churn_notebook.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {
    "editable": true
   },
   "outputs": [],
   "source": [
    "import shap\n",
    "import joblib\n",
    "import pandas as pd\n",
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "import seaborn as sns; sns.set()\n",
    "\n",
    "from sklearn.preprocessing import normalize\n",
    "from sklearn.model_selection import train_test_split\n",
    "\n",
    "from sklearn.linear_model import LogisticRegression\n",
    "from sklearn.ensemble import RandomForestClassifier\n",
    "from sklearn.model_selection import GridSearchCV\n",
    "\n",
    "from sklearn.metrics import plot_roc_curve, classification_report"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {
    "editable": true
   },
   "outputs": [],
   "source": [
    "import os\n",
    "os.environ['QT_QPA_PLATFORM']='offscreen'"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {
    "editable": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "True"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "os.path.exists(\"images/eda/Heatmap.png\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {
    "editable": 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>Unnamed: 0</th>\n",
       "      <th>CLIENTNUM</th>\n",
       "      <th>Attrition_Flag</th>\n",
       "      <th>Customer_Age</th>\n",
       "      <th>Gender</th>\n",
       "      <th>Dependent_count</th>\n",
       "      <th>Education_Level</th>\n",
       "      <th>Marital_Status</th>\n",
       "      <th>Income_Category</th>\n",
       "      <th>Card_Category</th>\n",
       "      <th>...</th>\n",
       "      <th>Months_Inactive_12_mon</th>\n",
       "      <th>Contacts_Count_12_mon</th>\n",
       "      <th>Credit_Limit</th>\n",
       "      <th>Total_Revolving_Bal</th>\n",
       "      <th>Avg_Open_To_Buy</th>\n",
       "      <th>Total_Amt_Chng_Q4_Q1</th>\n",
       "      <th>Total_Trans_Amt</th>\n",
       "      <th>Total_Trans_Ct</th>\n",
       "      <th>Total_Ct_Chng_Q4_Q1</th>\n",
       "      <th>Avg_Utilization_Ratio</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0</td>\n",
       "      <td>768805383</td>\n",
       "      <td>Existing Customer</td>\n",
       "      <td>45</td>\n",
       "      <td>M</td>\n",
       "      <td>3</td>\n",
       "      <td>High School</td>\n",
       "      <td>Married</td>\n",
       "      <td>$60K - $80K</td>\n",
       "      <td>Blue</td>\n",
       "      <td>...</td>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "      <td>12691.0</td>\n",
       "      <td>777</td>\n",
       "      <td>11914.0</td>\n",
       "      <td>1.335</td>\n",
       "      <td>1144</td>\n",
       "      <td>42</td>\n",
       "      <td>1.625</td>\n",
       "      <td>0.061</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1</td>\n",
       "      <td>818770008</td>\n",
       "      <td>Existing Customer</td>\n",
       "      <td>49</td>\n",
       "      <td>F</td>\n",
       "      <td>5</td>\n",
       "      <td>Graduate</td>\n",
       "      <td>Single</td>\n",
       "      <td>Less than $40K</td>\n",
       "      <td>Blue</td>\n",
       "      <td>...</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>8256.0</td>\n",
       "      <td>864</td>\n",
       "      <td>7392.0</td>\n",
       "      <td>1.541</td>\n",
       "      <td>1291</td>\n",
       "      <td>33</td>\n",
       "      <td>3.714</td>\n",
       "      <td>0.105</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2</td>\n",
       "      <td>713982108</td>\n",
       "      <td>Existing Customer</td>\n",
       "      <td>51</td>\n",
       "      <td>M</td>\n",
       "      <td>3</td>\n",
       "      <td>Graduate</td>\n",
       "      <td>Married</td>\n",
       "      <td>$80K - $120K</td>\n",
       "      <td>Blue</td>\n",
       "      <td>...</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>3418.0</td>\n",
       "      <td>0</td>\n",
       "      <td>3418.0</td>\n",
       "      <td>2.594</td>\n",
       "      <td>1887</td>\n",
       "      <td>20</td>\n",
       "      <td>2.333</td>\n",
       "      <td>0.000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>3</td>\n",
       "      <td>769911858</td>\n",
       "      <td>Existing Customer</td>\n",
       "      <td>40</td>\n",
       "      <td>F</td>\n",
       "      <td>4</td>\n",
       "      <td>High School</td>\n",
       "      <td>Unknown</td>\n",
       "      <td>Less than $40K</td>\n",
       "      <td>Blue</td>\n",
       "      <td>...</td>\n",
       "      <td>4</td>\n",
       "      <td>1</td>\n",
       "      <td>3313.0</td>\n",
       "      <td>2517</td>\n",
       "      <td>796.0</td>\n",
       "      <td>1.405</td>\n",
       "      <td>1171</td>\n",
       "      <td>20</td>\n",
       "      <td>2.333</td>\n",
       "      <td>0.760</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>4</td>\n",
       "      <td>709106358</td>\n",
       "      <td>Existing Customer</td>\n",
       "      <td>40</td>\n",
       "      <td>M</td>\n",
       "      <td>3</td>\n",
       "      <td>Uneducated</td>\n",
       "      <td>Married</td>\n",
       "      <td>$60K - $80K</td>\n",
       "      <td>Blue</td>\n",
       "      <td>...</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>4716.0</td>\n",
       "      <td>0</td>\n",
       "      <td>4716.0</td>\n",
       "      <td>2.175</td>\n",
       "      <td>816</td>\n",
       "      <td>28</td>\n",
       "      <td>2.500</td>\n",
       "      <td>0.000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 22 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "   Unnamed: 0  CLIENTNUM     Attrition_Flag  Customer_Age Gender  \\\n",
       "0           0  768805383  Existing Customer            45      M   \n",
       "1           1  818770008  Existing Customer            49      F   \n",
       "2           2  713982108  Existing Customer            51      M   \n",
       "3           3  769911858  Existing Customer            40      F   \n",
       "4           4  709106358  Existing Customer            40      M   \n",
       "\n",
       "   Dependent_count Education_Level Marital_Status Income_Category  \\\n",
       "0                3     High School        Married     $60K - $80K   \n",
       "1                5        Graduate         Single  Less than $40K   \n",
       "2                3        Graduate        Married    $80K - $120K   \n",
       "3                4     High School        Unknown  Less than $40K   \n",
       "4                3      Uneducated        Married     $60K - $80K   \n",
       "\n",
       "  Card_Category          ...            Months_Inactive_12_mon  \\\n",
       "0          Blue          ...                                 1   \n",
       "1          Blue          ...                                 1   \n",
       "2          Blue          ...                                 1   \n",
       "3          Blue          ...                                 4   \n",
       "4          Blue          ...                                 1   \n",
       "\n",
       "   Contacts_Count_12_mon  Credit_Limit  Total_Revolving_Bal  Avg_Open_To_Buy  \\\n",
       "0                      3       12691.0                  777          11914.0   \n",
       "1                      2        8256.0                  864           7392.0   \n",
       "2                      0        3418.0                    0           3418.0   \n",
       "3                      1        3313.0                 2517            796.0   \n",
       "4                      0        4716.0                    0           4716.0   \n",
       "\n",
       "   Total_Amt_Chng_Q4_Q1  Total_Trans_Amt  Total_Trans_Ct  Total_Ct_Chng_Q4_Q1  \\\n",
       "0                 1.335             1144              42                1.625   \n",
       "1                 1.541             1291              33                3.714   \n",
       "2                 2.594             1887              20                2.333   \n",
       "3                 1.405             1171              20                2.333   \n",
       "4                 2.175              816              28                2.500   \n",
       "\n",
       "   Avg_Utilization_Ratio  \n",
       "0                  0.061  \n",
       "1                  0.105  \n",
       "2                  0.000  \n",
       "3                  0.760  \n",
       "4                  0.000  \n",
       "\n",
       "[5 rows x 22 columns]"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df = pd.read_csv(r\"./data/bank_data.csv\")\n",
    "df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {
    "editable": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(10127, 22)"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {
    "editable": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Unnamed: 0                  0\n",
       "CLIENTNUM                   0\n",
       "Attrition_Flag              0\n",
       "Customer_Age                0\n",
       "Gender                      0\n",
       "Dependent_count             0\n",
       "Education_Level             0\n",
       "Marital_Status              0\n",
       "Income_Category             0\n",
       "Card_Category               0\n",
       "Months_on_book              0\n",
       "Total_Relationship_Count    0\n",
       "Months_Inactive_12_mon      0\n",
       "Contacts_Count_12_mon       0\n",
       "Credit_Limit                0\n",
       "Total_Revolving_Bal         0\n",
       "Avg_Open_To_Buy             0\n",
       "Total_Amt_Chng_Q4_Q1        0\n",
       "Total_Trans_Amt             0\n",
       "Total_Trans_Ct              0\n",
       "Total_Ct_Chng_Q4_Q1         0\n",
       "Avg_Utilization_Ratio       0\n",
       "dtype: int64"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.isnull().sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {
    "editable": 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>Unnamed: 0</th>\n",
       "      <th>CLIENTNUM</th>\n",
       "      <th>Customer_Age</th>\n",
       "      <th>Dependent_count</th>\n",
       "      <th>Months_on_book</th>\n",
       "      <th>Total_Relationship_Count</th>\n",
       "      <th>Months_Inactive_12_mon</th>\n",
       "      <th>Contacts_Count_12_mon</th>\n",
       "      <th>Credit_Limit</th>\n",
       "      <th>Total_Revolving_Bal</th>\n",
       "      <th>Avg_Open_To_Buy</th>\n",
       "      <th>Total_Amt_Chng_Q4_Q1</th>\n",
       "      <th>Total_Trans_Amt</th>\n",
       "      <th>Total_Trans_Ct</th>\n",
       "      <th>Total_Ct_Chng_Q4_Q1</th>\n",
       "      <th>Avg_Utilization_Ratio</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>10127.000000</td>\n",
       "      <td>1.012700e+04</td>\n",
       "      <td>10127.000000</td>\n",
       "      <td>10127.000000</td>\n",
       "      <td>10127.000000</td>\n",
       "      <td>10127.000000</td>\n",
       "      <td>10127.000000</td>\n",
       "      <td>10127.000000</td>\n",
       "      <td>10127.000000</td>\n",
       "      <td>10127.000000</td>\n",
       "      <td>10127.000000</td>\n",
       "      <td>10127.000000</td>\n",
       "      <td>10127.000000</td>\n",
       "      <td>10127.000000</td>\n",
       "      <td>10127.000000</td>\n",
       "      <td>10127.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>5063.000000</td>\n",
       "      <td>7.391776e+08</td>\n",
       "      <td>46.325960</td>\n",
       "      <td>2.346203</td>\n",
       "      <td>35.928409</td>\n",
       "      <td>3.812580</td>\n",
       "      <td>2.341167</td>\n",
       "      <td>2.455317</td>\n",
       "      <td>8631.953698</td>\n",
       "      <td>1162.814061</td>\n",
       "      <td>7469.139637</td>\n",
       "      <td>0.759941</td>\n",
       "      <td>4404.086304</td>\n",
       "      <td>64.858695</td>\n",
       "      <td>0.712222</td>\n",
       "      <td>0.274894</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>2923.557422</td>\n",
       "      <td>3.690378e+07</td>\n",
       "      <td>8.016814</td>\n",
       "      <td>1.298908</td>\n",
       "      <td>7.986416</td>\n",
       "      <td>1.554408</td>\n",
       "      <td>1.010622</td>\n",
       "      <td>1.106225</td>\n",
       "      <td>9088.776650</td>\n",
       "      <td>814.987335</td>\n",
       "      <td>9090.685324</td>\n",
       "      <td>0.219207</td>\n",
       "      <td>3397.129254</td>\n",
       "      <td>23.472570</td>\n",
       "      <td>0.238086</td>\n",
       "      <td>0.275691</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>0.000000</td>\n",
       "      <td>7.080821e+08</td>\n",
       "      <td>26.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>13.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>1438.300000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>3.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>510.000000</td>\n",
       "      <td>10.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>2531.500000</td>\n",
       "      <td>7.130368e+08</td>\n",
       "      <td>41.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>31.000000</td>\n",
       "      <td>3.000000</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>2555.000000</td>\n",
       "      <td>359.000000</td>\n",
       "      <td>1324.500000</td>\n",
       "      <td>0.631000</td>\n",
       "      <td>2155.500000</td>\n",
       "      <td>45.000000</td>\n",
       "      <td>0.582000</td>\n",
       "      <td>0.023000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>5063.000000</td>\n",
       "      <td>7.179264e+08</td>\n",
       "      <td>46.000000</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>36.000000</td>\n",
       "      <td>4.000000</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>4549.000000</td>\n",
       "      <td>1276.000000</td>\n",
       "      <td>3474.000000</td>\n",
       "      <td>0.736000</td>\n",
       "      <td>3899.000000</td>\n",
       "      <td>67.000000</td>\n",
       "      <td>0.702000</td>\n",
       "      <td>0.176000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>7594.500000</td>\n",
       "      <td>7.731435e+08</td>\n",
       "      <td>52.000000</td>\n",
       "      <td>3.000000</td>\n",
       "      <td>40.000000</td>\n",
       "      <td>5.000000</td>\n",
       "      <td>3.000000</td>\n",
       "      <td>3.000000</td>\n",
       "      <td>11067.500000</td>\n",
       "      <td>1784.000000</td>\n",
       "      <td>9859.000000</td>\n",
       "      <td>0.859000</td>\n",
       "      <td>4741.000000</td>\n",
       "      <td>81.000000</td>\n",
       "      <td>0.818000</td>\n",
       "      <td>0.503000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>10126.000000</td>\n",
       "      <td>8.283431e+08</td>\n",
       "      <td>73.000000</td>\n",
       "      <td>5.000000</td>\n",
       "      <td>56.000000</td>\n",
       "      <td>6.000000</td>\n",
       "      <td>6.000000</td>\n",
       "      <td>6.000000</td>\n",
       "      <td>34516.000000</td>\n",
       "      <td>2517.000000</td>\n",
       "      <td>34516.000000</td>\n",
       "      <td>3.397000</td>\n",
       "      <td>18484.000000</td>\n",
       "      <td>139.000000</td>\n",
       "      <td>3.714000</td>\n",
       "      <td>0.999000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "         Unnamed: 0     CLIENTNUM  Customer_Age  Dependent_count  \\\n",
       "count  10127.000000  1.012700e+04  10127.000000     10127.000000   \n",
       "mean    5063.000000  7.391776e+08     46.325960         2.346203   \n",
       "std     2923.557422  3.690378e+07      8.016814         1.298908   \n",
       "min        0.000000  7.080821e+08     26.000000         0.000000   \n",
       "25%     2531.500000  7.130368e+08     41.000000         1.000000   \n",
       "50%     5063.000000  7.179264e+08     46.000000         2.000000   \n",
       "75%     7594.500000  7.731435e+08     52.000000         3.000000   \n",
       "max    10126.000000  8.283431e+08     73.000000         5.000000   \n",
       "\n",
       "       Months_on_book  Total_Relationship_Count  Months_Inactive_12_mon  \\\n",
       "count    10127.000000              10127.000000            10127.000000   \n",
       "mean        35.928409                  3.812580                2.341167   \n",
       "std          7.986416                  1.554408                1.010622   \n",
       "min         13.000000                  1.000000                0.000000   \n",
       "25%         31.000000                  3.000000                2.000000   \n",
       "50%         36.000000                  4.000000                2.000000   \n",
       "75%         40.000000                  5.000000                3.000000   \n",
       "max         56.000000                  6.000000                6.000000   \n",
       "\n",
       "       Contacts_Count_12_mon  Credit_Limit  Total_Revolving_Bal  \\\n",
       "count           10127.000000  10127.000000         10127.000000   \n",
       "mean                2.455317   8631.953698          1162.814061   \n",
       "std                 1.106225   9088.776650           814.987335   \n",
       "min                 0.000000   1438.300000             0.000000   \n",
       "25%                 2.000000   2555.000000           359.000000   \n",
       "50%                 2.000000   4549.000000          1276.000000   \n",
       "75%                 3.000000  11067.500000          1784.000000   \n",
       "max                 6.000000  34516.000000          2517.000000   \n",
       "\n",
       "       Avg_Open_To_Buy  Total_Amt_Chng_Q4_Q1  Total_Trans_Amt  Total_Trans_Ct  \\\n",
       "count     10127.000000          10127.000000     10127.000000    10127.000000   \n",
       "mean       7469.139637              0.759941      4404.086304       64.858695   \n",
       "std        9090.685324              0.219207      3397.129254       23.472570   \n",
       "min           3.000000              0.000000       510.000000       10.000000   \n",
       "25%        1324.500000              0.631000      2155.500000       45.000000   \n",
       "50%        3474.000000              0.736000      3899.000000       67.000000   \n",
       "75%        9859.000000              0.859000      4741.000000       81.000000   \n",
       "max       34516.000000              3.397000     18484.000000      139.000000   \n",
       "\n",
       "       Total_Ct_Chng_Q4_Q1  Avg_Utilization_Ratio  \n",
       "count         10127.000000           10127.000000  \n",
       "mean              0.712222               0.274894  \n",
       "std               0.238086               0.275691  \n",
       "min               0.000000               0.000000  \n",
       "25%               0.582000               0.023000  \n",
       "50%               0.702000               0.176000  \n",
       "75%               0.818000               0.503000  \n",
       "max               3.714000               0.999000  "
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {
    "editable": true
   },
   "outputs": [],
   "source": [
    "cat_columns = [\n",
    "    'Gender',\n",
    "    'Education_Level',\n",
    "    'Marital_Status',\n",
    "    'Income_Category',\n",
    "    'Card_Category'                \n",
    "]\n",
    "\n",
    "quant_columns = [\n",
    "    'Customer_Age',\n",
    "    'Dependent_count', \n",
    "    'Months_on_book',\n",
    "    'Total_Relationship_Count', \n",
    "    'Months_Inactive_12_mon',\n",
    "    'Contacts_Count_12_mon', \n",
    "    'Credit_Limit', \n",
    "    'Total_Revolving_Bal',\n",
    "    'Avg_Open_To_Buy', \n",
    "    'Total_Amt_Chng_Q4_Q1', \n",
    "    'Total_Trans_Amt',\n",
    "    'Total_Trans_Ct', \n",
    "    'Total_Ct_Chng_Q4_Q1', \n",
    "    'Avg_Utilization_Ratio'\n",
    "]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {
    "editable": true
   },
   "outputs": [],
   "source": [
    "df['Churn'] = df['Attrition_Flag'].apply(lambda val: 0 if val == \"Existing Customer\" else 1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {
    "editable": true
   },
   "outputs": [
    {
     "data": {
      "image/png": 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AAAAAaBOVAAAAAGgTlQAAAABoE5UAAAAAaBOVAAAAAGgTlQAAAABoE5UAAAAAaBOVAAAAAGgTlQAAAABoE5UAAAAAaBOVAAAAAGgTlQAAAABoE5UAAAAAaBOVAAAAAGgTlQAAAABoE5UAAAAAaBOVAAAAAGgTlQAAAABoE5UAAAAAaBOVAAAAAGgTlQAAAABoE5UAAAAAaBOVAAAAAGgTlQAAAABoE5UAAAAAaBOVAAAAAGgTlQAAAABoE5UAAAAAaBOVAAAAAGgTlQAAAABoE5UAAAAAaBOVAAAAAGgTlQAAAABoE5UAAAAAaBOVAAAAAGgTlQAAAABoE5UAAAAAaBOVAAAAAGgTlQAAAABoE5UAAAAAaBOVAAAAAGgTlQAAAABoO9o5eYzx40nemOTyqvrIGOP5SU4luSDJHUmuqqp7Z+fOtQYAAADA3jf5SqUxxhVJnp/krtnXS0nemeRVVXU8yQeSvOlc1gAAAADYHyZFpTHGY5OcTPIjSTZnh5+T5MGqunn29fVJXnmOawAAAADsA1OvVPqJJO+sqk9sO/a0JHee+aKqPpdkeYzxpHNYAwAAAGAf2PE1lcYYL0jy3CSvPf/bmW5l5cJFb4EDbHX12KK3wFk8JzCNWYFpzApMY1ZgmsM6K1NeqPslSb4pySfGGEny1CQ3JXlrkqefOWmM8eQkm1X1+THGXfOsdTa+vv5ANjY2dz5xDzusP3T7wdra/YveAtusrh7znMAEZgWmMSswjVmBaQ7KrCwvL7Uv4Nnx9reqelNVXVxVl1XVZUk+meRlSX4myQVjjBfOTr02yXtmn//+nGsAAAAA7AOT3/3tbFW1keTqJL8wxrg9W1c0vfZc1gAAAADYH6bc/vYVZlcrnfn8Q0kuf4Tz5loDAAAAYO+b+0olAAAAAA4vUQkAAACANlEJAAAAgDZRCQAAAIA2UQkAAACANlEJAAAAgDZRCQAAAIA2UQkAAACANlEJAAAAgDZRCQAAAIA2UQkAAACANlEJAAAAgDZRCQAAAIA2UQkAAACANlEJAAAAgDZRCQAAAIA2UQkAAACANlEJAAAAgDZRCQAAAIA2UQkAAACANlEJAAAAgDZRCQAAAIA2UQkAAACANlEJAAAAgDZRCQAAAIA2UQkAAACANlEJAAAAgDZRCQAAAIA2UQkAAACANlEJAAAAgDZRCQAAAIA2UQkAAACANlEJAAAAgDZRCQAAAIA2UQkAAACANlEJAAAAgDZRCQAAAIA2UQkAAACANlEJAAAAgDZRCQAAAIA2UQkAAACANlEJAAAAgDZRCQAAAIA2UQkAAACANlEJAAAAgDZRCQAAAIA2UQkAAACANlEJAAAAgDZRCQAAAIA2UQkAAACANlEJAAAAgDZRCQAAAIC2o1NOGmO8N8mfTbKR5IEkr66qW8cYx5PckGQlyXqSa6rq9tlj5loDAAAAYO+beqXS91fVt1TVtyZ5c5K3z45fn+RkVR1PcjLJqW2PmXcNAAAAgD1u0pVKVfWFbV9+XZKNMcZFSa5IcuXs+I1JrhtjrCZZmmetqtbO5ZsBAAAAYHdMfk2lMcYvjzHuSvJTSb4/yaVJ7qmq00ky+/ip2fF51wAAAADYByZdqZQkVfX3kmSMcXWSn0ny+vO1qSlWVi5c5H+eA2519diit8BZPCcwjVmBacwKTGNWYJrDOiuTo9IZVfWOMcYvJvlkkkvGGEeq6vQY40iSi5Pcna1b3OZZm2x9/YFsbGx2t7+nHNYfuv1gbe3+RW+BbVZXj3lOYAKzAtOYFZjGrMA0B2VWlpeX2hfw7Hj72xjjwjHGpdu+fnmSzye5N8mtSU7Mlk4kuaWq1qpqrrXWzgEAAABYmClXKj0+ya+NMR6f5HS2gtLLq2pzjHFtkhvGGG9Icl+Sa7Y9bt41AAAAAPa4HaNSVX02yfMfYe2jSZ73aK4BAAAAsPdNfvc3AAAAADhDVAIAAACgTVQCAAAAoE1UAgAAAKBNVAIAAACgTVQCAAAAoE1UAgAAAKBNVAIAAACgTVQCAAAAoE1UAgAAAKBNVAIAAACgTVQCAAAAoE1UAgAAAKBNVAIAAACgTVQCAAAAoE1UAgAAAKBNVAIAAACgTVQCAAAAoE1UAgAAAKBNVAIAAACgTVQCAAAAoE1UAgAAAKBNVAIAAACgTVQCAAAAoE1UAgAAAKBNVAIAAACgTVQCAAAAoE1UAgAAAKBNVAIAAACgTVQCAAAAoE1UAgAAAKBNVAIAAACgTVQCAAAAoE1UAgAAAKBNVAIAAACgTVQCAAAAoE1UAgAAAKBNVAIAAACgTVQCAAAAoE1UAgAAAKBNVAIAAACgTVQCAAAAoE1UAgAAAKBNVAIAAACgTVQCAAAAoE1UAgAAAKBNVAIAAACgTVQCAAAAoE1UAgAAAKBNVAIAAACgTVQCAAAAoE1UAgAAAKDt6E4njDFWkrwjyTOSPJTkfyT5B1W1NsZ4fpJTSS5IckeSq6rq3tnj5loDAAAAYO+bcqXSZpKfrqpRVc9M8rEkbxpjLCV5Z5JXVdXxJB9I8qYkmXcNAAAAgP1hx6hUVZ+vqvdvO/S7SZ6e5DlJHqyqm2fHr0/yytnn864BAAAAsA/sePvbdmOM5SQ/nOQ3kjwtyZ1n1qrqc2OM5THGk+Zdq6rPT93LysqFna1Dy+rqsUVvgbN4TmAaswLTmBWYxqzANId1VlpRKck/T/JAkuuSfPujv53p1tcfyMbG5iK3cM4O6w/dfrC2dv+it8A2q6vHPCcwgVmBacwKTGNWYJqDMivLy0vtC3gmv/vbGOPNSb4xyfdU1UaSu7J1G9yZ9Scn2ZxdbTTvGgAAAAD7wKSoNMb4qSTPTvJtVfXQ7PDvJ7lgjPHC2dfXJnnPOa4BAAAAsA/sePvbGOObk7wuyW1JPjTGSJJPVNW3jzGuTnJqjPG4JHckuSpJqmpjnjUAAAAA9ocdo1JV/VGSpUdY+1CSyx/NNQAAAAD2vsmvqQQAAAAAZ4hKAAAAALSJSgAAAAC0iUoAAAAAtIlKAAAAALSJSgAAAAC0iUoAAAAAtIlKAAAAALSJSgAAAAC0iUoAAAAAtIlKAAAAALSJSgAAAAC0iUoAAAAAtIlKAAAAALSJSgAAAAC0iUoAAAAAtIlKAAAAALQdXfQGAAAAgGmOfe0Fedxj/Sq/l/zpl04vegsL4ycRAAAA9onHPfZoXv5j71v0NtjmN9/yikVvYWHc/gYAAABAm6gEAAAAQJuoBAAAAECbqAQAAABAm6gEAAAAQJuoBAAAAECbqAQAAABAm6gEAAAAQJuoBAAAAECbqAQAAABAm6gEAAAAQJuoBAAAAECbqAQAAABAm6gEAAAAQJuoBAAAAECbqAQAAABAm6gEAAAAQJuoBAAAAECbqAQAAABAm6gEAAAAQJuoBAAAAECbqAQAAABAm6gEAAAAQJuoBAAAAECbqAQAAABAm6gEAAAAQJuoBAAAAECbqAQAAABAm6gEAAAAQJuoBAAAAECbqAQAAABAm6gEAAAAQJuoBAAAAEDb0Z1OGGO8Ocl3JrksyeVV9ZHZ8eNJbkiykmQ9yTVVdfu5rAEAAACwP0y5Uum9SV6c5M6zjl+f5GRVHU9yMsmpR2ENAAAAgH1gxyuVqurmJBlj/N9jY4yLklyR5MrZoRuTXDfGWE2yNM9aVa2d83cDAAAAwK7YMSo9gkuT3FNVp5Okqk6PMT41O74051orKq2sXDjn1mFnq6vHFr0FzuI5gWnMCkxjVmAaswLTHNZZmTcqLdz6+gPZ2Nhc9DbOyWH9odsP1tbuX/QW2GZ19ZjnBCYwKzCNWYFpzMre5PfIvekgzMry8lL7Ap553/3t7iSXjDGOJMns48Wz4/OuAQAAALBPzBWVqureJLcmOTE7dCLJLVW1Nu/avN8AAAAAALtvx9vfxhhvTfIdSb4+yW+PMdar6puTXJvkhjHGG5Lcl+SabQ+bdw0AAACAfWDKu7+9JslrHub4R5M87xEeM9caAAAAAPvDvK+pBAAAAMAhJioBAAAA0CYqAQAAANAmKgEAAADQJioBAAAA0CYqAQAAANAmKgEAAADQJioBAAAA0CYqAQAAANAmKgEAAADQJioBAAAA0CYqAQAAANAmKgEAAADQJioBAAAA0CYqAQAAANAmKgEAAADQJioBAAAA0CYqAQAAANAmKgEAAADQJioBAAAA0CYqAQAAANAmKgEAAADQJioBAAAA0CYqAQAAANAmKgEAAADQJioBAAAA0CYqAQAAANAmKgEAAADQJioBAAAA0CYqAQAAANAmKgEAAADQJioBAAAA0CYqAQAAANAmKgEAAADQJioBAAAA0CYqAQAAANAmKgEAAADQJioBAAAA0CYqAQAAANAmKgEAAADQJioBAAAA0CYqAQAAANAmKgEAAADQJioBAAAA0CYqAQAAANAmKgEAAADQJioBAAAA0CYqAQAAANAmKgEAAADQJioBAAAA0CYqAQAAANAmKgEAAADQdnRR/+ExxvEkNyRZSbKe5Jqqun1R+wEAAABgukVeqXR9kpNVdTzJySSnFrgXAAAAABoWcqXSGOOiJFckuXJ26MYk140xVqtqbYeHH0mS5eWl87jD3XPREy9Y9BZ4GAfl5+sg8ZzANGYFpjErMI1Z2Zv8Hrn3HIRZ2fY9HJn6mKXNzc3zs5uvYozx7CT/qqq+eduxP05yVVV9eIeHvzDJ75zP/QEAAAAcUi9KcvOUExf2mkrn4Pey9Q1+OsnpBe8FAAAA4CA4kuQp2eoukywqKt2d5JIxxpGqOj3GOJLk4tnxnTyUicUMAAAAgMk+1jl5IS/UXVX3Jrk1yYnZoRNJbpnwekoAAAAA7AELeU2lJBljfFOSG5I8Mcl9Sa6pqlrIZgAAAABoWVhUAgAAAGD/WsjtbwAAAADsb6ISAAAAAG2iEgAAAABtohIAAAAAbUcXvYGDboxxPFvvcreSZD1b73J3+1nnHEny1iR/I8lmkjdV1S/v9l5hkSbOyuuTfG+SL8/+vK6qbtrtvcIiTZmVbeeOJLckeVtV/aPd2yUs3tRZGWO8Msnrkyxl699hL62qz+7mXmGRJv4b7KIk/zLJpUkek+Q/JHlNVX15l7cLCzHGeHOS70xyWZLLq+ojD3POofy93pVK59/1SU5W1fEkJ5Ocephzvi/Jn0vyjUlekOSNY4zLdm2HsDdMmZX/kuS5VfUtSX4wybvHGBfs4h5hL5gyK2f+YXMqyXt3cW+wl+w4K2OM5yR5Y5Irq+ovJnlhki/s5iZhD5jy98rrkvz3qnpmksuTPDvJd+zeFmHh3pvkxUnu/CrnHMrf60Wl82hW9K9IcuPs0I1JrhhjrJ516vck+aWq2qiqtWz9wH737u0UFmvqrFTVTVX1v2df/mG2/q/yyq5tFBas8fdKkrw2yb9LctsubQ/2jMas/MMkb66qzyRJVX2hqh7cvZ3CYjVmZTPJsTHGcpLHZutqpXt2baOwYFV1c1XdvcNph/L3elHp/Lo0yT1VdTpJZh8/NTu+3dPylcXzroc5Bw6yqbOy3TVJPlZVn9yF/cFeMWlWxhjPTPKyJD+76zuEvWHq3yt/Ick3jDE+MMb48Bjjn40xlnZ5r7BIU2flJ5McT/LpJJ9JclNVfXA3Nwr7wKH8vV5UAvadMcZLsvWPmxOL3gvsNWOMr0nyS0muPfNLAvCIjiZ5ZpIrk7wkyd9McvVCdwR703dn6yrxpyS5JMmLxxjftdgtAXuBqHR+3Z3kktnrWpx5fYuLZ8e3uyvJ07d9/bSHOQcOsqmzkjHGC5K8M8m3VVXt6i5h8abMylOSPCPJb40x7kjyo0n+/hjjF3d3q7BQU/9euTPJr1fVQ1V1f5L3JflLu7pTWKyps/LqJO+a3dbzhWzNyl/d1Z3C3ncof68Xlc6jqro3ya35f1dTnEhyy+z+yu1+LVv/4F+e3b/8bUn+9e7tFBZr6qyMMZ6b5N1JvquqPry7u4TFmzIrVXVXVT25qi6rqsuS/Fy27u//oV3fMCxI499gv5Lkr48xlmZX+f21JH+wezuFxWrMyiey9Y5WGWM8JslLk/x/734Fh9yh/L1eVDr/rk3y6jHGbdkq/NcmyRjjt2bvOJIk70jy8SS3J/mAzju8AAAArElEQVTdJD9RVR9fxGZhgabMytuSXJDk1Bjj1tmfyxezXViYKbMCTJuVX01yb5I/ztYv1n+U5F8sYK+wSFNm5UeTvGiM8d+yNSu3ZetWazgUxhhvHWN8MslTk/z2GOOPZscP/e/1S5ubm4veAwAAAAD7jCuVAAAAAGgTlQAAAABoE5UAAAAAaBOVAAAAAGgTlQAAAABoE5UAAAAAaBOVAAAAAGgTlQAAAABo+z+VpGiIsJw5+wAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7fa94ac76470>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(20,10)) \n",
    "df['Churn'].hist();"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {
    "editable": true
   },
   "outputs": [
    {
     "data": {
      "image/png": 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4mySzTym1vw6opakHgG26Ksm/qaqrklzMWlA6McZYrarbk3yyqn45yVNZe9NGYBvGGCtVdWuSB2Yf2XwqyS3TTgVz54eSfH72f3IPJflmkg/aX7Bjbs/amwffk+S5JLeOMf6n14awY34+yR2XHbO/DqiF1dXVje8FAAAAAOu4/A0AAACANlEJAAAAgDZRCQAAAIA2UQkAAACANlEJAAAAgDZRCQAAAIA2UQkAAACANlEJAAAAgLb/C2CdlujpCC8RAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7fa94a9e59b0>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(20,10)) \n",
    "df['Customer_Age'].hist();"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {
    "editable": true
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7fa94a94c278>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(20,10)) \n",
    "df.Marital_Status.value_counts('normalize').plot(kind='bar');"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {
    "editable": true
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7fa94a152a90>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(20,10)) \n",
    "# distplot is deprecated. Use histplot instead\n",
    "# sns.distplot(df['Total_Trans_Ct']);\n",
    "# Show distributions of 'Total_Trans_Ct' and add a smooth curve obtained using a kernel density estimate\n",
    "sns.histplot(df['Total_Trans_Ct'], stat='density', kde=True);"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {
    "editable": true
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7fa94a10ccc0>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(20,10)) \n",
    "sns.heatmap(df.corr(), annot=False, cmap='Dark2_r', linewidths = 2)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {
    "editable": true
   },
   "outputs": [],
   "source": [
    "y = df['Churn']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {
    "editable": true
   },
   "outputs": [
    {
     "ename": "NameError",
     "evalue": "name 'pd' is not defined",
     "output_type": "error",
     "traceback": [
      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[0;31mNameError\u001b[0m                                 Traceback (most recent call last)",
      "\u001b[0;32m<ipython-input-6-4763351fe45d>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m()\u001b[0m\n\u001b[0;32m----> 1\u001b[0;31m \u001b[0mX\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mpd\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mDataFrame\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m",
      "\u001b[0;31mNameError\u001b[0m: name 'pd' is not defined"
     ]
    }
   ],
   "source": [
    "X = pd.DataFrame()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {
    "editable": true
   },
   "outputs": [],
   "source": [
    "\n",
    "# gender encoded column\n",
    "gender_lst = []\n",
    "gender_groups = df.groupby('Gender').mean()['Churn']\n",
    "\n",
    "for val in df['Gender']:\n",
    "    gender_lst.append(gender_groups.loc[val])\n",
    "    \n",
    "df['Gender_Churn'] = gender_lst  \n",
    "\n",
    "\n",
    "#education encoded column\n",
    "edu_lst = []\n",
    "edu_groups = df.groupby('Education_Level').mean()['Churn']\n",
    "\n",
    "for val in df['Education_Level']:\n",
    "    edu_lst.append(edu_groups.loc[val])\n",
    "\n",
    "df['Education_Level_Churn'] = edu_lst\n",
    "\n",
    "\n",
    "#marital encoded column\n",
    "marital_lst = []\n",
    "marital_groups = df.groupby('Marital_Status').mean()['Churn']\n",
    "\n",
    "for val in df['Marital_Status']:\n",
    "    marital_lst.append(marital_groups.loc[val])\n",
    "    \n",
    "df['Marital_Status_Churn'] = marital_lst\n",
    "\n",
    "\n",
    "#income encoded column\n",
    "income_lst = []\n",
    "income_groups = df.groupby('Income_Category').mean()['Churn']\n",
    "\n",
    "for val in df['Income_Category']:\n",
    "    income_lst.append(income_groups.loc[val])\n",
    "\n",
    "df['Income_Category_Churn'] = income_lst\n",
    "\n",
    "\n",
    "#card encoded column\n",
    "card_lst = []\n",
    "card_groups = df.groupby('Card_Category').mean()['Churn']\n",
    " \n",
    "for val in df['Card_Category']:\n",
    "    card_lst.append(card_groups.loc[val])\n",
    "\n",
    "df['Card_Category_Churn'] = card_lst"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {
    "editable": true
   },
   "outputs": [],
   "source": [
    "keep_cols = ['Customer_Age', 'Dependent_count', 'Months_on_book',\n",
    "             'Total_Relationship_Count', 'Months_Inactive_12_mon',\n",
    "             'Contacts_Count_12_mon', 'Credit_Limit', 'Total_Revolving_Bal',\n",
    "             'Avg_Open_To_Buy', 'Total_Amt_Chng_Q4_Q1', 'Total_Trans_Amt',\n",
    "             'Total_Trans_Ct', 'Total_Ct_Chng_Q4_Q1', 'Avg_Utilization_Ratio',\n",
    "             'Gender_Churn', 'Education_Level_Churn', 'Marital_Status_Churn', \n",
    "             'Income_Category_Churn', 'Card_Category_Churn']\n",
    "\n",
    "X[keep_cols] = df[keep_cols]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "editable": true
   },
   "outputs": [],
   "source": [
    "import pytest"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {
    "editable": 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>Customer_Age</th>\n",
       "      <th>Dependent_count</th>\n",
       "      <th>Months_on_book</th>\n",
       "      <th>Total_Relationship_Count</th>\n",
       "      <th>Months_Inactive_12_mon</th>\n",
       "      <th>Contacts_Count_12_mon</th>\n",
       "      <th>Credit_Limit</th>\n",
       "      <th>Total_Revolving_Bal</th>\n",
       "      <th>Avg_Open_To_Buy</th>\n",
       "      <th>Total_Amt_Chng_Q4_Q1</th>\n",
       "      <th>Total_Trans_Amt</th>\n",
       "      <th>Total_Trans_Ct</th>\n",
       "      <th>Total_Ct_Chng_Q4_Q1</th>\n",
       "      <th>Avg_Utilization_Ratio</th>\n",
       "      <th>Gender_Churn</th>\n",
       "      <th>Education_Level_Churn</th>\n",
       "      <th>Marital_Status_Churn</th>\n",
       "      <th>Income_Category_Churn</th>\n",
       "      <th>Card_Category_Churn</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>45</td>\n",
       "      <td>3</td>\n",
       "      <td>39</td>\n",
       "      <td>5</td>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "      <td>12691.0</td>\n",
       "      <td>777</td>\n",
       "      <td>11914.0</td>\n",
       "      <td>1.335</td>\n",
       "      <td>1144</td>\n",
       "      <td>42</td>\n",
       "      <td>1.625</td>\n",
       "      <td>0.061</td>\n",
       "      <td>0.146152</td>\n",
       "      <td>0.152012</td>\n",
       "      <td>0.151269</td>\n",
       "      <td>0.134807</td>\n",
       "      <td>0.160979</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>49</td>\n",
       "      <td>5</td>\n",
       "      <td>44</td>\n",
       "      <td>6</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>8256.0</td>\n",
       "      <td>864</td>\n",
       "      <td>7392.0</td>\n",
       "      <td>1.541</td>\n",
       "      <td>1291</td>\n",
       "      <td>33</td>\n",
       "      <td>3.714</td>\n",
       "      <td>0.105</td>\n",
       "      <td>0.173572</td>\n",
       "      <td>0.155691</td>\n",
       "      <td>0.169414</td>\n",
       "      <td>0.171862</td>\n",
       "      <td>0.160979</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>51</td>\n",
       "      <td>3</td>\n",
       "      <td>36</td>\n",
       "      <td>4</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>3418.0</td>\n",
       "      <td>0</td>\n",
       "      <td>3418.0</td>\n",
       "      <td>2.594</td>\n",
       "      <td>1887</td>\n",
       "      <td>20</td>\n",
       "      <td>2.333</td>\n",
       "      <td>0.000</td>\n",
       "      <td>0.146152</td>\n",
       "      <td>0.155691</td>\n",
       "      <td>0.151269</td>\n",
       "      <td>0.157655</td>\n",
       "      <td>0.160979</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>40</td>\n",
       "      <td>4</td>\n",
       "      <td>34</td>\n",
       "      <td>3</td>\n",
       "      <td>4</td>\n",
       "      <td>1</td>\n",
       "      <td>3313.0</td>\n",
       "      <td>2517</td>\n",
       "      <td>796.0</td>\n",
       "      <td>1.405</td>\n",
       "      <td>1171</td>\n",
       "      <td>20</td>\n",
       "      <td>2.333</td>\n",
       "      <td>0.760</td>\n",
       "      <td>0.173572</td>\n",
       "      <td>0.152012</td>\n",
       "      <td>0.172230</td>\n",
       "      <td>0.171862</td>\n",
       "      <td>0.160979</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>40</td>\n",
       "      <td>3</td>\n",
       "      <td>21</td>\n",
       "      <td>5</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>4716.0</td>\n",
       "      <td>0</td>\n",
       "      <td>4716.0</td>\n",
       "      <td>2.175</td>\n",
       "      <td>816</td>\n",
       "      <td>28</td>\n",
       "      <td>2.500</td>\n",
       "      <td>0.000</td>\n",
       "      <td>0.146152</td>\n",
       "      <td>0.159381</td>\n",
       "      <td>0.151269</td>\n",
       "      <td>0.134807</td>\n",
       "      <td>0.160979</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   Customer_Age  Dependent_count  Months_on_book  Total_Relationship_Count  \\\n",
       "0            45                3              39                         5   \n",
       "1            49                5              44                         6   \n",
       "2            51                3              36                         4   \n",
       "3            40                4              34                         3   \n",
       "4            40                3              21                         5   \n",
       "\n",
       "   Months_Inactive_12_mon  Contacts_Count_12_mon  Credit_Limit  \\\n",
       "0                       1                      3       12691.0   \n",
       "1                       1                      2        8256.0   \n",
       "2                       1                      0        3418.0   \n",
       "3                       4                      1        3313.0   \n",
       "4                       1                      0        4716.0   \n",
       "\n",
       "   Total_Revolving_Bal  Avg_Open_To_Buy  Total_Amt_Chng_Q4_Q1  \\\n",
       "0                  777          11914.0                 1.335   \n",
       "1                  864           7392.0                 1.541   \n",
       "2                    0           3418.0                 2.594   \n",
       "3                 2517            796.0                 1.405   \n",
       "4                    0           4716.0                 2.175   \n",
       "\n",
       "   Total_Trans_Amt  Total_Trans_Ct  Total_Ct_Chng_Q4_Q1  \\\n",
       "0             1144              42                1.625   \n",
       "1             1291              33                3.714   \n",
       "2             1887              20                2.333   \n",
       "3             1171              20                2.333   \n",
       "4              816              28                2.500   \n",
       "\n",
       "   Avg_Utilization_Ratio  Gender_Churn  Education_Level_Churn  \\\n",
       "0                  0.061      0.146152               0.152012   \n",
       "1                  0.105      0.173572               0.155691   \n",
       "2                  0.000      0.146152               0.155691   \n",
       "3                  0.760      0.173572               0.152012   \n",
       "4                  0.000      0.146152               0.159381   \n",
       "\n",
       "   Marital_Status_Churn  Income_Category_Churn  Card_Category_Churn  \n",
       "0              0.151269               0.134807             0.160979  \n",
       "1              0.169414               0.171862             0.160979  \n",
       "2              0.151269               0.157655             0.160979  \n",
       "3              0.172230               0.171862             0.160979  \n",
       "4              0.151269               0.134807             0.160979  "
      ]
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "X.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {
    "editable": true
   },
   "outputs": [],
   "source": [
    "# This cell may take up to 15-20 minutes to run\n",
    "# train test split \n",
    "X_train, X_test, y_train, y_test = train_test_split(X, y, test_size= 0.3, random_state=42)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 68,
   "metadata": {
    "editable": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "7088"
      ]
     },
     "execution_count": 68,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "len(X_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 69,
   "metadata": {
    "editable": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "3039"
      ]
     },
     "execution_count": 69,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "len(X_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 67,
   "metadata": {
    "editable": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "10127"
      ]
     },
     "execution_count": 67,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "len(X_train.append(X_test))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {
    "editable": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "random forest results\n",
      "test results\n",
      "              precision    recall  f1-score   support\n",
      "\n",
      "           0       0.96      0.99      0.98      2543\n",
      "           1       0.93      0.80      0.86       496\n",
      "\n",
      "    accuracy                           0.96      3039\n",
      "   macro avg       0.95      0.90      0.92      3039\n",
      "weighted avg       0.96      0.96      0.96      3039\n",
      "\n",
      "train results\n",
      "              precision    recall  f1-score   support\n",
      "\n",
      "           0       1.00      1.00      1.00      5957\n",
      "           1       1.00      1.00      1.00      1131\n",
      "\n",
      "    accuracy                           1.00      7088\n",
      "   macro avg       1.00      1.00      1.00      7088\n",
      "weighted avg       1.00      1.00      1.00      7088\n",
      "\n",
      "logistic regression results\n",
      "test results\n",
      "              precision    recall  f1-score   support\n",
      "\n",
      "           0       0.90      0.96      0.93      2543\n",
      "           1       0.71      0.45      0.55       496\n",
      "\n",
      "    accuracy                           0.88      3039\n",
      "   macro avg       0.81      0.71      0.74      3039\n",
      "weighted avg       0.87      0.88      0.87      3039\n",
      "\n",
      "train results\n",
      "              precision    recall  f1-score   support\n",
      "\n",
      "           0       0.91      0.96      0.94      5957\n",
      "           1       0.72      0.50      0.59      1131\n",
      "\n",
      "    accuracy                           0.89      7088\n",
      "   macro avg       0.82      0.73      0.76      7088\n",
      "weighted avg       0.88      0.89      0.88      7088\n",
      "\n"
     ]
    }
   ],
   "source": [
    "# grid search\n",
    "rfc = RandomForestClassifier(random_state=42)\n",
    "# Use a different solver if the default 'lbfgs' fails to converge\n",
    "# Reference: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression\n",
    "lrc = LogisticRegression(solver='lbfgs', max_iter=3000)\n",
    "\n",
    "param_grid = { \n",
    "    'n_estimators': [200, 500],\n",
    "    'max_features': ['auto', 'sqrt'],\n",
    "    'max_depth' : [4,5,100],\n",
    "    'criterion' :['gini', 'entropy']\n",
    "}\n",
    "\n",
    "cv_rfc = GridSearchCV(estimator=rfc, param_grid=param_grid, cv=5)\n",
    "cv_rfc.fit(X_train, y_train)\n",
    "\n",
    "lrc.fit(X_train, y_train)\n",
    "\n",
    "y_train_preds_rf = cv_rfc.best_estimator_.predict(X_train)\n",
    "y_test_preds_rf = cv_rfc.best_estimator_.predict(X_test)\n",
    "\n",
    "y_train_preds_lr = lrc.predict(X_train)\n",
    "y_test_preds_lr = lrc.predict(X_test)\n",
    "\n",
    "# scores\n",
    "print('random forest results')\n",
    "print('test results')\n",
    "print(classification_report(y_test, y_test_preds_rf))\n",
    "print('train results')\n",
    "print(classification_report(y_train, y_train_preds_rf))\n",
    "\n",
    "print('logistic regression results')\n",
    "print('test results')\n",
    "print(classification_report(y_test, y_test_preds_lr))\n",
    "print('train results')\n",
    "print(classification_report(y_train, y_train_preds_lr))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {
    "editable": true
   },
   "outputs": [
    {
     "data": {
      "image/png": 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6+2sFcGWdY9OAaXEEJSKS76JuUNgC+CUwGOhCsM7ABGCUu2+KLzwRkfwUtVvg18CxwCXAQeG/g4BRMcWVE3QzS0QaK+oNrVOAA9z9s/D53HAB7TnA5bFElgN0M0tEGitqy7UEqKpzrAooymw4uUc3s0SkMaK2XJ8CnjWz64GFQA9gOPB0XIGJiOSzqC3XnwP/IFgp6x3gPmBGeFxEROqIOhRrA3B1+CUiIimk2kOrF0FrtQ8wCzg3nPYqIiINSNUt8HuCMa3nAJ8RTIEVEZEUUiXXfgSt1WeB8wlWwmoWNMZVRLZHqj7XUnf/EsDdV5tZ68ZeyMx6A+OBMoIFX4a4+7x6yhowG7jL3a9o7DW3h8a4isj2SJVcW5nZ8ITnres8x91vjHite4Cx7v6omZ0FjAO+XbeQmZWE35sa8byx0RhXEWmsVN0CTwC9Er6eqvO8Z5SLmNnOQF9gUnhoEtDXzMqTFP8FwYIw70U5dxzUJSAi26vBlqu7D87QdboBi929MjxvpZl9Eh6vqClkZvsB3wW+BVzXmAuVlbVN+zXl5e22ej5rXjDL98iDe2zzvVyWT7GmorrkpkKpSzbqEXWGVuzMrCXB5IQfhcm3UedZvnwNVVXVkcuXl7ejomL1Vsc2bazEurWnX8+ybb6Xq5LVI1+pLrmpUOqSbj2Ki4sa1Whr1O6vjfAx0CXsT63pV901PF5jF2BP4Hkzm0+w8tb5ZnZvlmIUEcmYrLRc3X2Zmc0BTgceDf+dHS7CXVNmIdCx5rmZ3QC0barRAiIi2yNbLVeAYcBFZvYecFH4HDN73swOzGIcIiKxi9xyNbNvAacBndz9BDPrC7Rz979Heb27/5ckkxDc/Zh6yt8QNTYRkVwTqeVqZj8hWGPgY4I7+QAbgZtjiktEJK9F7Ra4HDjS3UeyZdHsd4G9YolKRCTPRU2u7YAF4eOacU4tCFqvIiJSR9Tk+gpQ9679T4FI/a0iIs1N1OR6EXCamb0PtDOzuQTbbF8aW2RNRFNfRSQTou5EsNjM+gEDgO4EN7ZerZnOWki0GpaIZELkoVjuXkWwb9aM+MLJDVoNS0S2V6TkamYfseVG1lbcfY+MRiQiUgCitlzPq/N8F4J+2ElJyoqINHtR+1xfqnvMzF4Cnkf7aomIbGN71hb4ElCXgIhIElH7XIfXOdQGOBb4c8YjEhEpAFH7XHvVeb4WGAs8nNFomljNGFfr1r6pQxGRPJcyuYYLW/8FeMLd18cfUtPRGFcRyZSUfa7hRIExhZ5Ya2iMq4hkQtQbWtPNLOm6qyIisq2ofa7FwBQze4Vg6mvthAJ3PzeOwERE8lnU5DoPuC3OQERECkmDydXMTnf3Se5+XbYCEhEpBKn6XMdlJQoRkQKTKrkWZSUKEZECk6rPtSTc9bXeJOvuf81sSCIi+S9Vcm1FsOtrfcm1Gq0vICKyjVTJdW1zWa9VU19FJJO2Z1WsgqKpryKSSbqhlUBTX0UkUxpMru7eLluBiIgUEnULiIjEQMlVRCQGSq4iIjFQchURiYGSq4hIDJRcRURioOQqIhIDJVcRkRgouYqIxEDJVUQkBkquIiIxUHJly3KDIiKZouSKlhsUkcyLurX2djOz3sB4oAxYDgxx93l1ylwHnAZsDr+udvc/ZSU+LTcoIhmUzZbrPcBYd+8NjCX5zrKvAf3dfX/gXGCymbXOYowiIhmRleRqZjsDfYFJ4aFJQF8zK08s5+5/cvd14dO3CBbrLstGjCIimZStlms3YLG7VwKE/34SHq/PEOADd1+UhfhERDIqa32u6TCzbwI3AUel+9qysrZpX69laQkA5eX5vfFCvsefSHXJTYVSl2zUI1vJ9WOgi5mVuHulmZUAu4bHt2JmA4BHge+7u6d7oeXL11BVVR25fHl5OzZtrASgomJ1upfLGeXl7fI6/kSqS24qlLqkW4/i4qJGNdqy0i3g7suAOcDp4aHTgdnuXpFYzsz6A5OBk9x9VjZiExGJQzZHCwwDLjKz94CLwueY2fNmdmBY5i6gNTDOzOaEX/vGGdQLr87XBAIRybis9bm6+3+Bg5McPybhcf9sxVPj77OD+2WaQCAimaQZWmgCgYhknpKriEgMlFxFRGKg5CoiEgMlVxGRGCi5iojEQMlVRCQGSq4iIjFQchURiUGzTq4vz1nMOx8sb+owRKQANevkqr2zRCQuzTq5AvTZs0xTX0Uk45p9chURiYOSq4hIDJRcRURioOQqIhIDJVcRkRgouYqIxEDJVUQkBkquIiIxUHIVEYmBkquISAyUXEVEYqDkKiISAyVXEZEYKLmKiMRAyVVEJAZKriIiMVByFRGJgZKriEgMlFxFRGKg5CoiEoMWTR2ApFZZuZkVKyrYvHljvWWWLSumqqoqi1HFR3XJTYVSl/rqUVxcQuvWbWnbdieKioq2+zpKrnlgxYoKdtihDTvu2LneX3qLFsVs3pz///FBdclVhVKXZPWorq6msnIzq1evZMWKCr761Z23+zrqFsgDmzdvZMcdv5KRv6Yisq2ioiJatGhJ+/ZlbNy4PiPnVHLNE0qsIvErKioGqjNyLiVXEZEYKLlK2k466Xg+/PD9jJzrs88quOiiCxoss2TJJzzzzJStjl1xxcUsXryowdc98MA4jjvuKM455wzOOOOH3HDDNXz55ZfbHXNcovwsGuOJJyYxceKErY5NmfIkhx12IPPm+VbHk/1uhw4dzKxZb9Q+//Of/8iPfhT8TM888ySuv/6XfPrppxmJ9fPPl3PppT/ltNNO5OyzT2fu3HeSllu/fj0jRlzL4MGncNZZpzB8+C9Zt24tAOvWreOmm4YzZMipnHHGD7eq+5QpTzJ+/IMZiTUVJVdpUh07ljNmzLgGyyxZ8gnPPvuHrY6NHn0nXbp0TXn+o48+locfnsj48Y+zbNlSnn568nbFW9fmzZszdq4oP4t0rV+/nqeeepwf/vDkrY5Pn/4s/fr1Z/r0Z9M633PPTeWRRx7kxhtHMXHi0zz22FN8//s/5PPPP8tIvPfc83v23/8AHn98CpdddhU33ngt1dXbfkx/9tkpbNq0iUcemcyECZOpqqrkD394CoAJEx6iZcuWjB//OA888Ch/+tPzvPPO2wB873s/4Nlnp7J27ZqMxNsQjRaQjHn33bncccdo1q//kh12aM0ll1zBXnvtA8DTT0/myScfp23bdgwYcChTpjzB9OkvsWTJJ5x33mCmT3+J9evXM3Lk9cyf/yElJS3o3r0HN900ittvv5UlSxZzzjln0LVrV0aOvJWTTjqeW2/9LXvs0ZOKimXcccdtLFr0MQBHHvldBg/+0VaxtWzZkj599mPp0qW1x+bOfYd77hnD2rVBi+e884bxjW8cFineE088hTfeeI3vfncQxx77fe699y7mzHmTTZs2s+eee3L55b+kTZs2TJ36NJMmPUbLlqVUV1dx442j6NatO7fffiuzZr1Oy5altGnTmrvvfnCrnwXAv//9L8aN+z1VVVW0b9+Bn//8arp27casWW9w5523s/fe+zB37ttAESNG/Irddtt9m9/Jyy+/xNe/3pdWrXaoPfbBB++zcuUKbr75Vs47bzA/+cnPKC0tjfQ7fuih+7jqqmvp1q177bG+fQ+M9Noo/va3F3nyyecA2H//r1NaWsp///t/tf+Ptihiw4b1tX/cvvxyPeXlnQB4//33GDToeIqKimjdujUHHNCXv/zlj/Tpsy8tWrTgoIMO4aWX/sL3vveDjMWdjJJrHprx9hJeeWvJVseKiiDJH/i0HbbfLhy67y5pv27Tpk1cc82V/PKXw+nf/2DeeOM1rrnmSiZPnsqCBfOZMOFhHnpoIh06dOB3v/tN0nPMnPkqq1ev5vHHn2bz5iq++OILAC677ErGjv0dDzwwIenrbrzxOgYMOJSbb74NgJUrV25TZt26tcyZM4sLLvgpAKtXr2b06F9x22130rFjRz777DPOP38IjzwymaVLP20w3lWrVrHbbrszdGjwEf7hh+9nxx135L77HgHgrrvuZMKEh7jggp8yZszveOSRx+nUqTMbN26kqqqK999/jzfeeI2JE5+iuLi4tp6JVqz4nJEjhzNmzL3svvseTJs2lREjruW++8YD8NFHH3D11cO58sprGD/+AcaPf4Drrx+5zXlmz36TvffeOjFNm/YMgwYdR+fOu9CzZ2/++c+/c8QRRyX92Sb6/PPPWbZsKXvv3SdlWYA//nEakydPTPq9wYPP4YgjvrPVsVWrVlJdXU379u1rj3Xq1Jlly5Zuk1y///0TmTv3LY4/PjjHwQcfwne+czQAZnvx8ssvMXDg/7BmzRpmznyV7t171L523333Y8aMfxZOcjWz3sB4oAxYDgxx93l1ypQAdwJHE9yyG+Xu92crRmm8hQsX0LJlS/r3PxiAAw88iJYtW7Jw4QJmz36TAQMOpUOHDgAcc8zx/PnPz29zjp49e7Fw4Xxuu+0W9t+/X20rsiHr1q3jnXfe4re/HVt7LPHN+cIL03n99ZksWrSQQw8dWNvKeued/7BkySdcccXFtWWLiopYvPhj3n77rQbjLS1txbe/vSUZzZjxD9auXcvLL/8VgE2bNtKzZ6/w59CfX/3qRg4/fCADBhxGly5d2XXXrlRVVTJq1E307Xsg3/jG4dvUa+7cd9hzz97svvseYQzf4ze/+XVtv2L37j3o3ftrAOyzz77MmPHPpD+fioplW51/06ZN/OUvLzBu3EO1dZs+/dna5FrfqJSioqKkH88bMmjQcQwadFxar4nqjTdeA+CZZ14AYMSIa5k4cQJnnDGYs846h7Fjf8fQoYNp374DBxzQj1WrtvzBLSsrY9myZbHElSibLdd7gLHu/qiZnQWMA75dp8yZQE+gF0ESnm1mL7r7/CzGmfMO3Xfb1mVTD/Curq5O+sYMWtTVQOqhZF26dOWxx55k9uw3mDHjFe69dyzjxz++XXEdffSxXHjhJSxf/hk/+cl5TJ36ND/4wUlUV8Oee/Zi7Nj7tnnNW2/9p8F4W7feYau6VlfD5Zf/gn79+m9TdtSo0bz99tu8+eYbXHzxMK644pcMGHAoEyY8wezZb/Lmm69z991jePDBR+u8spqGRt+VlraqfVxcXExlZWXScq1atWLjxg21z1955e+sW7eWn/3sxwBUVVWxYsXnLF36KZ06daZ9+/asWrVqq3OsWrWSDh2+SllZGeXlO/Puu3M56KBD6g8ulG7Ldaedgj+KK1eurP0DuXTpp+y8c6dtXv/MM0/z3e8eS6tWwc/hiCOO4oUXpnPGGYPZYYcduPzyq2rLjh49ih49tnSZbNiwsfZ1ccrKDS0z2xnoC0wKD00C+ppZeZ2ipwL3uXuVu1cAU4GTkZzXo8dubNy4sfau8qxZb7B582a6devBAQf049//nlH7cf2FF6YlPceyZUspLi7hm9/8FhdffDkrV65g9eov2HHHtvXegGjTpg19+uzHE09seRMn6xYoK+vIz352BQ8/fD8bNmygT5/9WLRo4VZ3wd99dy7V1dWR461x2GEDmTz5MTZsCAafr1u3lvnzP2Lz5s0sXryIvffuw+DB53DQQYcwb56zYsUKNmzYwCGHfINhwy6kbdu2fPLJ4q3Ouc8++/H++++xYMF8IEhUvXoZbdrs2GAsde2xR08WLlxQ+3z69Ge59NIreeqp53jqqeeYMmU6gwYdxx//GNTxwAMPZtq0qbXJ+tVXZ1BcXEzXrt0AOPvsoYwZc/tWIzVmznw16V39QYOO4+GHJyb9qptYa3zrW0cydWpwY+o//5nDhg0bMNtrm3K77LIrr732KtXV1VRVVTFz5qvsvvueAKxdu6b2d/H++/P45z//xoknbkkj8+d/RM+evaP/EBspWy3XbsBid68EcPdKM/skPF6RUK47sCDh+cKwTGRlZW0jlz3msOCvWXl5u3QukXXLlhXTokXqv4NRymTKJZf8lJKSktrnjz32BKNGjeb222/lyy+/pHXr1txyy220bt2Kvfb6GmeddTbDhv2IsrIy+vc/mLZt29GiRTElJcVAES1aFDN//ofcddedQNCiGjLkXDp37kTHjmX06NGDIUNOpUeP3bjllqBvtaQk+LmMGHEzo0enq0vZAAAML0lEQVSPYsiQUykuLuY73xnEkCHnUFxcRHFxUe3PZeDAgUyevBvTpv2BU089g9tuu4MxY+7gzjt/w6ZNm+jSpSujR98ROd4a55zzI+67bxznn382RUVFFBUVMXTo/9K9ezduuul61qxZQ1FREZ06deLCCy9myZIl3HLLTVRWVlJZWcmAAYey//77h8OZgnOXl5dx/fU3MWLEtVRWbqZDhw6MGDGyNoaioi2/77rPE33720dw2223cMEFP6aiooI5c2Zx0023bFV20KBjGDlyBEOHns/QoecxZswdnHvumRQXF/OVr3yFX//6N+ywQ3DD66STTqZNm9Zcd91VbNiwgaKiInr16s2FF/4sI///LrzwYq6//lpOO+0HtGrVihtuGElpaZCmbr75Rg4//JsMHPhNzj//Am65ZSRDhpwKQK9evRk69DxatChm6dIlXHPNVZSUlFBa2ooRI26mc+ctrd+ZM//FsGE/rTfe4uLijOSEonT7URrDzPoBj7j7PgnH/g84y91nJRx7GzjX3V8Pn18JdHX3i+ueM4ndgI+WL19DVVX0OpWXt6OiYnXk8k3h008X0LlzjwbLNHW3QCrr1q2tbXU98MA4Fi9exPDhNyUtmwt1SSfehuRCXS677EKGDbuwto+2sXKhLttrwYL5jB79K8aMubfeMnXfb8XFRTWNtt2B+VGvla2W68dAFzMrCVutJcCu4fFEC4EewOvh87otWclTd9/9e95++z9s3ryJXXftwpVXXtPUITUo3+JtyKWXXsmiRQubOoycsGzZp1x55dVZuVZWkqu7LzOzOcDpwKPhv7PDftVETwLnm9kUghtaJwADsxGjxCvxBkM+yLd4G9KtW/etxqU2Z/37H5K1Fng2Z2gNAy4ys/eAi8LnmNnzZlYzCnkC8CEwD/g3cKO7f5jFGEVEMiJrQ7Hc/b/AwUmOH5PwuBL4cbZiyif1DXUSkcyprq4iyrDBKLS2QB5o0aKUtWu/SHsQt4hEU11dzebNm1i58jNKS3dI/YIINP01D3ToUM6KFRWsWbPt+M0axcWFsQUHqC65qlDqUl89Erd5yQQl1zxQUtKCjh0bnu+fD0PKolJdclOh1CVb9VC3gIhIDJRcRURiUEjdAiUQzKZIV2Nek4sKpR6guuSqQqlLOvVIKFvSULm6sjL9NUsOA5KvuyYisv0OB16JWriQkmsroD+wBEi+/pqISPpKgF0IpuVvSFG2ViElVxGRnKEbWiIiMVByFRGJgZKriEgMlFxFRGKg5CoiEgMlVxGRGCi5iojEoJCmv9bLzHoD4wm2jlkODHH3eXXKlAB3AkcD1cAod78/27GmErEu1wGnAZvDr6vd/U/ZjrUhUeqRUNaA2cBd7n5F9qKMJmpdzOwU4DqC1ZirgSPdfWk2Y00l4v+vnYGHCHZmLgX+Clzs7puzHG69zGw08EOCjUv3dfdt9v6O+z3fXFqu9wBj3b03MBYYl6TMmUBPoBcwALjBzHbLWoTRRanLa0B/d98fOBeYbGatsxhjFFHqUfMGGAdMzWJs6UpZl3AroxuAo9y9D8F07VXZDDKiKL+Xq4F33X0/YF+gH3Bi9kKMZCrB/nsNbXAa63u+4JNr+Fe2LzApPDQJ6Gtm5XWKngrc5+5V4caJU4GTsxdpalHr4u5/cvd14dO3CFpKZVkLNIU0ficAvwCmAe9lKby0pFGXS4HR7v4pgLuvcvf12Ys0tTTqUg20M7NigmnnpcDirAUagbu/4u51d5euK9b3fMEnV4KPLovD/blq9un6JDyeqO423guTlGlqUeuSaAjwgbsvykJ8UUWqh5ntB3wX+G3WI4wu6u9kb2APM/uHmc0ys2vNLNeWmIpal5uA3gTreHwK/MndZ2Qz0AyJ9T3fHJJrs2Vm3yR4I5ze1LGky8xaAvcBw2re7HmuBbAfcBTwTWAQMLhJI2q8kwk+Ee0CdAEGmtlJTRtS7mkOyfVjoEvYd1fTh7dreDzRQqBHwvPuSco0tah1wcwGAI8CJ7i7ZzXK1KLUYxdgT+B5M5sPXAKcb2b3ZjfUlKL+ThYAT7n7BndfDTwDHJTVSFOLWpeLgMfCj9OrCOryraxGmhmxvucLPrm6+zJgDltab6cDs8M+lkRPErx5i8M+phOAp7MXaWpR62Jm/YHJwEnuPiu7UaYWpR7uvtDdO7r7bu6+G3AHQf/Y/2Y94Aak8f9rIvAdMysKW+VHAP/JXqSppVGXjwjusGNmpcCRwDZ34/NArO/5gk+uoWHARWb2HsFf3WEAZvZ8eBcXYALwITAP+Ddwo7t/2BTBphClLncBrYFxZjYn/Nq3acKtV5R65IsodXkcWAb8H0ECmws80ASxphKlLpcAh5vZ2wR1eY+gCydnmNmdZrYI6Aq8aGZzw+NZe89rPVcRkRg0l5ariEhWKbmKiMRAyVVEJAZKriIiMVByFRGJgZKrZJyZPWpmNzR1HKmYmZvZ4Q18/89mdmY2Y5LC0SyWHJTGCWdGdQISp5/2dvdPmiCWR4FTgI3h1xvAhe7e6AVd3N0Szj8S6Oru5yR8/zuNDrgeZtYC2ASsI1gAZSXBAilXuXtVhNcfCdwfTqyQHKaWq6RyvLu3TfjKemJN8Ct3b0uwuMbnwINNGMv22iesy7cJ1hg4u4njkQxTy1XSFi419wTBmqQ7EMzS+bG7v5uk7M7Aw8A3gCrgHXcfGH6vKzAmPM8agiX5xqa6vruvNbNJBIs6Y2Y7ALcSLChSRTD19xfuvjHF9RcBZwFtgSuBonABEnf3fmb2CnB/eL6lwEHu/t/wtZ0JpoF2dfflZvY9gkVyehBMBR2WbIHmJHV5z8z+BXw94Wd2HnA5weyiZcAt7n6/me0EPAe0MrM1YfE9gM8IlmYcCuwEvEjw+1iR6voSH7VcpbGmESwy3JkgmUyop9zPCaYYlodlr4PaRUGmAa8TrKx0FPBzMzsi1YXNrB1wBsHuBADDgQMJVp06ADgU+GVD10/k7tMIkvNjYeu8X53vf0mw1mfi6mKnAi+FibU/wfTP8wjWzX0QeCacd5+qLnuF8b6fcHgpcCzwFeB8YIyZ7RcuknI8sDDhk8Qy4LKw/ECChLyWYIV9aUJquUoqU82sZvuOl939hLBv8OGaAuHNqwoz29Hd19Z5/SaC1a26u/sHwN/D44cAX3H3X4XP3zezBwi2p3mpnlh+YWaXAF8CMwl2WYBgRfnzaxYYMbMbgd8BIxq4fromEiSs68PnZ4TXAPhfgi1oXg+fP2hm1wD9gfrWOX0r/APTBniMhBX/3f25hHJ/NbOXgMMJlvlL5gLgPHdfDLW/j/fN7Owo/bgSDyVXSeUEd38x8UCYFG4BTgI6EnzcJnxcN7mOIkhyL5lZJXCPu99G8PG5u5mtTChbArzcQCyj3P2GJMd3YetFjxcQtIYbun66XgTam1k/gptQ+xAstQdBXc40s0sTypcmxJDMfgRL3p0KjCRIshsBzOw4ghZ2L4JPl20IWvj16Q48Z2aJibQa2JlgMWtpAkqu0hhDgGMIbsYsIPgoXEGwncxW3P0Lgi1OLg1X5vqbmb1GsG7mPHffKwPxLCFIcDXr1nYn3Hakvuu7e90WbIMrGLn7ZjN7kqBrYBXwTEIr/WNghLv/Op2gw1blJDM7AbgWuCLc6+wpghb8dHffZGbT2PKzTRbnIuAMd5+ZzvUlXkqu0hjtgA0Eu4O2AW6ur6CZHU+wzN6HBEmpMvz6N7DRzC4n2AhvE8FWKKXu/maa8UwChpvZLIIkdB3BQuENXb+upQTL6BW5e32JdiLB0oFrgMRdaO8FnjSzvxIMEduRYPHovybpJknmFuAVM/s1wc+hlOCPVWXYij0iPG9NnB3NrF246DYEmwr+ysx+5O4Lw5t4h7j7sxGuLTHRDS1pjIcI9lb6hGBd0n81UNYItl5eQ9D/+Ltw87jNBK3fg4D5BHe8xxHcxEnXCIKFp98m6JecSZCw6r1+knNMJkhqn4ct62T+RbBVeTnw55qDYYvxx8DdwAqC9U3Pihq8u88BXgWucPeVBC3tPxAMNzuJ4MZfTdl3CBZ0nm9mK8NEejvwAkHXx+owzv5Rry/x0HquIiIxUMtVRCQGSq4iIjFQchURiYGSq4hIDJRcRURioOQqIhIDJVcRkRgouYqIxEDJVUQkBv8PfSaWH3Q4r7MAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7fa949d97390>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "lrc_plot = plot_roc_curve(lrc, X_test, y_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 83,
   "metadata": {
    "editable": true
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7fa14dbb66a0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# plots\n",
    "plt.figure(figsize=(15, 8))\n",
    "ax = plt.gca()\n",
    "rfc_disp = plot_roc_curve(cv_rfc.best_estimator_, X_test, y_test, ax=ax, alpha=0.8)\n",
    "lrc_plot.plot(ax=ax, alpha=0.8)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {
    "editable": true
   },
   "outputs": [
    {
     "ename": "NameError",
     "evalue": "name 'cv_rfc' is not defined",
     "output_type": "error",
     "traceback": [
      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[0;31mNameError\u001b[0m                                 Traceback (most recent call last)",
      "\u001b[0;32m<ipython-input-21-0e70102ca57b>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m()\u001b[0m\n\u001b[1;32m      1\u001b[0m \u001b[0;31m# save best model\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m----> 2\u001b[0;31m \u001b[0mjoblib\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mdump\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mcv_rfc\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mbest_estimator_\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m'./models/rfc_model.pkl'\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m      3\u001b[0m \u001b[0mjoblib\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mdump\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mlrc\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m'./models/logistic_model.pkl'\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;31mNameError\u001b[0m: name 'cv_rfc' is not defined"
     ]
    }
   ],
   "source": [
    "# save best model\n",
    "joblib.dump(cv_rfc.best_estimator_, './models/rfc_model.pkl')\n",
    "joblib.dump(lrc, './models/logistic_model.pkl')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {
    "editable": true
   },
   "outputs": [],
   "source": [
    "rfc_model = joblib.load('./models/rfc_model.pkl')\n",
    "lr_model = joblib.load('./models/logistic_model.pkl')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "metadata": {
    "editable": true
   },
   "outputs": [
    {
     "data": {
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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7fa949579ba8>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "lrc_plot = plot_roc_curve(lr_model, X_test, y_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "metadata": {
    "editable": true
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7fa949570080>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(15, 8))\n",
    "ax = plt.gca()\n",
    "rfc_disp = plot_roc_curve(rfc_model, X_test, y_test, ax=ax, alpha=0.8)\n",
    "lrc_plot.plot(ax=ax, alpha=0.8)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "metadata": {
    "editable": true
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7fa94a06fc50>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "explainer = shap.TreeExplainer(cv_rfc.best_estimator_)\n",
    "shap_values = explainer.shap_values(X_test)\n",
    "shap.summary_plot(shap_values, X_test, plot_type=\"bar\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "metadata": {
    "editable": true
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7fa94a119b70>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Calculate feature importances\n",
    "importances = cv_rfc.best_estimator_.feature_importances_\n",
    "# Sort feature importances in descending order\n",
    "indices = np.argsort(importances)[::-1]\n",
    "\n",
    "# Rearrange feature names so they match the sorted feature importances\n",
    "names = [X.columns[i] for i in indices]\n",
    "\n",
    "# Create plot\n",
    "plt.figure(figsize=(20,5))\n",
    "\n",
    "# Create plot title\n",
    "plt.title(\"Feature Importance\")\n",
    "plt.ylabel('Importance')\n",
    "\n",
    "# Add bars\n",
    "plt.bar(range(X.shape[1]), importances[indices])\n",
    "\n",
    "# Add feature names as x-axis labels\n",
    "plt.xticks(range(X.shape[1]), names, rotation=90);"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 62,
   "metadata": {
    "editable": true
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7fa949047780>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.rc('figure', figsize=(8, 5))\n",
    "#plt.text(0.01, 0.05, str(model.summary()), {'fontsize': 12}) old approach\n",
    "plt.text(0.01, 1.25, str('Random Forest Train'), {'fontsize': 10}, fontproperties = 'monospace')\n",
    "plt.text(0.01, 0.05, str(classification_report(y_test, y_test_preds_rf)), {'fontsize': 10}, fontproperties = 'monospace') # approach improved by OP -> monospace!\n",
    "plt.text(0.01, 0.6, str('Random Forest Test'), {'fontsize': 10}, fontproperties = 'monospace')\n",
    "plt.text(0.01, 0.7, str(classification_report(y_train, y_train_preds_rf)), {'fontsize': 10}, fontproperties = 'monospace') # approach improved by OP -> monospace!\n",
    "plt.axis('off');\n",
    "plt.savefig(\"abc.png\", bbox_inches=\"tight\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 63,
   "metadata": {
    "editable": true
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7fa94950a0f0>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.rc('figure', figsize=(5, 5))\n",
    "plt.text(0.01, 1.25, str('Logistic Regression Train'), {'fontsize': 10}, fontproperties = 'monospace')\n",
    "plt.text(0.01, 0.05, str(classification_report(y_train, y_train_preds_lr)), {'fontsize': 10}, fontproperties = 'monospace') # approach improved by OP -> monospace!\n",
    "plt.text(0.01, 0.6, str('Logistic Regression Test'), {'fontsize': 10}, fontproperties = 'monospace')\n",
    "plt.text(0.01, 0.7, str(classification_report(y_test, y_test_preds_lr)), {'fontsize': 10}, fontproperties = 'monospace') # approach improved by OP -> monospace!\n",
    "plt.axis('off');"
   ]
  }
 ],
 "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": "3.6.3"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 4
}