-11-15T15:24:05.102649Z"
    },
    "hidden": true
   },
   "outputs": [],
   "source": [
    "df3 = pd.read_csv('/home/leonardo/repo/blocker_fraud_company/data/df_sample.csv')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "heading_collapsed": true,
    "hidden": true
   },
   "source": [
    "## 3.1  Mind map Hypotheses "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-11-15T15:24:06.473317Z",
     "start_time": "2020-11-15T15:24:06.366367Z"
    },
    "hidden": true
   },
   "outputs": [
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<IPython.core.display.Image object>"
      ]
     },
     "execution_count": 26,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "Image('img/Fraud_detection.png')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "hidden": true
   },
   "source": [
    "## 3.2 Hypothesis creation"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-11-11T16:49:31.211204Z",
     "start_time": "2020-11-11T16:49:31.156534Z"
    },
    "heading_collapsed": true,
    "hidden": true
   },
   "source": [
    "### 3.2.1 Type of transactions"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-11-15T15:24:06.492077Z",
     "start_time": "2020-11-15T15:24:06.479795Z"
    },
    "hidden": true
   },
   "outputs": [],
   "source": [
    "#type of transactions already in the dataset "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "heading_collapsed": true,
    "hidden": true
   },
   "source": [
    "### 3.2.2 Temporal "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-11-15T15:24:06.833382Z",
     "start_time": "2020-11-15T15:24:06.494769Z"
    },
    "hidden": true
   },
   "outputs": [],
   "source": [
    "# week of month\n",
    "df3['week_of_month'] = df3['step'].apply(lambda x: x/(24*7)  ).astype(int)\n",
    "#day\n",
    "df3['day_of_month'] = df3['step'].apply(lambda x: x/24).astype(int)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-11-15T15:24:06.878946Z",
     "start_time": "2020-11-15T15:24:06.836674Z"
    },
    "hidden": true
   },
   "outputs": [
    {
     "data": {
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
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       "      <th>Unnamed: 1</th>\n",
       "      <th>step</th>\n",
       "      <th>type</th>\n",
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       "      <th>name_orig</th>\n",
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       "      <th>is_fraud.1</th>\n",
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       "      <th>week_of_month</th>\n",
       "      <th>day_of_month</th>\n",
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       "  </thead>\n",
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       "      <td>CASH_OUT</td>\n",
       "      <td>244173.70</td>\n",
       "      <td>C248730046</td>\n",
       "      <td>49537.00</td>\n",
       "      <td>0.0</td>\n",
       "      <td>C1876543998</td>\n",
       "      <td>0.00</td>\n",
       "      <td>244173.70</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>11</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>0</td>\n",
       "      <td>3977715</td>\n",
       "      <td>297</td>\n",
       "      <td>CASH_OUT</td>\n",
       "      <td>320636.56</td>\n",
       "      <td>C1490350516</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.0</td>\n",
       "      <td>C2098652030</td>\n",
       "      <td>4395126.25</td>\n",
       "      <td>4715762.80</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>12</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>0</td>\n",
       "      <td>2359639</td>\n",
       "      <td>190</td>\n",
       "      <td>CASH_OUT</td>\n",
       "      <td>406650.21</td>\n",
       "      <td>C1985972159</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.0</td>\n",
       "      <td>C2034144661</td>\n",
       "      <td>3860832.38</td>\n",
       "      <td>4267482.59</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>7</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>357370</th>\n",
       "      <td>1</td>\n",
       "      <td>6360920</td>\n",
       "      <td>717</td>\n",
       "      <td>CASH_OUT</td>\n",
       "      <td>751854.42</td>\n",
       "      <td>C1391223821</td>\n",
       "      <td>751854.42</td>\n",
       "      <td>0.0</td>\n",
       "      <td>C450487671</td>\n",
       "      <td>11716.66</td>\n",
       "      <td>763571.08</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>4</td>\n",
       "      <td>29</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>357371</th>\n",
       "      <td>1</td>\n",
       "      <td>3273908</td>\n",
       "      <td>251</td>\n",
       "      <td>CASH_OUT</td>\n",
       "      <td>2693520.61</td>\n",
       "      <td>C1734542824</td>\n",
       "      <td>2693520.61</td>\n",
       "      <td>0.0</td>\n",
       "      <td>C1750548903</td>\n",
       "      <td>2505748.79</td>\n",
       "      <td>5199269.41</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>10</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>357372</th>\n",
       "      <td>1</td>\n",
       "      <td>4667</td>\n",
       "      <td>4</td>\n",
       "      <td>TRANSFER</td>\n",
       "      <td>169941.73</td>\n",
       "      <td>C540962910</td>\n",
       "      <td>169941.73</td>\n",
       "      <td>0.0</td>\n",
       "      <td>C2127862399</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>357373</th>\n",
       "      <td>1</td>\n",
       "      <td>6259987</td>\n",
       "      <td>608</td>\n",
       "      <td>TRANSFER</td>\n",
       "      <td>388171.95</td>\n",
       "      <td>C1413288238</td>\n",
       "      <td>388171.95</td>\n",
       "      <td>0.0</td>\n",
       "      <td>C1384608658</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>25</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>357374</th>\n",
       "      <td>1</td>\n",
       "      <td>6202687</td>\n",
       "      <td>583</td>\n",
       "      <td>CASH_OUT</td>\n",
       "      <td>1764389.11</td>\n",
       "      <td>C1297057320</td>\n",
       "      <td>1764389.11</td>\n",
       "      <td>0.0</td>\n",
       "      <td>C2133001459</td>\n",
       "      <td>7116057.41</td>\n",
       "      <td>8880446.51</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>24</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>357375 rows × 15 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "        is_fraud  Unnamed: 1  step      type      amount    name_orig  \\\n",
       "0              0     3807078   281  CASH_OUT   118035.33  C1458573557   \n",
       "1              0     2280345   187  CASH_OUT   108902.87  C1281506030   \n",
       "2              0     3817919   281  CASH_OUT   244173.70   C248730046   \n",
       "3              0     3977715   297  CASH_OUT   320636.56  C1490350516   \n",
       "4              0     2359639   190  CASH_OUT   406650.21  C1985972159   \n",
       "...          ...         ...   ...       ...         ...          ...   \n",
       "357370         1     6360920   717  CASH_OUT   751854.42  C1391223821   \n",
       "357371         1     3273908   251  CASH_OUT  2693520.61  C1734542824   \n",
       "357372         1        4667     4  TRANSFER   169941.73   C540962910   \n",
       "357373         1     6259987   608  TRANSFER   388171.95  C1413288238   \n",
       "357374         1     6202687   583  CASH_OUT  1764389.11  C1297057320   \n",
       "\n",
       "        oldbalance_org  newbalance_orig    name_dest  oldbalance_dest  \\\n",
       "0                 0.00              0.0  C1011327941        323412.58   \n",
       "1             25372.00              0.0   C224713062        307213.63   \n",
       "2             49537.00              0.0  C1876543998             0.00   \n",
       "3                 0.00              0.0  C2098652030       4395126.25   \n",
       "4                 0.00              0.0  C2034144661       3860832.38   \n",
       "...                ...              ...          ...              ...   \n",
       "357370       751854.42              0.0   C450487671         11716.66   \n",
       "357371      2693520.61              0.0  C1750548903       2505748.79   \n",
       "357372       169941.73              0.0  C2127862399             0.00   \n",
       "357373       388171.95              0.0  C1384608658             0.00   \n",
       "357374      1764389.11              0.0  C2133001459       7116057.41   \n",
       "\n",
       "        newbalance_dest  is_fraud.1  is_flagged_fraud  week_of_month  \\\n",
       "0             441447.92           0                 0              1   \n",
       "1             416116.50           0                 0              1   \n",
       "2             244173.70           0                 0              1   \n",
       "3            4715762.80           0                 0              1   \n",
       "4            4267482.59           0                 0              1   \n",
       "...                 ...         ...               ...            ...   \n",
       "357370        763571.08           1                 0              4   \n",
       "357371       5199269.41           1                 0              1   \n",
       "357372             0.00           1                 0              0   \n",
       "357373             0.00           1                 0              3   \n",
       "357374       8880446.51           1                 0              3   \n",
       "\n",
       "        day_of_month  \n",
       "0                 11  \n",
       "1                  7  \n",
       "2                 11  \n",
       "3                 12  \n",
       "4                  7  \n",
       "...              ...  \n",
       "357370            29  \n",
       "357371            10  \n",
       "357372             0  \n",
       "357373            25  \n",
       "357374            24  \n",
       "\n",
       "[357375 rows x 15 columns]"
      ]
     },
     "execution_count": 29,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df3"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-11-11T16:49:31.368314Z",
     "start_time": "2020-11-11T16:49:31.289938Z"
    },
    "hidden": true
   },
   "source": [
    "### 3.2.3 Client Hypothesis"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "hidden": true
   },
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-11-15T15:24:06.932231Z",
     "start_time": "2020-11-15T15:24:06.881822Z"
    },
    "hidden": true
   },
   "outputs": [],
   "source": [
    "#bank balance variation\n",
    "df3['balance_variation_dest'] = df3['oldbalance_dest'] - df3['newbalance_dest']\n",
    "df3['balance_variation_org'] = df3['oldbalance_org'] - df3['newbalance_orig']\n",
    "\n",
    "#who started the transaction: in case of future studies\n",
    "# who received: in case of future studies"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-11-15T15:24:07.039692Z",
     "start_time": "2020-11-15T15:24:06.935860Z"
    },
    "hidden": true
   },
   "outputs": [
    {
     "data": {
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>is_fraud</th>\n",
       "      <th>Unnamed: 1</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>is_fraud.1</th>\n",
       "      <th>is_flagged_fraud</th>\n",
       "      <th>week_of_month</th>\n",
       "      <th>day_of_month</th>\n",
       "      <th>balance_variation_dest</th>\n",
       "      <th>balance_variation_org</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0</td>\n",
       "      <td>3807078</td>\n",
       "      <td>281</td>\n",
       "      <td>CASH_OUT</td>\n",
       "      <td>118035.33</td>\n",
       "      <td>C1458573557</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.0</td>\n",
       "      <td>C1011327941</td>\n",
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       "      <td>441447.92</td>\n",
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       "      <td>1</td>\n",
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       "      <td>-118035.34</td>\n",
       "      <td>0.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>0</td>\n",
       "      <td>2280345</td>\n",
       "      <td>187</td>\n",
       "      <td>CASH_OUT</td>\n",
       "      <td>108902.87</td>\n",
       "      <td>C1281506030</td>\n",
       "      <td>25372.00</td>\n",
       "      <td>0.0</td>\n",
       "      <td>C224713062</td>\n",
       "      <td>307213.63</td>\n",
       "      <td>416116.50</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>7</td>\n",
       "      <td>-108902.87</td>\n",
       "      <td>25372.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>0</td>\n",
       "      <td>3817919</td>\n",
       "      <td>281</td>\n",
       "      <td>CASH_OUT</td>\n",
       "      <td>244173.70</td>\n",
       "      <td>C248730046</td>\n",
       "      <td>49537.00</td>\n",
       "      <td>0.0</td>\n",
       "      <td>C1876543998</td>\n",
       "      <td>0.00</td>\n",
       "      <td>244173.70</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>11</td>\n",
       "      <td>-244173.70</td>\n",
       "      <td>49537.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>0</td>\n",
       "      <td>3977715</td>\n",
       "      <td>297</td>\n",
       "      <td>CASH_OUT</td>\n",
       "      <td>320636.56</td>\n",
       "      <td>C1490350516</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.0</td>\n",
       "      <td>C2098652030</td>\n",
       "      <td>4395126.25</td>\n",
       "      <td>4715762.80</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>12</td>\n",
       "      <td>-320636.55</td>\n",
       "      <td>0.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>0</td>\n",
       "      <td>2359639</td>\n",
       "      <td>190</td>\n",
       "      <td>CASH_OUT</td>\n",
       "      <td>406650.21</td>\n",
       "      <td>C1985972159</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.0</td>\n",
       "      <td>C2034144661</td>\n",
       "      <td>3860832.38</td>\n",
       "      <td>4267482.59</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>7</td>\n",
       "      <td>-406650.21</td>\n",
       "      <td>0.00</td>\n",
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       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
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       "    <tr>\n",
       "      <th>357370</th>\n",
       "      <td>1</td>\n",
       "      <td>6360920</td>\n",
       "      <td>717</td>\n",
       "      <td>CASH_OUT</td>\n",
       "      <td>751854.42</td>\n",
       "      <td>C1391223821</td>\n",
       "      <td>751854.42</td>\n",
       "      <td>0.0</td>\n",
       "      <td>C450487671</td>\n",
       "      <td>11716.66</td>\n",
       "      <td>763571.08</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>4</td>\n",
       "      <td>29</td>\n",
       "      <td>-751854.42</td>\n",
       "      <td>751854.42</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>357371</th>\n",
       "      <td>1</td>\n",
       "      <td>3273908</td>\n",
       "      <td>251</td>\n",
       "      <td>CASH_OUT</td>\n",
       "      <td>2693520.61</td>\n",
       "      <td>C1734542824</td>\n",
       "      <td>2693520.61</td>\n",
       "      <td>0.0</td>\n",
       "      <td>C1750548903</td>\n",
       "      <td>2505748.79</td>\n",
       "      <td>5199269.41</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>10</td>\n",
       "      <td>-2693520.62</td>\n",
       "      <td>2693520.61</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>357372</th>\n",
       "      <td>1</td>\n",
       "      <td>4667</td>\n",
       "      <td>4</td>\n",
       "      <td>TRANSFER</td>\n",
       "      <td>169941.73</td>\n",
       "      <td>C540962910</td>\n",
       "      <td>169941.73</td>\n",
       "      <td>0.0</td>\n",
       "      <td>C2127862399</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0.00</td>\n",
       "      <td>169941.73</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>357373</th>\n",
       "      <td>1</td>\n",
       "      <td>6259987</td>\n",
       "      <td>608</td>\n",
       "      <td>TRANSFER</td>\n",
       "      <td>388171.95</td>\n",
       "      <td>C1413288238</td>\n",
       "      <td>388171.95</td>\n",
       "      <td>0.0</td>\n",
       "      <td>C1384608658</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>25</td>\n",
       "      <td>0.00</td>\n",
       "      <td>388171.95</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>357374</th>\n",
       "      <td>1</td>\n",
       "      <td>6202687</td>\n",
       "      <td>583</td>\n",
       "      <td>CASH_OUT</td>\n",
       "      <td>1764389.11</td>\n",
       "      <td>C1297057320</td>\n",
       "      <td>1764389.11</td>\n",
       "      <td>0.0</td>\n",
       "      <td>C2133001459</td>\n",
       "      <td>7116057.41</td>\n",
       "      <td>8880446.51</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>24</td>\n",
       "      <td>-1764389.10</td>\n",
       "      <td>1764389.11</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>357375 rows × 17 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "        is_fraud  Unnamed: 1  step      type      amount    name_orig  \\\n",
       "0              0     3807078   281  CASH_OUT   118035.33  C1458573557   \n",
       "1              0     2280345   187  CASH_OUT   108902.87  C1281506030   \n",
       "2              0     3817919   281  CASH_OUT   244173.70   C248730046   \n",
       "3              0     3977715   297  CASH_OUT   320636.56  C1490350516   \n",
       "4              0     2359639   190  CASH_OUT   406650.21  C1985972159   \n",
       "...          ...         ...   ...       ...         ...          ...   \n",
       "357370         1     6360920   717  CASH_OUT   751854.42  C1391223821   \n",
       "357371         1     3273908   251  CASH_OUT  2693520.61  C1734542824   \n",
       "357372         1        4667     4  TRANSFER   169941.73   C540962910   \n",
       "357373         1     6259987   608  TRANSFER   388171.95  C1413288238   \n",
       "357374         1     6202687   583  CASH_OUT  1764389.11  C1297057320   \n",
       "\n",
       "        oldbalance_org  newbalance_orig    name_dest  oldbalance_dest  \\\n",
       "0                 0.00              0.0  C1011327941        323412.58   \n",
       "1             25372.00              0.0   C224713062        307213.63   \n",
       "2             49537.00              0.0  C1876543998             0.00   \n",
       "3                 0.00              0.0  C2098652030       4395126.25   \n",
       "4                 0.00              0.0  C2034144661       3860832.38   \n",
       "...                ...              ...          ...              ...   \n",
       "357370       751854.42              0.0   C450487671         11716.66   \n",
       "357371      2693520.61              0.0  C1750548903       2505748.79   \n",
       "357372       169941.73              0.0  C2127862399             0.00   \n",
       "357373       388171.95              0.0  C1384608658             0.00   \n",
       "357374      1764389.11              0.0  C2133001459       7116057.41   \n",
       "\n",
       "        newbalance_dest  is_fraud.1  is_flagged_fraud  week_of_month  \\\n",
       "0             441447.92           0                 0              1   \n",
       "1             416116.50           0                 0              1   \n",
       "2             244173.70           0                 0              1   \n",
       "3            4715762.80           0                 0              1   \n",
       "4            4267482.59           0                 0              1   \n",
       "...                 ...         ...               ...            ...   \n",
       "357370        763571.08           1                 0              4   \n",
       "357371       5199269.41           1                 0              1   \n",
       "357372             0.00           1                 0              0   \n",
       "357373             0.00           1                 0              3   \n",
       "357374       8880446.51           1                 0              3   \n",
       "\n",
       "        day_of_month  balance_variation_dest  balance_variation_org  \n",
       "0                 11              -118035.34                   0.00  \n",
       "1                  7              -108902.87               25372.00  \n",
       "2                 11              -244173.70               49537.00  \n",
       "3                 12              -320636.55                   0.00  \n",
       "4                  7              -406650.21                   0.00  \n",
       "...              ...                     ...                    ...  \n",
       "357370            29              -751854.42              751854.42  \n",
       "357371            10             -2693520.62             2693520.61  \n",
       "357372             0                    0.00              169941.73  \n",
       "357373            25                    0.00              388171.95  \n",
       "357374            24             -1764389.10             1764389.11  \n",
       "\n",
       "[357375 rows x 17 columns]"
      ]
     },
     "execution_count": 31,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df3"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "heading_collapsed": true,
    "hidden": true
   },
   "source": [
    "### Transactions Hypothesis "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-11-15T15:24:08.308361Z",
     "start_time": "2020-11-15T15:24:07.042802Z"
    },
    "hidden": true
   },
   "outputs": [],
   "source": [
    "#amount of transactions (how many times a client made a transaction)\n",
    "\n",
    "count_map = df3['name_dest'].value_counts().to_dict()\n",
    "df3['amount_of_transactions']=df3['name_dest'].map(count_map)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-11-15T15:24:08.335672Z",
     "start_time": "2020-11-15T15:24:08.311153Z"
    },
    "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>is_fraud</th>\n",
       "      <th>Unnamed: 1</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>is_fraud.1</th>\n",
       "      <th>is_flagged_fraud</th>\n",
       "      <th>week_of_month</th>\n",
       "      <th>day_of_month</th>\n",
       "      <th>balance_variation_dest</th>\n",
       "      <th>balance_variation_org</th>\n",
       "      <th>amount_of_transactions</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0</td>\n",
       "      <td>3807078</td>\n",
       "      <td>281</td>\n",
       "      <td>CASH_OUT</td>\n",
       "      <td>118035.33</td>\n",
       "      <td>C1458573557</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>C1011327941</td>\n",
       "      <td>323412.58</td>\n",
       "      <td>441447.92</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>11</td>\n",
       "      <td>-118035.34</td>\n",
       "      <td>0.0</td>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>0</td>\n",
       "      <td>2280345</td>\n",
       "      <td>187</td>\n",
       "      <td>CASH_OUT</td>\n",
       "      <td>108902.87</td>\n",
       "      <td>C1281506030</td>\n",
       "      <td>25372.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>C224713062</td>\n",
       "      <td>307213.63</td>\n",
       "      <td>416116.50</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>7</td>\n",
       "      <td>-108902.87</td>\n",
       "      <td>25372.0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>0</td>\n",
       "      <td>3817919</td>\n",
       "      <td>281</td>\n",
       "      <td>CASH_OUT</td>\n",
       "      <td>244173.70</td>\n",
       "      <td>C248730046</td>\n",
       "      <td>49537.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>C1876543998</td>\n",
       "      <td>0.00</td>\n",
       "      <td>244173.70</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>11</td>\n",
       "      <td>-244173.70</td>\n",
       "      <td>49537.0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>0</td>\n",
       "      <td>3977715</td>\n",
       "      <td>297</td>\n",
       "      <td>CASH_OUT</td>\n",
       "      <td>320636.56</td>\n",
       "      <td>C1490350516</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>C2098652030</td>\n",
       "      <td>4395126.25</td>\n",
       "      <td>4715762.80</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>12</td>\n",
       "      <td>-320636.55</td>\n",
       "      <td>0.0</td>\n",
       "      <td>8</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>0</td>\n",
       "      <td>2359639</td>\n",
       "      <td>190</td>\n",
       "      <td>CASH_OUT</td>\n",
       "      <td>406650.21</td>\n",
       "      <td>C1985972159</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>C2034144661</td>\n",
       "      <td>3860832.38</td>\n",
       "      <td>4267482.59</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>7</td>\n",
       "      <td>-406650.21</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   is_fraud  Unnamed: 1  step      type     amount    name_orig  \\\n",
       "0         0     3807078   281  CASH_OUT  118035.33  C1458573557   \n",
       "1         0     2280345   187  CASH_OUT  108902.87  C1281506030   \n",
       "2         0     3817919   281  CASH_OUT  244173.70   C248730046   \n",
       "3         0     3977715   297  CASH_OUT  320636.56  C1490350516   \n",
       "4         0     2359639   190  CASH_OUT  406650.21  C1985972159   \n",
       "\n",
       "   oldbalance_org  newbalance_orig    name_dest  oldbalance_dest  \\\n",
       "0             0.0              0.0  C1011327941        323412.58   \n",
       "1         25372.0              0.0   C224713062        307213.63   \n",
       "2         49537.0              0.0  C1876543998             0.00   \n",
       "3             0.0              0.0  C2098652030       4395126.25   \n",
       "4             0.0              0.0  C2034144661       3860832.38   \n",
       "\n",
       "   newbalance_dest  is_fraud.1  is_flagged_fraud  week_of_month  day_of_month  \\\n",
       "0        441447.92           0                 0              1            11   \n",
       "1        416116.50           0                 0              1             7   \n",
       "2        244173.70           0                 0              1            11   \n",
       "3       4715762.80           0                 0              1            12   \n",
       "4       4267482.59           0                 0              1             7   \n",
       "\n",
       "   balance_variation_dest  balance_variation_org  amount_of_transactions  \n",
       "0              -118035.34                    0.0                       4  \n",
       "1              -108902.87                25372.0                       1  \n",
       "2              -244173.70                49537.0                       1  \n",
       "3              -320636.55                    0.0                       8  \n",
       "4              -406650.21                    0.0                       1  "
      ]
     },
     "execution_count": 33,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df3.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "heading_collapsed": true
   },
   "source": [
    "# 4. EXPLORATORY DATA ANALYSIS"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-11-15T15:24:08.440654Z",
     "start_time": "2020-11-15T15:24:08.337927Z"
    },
    "hidden": true
   },
   "outputs": [],
   "source": [
    "df4= df3.copy()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-11-15T15:24:08.520645Z",
     "start_time": "2020-11-15T15:24:08.443408Z"
    },
    "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": [
    "jupyter_settings()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "heading_collapsed": true,
    "hidden": true
   },
   "source": [
    "## 4.1 Univariate analysis"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-11-11T20:27:15.291031Z",
     "start_time": "2020-11-11T20:27:14.448021Z"
    },
    "hidden": true
   },
   "source": [
    "### Numerical Variable"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-11-15T15:24:12.821942Z",
     "start_time": "2020-11-15T15:24:08.523950Z"
    },
    "hidden": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[<AxesSubplot:title={'center':'is_fraud'}>,\n",
       "        <AxesSubplot:title={'center':'Unnamed: 1'}>,\n",
       "        <AxesSubplot:title={'center':'step'}>,\n",
       "        <AxesSubplot:title={'center':'amount'}>],\n",
       "       [<AxesSubplot:title={'center':'oldbalance_org'}>,\n",
       "        <AxesSubplot:title={'center':'newbalance_orig'}>,\n",
       "        <AxesSubplot:title={'center':'oldbalance_dest'}>,\n",
       "        <AxesSubplot:title={'center':'newbalance_dest'}>],\n",
       "       [<AxesSubplot:title={'center':'is_fraud.1'}>,\n",
       "        <AxesSubplot:title={'center':'is_flagged_fraud'}>,\n",
       "        <AxesSubplot:title={'center':'week_of_month'}>,\n",
       "        <AxesSubplot:title={'center':'day_of_month'}>],\n",
       "       [<AxesSubplot:title={'center':'balance_variation_dest'}>,\n",
       "        <AxesSubplot:title={'center':'balance_variation_org'}>,\n",
       "        <AxesSubplot:title={'center':'amount_of_transactions'}>,\n",
       "        <AxesSubplot:>]], dtype=object)"
      ]
     },
     "execution_count": 36,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
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      "text/plain": [
       "<Figure size 1800x864 with 16 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "num_attributes = df4.select_dtypes(include=['int64','float64'])\n",
    "num_attributes.hist()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "heading_collapsed": true,
    "hidden": true
   },
   "source": [
    "### Categorical Variables"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-11-15T15:24:13.503653Z",
     "start_time": "2020-11-15T15:24:12.824415Z"
    },
    "hidden": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:xlabel='amount', ylabel='Density'>"
      ]
     },
     "execution_count": 37,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1800x864 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "a = df4[(df4['is_fraud']==1)]\n",
    "#types \n",
    "plt.subplot(1,2,1)\n",
    "sns.countplot(a['type'])\n",
    "\n",
    "plt.subplot(1,2,2)\n",
    "sns.kdeplot(a[a['type'] =='CASH_OUT']['amount'],label='cash_out',shade=True)\n",
    "sns.kdeplot(a[a['type'] =='TRANSFER']['amount'],label='transfer',shade=True)\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "heading_collapsed": true,
    "hidden": true
   },
   "source": [
    "## 4.2 Bivariate analisys"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "hidden": true
   },
   "source": [
    " **1**- There is more fraud in the first and last week of month"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "hidden": true
   },
   "source": [
    "**False** The last week is where there is the smaller amount of frauds"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-11-15T15:24:13.829351Z",
     "start_time": "2020-11-15T15:24:13.506659Z"
    },
    "hidden": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:xlabel='week_of_month', ylabel='is_fraud'>"
      ]
     },
     "execution_count": 38,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1800x864 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "aux1 = df4[['week_of_month','is_fraud']].groupby('week_of_month').sum().reset_index()\n",
    "sns.barplot(x='week_of_month',y='is_fraud',data=aux1)\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "hidden": true
   },
   "source": [
    "**2**- There is more fraud in the first days of month"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "hidden": true
   },
   "source": [
    "**True** There is more fraud in the first days of month"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-11-15T15:24:16.463134Z",
     "start_time": "2020-11-15T15:24:13.832444Z"
    },
    "hidden": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:xlabel='day_of_month', ylabel='Density'>"
      ]
     },
     "execution_count": 39,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1800x864 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "a = df4[(df4['is_fraud']==1)]\n",
    "sns.distplot(df4['day_of_month'])\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "heading_collapsed": true,
    "hidden": true
   },
   "source": [
    "## 4.3 Multivariate analysis"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "hidden": true
   },
   "source": [
    "### Numerical attributes"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-11-15T15:24:19.193707Z",
     "start_time": "2020-11-15T15:24:16.465821Z"
    },
    "hidden": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:>"
      ]
     },
     "execution_count": 40,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1800x864 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "correlation = num_attributes.corr(method='pearson')\n",
    "sns.heatmap(correlation,annot=True)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "heading_collapsed": true
   },
   "source": [
    "# 5. Data Preparation"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-11-15T15:24:19.234582Z",
     "start_time": "2020-11-15T15:24:19.196239Z"
    },
    "hidden": true
   },
   "outputs": [],
   "source": [
    "df5 = df4.copy()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "ExecuteTime": {
     "start_time": "2020-11-12T02:11:51.508Z"
    },
    "heading_collapsed": true,
    "hidden": true
   },
   "source": [
    "## 5.1 Rescaling"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-11-15T15:24:19.272814Z",
     "start_time": "2020-11-15T15:24:19.236725Z"
    },
    "hidden": true
   },
   "outputs": [],
   "source": [
    "rs= RobustScaler() #when there's big outliers\n",
    "mms = MinMaxScaler() #when there isn't big outliers"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-11-15T15:24:19.360835Z",
     "start_time": "2020-11-15T15:24:19.276690Z"
    },
    "hidden": true
   },
   "outputs": [],
   "source": [
    "a = df5.select_dtypes(include=['float64','int64'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-11-15T15:24:19.661879Z",
     "start_time": "2020-11-15T15:24:19.363346Z"
    },
    "hidden": true
   },
   "outputs": [],
   "source": [
    "#step    \n",
    "df5['step'] = mms.fit_transform(df5[['step']].values)\n",
    "\n",
    "#amount   \n",
    "\n",
    "df5['amount'] = rs.fit_transform(df5[['amount']].values)\n",
    "\n",
    "#oldbalance_org \n",
    "df5['oldbalance_org'] = rs.fit_transform(df5[['oldbalance_org']].values)\n",
    "\n",
    "#newbalance_orig \n",
    "df5['newbalance_orig '] = rs.fit_transform(df5[['newbalance_orig' ]].values)\n",
    "\n",
    "#oldbalance_dest\n",
    "df5['oldbalance_dest '] = rs.fit_transform(df5[['oldbalance_dest' ]].values)\n",
    "\n",
    "                                                \n",
    "#newbalance_dest \n",
    "df5['newbalance_dest'] = rs.fit_transform(df5[['newbalance_dest']].values)\n",
    "                                                \n",
    "\n",
    "#balance_variation_dest \n",
    "df5['balance_variation_dest'] = rs.fit_transform(df5[['balance_variation_dest']].values)\n",
    "\n",
    "#balance_variation_org   \n",
    "df5['balance_variation_org'] = rs.fit_transform(df5[['balance_variation_org']].values)\n",
    "\n",
    "#amount_of_transactions      \n",
    "df5['amount_of_transactions'] = mms.fit_transform(df5[['amount_of_transactions']].values)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "hidden": true
   },
   "source": [
    "## 5.2 Enconding"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-11-15T15:24:19.808479Z",
     "start_time": "2020-11-15T15:24:19.664942Z"
    },
    "hidden": true
   },
   "outputs": [],
   "source": [
    "#TYPE\n",
    "\n",
    "#BY ONE HOT ENCODING\n",
    "df5 = pd.get_dummies( df5, prefix=['type'], columns=['type'] )"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# 6. FEATURE SELECTION"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-11-15T15:24:19.835779Z",
     "start_time": "2020-11-15T15:24:19.811346Z"
    }
   },
   "outputs": [],
   "source": [
    "df6 = df5.copy()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 6.1 Spliting dataframe into training and test"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-11-15T15:24:19.966098Z",
     "start_time": "2020-11-15T15:24:19.838853Z"
    }
   },
   "outputs": [],
   "source": [
    "cols_drop = ['Unnamed: 1', 'step','name_orig','name_dest','oldbalance_dest']\n",
    "df6 = df6.drop(cols_drop,axis=1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-11-15T15:24:20.064168Z",
     "start_time": "2020-11-15T15:24:19.969237Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "X_train shape: (250162, 15)\n",
      "X_test: (107213, 15)\n",
      "\n",
      "_train: (250162,)\n",
      "y_test: (107213,)\n"
     ]
    }
   ],
   "source": [
    "X= df6.drop('is_fraud',axis=1)\n",
    "Y= df6['is_fraud']\n",
    "\n",
    "#training dataset\n",
    "X_train, X_test, y_train, y_test = train_test_split(X, Y, train_size=0.7)\n",
    "\n",
    "\n",
    "print( 'X_train shape: {}'.format( X_train.shape ) )\n",
    "print( 'X_test: {}'.format( X_test.shape ) )\n",
    "\n",
    "\n",
    "print( '\\n_train: {}'.format( y_train.shape ) )\n",
    "print( 'y_test: {}'.format( y_test.shape ) )\n",
    "\n",
    "\n",
    "#test dataset"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-11-15T15:24:20.073176Z",
     "start_time": "2020-11-15T15:24:20.067237Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(250162, 15)"
      ]
     },
     "execution_count": 49,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "X_train.shape"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-11-15T15:13:38.925318Z",
     "start_time": "2020-11-15T15:13:38.921700Z"
    }
   },
   "source": [
    "# 6.1.1 Balacing data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 51,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-11-15T15:24:59.189762Z",
     "start_time": "2020-11-15T15:24:58.833665Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Before OverSampling, counts of label '1': 734\n",
      "Before OverSampling, counts of label '0': 249428 \n",
      "\n",
      "After OverSampling, the shape of train_X: (498856, 15)\n",
      "After OverSampling, the shape of train_y: (498856,) \n",
      "\n",
      "After OverSampling, counts of label '1': 249428\n",
      "After OverSampling, counts of label '0': 249428\n"
     ]
    }
   ],
   "source": [
    "#the data is quite imbalanced then we will apply the SMOTE module \n",
    "print(\"Before OverSampling, counts of label '1': {}\".format(sum(y_train== 1))) \n",
    "print(\"Before OverSampling, counts of label '0': {} \\n\".format(sum(y_train== 0))) \n",
    "  \n",
    "sm = SMOTE(random_state = 2) \n",
    "X_train_res, y_train_res = sm.fit_sample(X_train, y_train.ravel()) \n",
    "  \n",
    "print('After OverSampling, the shape of train_X: {}'.format(X_train_res.shape)) \n",
    "print('After OverSampling, the shape of train_y: {} \\n'.format(y_train_res.shape)) \n",
    "  \n",
    "print(\"After OverSampling, counts of label '1': {}\".format(sum(y_train_res == 1))) \n",
    "print(\"After OverSampling, counts of label '0': {}\".format(sum(y_train_res == 0)))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 6.2 Boruta as Feature Selector"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-11-15T15:24:20.416293Z",
     "start_time": "2020-11-15T15:23:25.348Z"
    }
   },
   "outputs": [],
   "source": [
    "#training and test dataset for Boruta\n",
    "#X_train_n = X_train.drop('is_fraud.1',axis=1).values\n",
    "#y_train_n = y_train.values.ravel()\n",
    "\n",
    "# define RandomForestRegressor\n",
    "#rf = RandomForestRegressor( n_jobs=-1 )\n",
    "\n",
    "# define Boruta\n",
    "#boruta = BorutaPy( rf, n_estimators='auto', verbose=2, random_state = 11 ).fit( X_train_n, y_train_n )"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 6.2.1. Best Features from Boruta"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-11-15T15:24:20.418047Z",
     "start_time": "2020-11-15T15:23:25.356Z"
    }
   },
   "outputs": [],
   "source": [
    "#cols_selected = boruta.support_.tolist()\n",
    "## best features\n",
    "#x_train_fs = X_train.drop('is_fraud.1',axis=1)\n",
    "#cols_selected_boruta = x_train_fs.iloc[:, cols_selected].columns.to_list()\n",
    "\n",
    "## not selected boruta\n",
    "#cols_not_selected_boruta = list( np.setdiff1d( x_train_fs.columns, cols_selected_boruta ) )\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# 6.2.3 Manual Features selection"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-11-15T15:24:20.419467Z",
     "start_time": "2020-11-15T15:23:25.362Z"
    }
   },
   "outputs": [],
   "source": [
    "cols_selected_boruta =['amount',\n",
    " 'oldbalance_org',\n",
    " 'newbalance_orig',\n",
    " 'newbalance_dest',\n",
    " 'day_of_month',\n",
    " 'balance_variation_dest',\n",
    " 'balance_variation_org',\n",
    " 'oldbalance_dest ',\n",
    " 'type_CASH_OUT',\n",
    " 'type_TRANSFER']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-11-15T15:24:20.421167Z",
     "start_time": "2020-11-15T15:23:25.367Z"
    }
   },
   "outputs": [],
   "source": [
    "# columns to add\n",
    "feat_to_add = ['is_fraud']\n",
    "cols_selected_boruta_full = cols_selected_boruta.copy()\n",
    "cols_selected_boruta_full.extend( feat_to_add )"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-11-13T20:05:27.848107Z",
     "start_time": "2020-11-13T20:05:27.844891Z"
    },
    "heading_collapsed": true
   },
   "source": [
    "# 7.0 Machine Learning Modeling "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-11-15T15:24:20.422552Z",
     "start_time": "2020-11-15T15:23:25.373Z"
    },
    "hidden": true
   },
   "outputs": [],
   "source": [
    "x_train = X_train[cols_selected_boruta]\n",
    "x_test = X_test[cols_selected_boruta]\n",
    "x_training = X_train[cols_selected_boruta]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "hidden": true
   },
   "source": [
    "## 7.1 Random Forest Regressor "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-11-15T15:24:20.423941Z",
     "start_time": "2020-11-15T15:23:25.379Z"
    },
    "hidden": true
   },
   "outputs": [],
   "source": [
    "# model\n",
    "rf = RandomForestRegressor(n_estimators=100,n_jobs=1,random_state=42).fit(x_train,y_train)\n",
    "\n",
    "# prediction\n",
    "yhat_rf = rf.predict( x_test )\n",
    "\n",
    "# performance\n",
    "rf_result = ml_error( 'Random Forest Regressor', y_test , yhat_rf )\n",
    "rf"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "hidden": true
   },
   "source": [
    "### 7.1.1. Random Forest Regressor - Cross Validation"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-11-15T15:24:20.425498Z",
     "start_time": "2020-11-15T15:23:25.386Z"
    },
    "hidden": true
   },
   "outputs": [],
   "source": [
    "result = list()\n",
    "kf = KFold(2,shuffle=True,random_state=0)\n",
    "clean_df = df6[cols_selected_boruta]\n",
    "\n",
    "for train_rows, test_rows in kf.split(X):\n",
    "    X_treino,X_valid = X.iloc[train_rows],  X.iloc[test_rows]\n",
    "    y_treino,y_valid = Y.iloc[train_rows],  Y.iloc[test_rows]\n",
    "    \n",
    "    model = RandomForestRegressor(n_estimators=100,n_jobs=1,random_state=42)\n",
    "    model.fit(X_treino, y_treino)\n",
    "    \n",
    "    p = model.predict(X_valid)\n",
    "    \n",
    "    acc = np.mean(y_valid == p)\n",
    "    result.append(acc)\n",
    "\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "hidden": true
   },
   "source": [
    "## 7.2 XGboost Regressor"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-11-15T15:24:20.427195Z",
     "start_time": "2020-11-15T15:23:25.393Z"
    },
    "hidden": true
   },
   "outputs": [],
   "source": [
    "# model\n",
    "model_xgb = xgb.XGBRegressor( objective='reg:squarederror',\n",
    "                              n_estimators=100, \n",
    "                              eta=0.01, \n",
    "                              max_depth=10, \n",
    "                              subsample=0.7,\n",
    "                              colsample_bytee=0.9 ).fit( x_train, y_train )\n",
    "\n",
    "# prediction\n",
    "yhat_xgb = model_xgb.predict( x_test )\n",
    "\n",
    "# performance\n",
    "xgb_result = ml_error( 'XGBoost Regressor',  y_test, yhat_xgb )\n",
    "xgb_result"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "hidden": true
   },
   "source": [
    "### 7.2.1. XGBoost Regressor - Cross Validation"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-11-15T15:24:20.428703Z",
     "start_time": "2020-11-15T15:23:25.402Z"
    },
    "hidden": true
   },
   "outputs": [],
   "source": [
    "xgb_result_cv = cross_validation( x_training, 5, 'XGBoost Regressor', model_xgb, verbose=True )\n",
    "xgb_result_cv"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "hidden": true
   },
   "source": [
    "## 7.3 Kneighbors Classifier"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-11-15T15:24:20.430116Z",
     "start_time": "2020-11-15T15:23:25.409Z"
    },
    "hidden": true
   },
   "outputs": [],
   "source": [
    "# model\n",
    "model_knc = KNeighborsClassifier().fit( x_train, y_train )\n",
    "\n",
    "# prediction\n",
    "yhat_knc = model_knc.predict( x_test )\n",
    "\n",
    "# performance\n",
    "knc_result = ml_error( 'Kneighbors Classifier', y_test, yhat_knc )\n",
    "knc_result\n",
    "\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "hidden": true
   },
   "source": [
    "## 7.3 Comparing Model Performace"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "heading_collapsed": true,
    "hidden": true
   },
   "source": [
    "### 7.3.1 Single Performace"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-11-15T15:24:20.431405Z",
     "start_time": "2020-11-15T15:23:25.417Z"
    },
    "hidden": true
   },
   "outputs": [],
   "source": [
    "modelling_result = pd.concat( [rf_result, xgb_result] )\n",
    "modelling_result.sort_values( 'RMSE' )"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "hidden": true
   },
   "source": [
    "### 7.3.1 Real Performace (by CrossValidation)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-11-15T15:24:20.432950Z",
     "start_time": "2020-11-15T15:23:25.425Z"
    },
    "hidden": true
   },
   "outputs": [],
   "source": [
    "result"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-11-13T22:38:54.662022Z",
     "start_time": "2020-11-13T22:38:54.656686Z"
    },
    "hidden": true
   },
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "hidden": true
   },
   "outputs": [],
   "source": []
  }
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