CreditCardData.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Problem: A manager at the bank is disturbed with more and more customers leaving their credit card services. They would really appreciate if one could predict for them who is gonna get churned so they can proactively go to the customer to provide them better services and turn customers' decisions in the opposite direction"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Hypothesis: The bank's credit terms are too strict to maintain customers"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import numpy as np\n",
    "from sklearn.model_selection import train_test_split\n",
    "from sklearn import tree\n",
    "from sklearn.naive_bayes import MultinomialNB\n",
    "from sklearn.svm import SVC\n",
    "from sklearn.linear_model import LogisticRegression\n",
    "from yellowbrick.classifier import ClassificationReport\n",
    "from yellowbrick.model_selection import FeatureImportances\n",
    "from sklearn import datasets\n",
    "from sklearn.tree import DecisionTreeClassifier\n",
    "from sklearn.linear_model import LogisticRegression\n",
    "from sklearn.metrics import roc_curve, roc_auc_score\n",
    "from sklearn.model_selection import train_test_split\n",
    "import matplotlib.pyplot as plt\n",
    "from sklearn.preprocessing import StandardScaler\n",
    "from scipy.stats import randint\n",
    "from sklearn.model_selection import RandomizedSearchCV\n",
    "from sklearn.metrics import accuracy_score\n",
    "from yellowbrick.datasets import load_credit\n",
    "from yellowbrick.classifier import confusion_matrix\n",
    "from sklearn.linear_model import LogisticRegression\n",
    "from sklearn.model_selection import train_test_split as tts\n",
    "from sklearn.metrics import precision_recall_curve\n",
    "import matplotlib.pyplot as plt\n",
    "from yellowbrick.datasets import load_spam\n",
    "from sklearn.linear_model import RidgeClassifier\n",
    "from yellowbrick.classifier import PrecisionRecallCurve\n",
    "from sklearn.model_selection import train_test_split as tts\n",
    "from imblearn.over_sampling import RandomOverSampler"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></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>Months_on_book</th>\n",
       "      <th>...</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>Naive_Bayes_Classifier_Attrition_Flag_Card_Category_Contacts_Count_12_mon_Dependent_count_Education_Level_Months_Inactive_12_mon_1</th>\n",
       "      <th>Naive_Bayes_Classifier_Attrition_Flag_Card_Category_Contacts_Count_12_mon_Dependent_count_Education_Level_Months_Inactive_12_mon_2</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\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>39</td>\n",
       "      <td>...</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>0.000093</td>\n",
       "      <td>0.99991</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\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>44</td>\n",
       "      <td>...</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>0.000057</td>\n",
       "      <td>0.99994</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\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>36</td>\n",
       "      <td>...</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>0.000021</td>\n",
       "      <td>0.99998</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\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>34</td>\n",
       "      <td>...</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>0.000134</td>\n",
       "      <td>0.99987</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\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>21</td>\n",
       "      <td>...</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>0.000022</td>\n",
       "      <td>0.99998</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 23 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "   CLIENTNUM     Attrition_Flag  Customer_Age Gender  Dependent_count  \\\n",
       "0  768805383  Existing Customer            45      M                3   \n",
       "1  818770008  Existing Customer            49      F                5   \n",
       "2  713982108  Existing Customer            51      M                3   \n",
       "3  769911858  Existing Customer            40      F                4   \n",
       "4  709106358  Existing Customer            40      M                3   \n",
       "\n",
       "  Education_Level Marital_Status Income_Category Card_Category  \\\n",
       "0     High School        Married     $60K - $80K          Blue   \n",
       "1        Graduate         Single  Less than $40K          Blue   \n",
       "2        Graduate        Married    $80K - $120K          Blue   \n",
       "3     High School        Unknown  Less than $40K          Blue   \n",
       "4      Uneducated        Married     $60K - $80K          Blue   \n",
       "\n",
       "   Months_on_book  ...  Credit_Limit  Total_Revolving_Bal  Avg_Open_To_Buy  \\\n",
       "0              39  ...       12691.0                  777          11914.0   \n",
       "1              44  ...        8256.0                  864           7392.0   \n",
       "2              36  ...        3418.0                    0           3418.0   \n",
       "3              34  ...        3313.0                 2517            796.0   \n",
       "4              21  ...        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",
       "   Naive_Bayes_Classifier_Attrition_Flag_Card_Category_Contacts_Count_12_mon_Dependent_count_Education_Level_Months_Inactive_12_mon_1  \\\n",
       "0                                           0.000093                                                                                    \n",
       "1                                           0.000057                                                                                    \n",
       "2                                           0.000021                                                                                    \n",
       "3                                           0.000134                                                                                    \n",
       "4                                           0.000022                                                                                    \n",
       "\n",
       "   Naive_Bayes_Classifier_Attrition_Flag_Card_Category_Contacts_Count_12_mon_Dependent_count_Education_Level_Months_Inactive_12_mon_2  \n",
       "0                                            0.99991                                                                                   \n",
       "1                                            0.99994                                                                                   \n",
       "2                                            0.99998                                                                                   \n",
       "3                                            0.99987                                                                                   \n",
       "4                                            0.99998                                                                                   \n",
       "\n",
       "[5 rows x 23 columns]"
      ]
     },
     "execution_count": 2,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_credit = pd.read_csv('BankChurners.csv')\n",
    "df_credit.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></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",
       "      <th>Naive_Bayes_Classifier_Attrition_Flag_Card_Category_Contacts_Count_12_mon_Dependent_count_Education_Level_Months_Inactive_12_mon_1</th>\n",
       "      <th>Naive_Bayes_Classifier_Attrition_Flag_Card_Category_Contacts_Count_12_mon_Dependent_count_Education_Level_Months_Inactive_12_mon_2</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\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",
       "      <td>10127.000000</td>\n",
       "      <td>10127.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\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",
       "      <td>0.159997</td>\n",
       "      <td>0.840003</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\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",
       "      <td>0.365301</td>\n",
       "      <td>0.365301</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\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",
       "      <td>0.000008</td>\n",
       "      <td>0.000420</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\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",
       "      <td>0.000099</td>\n",
       "      <td>0.999660</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\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",
       "      <td>0.000181</td>\n",
       "      <td>0.999820</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\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",
       "      <td>0.000337</td>\n",
       "      <td>0.999900</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\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",
       "      <td>0.999580</td>\n",
       "      <td>0.999990</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "          CLIENTNUM  Customer_Age  Dependent_count  Months_on_book  \\\n",
       "count  1.012700e+04  10127.000000     10127.000000    10127.000000   \n",
       "mean   7.391776e+08     46.325960         2.346203       35.928409   \n",
       "std    3.690378e+07      8.016814         1.298908        7.986416   \n",
       "min    7.080821e+08     26.000000         0.000000       13.000000   \n",
       "25%    7.130368e+08     41.000000         1.000000       31.000000   \n",
       "50%    7.179264e+08     46.000000         2.000000       36.000000   \n",
       "75%    7.731435e+08     52.000000         3.000000       40.000000   \n",
       "max    8.283431e+08     73.000000         5.000000       56.000000   \n",
       "\n",
       "       Total_Relationship_Count  Months_Inactive_12_mon  \\\n",
       "count              10127.000000            10127.000000   \n",
       "mean                   3.812580                2.341167   \n",
       "std                    1.554408                1.010622   \n",
       "min                    1.000000                0.000000   \n",
       "25%                    3.000000                2.000000   \n",
       "50%                    4.000000                2.000000   \n",
       "75%                    5.000000                3.000000   \n",
       "max                    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   \n",
       "\n",
       "       Naive_Bayes_Classifier_Attrition_Flag_Card_Category_Contacts_Count_12_mon_Dependent_count_Education_Level_Months_Inactive_12_mon_1  \\\n",
       "count                                       10127.000000                                                                                    \n",
       "mean                                            0.159997                                                                                    \n",
       "std                                             0.365301                                                                                    \n",
       "min                                             0.000008                                                                                    \n",
       "25%                                             0.000099                                                                                    \n",
       "50%                                             0.000181                                                                                    \n",
       "75%                                             0.000337                                                                                    \n",
       "max                                             0.999580                                                                                    \n",
       "\n",
       "       Naive_Bayes_Classifier_Attrition_Flag_Card_Category_Contacts_Count_12_mon_Dependent_count_Education_Level_Months_Inactive_12_mon_2  \n",
       "count                                       10127.000000                                                                                   \n",
       "mean                                            0.840003                                                                                   \n",
       "std                                             0.365301                                                                                   \n",
       "min                                             0.000420                                                                                   \n",
       "25%                                             0.999660                                                                                   \n",
       "50%                                             0.999820                                                                                   \n",
       "75%                                             0.999900                                                                                   \n",
       "max                                             0.999990                                                                                   "
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_credit.describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x21ac97d0850>"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 576x396 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "df_credit.Attrition_Flag.value_counts().plot(kind='bar')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x21ac9f2a430>"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 576x396 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "df_credit.Income_Category.value_counts().plot(kind='bar')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [],
   "source": [
    "df_credit.insert(0,\"Age_groups\",\"\")\n",
    "df_credit[\"Age_groups\"] = pd.cut(df_credit['Customer_Age'], 10)\n",
    "df_credit.insert(0,\"Credit_Limit_Groups\",\"\")\n",
    "df_credit[\"Credit_Limit_Groups\"] = pd.cut(df_credit['Credit_Limit'], 10)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x21aca033fa0>"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 576x396 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "df_credit.Age_groups.value_counts().plot(kind='bar')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [],
   "source": [
    "df = pd.get_dummies(df_credit[['Attrition_Flag','Gender','Education_Level','Marital_Status','Income_Category','Card_Category',\n",
    "                              'Age_groups','Credit_Limit_Groups']],dummy_na=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "\n",
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       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
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       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Attrition_Flag_Attrited Customer</th>\n",
       "      <th>Attrition_Flag_Existing Customer</th>\n",
       "      <th>Attrition_Flag_nan</th>\n",
       "      <th>Gender_F</th>\n",
       "      <th>Gender_M</th>\n",
       "      <th>Gender_nan</th>\n",
       "      <th>Education_Level_College</th>\n",
       "      <th>Education_Level_Doctorate</th>\n",
       "      <th>Education_Level_Graduate</th>\n",
       "      <th>Education_Level_High School</th>\n",
       "      <th>...</th>\n",
       "      <th>Credit_Limit_Groups_(4746.07, 8053.84]</th>\n",
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       "      <th>10123</th>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
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       "    </tr>\n",
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       "    <tr>\n",
       "      <th>10125</th>\n",
       "      <td>1</td>\n",
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       "    <tr>\n",
       "      <th>10126</th>\n",
       "      <td>1</td>\n",
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       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>10127 rows × 53 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "       Attrition_Flag_Attrited Customer  Attrition_Flag_Existing Customer  \\\n",
       "0                                     0                                 1   \n",
       "1                                     0                                 1   \n",
       "2                                     0                                 1   \n",
       "3                                     0                                 1   \n",
       "4                                     0                                 1   \n",
       "...                                 ...                               ...   \n",
       "10122                                 0                                 1   \n",
       "10123                                 1                                 0   \n",
       "10124                                 1                                 0   \n",
       "10125                                 1                                 0   \n",
       "10126                                 1                                 0   \n",
       "\n",
       "       Attrition_Flag_nan  Gender_F  Gender_M  Gender_nan  \\\n",
       "0                       0         0         1           0   \n",
       "1                       0         1         0           0   \n",
       "2                       0         0         1           0   \n",
       "3                       0         1         0           0   \n",
       "4                       0         0         1           0   \n",
       "...                   ...       ...       ...         ...   \n",
       "10122                   0         0         1           0   \n",
       "10123                   0         0         1           0   \n",
       "10124                   0         1         0           0   \n",
       "10125                   0         0         1           0   \n",
       "10126                   0         1         0           0   \n",
       "\n",
       "       Education_Level_College  Education_Level_Doctorate  \\\n",
       "0                            0                          0   \n",
       "1                            0                          0   \n",
       "2                            0                          0   \n",
       "3                            0                          0   \n",
       "4                            0                          0   \n",
       "...                        ...                        ...   \n",
       "10122                        0                          0   \n",
       "10123                        0                          0   \n",
       "10124                        0                          0   \n",
       "10125                        0                          0   \n",
       "10126                        0                          0   \n",
       "\n",
       "       Education_Level_Graduate  Education_Level_High School  ...  \\\n",
       "0                             0                            1  ...   \n",
       "1                             1                            0  ...   \n",
       "2                             1                            0  ...   \n",
       "3                             0                            1  ...   \n",
       "4                             0                            0  ...   \n",
       "...                         ...                          ...  ...   \n",
       "10122                         1                            0  ...   \n",
       "10123                         0                            0  ...   \n",
       "10124                         0                            1  ...   \n",
       "10125                         1                            0  ...   \n",
       "10126                         1                            0  ...   \n",
       "\n",
       "       Credit_Limit_Groups_(4746.07, 8053.84]  \\\n",
       "0                                           0   \n",
       "1                                           0   \n",
       "2                                           0   \n",
       "3                                           0   \n",
       "4                                           0   \n",
       "...                                       ...   \n",
       "10122                                       0   \n",
       "10123                                       0   \n",
       "10124                                       1   \n",
       "10125                                       1   \n",
       "10126                                       0   \n",
       "\n",
       "       Credit_Limit_Groups_(8053.84, 11361.61]  \\\n",
       "0                                            0   \n",
       "1                                            1   \n",
       "2                                            0   \n",
       "3                                            0   \n",
       "4                                            0   \n",
       "...                                        ...   \n",
       "10122                                        0   \n",
       "10123                                        0   \n",
       "10124                                        0   \n",
       "10125                                        0   \n",
       "10126                                        1   \n",
       "\n",
       "       Credit_Limit_Groups_(11361.61, 14669.38]  \\\n",
       "0                                             1   \n",
       "1                                             0   \n",
       "2                                             0   \n",
       "3                                             0   \n",
       "4                                             0   \n",
       "...                                         ...   \n",
       "10122                                         0   \n",
       "10123                                         0   \n",
       "10124                                         0   \n",
       "10125                                         0   \n",
       "10126                                         0   \n",
       "\n",
       "       Credit_Limit_Groups_(14669.38, 17977.15]  \\\n",
       "0                                             0   \n",
       "1                                             0   \n",
       "2                                             0   \n",
       "3                                             0   \n",
       "4                                             0   \n",
       "...                                         ...   \n",
       "10122                                         0   \n",
       "10123                                         0   \n",
       "10124                                         0   \n",
       "10125                                         0   \n",
       "10126                                         0   \n",
       "\n",
       "       Credit_Limit_Groups_(17977.15, 21284.92]  \\\n",
       "0                                             0   \n",
       "1                                             0   \n",
       "2                                             0   \n",
       "3                                             0   \n",
       "4                                             0   \n",
       "...                                         ...   \n",
       "10122                                         0   \n",
       "10123                                         0   \n",
       "10124                                         0   \n",
       "10125                                         0   \n",
       "10126                                         0   \n",
       "\n",
       "       Credit_Limit_Groups_(21284.92, 24592.69]  \\\n",
       "0                                             0   \n",
       "1                                             0   \n",
       "2                                             0   \n",
       "3                                             0   \n",
       "4                                             0   \n",
       "...                                         ...   \n",
       "10122                                         0   \n",
       "10123                                         0   \n",
       "10124                                         0   \n",
       "10125                                         0   \n",
       "10126                                         0   \n",
       "\n",
       "       Credit_Limit_Groups_(24592.69, 27900.46]  \\\n",
       "0                                             0   \n",
       "1                                             0   \n",
       "2                                             0   \n",
       "3                                             0   \n",
       "4                                             0   \n",
       "...                                         ...   \n",
       "10122                                         0   \n",
       "10123                                         0   \n",
       "10124                                         0   \n",
       "10125                                         0   \n",
       "10126                                         0   \n",
       "\n",
       "       Credit_Limit_Groups_(27900.46, 31208.23]  \\\n",
       "0                                             0   \n",
       "1                                             0   \n",
       "2                                             0   \n",
       "3                                             0   \n",
       "4                                             0   \n",
       "...                                         ...   \n",
       "10122                                         0   \n",
       "10123                                         0   \n",
       "10124                                         0   \n",
       "10125                                         0   \n",
       "10126                                         0   \n",
       "\n",
       "       Credit_Limit_Groups_(31208.23, 34516.0]  Credit_Limit_Groups_nan  \n",
       "0                                            0                        0  \n",
       "1                                            0                        0  \n",
       "2                                            0                        0  \n",
       "3                                            0                        0  \n",
       "4                                            0                        0  \n",
       "...                                        ...                      ...  \n",
       "10122                                        0                        0  \n",
       "10123                                        0                        0  \n",
       "10124                                        0                        0  \n",
       "10125                                        0                        0  \n",
       "10126                                        0                        0  \n",
       "\n",
       "[10127 rows x 53 columns]"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [],
   "source": [
    "credit_dummy = df['Attrition_Flag_Attrited Customer']\n",
    "other_data = df.iloc[:,3:]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [],
   "source": [
    "X_train, X_test, y_train, y_test = train_test_split(other_data, credit_dummy, test_size = 0.2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [],
   "source": [
    "model = tree.DecisionTreeClassifier()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "DecisionTreeClassifier()"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "model.fit(X_train,y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.78035538005923"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pred = model.predict(X_test)\n",
    "score = accuracy_score(y_test,pred)\n",
    "score"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\ProgramData\\Anaconda3\\lib\\site-packages\\sklearn\\base.py:209: FutureWarning: From version 0.24, get_params will raise an AttributeError if a parameter cannot be retrieved as an instance attribute. Previously it would return None.\n",
      "  warnings.warn('From version 0.24, get_params will raise an '\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 576x396 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": [
       "<Figure size 576x396 with 0 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "#Load the classification dataset\n",
    "X = other_data\n",
    "y = credit_dummy\n",
    "\n",
    "#Create the train and test data\n",
    "X_train, X_test, y_train, y_test = tts(X, y, test_size=0.2)\n",
    "\n",
    "# Instantiate the visualizer with the classification model\n",
    "confusion_matrix(\n",
    "    tree.DecisionTreeClassifier(),\n",
    "    X_train, y_train, X_test, y_test,\n",
    "    classes=['Active', 'Attrited']\n",
    ")\n",
    "plt.tight_layout()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\ProgramData\\Anaconda3\\lib\\site-packages\\sklearn\\base.py:209: FutureWarning: From version 0.24, get_params will raise an AttributeError if a parameter cannot be retrieved as an instance attribute. Previously it would return None.\n",
      "  warnings.warn('From version 0.24, get_params will raise an '\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 576x396 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x21acb0d50d0>"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "target = ['Active', 'Attrited']\n",
    "visualizer = ClassificationReport(model, classes=target, support=True)\n",
    "visualizer.fit(X_train, y_train)      \n",
    "visualizer.score(X_test, y_test)  \n",
    "visualizer.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [],
   "source": [
    "model = MultinomialNB()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "MultinomialNB()"
      ]
     },
     "execution_count": 28,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "model.fit(X_train,y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.8484698914116485"
      ]
     },
     "execution_count": 29,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "model.score(X_test, y_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\ProgramData\\Anaconda3\\lib\\site-packages\\sklearn\\base.py:209: FutureWarning: From version 0.24, get_params will raise an AttributeError if a parameter cannot be retrieved as an instance attribute. Previously it would return None.\n",
      "  warnings.warn('From version 0.24, get_params will raise an '\n",
      "C:\\ProgramData\\Anaconda3\\lib\\site-packages\\sklearn\\metrics\\_classification.py:1221: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
      "  _warn_prf(average, modifier, msg_start, len(result))\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 576x396 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x21acb2dbeb0>"
      ]
     },
     "execution_count": 30,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "target = ['Active', 'Attrited']\n",
    "visualizer = ClassificationReport(model, classes=target, support=True)\n",
    "visualizer.fit(X_train, y_train)      \n",
    "visualizer.score(X_test, y_test)  \n",
    "visualizer.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\ProgramData\\Anaconda3\\lib\\site-packages\\sklearn\\base.py:209: FutureWarning: From version 0.24, get_params will raise an AttributeError if a parameter cannot be retrieved as an instance attribute. Previously it would return None.\n",
      "  warnings.warn('From version 0.24, get_params will raise an '\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 576x396 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x21acb162970>"
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from yellowbrick.model_selection import FeatureImportances\n",
    "viz = FeatureImportances(model)\n",
    "viz.fit(X_train, y_train)\n",
    "viz.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.8.3"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 4
}