Python/ml/Churn_Risk_Score_Prediction.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "XjITw8ZGTEHV"
   },
   "source": [
    "# Churn Risk Score Prediction"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Link to the Dataset: [Churn Risk Rate](https://www.kaggle.com/imsparsh/churn-risk-rate-hackerearth-ml?select=train.csv)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Importing Libraries"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "from sklearn import preprocessing  \n",
    "from sklearn import metrics\n",
    "import seaborn as sns\n",
    "from sklearn.model_selection import train_test_split\n",
    "from sklearn.tree import DecisionTreeClassifier\n",
    "from sklearn import tree\n",
    "from matplotlib.backends.backend_tkagg import FigureCanvasTkAgg\n",
    "import matplotlib.pyplot as plt"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Getting Data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 640
    },
    "id": "NTpYjZZiUYrM",
    "outputId": "03362eba-a49c-4f28-9d8e-38fb85760745"
   },
   "outputs": [
    {
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>customer_id</th>\n",
       "      <th>Name</th>\n",
       "      <th>age</th>\n",
       "      <th>gender</th>\n",
       "      <th>security_no</th>\n",
       "      <th>region_category</th>\n",
       "      <th>membership_category</th>\n",
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       "      <th>joined_through_referral</th>\n",
       "      <th>referral_id</th>\n",
       "      <th>...</th>\n",
       "      <th>avg_time_spent</th>\n",
       "      <th>avg_transaction_value</th>\n",
       "      <th>avg_frequency_login_days</th>\n",
       "      <th>points_in_wallet</th>\n",
       "      <th>used_special_discount</th>\n",
       "      <th>offer_application_preference</th>\n",
       "      <th>past_complaint</th>\n",
       "      <th>complaint_status</th>\n",
       "      <th>feedback</th>\n",
       "      <th>churn_risk_score</th>\n",
       "    </tr>\n",
       "  </thead>\n",
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       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>fffe4300490044003600300030003800</td>\n",
       "      <td>Pattie Morrisey</td>\n",
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       "      <td>Village</td>\n",
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       "      <td>No</td>\n",
       "      <td>xxxxxxxx</td>\n",
       "      <td>...</td>\n",
       "      <td>300.630000</td>\n",
       "      <td>53005.25</td>\n",
       "      <td>17.0</td>\n",
       "      <td>781.750000</td>\n",
       "      <td>Yes</td>\n",
       "      <td>Yes</td>\n",
       "      <td>No</td>\n",
       "      <td>Not Applicable</td>\n",
       "      <td>Products always in Stock</td>\n",
       "      <td>2</td>\n",
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       "    <tr>\n",
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       "      <td>Traci Peery</td>\n",
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       "      <td>5K0N3X1</td>\n",
       "      <td>City</td>\n",
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       "      <td>2017-08-28</td>\n",
       "      <td>?</td>\n",
       "      <td>CID21329</td>\n",
       "      <td>...</td>\n",
       "      <td>306.340000</td>\n",
       "      <td>12838.38</td>\n",
       "      <td>10.0</td>\n",
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       "      <td>Solved</td>\n",
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       "      <td>Merideth Mcmeen</td>\n",
       "      <td>44</td>\n",
       "      <td>F</td>\n",
       "      <td>1F2TCL3</td>\n",
       "      <td>Town</td>\n",
       "      <td>No Membership</td>\n",
       "      <td>2016-11-11</td>\n",
       "      <td>Yes</td>\n",
       "      <td>CID12313</td>\n",
       "      <td>...</td>\n",
       "      <td>516.160000</td>\n",
       "      <td>21027.00</td>\n",
       "      <td>22.0</td>\n",
       "      <td>500.690000</td>\n",
       "      <td>No</td>\n",
       "      <td>Yes</td>\n",
       "      <td>Yes</td>\n",
       "      <td>Solved in Follow-up</td>\n",
       "      <td>Poor Website</td>\n",
       "      <td>5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>fffe43004900440036003000330031003600</td>\n",
       "      <td>Eufemia Cardwell</td>\n",
       "      <td>37</td>\n",
       "      <td>M</td>\n",
       "      <td>VJGJ33N</td>\n",
       "      <td>City</td>\n",
       "      <td>No Membership</td>\n",
       "      <td>2016-10-29</td>\n",
       "      <td>Yes</td>\n",
       "      <td>CID3793</td>\n",
       "      <td>...</td>\n",
       "      <td>53.270000</td>\n",
       "      <td>25239.56</td>\n",
       "      <td>6.0</td>\n",
       "      <td>567.660000</td>\n",
       "      <td>No</td>\n",
       "      <td>Yes</td>\n",
       "      <td>Yes</td>\n",
       "      <td>Unsolved</td>\n",
       "      <td>Poor Website</td>\n",
       "      <td>5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>fffe43004900440031003900350030003600</td>\n",
       "      <td>Meghan Kosak</td>\n",
       "      <td>31</td>\n",
       "      <td>F</td>\n",
       "      <td>SVZXCWB</td>\n",
       "      <td>City</td>\n",
       "      <td>No Membership</td>\n",
       "      <td>2017-09-12</td>\n",
       "      <td>No</td>\n",
       "      <td>xxxxxxxx</td>\n",
       "      <td>...</td>\n",
       "      <td>113.130000</td>\n",
       "      <td>24483.66</td>\n",
       "      <td>16.0</td>\n",
       "      <td>663.060000</td>\n",
       "      <td>No</td>\n",
       "      <td>Yes</td>\n",
       "      <td>Yes</td>\n",
       "      <td>Solved</td>\n",
       "      <td>Poor Website</td>\n",
       "      <td>5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
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       "    <tr>\n",
       "      <th>36987</th>\n",
       "      <td>fffe43004900440035003500390036003100</td>\n",
       "      <td>Cuc Tarr</td>\n",
       "      <td>46</td>\n",
       "      <td>F</td>\n",
       "      <td>6F51HFO</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Basic Membership</td>\n",
       "      <td>2017-09-21</td>\n",
       "      <td>No</td>\n",
       "      <td>xxxxxxxx</td>\n",
       "      <td>...</td>\n",
       "      <td>-650.682759</td>\n",
       "      <td>27277.68</td>\n",
       "      <td>6.0</td>\n",
       "      <td>639.510000</td>\n",
       "      <td>No</td>\n",
       "      <td>Yes</td>\n",
       "      <td>Yes</td>\n",
       "      <td>No Information Available</td>\n",
       "      <td>No reason specified</td>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>36988</th>\n",
       "      <td>fffe43004900440033003500380036003600</td>\n",
       "      <td>Jenni Stronach</td>\n",
       "      <td>29</td>\n",
       "      <td>F</td>\n",
       "      <td>21KSM8Y</td>\n",
       "      <td>Town</td>\n",
       "      <td>Basic Membership</td>\n",
       "      <td>2016-06-27</td>\n",
       "      <td>No</td>\n",
       "      <td>xxxxxxxx</td>\n",
       "      <td>...</td>\n",
       "      <td>-638.123421</td>\n",
       "      <td>11069.71</td>\n",
       "      <td>28.0</td>\n",
       "      <td>527.990000</td>\n",
       "      <td>Yes</td>\n",
       "      <td>No</td>\n",
       "      <td>No</td>\n",
       "      <td>Not Applicable</td>\n",
       "      <td>Poor Customer Service</td>\n",
       "      <td>5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>36989</th>\n",
       "      <td>fffe4300490044003500330034003100</td>\n",
       "      <td>Luciana Kinch</td>\n",
       "      <td>23</td>\n",
       "      <td>F</td>\n",
       "      <td>XK1IM9H</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Basic Membership</td>\n",
       "      <td>2016-09-11</td>\n",
       "      <td>Yes</td>\n",
       "      <td>CID3838</td>\n",
       "      <td>...</td>\n",
       "      <td>154.940000</td>\n",
       "      <td>38127.56</td>\n",
       "      <td>Error</td>\n",
       "      <td>680.470000</td>\n",
       "      <td>No</td>\n",
       "      <td>Yes</td>\n",
       "      <td>Yes</td>\n",
       "      <td>Unsolved</td>\n",
       "      <td>Poor Website</td>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>36990</th>\n",
       "      <td>fffe43004900440031003200390039003000</td>\n",
       "      <td>Tawana Ardoin</td>\n",
       "      <td>53</td>\n",
       "      <td>M</td>\n",
       "      <td>K6VTP1Z</td>\n",
       "      <td>Village</td>\n",
       "      <td>Platinum Membership</td>\n",
       "      <td>2017-06-15</td>\n",
       "      <td>No</td>\n",
       "      <td>xxxxxxxx</td>\n",
       "      <td>...</td>\n",
       "      <td>482.610000</td>\n",
       "      <td>2378.86</td>\n",
       "      <td>20.0</td>\n",
       "      <td>197.264414</td>\n",
       "      <td>Yes</td>\n",
       "      <td>Yes</td>\n",
       "      <td>No</td>\n",
       "      <td>Not Applicable</td>\n",
       "      <td>No reason specified</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>36991</th>\n",
       "      <td>fffe43004900440033003600340034003200</td>\n",
       "      <td>Verlene Beaulieu</td>\n",
       "      <td>35</td>\n",
       "      <td>M</td>\n",
       "      <td>LBX0GLR</td>\n",
       "      <td>Town</td>\n",
       "      <td>Silver Membership</td>\n",
       "      <td>2015-10-23</td>\n",
       "      <td>No</td>\n",
       "      <td>xxxxxxxx</td>\n",
       "      <td>...</td>\n",
       "      <td>79.180000</td>\n",
       "      <td>2189.68</td>\n",
       "      <td>Error</td>\n",
       "      <td>719.970000</td>\n",
       "      <td>Yes</td>\n",
       "      <td>No</td>\n",
       "      <td>No</td>\n",
       "      <td>Not Applicable</td>\n",
       "      <td>Quality Customer Care</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>36992 rows × 25 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "                                customer_id              Name  age gender  \\\n",
       "0          fffe4300490044003600300030003800   Pattie Morrisey   18      F   \n",
       "1      fffe43004900440032003100300035003700       Traci Peery   32      F   \n",
       "2          fffe4300490044003100390032003600   Merideth Mcmeen   44      F   \n",
       "3      fffe43004900440036003000330031003600  Eufemia Cardwell   37      M   \n",
       "4      fffe43004900440031003900350030003600      Meghan Kosak   31      F   \n",
       "...                                     ...               ...  ...    ...   \n",
       "36987  fffe43004900440035003500390036003100          Cuc Tarr   46      F   \n",
       "36988  fffe43004900440033003500380036003600    Jenni Stronach   29      F   \n",
       "36989      fffe4300490044003500330034003100     Luciana Kinch   23      F   \n",
       "36990  fffe43004900440031003200390039003000     Tawana Ardoin   53      M   \n",
       "36991  fffe43004900440033003600340034003200  Verlene Beaulieu   35      M   \n",
       "\n",
       "      security_no region_category  membership_category joining_date  \\\n",
       "0         XW0DQ7H         Village  Platinum Membership   2017-08-17   \n",
       "1         5K0N3X1            City   Premium Membership   2017-08-28   \n",
       "2         1F2TCL3            Town        No Membership   2016-11-11   \n",
       "3         VJGJ33N            City        No Membership   2016-10-29   \n",
       "4         SVZXCWB            City        No Membership   2017-09-12   \n",
       "...           ...             ...                  ...          ...   \n",
       "36987     6F51HFO             NaN     Basic Membership   2017-09-21   \n",
       "36988     21KSM8Y            Town     Basic Membership   2016-06-27   \n",
       "36989     XK1IM9H             NaN     Basic Membership   2016-09-11   \n",
       "36990     K6VTP1Z         Village  Platinum Membership   2017-06-15   \n",
       "36991     LBX0GLR            Town    Silver Membership   2015-10-23   \n",
       "\n",
       "      joined_through_referral referral_id  ... avg_time_spent  \\\n",
       "0                          No    xxxxxxxx  ...     300.630000   \n",
       "1                           ?    CID21329  ...     306.340000   \n",
       "2                         Yes    CID12313  ...     516.160000   \n",
       "3                         Yes     CID3793  ...      53.270000   \n",
       "4                          No    xxxxxxxx  ...     113.130000   \n",
       "...                       ...         ...  ...            ...   \n",
       "36987                      No    xxxxxxxx  ...    -650.682759   \n",
       "36988                      No    xxxxxxxx  ...    -638.123421   \n",
       "36989                     Yes     CID3838  ...     154.940000   \n",
       "36990                      No    xxxxxxxx  ...     482.610000   \n",
       "36991                      No    xxxxxxxx  ...      79.180000   \n",
       "\n",
       "      avg_transaction_value avg_frequency_login_days points_in_wallet  \\\n",
       "0                  53005.25                     17.0       781.750000   \n",
       "1                  12838.38                     10.0              NaN   \n",
       "2                  21027.00                     22.0       500.690000   \n",
       "3                  25239.56                      6.0       567.660000   \n",
       "4                  24483.66                     16.0       663.060000   \n",
       "...                     ...                      ...              ...   \n",
       "36987              27277.68                      6.0       639.510000   \n",
       "36988              11069.71                     28.0       527.990000   \n",
       "36989              38127.56                    Error       680.470000   \n",
       "36990               2378.86                     20.0       197.264414   \n",
       "36991               2189.68                    Error       719.970000   \n",
       "\n",
       "       used_special_discount  offer_application_preference  past_complaint  \\\n",
       "0                        Yes                           Yes              No   \n",
       "1                        Yes                            No             Yes   \n",
       "2                         No                           Yes             Yes   \n",
       "3                         No                           Yes             Yes   \n",
       "4                         No                           Yes             Yes   \n",
       "...                      ...                           ...             ...   \n",
       "36987                     No                           Yes             Yes   \n",
       "36988                    Yes                            No              No   \n",
       "36989                     No                           Yes             Yes   \n",
       "36990                    Yes                           Yes              No   \n",
       "36991                    Yes                            No              No   \n",
       "\n",
       "               complaint_status                  feedback churn_risk_score  \n",
       "0                Not Applicable  Products always in Stock                2  \n",
       "1                        Solved     Quality Customer Care                1  \n",
       "2           Solved in Follow-up              Poor Website                5  \n",
       "3                      Unsolved              Poor Website                5  \n",
       "4                        Solved              Poor Website                5  \n",
       "...                         ...                       ...              ...  \n",
       "36987  No Information Available       No reason specified                4  \n",
       "36988            Not Applicable     Poor Customer Service                5  \n",
       "36989                  Unsolved              Poor Website                4  \n",
       "36990            Not Applicable       No reason specified                3  \n",
       "36991            Not Applicable     Quality Customer Care                2  \n",
       "\n",
       "[36992 rows x 25 columns]"
      ]
     },
     "execution_count": 2,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df = pd.read_csv('train.csv')\n",
    "df"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Data Preprocessing"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "98Bmyyr8fSiG",
    "outputId": "a0d5c9cd-4dab-4a1d-979c-7271c8db4180"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 36992 entries, 0 to 36991\n",
      "Data columns (total 25 columns):\n",
      " #   Column                        Non-Null Count  Dtype  \n",
      "---  ------                        --------------  -----  \n",
      " 0   customer_id                   36992 non-null  object \n",
      " 1   Name                          36992 non-null  object \n",
      " 2   age                           36992 non-null  int64  \n",
      " 3   gender                        36992 non-null  object \n",
      " 4   security_no                   36992 non-null  object \n",
      " 5   region_category               31564 non-null  object \n",
      " 6   membership_category           36992 non-null  object \n",
      " 7   joining_date                  36992 non-null  object \n",
      " 8   joined_through_referral       36992 non-null  object \n",
      " 9   referral_id                   36992 non-null  object \n",
      " 10  preferred_offer_types         36704 non-null  object \n",
      " 11  medium_of_operation           36992 non-null  object \n",
      " 12  internet_option               36992 non-null  object \n",
      " 13  last_visit_time               36992 non-null  object \n",
      " 14  days_since_last_login         36992 non-null  int64  \n",
      " 15  avg_time_spent                36992 non-null  float64\n",
      " 16  avg_transaction_value         36992 non-null  float64\n",
      " 17  avg_frequency_login_days      36992 non-null  object \n",
      " 18  points_in_wallet              33549 non-null  float64\n",
      " 19  used_special_discount         36992 non-null  object \n",
      " 20  offer_application_preference  36992 non-null  object \n",
      " 21  past_complaint                36992 non-null  object \n",
      " 22  complaint_status              36992 non-null  object \n",
      " 23  feedback                      36992 non-null  object \n",
      " 24  churn_risk_score              36992 non-null  int64  \n",
      "dtypes: float64(3), int64(3), object(19)\n",
      "memory usage: 7.1+ MB\n"
     ]
    }
   ],
   "source": [
    "df.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 623
    },
    "id": "omNBdYkwWmKr",
    "outputId": "887816b6-f916-44f8-9b04-eb4991b927d5"
   },
   "outputs": [
    {
     "data": {
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       "    .dataframe tbody tr th:only-of-type {\n",
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       "\n",
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       "\n",
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       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>customer_id</th>\n",
       "      <th>age</th>\n",
       "      <th>gender</th>\n",
       "      <th>security_no</th>\n",
       "      <th>region_category</th>\n",
       "      <th>membership_category</th>\n",
       "      <th>joining_date</th>\n",
       "      <th>joined_through_referral</th>\n",
       "      <th>preferred_offer_types</th>\n",
       "      <th>medium_of_operation</th>\n",
       "      <th>...</th>\n",
       "      <th>avg_time_spent</th>\n",
       "      <th>avg_transaction_value</th>\n",
       "      <th>avg_frequency_login_days</th>\n",
       "      <th>points_in_wallet</th>\n",
       "      <th>used_special_discount</th>\n",
       "      <th>offer_application_preference</th>\n",
       "      <th>past_complaint</th>\n",
       "      <th>complaint_status</th>\n",
       "      <th>feedback</th>\n",
       "      <th>churn_risk_score</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>fffe4300490044003600300030003800</td>\n",
       "      <td>18</td>\n",
       "      <td>F</td>\n",
       "      <td>XW0DQ7H</td>\n",
       "      <td>Village</td>\n",
       "      <td>Platinum Membership</td>\n",
       "      <td>2017-08-17</td>\n",
       "      <td>No</td>\n",
       "      <td>Gift Vouchers/Coupons</td>\n",
       "      <td>?</td>\n",
       "      <td>...</td>\n",
       "      <td>300.630000</td>\n",
       "      <td>53005.25</td>\n",
       "      <td>17.0</td>\n",
       "      <td>781.750000</td>\n",
       "      <td>Yes</td>\n",
       "      <td>Yes</td>\n",
       "      <td>No</td>\n",
       "      <td>Not Applicable</td>\n",
       "      <td>Products always in Stock</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>fffe43004900440032003100300035003700</td>\n",
       "      <td>32</td>\n",
       "      <td>F</td>\n",
       "      <td>5K0N3X1</td>\n",
       "      <td>City</td>\n",
       "      <td>Premium Membership</td>\n",
       "      <td>2017-08-28</td>\n",
       "      <td>?</td>\n",
       "      <td>Gift Vouchers/Coupons</td>\n",
       "      <td>Desktop</td>\n",
       "      <td>...</td>\n",
       "      <td>306.340000</td>\n",
       "      <td>12838.38</td>\n",
       "      <td>10.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Yes</td>\n",
       "      <td>No</td>\n",
       "      <td>Yes</td>\n",
       "      <td>Solved</td>\n",
       "      <td>Quality Customer Care</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>fffe4300490044003100390032003600</td>\n",
       "      <td>44</td>\n",
       "      <td>F</td>\n",
       "      <td>1F2TCL3</td>\n",
       "      <td>Town</td>\n",
       "      <td>No Membership</td>\n",
       "      <td>2016-11-11</td>\n",
       "      <td>Yes</td>\n",
       "      <td>Gift Vouchers/Coupons</td>\n",
       "      <td>Desktop</td>\n",
       "      <td>...</td>\n",
       "      <td>516.160000</td>\n",
       "      <td>21027.00</td>\n",
       "      <td>22.0</td>\n",
       "      <td>500.690000</td>\n",
       "      <td>No</td>\n",
       "      <td>Yes</td>\n",
       "      <td>Yes</td>\n",
       "      <td>Solved in Follow-up</td>\n",
       "      <td>Poor Website</td>\n",
       "      <td>5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>fffe43004900440036003000330031003600</td>\n",
       "      <td>37</td>\n",
       "      <td>M</td>\n",
       "      <td>VJGJ33N</td>\n",
       "      <td>City</td>\n",
       "      <td>No Membership</td>\n",
       "      <td>2016-10-29</td>\n",
       "      <td>Yes</td>\n",
       "      <td>Gift Vouchers/Coupons</td>\n",
       "      <td>Desktop</td>\n",
       "      <td>...</td>\n",
       "      <td>53.270000</td>\n",
       "      <td>25239.56</td>\n",
       "      <td>6.0</td>\n",
       "      <td>567.660000</td>\n",
       "      <td>No</td>\n",
       "      <td>Yes</td>\n",
       "      <td>Yes</td>\n",
       "      <td>Unsolved</td>\n",
       "      <td>Poor Website</td>\n",
       "      <td>5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>fffe43004900440031003900350030003600</td>\n",
       "      <td>31</td>\n",
       "      <td>F</td>\n",
       "      <td>SVZXCWB</td>\n",
       "      <td>City</td>\n",
       "      <td>No Membership</td>\n",
       "      <td>2017-09-12</td>\n",
       "      <td>No</td>\n",
       "      <td>Credit/Debit Card Offers</td>\n",
       "      <td>Smartphone</td>\n",
       "      <td>...</td>\n",
       "      <td>113.130000</td>\n",
       "      <td>24483.66</td>\n",
       "      <td>16.0</td>\n",
       "      <td>663.060000</td>\n",
       "      <td>No</td>\n",
       "      <td>Yes</td>\n",
       "      <td>Yes</td>\n",
       "      <td>Solved</td>\n",
       "      <td>Poor Website</td>\n",
       "      <td>5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>36987</th>\n",
       "      <td>fffe43004900440035003500390036003100</td>\n",
       "      <td>46</td>\n",
       "      <td>F</td>\n",
       "      <td>6F51HFO</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Basic Membership</td>\n",
       "      <td>2017-09-21</td>\n",
       "      <td>No</td>\n",
       "      <td>Credit/Debit Card Offers</td>\n",
       "      <td>Desktop</td>\n",
       "      <td>...</td>\n",
       "      <td>-650.682759</td>\n",
       "      <td>27277.68</td>\n",
       "      <td>6.0</td>\n",
       "      <td>639.510000</td>\n",
       "      <td>No</td>\n",
       "      <td>Yes</td>\n",
       "      <td>Yes</td>\n",
       "      <td>No Information Available</td>\n",
       "      <td>No reason specified</td>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>36988</th>\n",
       "      <td>fffe43004900440033003500380036003600</td>\n",
       "      <td>29</td>\n",
       "      <td>F</td>\n",
       "      <td>21KSM8Y</td>\n",
       "      <td>Town</td>\n",
       "      <td>Basic Membership</td>\n",
       "      <td>2016-06-27</td>\n",
       "      <td>No</td>\n",
       "      <td>Without Offers</td>\n",
       "      <td>Smartphone</td>\n",
       "      <td>...</td>\n",
       "      <td>-638.123421</td>\n",
       "      <td>11069.71</td>\n",
       "      <td>28.0</td>\n",
       "      <td>527.990000</td>\n",
       "      <td>Yes</td>\n",
       "      <td>No</td>\n",
       "      <td>No</td>\n",
       "      <td>Not Applicable</td>\n",
       "      <td>Poor Customer Service</td>\n",
       "      <td>5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>36989</th>\n",
       "      <td>fffe4300490044003500330034003100</td>\n",
       "      <td>23</td>\n",
       "      <td>F</td>\n",
       "      <td>XK1IM9H</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Basic Membership</td>\n",
       "      <td>2016-09-11</td>\n",
       "      <td>Yes</td>\n",
       "      <td>Gift Vouchers/Coupons</td>\n",
       "      <td>Desktop</td>\n",
       "      <td>...</td>\n",
       "      <td>154.940000</td>\n",
       "      <td>38127.56</td>\n",
       "      <td>Error</td>\n",
       "      <td>680.470000</td>\n",
       "      <td>No</td>\n",
       "      <td>Yes</td>\n",
       "      <td>Yes</td>\n",
       "      <td>Unsolved</td>\n",
       "      <td>Poor Website</td>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>36990</th>\n",
       "      <td>fffe43004900440031003200390039003000</td>\n",
       "      <td>53</td>\n",
       "      <td>M</td>\n",
       "      <td>K6VTP1Z</td>\n",
       "      <td>Village</td>\n",
       "      <td>Platinum Membership</td>\n",
       "      <td>2017-06-15</td>\n",
       "      <td>No</td>\n",
       "      <td>Gift Vouchers/Coupons</td>\n",
       "      <td>Smartphone</td>\n",
       "      <td>...</td>\n",
       "      <td>482.610000</td>\n",
       "      <td>2378.86</td>\n",
       "      <td>20.0</td>\n",
       "      <td>197.264414</td>\n",
       "      <td>Yes</td>\n",
       "      <td>Yes</td>\n",
       "      <td>No</td>\n",
       "      <td>Not Applicable</td>\n",
       "      <td>No reason specified</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>36991</th>\n",
       "      <td>fffe43004900440033003600340034003200</td>\n",
       "      <td>35</td>\n",
       "      <td>M</td>\n",
       "      <td>LBX0GLR</td>\n",
       "      <td>Town</td>\n",
       "      <td>Silver Membership</td>\n",
       "      <td>2015-10-23</td>\n",
       "      <td>No</td>\n",
       "      <td>Gift Vouchers/Coupons</td>\n",
       "      <td>Desktop</td>\n",
       "      <td>...</td>\n",
       "      <td>79.180000</td>\n",
       "      <td>2189.68</td>\n",
       "      <td>Error</td>\n",
       "      <td>719.970000</td>\n",
       "      <td>Yes</td>\n",
       "      <td>No</td>\n",
       "      <td>No</td>\n",
       "      <td>Not Applicable</td>\n",
       "      <td>Quality Customer Care</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>36992 rows × 23 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "                                customer_id  age gender security_no  \\\n",
       "0          fffe4300490044003600300030003800   18      F     XW0DQ7H   \n",
       "1      fffe43004900440032003100300035003700   32      F     5K0N3X1   \n",
       "2          fffe4300490044003100390032003600   44      F     1F2TCL3   \n",
       "3      fffe43004900440036003000330031003600   37      M     VJGJ33N   \n",
       "4      fffe43004900440031003900350030003600   31      F     SVZXCWB   \n",
       "...                                     ...  ...    ...         ...   \n",
       "36987  fffe43004900440035003500390036003100   46      F     6F51HFO   \n",
       "36988  fffe43004900440033003500380036003600   29      F     21KSM8Y   \n",
       "36989      fffe4300490044003500330034003100   23      F     XK1IM9H   \n",
       "36990  fffe43004900440031003200390039003000   53      M     K6VTP1Z   \n",
       "36991  fffe43004900440033003600340034003200   35      M     LBX0GLR   \n",
       "\n",
       "      region_category  membership_category joining_date  \\\n",
       "0             Village  Platinum Membership   2017-08-17   \n",
       "1                City   Premium Membership   2017-08-28   \n",
       "2                Town        No Membership   2016-11-11   \n",
       "3                City        No Membership   2016-10-29   \n",
       "4                City        No Membership   2017-09-12   \n",
       "...               ...                  ...          ...   \n",
       "36987             NaN     Basic Membership   2017-09-21   \n",
       "36988            Town     Basic Membership   2016-06-27   \n",
       "36989             NaN     Basic Membership   2016-09-11   \n",
       "36990         Village  Platinum Membership   2017-06-15   \n",
       "36991            Town    Silver Membership   2015-10-23   \n",
       "\n",
       "      joined_through_referral     preferred_offer_types medium_of_operation  \\\n",
       "0                          No     Gift Vouchers/Coupons                   ?   \n",
       "1                           ?     Gift Vouchers/Coupons             Desktop   \n",
       "2                         Yes     Gift Vouchers/Coupons             Desktop   \n",
       "3                         Yes     Gift Vouchers/Coupons             Desktop   \n",
       "4                          No  Credit/Debit Card Offers          Smartphone   \n",
       "...                       ...                       ...                 ...   \n",
       "36987                      No  Credit/Debit Card Offers             Desktop   \n",
       "36988                      No            Without Offers          Smartphone   \n",
       "36989                     Yes     Gift Vouchers/Coupons             Desktop   \n",
       "36990                      No     Gift Vouchers/Coupons          Smartphone   \n",
       "36991                      No     Gift Vouchers/Coupons             Desktop   \n",
       "\n",
       "       ... avg_time_spent avg_transaction_value  avg_frequency_login_days  \\\n",
       "0      ...     300.630000              53005.25                      17.0   \n",
       "1      ...     306.340000              12838.38                      10.0   \n",
       "2      ...     516.160000              21027.00                      22.0   \n",
       "3      ...      53.270000              25239.56                       6.0   \n",
       "4      ...     113.130000              24483.66                      16.0   \n",
       "...    ...            ...                   ...                       ...   \n",
       "36987  ...    -650.682759              27277.68                       6.0   \n",
       "36988  ...    -638.123421              11069.71                      28.0   \n",
       "36989  ...     154.940000              38127.56                     Error   \n",
       "36990  ...     482.610000               2378.86                      20.0   \n",
       "36991  ...      79.180000               2189.68                     Error   \n",
       "\n",
       "       points_in_wallet  used_special_discount offer_application_preference  \\\n",
       "0            781.750000                    Yes                          Yes   \n",
       "1                   NaN                    Yes                           No   \n",
       "2            500.690000                     No                          Yes   \n",
       "3            567.660000                     No                          Yes   \n",
       "4            663.060000                     No                          Yes   \n",
       "...                 ...                    ...                          ...   \n",
       "36987        639.510000                     No                          Yes   \n",
       "36988        527.990000                    Yes                           No   \n",
       "36989        680.470000                     No                          Yes   \n",
       "36990        197.264414                    Yes                          Yes   \n",
       "36991        719.970000                    Yes                           No   \n",
       "\n",
       "       past_complaint          complaint_status                  feedback  \\\n",
       "0                  No            Not Applicable  Products always in Stock   \n",
       "1                 Yes                    Solved     Quality Customer Care   \n",
       "2                 Yes       Solved in Follow-up              Poor Website   \n",
       "3                 Yes                  Unsolved              Poor Website   \n",
       "4                 Yes                    Solved              Poor Website   \n",
       "...               ...                       ...                       ...   \n",
       "36987             Yes  No Information Available       No reason specified   \n",
       "36988              No            Not Applicable     Poor Customer Service   \n",
       "36989             Yes                  Unsolved              Poor Website   \n",
       "36990              No            Not Applicable       No reason specified   \n",
       "36991              No            Not Applicable     Quality Customer Care   \n",
       "\n",
       "      churn_risk_score  \n",
       "0                    2  \n",
       "1                    1  \n",
       "2                    5  \n",
       "3                    5  \n",
       "4                    5  \n",
       "...                ...  \n",
       "36987                4  \n",
       "36988                5  \n",
       "36989                4  \n",
       "36990                3  \n",
       "36991                2  \n",
       "\n",
       "[36992 rows x 23 columns]"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df = df.drop(['Name', 'referral_id'], axis=1)  # dropping unnecessary columns \n",
    "df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {
    "id": "BRpXFNLef16c"
   },
   "outputs": [],
   "source": [
    "label_encoder = preprocessing.LabelEncoder()  # encoding data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 422
    },
    "id": "ILwQflBOhind",
    "outputId": "4b4a2a9c-daa5-4a5a-d3c1-8c0069d344da"
   },
   "outputs": [
    {
     "data": {
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       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>5910</td>\n",
       "      <td>34</td>\n",
       "      <td>0</td>\n",
       "      <td>1534</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>680</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>...</td>\n",
       "      <td>19239</td>\n",
       "      <td>8374</td>\n",
       "      <td>779</td>\n",
       "      <td>4792</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "      <td>3</td>\n",
       "      <td>5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>32400</td>\n",
       "      <td>27</td>\n",
       "      <td>1</td>\n",
       "      <td>32501</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>667</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>...</td>\n",
       "      <td>19546</td>\n",
       "      <td>11560</td>\n",
       "      <td>1637</td>\n",
       "      <td>7111</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>4</td>\n",
       "      <td>3</td>\n",
       "      <td>5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>6070</td>\n",
       "      <td>21</td>\n",
       "      <td>0</td>\n",
       "      <td>29819</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>985</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>...</td>\n",
       "      <td>3123</td>\n",
       "      <td>10936</td>\n",
       "      <td>743</td>\n",
       "      <td>12218</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>3</td>\n",
       "      <td>5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>36987</th>\n",
       "      <td>29660</td>\n",
       "      <td>36</td>\n",
       "      <td>0</td>\n",
       "      <td>6692</td>\n",
       "      <td>3</td>\n",
       "      <td>0</td>\n",
       "      <td>994</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>...</td>\n",
       "      <td>732</td>\n",
       "      <td>13097</td>\n",
       "      <td>1637</td>\n",
       "      <td>10708</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>36988</th>\n",
       "      <td>16761</td>\n",
       "      <td>19</td>\n",
       "      <td>0</td>\n",
       "      <td>2204</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>543</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>3</td>\n",
       "      <td>...</td>\n",
       "      <td>697</td>\n",
       "      <td>814</td>\n",
       "      <td>809</td>\n",
       "      <td>5785</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>36989</th>\n",
       "      <td>27942</td>\n",
       "      <td>13</td>\n",
       "      <td>0</td>\n",
       "      <td>34476</td>\n",
       "      <td>3</td>\n",
       "      <td>0</td>\n",
       "      <td>619</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>...</td>\n",
       "      <td>6497</td>\n",
       "      <td>21410</td>\n",
       "      <td>1653</td>\n",
       "      <td>13391</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>4</td>\n",
       "      <td>3</td>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>36990</th>\n",
       "      <td>1923</td>\n",
       "      <td>43</td>\n",
       "      <td>1</td>\n",
       "      <td>21020</td>\n",
       "      <td>2</td>\n",
       "      <td>3</td>\n",
       "      <td>896</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "      <td>...</td>\n",
       "      <td>18479</td>\n",
       "      <td>10433</td>\n",
       "      <td>774</td>\n",
       "      <td>2601</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>36991</th>\n",
       "      <td>17113</td>\n",
       "      <td>25</td>\n",
       "      <td>1</td>\n",
       "      <td>22137</td>\n",
       "      <td>1</td>\n",
       "      <td>5</td>\n",
       "      <td>295</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>...</td>\n",
       "      <td>23491</td>\n",
       "      <td>9007</td>\n",
       "      <td>1653</td>\n",
       "      <td>16256</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>5</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>36992 rows × 23 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "       customer_id  age  gender  security_no  region_category  \\\n",
       "0            32243    8       0        34811                2   \n",
       "1             7055   22       0         5804                0   \n",
       "2             5910   34       0         1534                1   \n",
       "3            32400   27       1        32501                0   \n",
       "4             6070   21       0        29819                0   \n",
       "...            ...  ...     ...          ...              ...   \n",
       "36987        29660   36       0         6692                3   \n",
       "36988        16761   19       0         2204                1   \n",
       "36989        27942   13       0        34476                3   \n",
       "36990         1923   43       1        21020                2   \n",
       "36991        17113   25       1        22137                1   \n",
       "\n",
       "       membership_category  joining_date  joined_through_referral  \\\n",
       "0                        3           959                        1   \n",
       "1                        4           970                        0   \n",
       "2                        2           680                        2   \n",
       "3                        2           667                        2   \n",
       "4                        2           985                        1   \n",
       "...                    ...           ...                      ...   \n",
       "36987                    0           994                        1   \n",
       "36988                    0           543                        1   \n",
       "36989                    0           619                        2   \n",
       "36990                    3           896                        1   \n",
       "36991                    5           295                        1   \n",
       "\n",
       "       preferred_offer_types  medium_of_operation  ...  avg_time_spent  \\\n",
       "0                          1                    0  ...           12869   \n",
       "1                          1                    2  ...           13051   \n",
       "2                          1                    2  ...           19239   \n",
       "3                          1                    2  ...           19546   \n",
       "4                          0                    3  ...            3123   \n",
       "...                      ...                  ...  ...             ...   \n",
       "36987                      0                    2  ...             732   \n",
       "36988                      2                    3  ...             697   \n",
       "36989                      1                    2  ...            6497   \n",
       "36990                      1                    3  ...           18479   \n",
       "36991                      1                    2  ...           23491   \n",
       "\n",
       "       avg_transaction_value  avg_frequency_login_days  points_in_wallet  \\\n",
       "0                      30769                       746             20503   \n",
       "1                       2199                       732             23699   \n",
       "2                       8374                       779              4792   \n",
       "3                      11560                      1637              7111   \n",
       "4                      10936                       743             12218   \n",
       "...                      ...                       ...               ...   \n",
       "36987                  13097                      1637             10708   \n",
       "36988                    814                       809              5785   \n",
       "36989                  21410                      1653             13391   \n",
       "36990                  10433                       774              2601   \n",
       "36991                   9007                      1653             16256   \n",
       "\n",
       "       used_special_discount  offer_application_preference  past_complaint  \\\n",
       "0                          1                             1               0   \n",
       "1                          1                             0               1   \n",
       "2                          0                             1               1   \n",
       "3                          0                             1               1   \n",
       "4                          0                             1               1   \n",
       "...                      ...                           ...             ...   \n",
       "36987                      0                             1               1   \n",
       "36988                      1                             0               0   \n",
       "36989                      0                             1               1   \n",
       "36990                      1                             1               0   \n",
       "36991                      1                             0               0   \n",
       "\n",
       "       complaint_status  feedback  churn_risk_score  \n",
       "0                     1         4                 2  \n",
       "1                     2         5                 1  \n",
       "2                     3         3                 5  \n",
       "3                     4         3                 5  \n",
       "4                     2         3                 5  \n",
       "...                 ...       ...               ...  \n",
       "36987                 0         0                 4  \n",
       "36988                 1         1                 5  \n",
       "36989                 4         3                 4  \n",
       "36990                 1         0                 3  \n",
       "36991                 1         5                 2  \n",
       "\n",
       "[36992 rows x 23 columns]"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "a = df.columns\n",
    "for i in a[:-1]:\n",
    "  df[i] = df[i].astype('|S')\n",
    "  df[i] = label_encoder.fit_transform(df[i])\n",
    "df  "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "lxpVBjlfkVAA",
    "outputId": "fc104603-7449-4ea8-c2fb-0fd66be4e8df"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "False"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df['churn_risk_score'].isnull().any()  # checking for and removing records with null values"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {
    "id": "1fYSRhANk6-5"
   },
   "outputs": [],
   "source": [
    "df = df.dropna(axis = 0, how ='any')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "FWzNivnplPUV",
    "outputId": "9c9701f2-689e-4f28-848f-1dbb4334ca6d"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "customer_id                     False\n",
       "age                             False\n",
       "gender                          False\n",
       "security_no                     False\n",
       "region_category                 False\n",
       "membership_category             False\n",
       "joining_date                    False\n",
       "joined_through_referral         False\n",
       "preferred_offer_types           False\n",
       "medium_of_operation             False\n",
       "internet_option                 False\n",
       "last_visit_time                 False\n",
       "days_since_last_login           False\n",
       "avg_time_spent                  False\n",
       "avg_transaction_value           False\n",
       "avg_frequency_login_days        False\n",
       "points_in_wallet                False\n",
       "used_special_discount           False\n",
       "offer_application_preference    False\n",
       "past_complaint                  False\n",
       "complaint_status                False\n",
       "feedback                        False\n",
       "churn_risk_score                False\n",
       "dtype: bool"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.isnull().any()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 422
    },
    "id": "JSKvngNBlaCq",
    "outputId": "11388c02-df98-4e96-c3bc-754f8a4bdae5"
   },
   "outputs": [
    {
     "data": {
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>customer_id</th>\n",
       "      <th>age</th>\n",
       "      <th>gender</th>\n",
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       "      <th>...</th>\n",
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       "      <th>past_complaint</th>\n",
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       "      <th>feedback</th>\n",
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       "    </tr>\n",
       "    <tr>\n",
       "      <th>36987</th>\n",
       "      <td>29660</td>\n",
       "      <td>36</td>\n",
       "      <td>0</td>\n",
       "      <td>6692</td>\n",
       "      <td>3</td>\n",
       "      <td>0</td>\n",
       "      <td>994</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>...</td>\n",
       "      <td>732</td>\n",
       "      <td>13097</td>\n",
       "      <td>1637</td>\n",
       "      <td>10708</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>36988</th>\n",
       "      <td>16761</td>\n",
       "      <td>19</td>\n",
       "      <td>0</td>\n",
       "      <td>2204</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>543</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>3</td>\n",
       "      <td>...</td>\n",
       "      <td>697</td>\n",
       "      <td>814</td>\n",
       "      <td>809</td>\n",
       "      <td>5785</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>36989</th>\n",
       "      <td>27942</td>\n",
       "      <td>13</td>\n",
       "      <td>0</td>\n",
       "      <td>34476</td>\n",
       "      <td>3</td>\n",
       "      <td>0</td>\n",
       "      <td>619</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>...</td>\n",
       "      <td>6497</td>\n",
       "      <td>21410</td>\n",
       "      <td>1653</td>\n",
       "      <td>13391</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>4</td>\n",
       "      <td>3</td>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>36990</th>\n",
       "      <td>1923</td>\n",
       "      <td>43</td>\n",
       "      <td>1</td>\n",
       "      <td>21020</td>\n",
       "      <td>2</td>\n",
       "      <td>3</td>\n",
       "      <td>896</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "      <td>...</td>\n",
       "      <td>18479</td>\n",
       "      <td>10433</td>\n",
       "      <td>774</td>\n",
       "      <td>2601</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>36991</th>\n",
       "      <td>17113</td>\n",
       "      <td>25</td>\n",
       "      <td>1</td>\n",
       "      <td>22137</td>\n",
       "      <td>1</td>\n",
       "      <td>5</td>\n",
       "      <td>295</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>...</td>\n",
       "      <td>23491</td>\n",
       "      <td>9007</td>\n",
       "      <td>1653</td>\n",
       "      <td>16256</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>5</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>36992 rows × 23 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "       customer_id  age  gender  security_no  region_category  \\\n",
       "0            32243    8       0        34811                2   \n",
       "1             7055   22       0         5804                0   \n",
       "2             5910   34       0         1534                1   \n",
       "3            32400   27       1        32501                0   \n",
       "4             6070   21       0        29819                0   \n",
       "...            ...  ...     ...          ...              ...   \n",
       "36987        29660   36       0         6692                3   \n",
       "36988        16761   19       0         2204                1   \n",
       "36989        27942   13       0        34476                3   \n",
       "36990         1923   43       1        21020                2   \n",
       "36991        17113   25       1        22137                1   \n",
       "\n",
       "       membership_category  joining_date  joined_through_referral  \\\n",
       "0                        3           959                        1   \n",
       "1                        4           970                        0   \n",
       "2                        2           680                        2   \n",
       "3                        2           667                        2   \n",
       "4                        2           985                        1   \n",
       "...                    ...           ...                      ...   \n",
       "36987                    0           994                        1   \n",
       "36988                    0           543                        1   \n",
       "36989                    0           619                        2   \n",
       "36990                    3           896                        1   \n",
       "36991                    5           295                        1   \n",
       "\n",
       "       preferred_offer_types  medium_of_operation  ...  avg_time_spent  \\\n",
       "0                          1                    0  ...           12869   \n",
       "1                          1                    2  ...           13051   \n",
       "2                          1                    2  ...           19239   \n",
       "3                          1                    2  ...           19546   \n",
       "4                          0                    3  ...            3123   \n",
       "...                      ...                  ...  ...             ...   \n",
       "36987                      0                    2  ...             732   \n",
       "36988                      2                    3  ...             697   \n",
       "36989                      1                    2  ...            6497   \n",
       "36990                      1                    3  ...           18479   \n",
       "36991                      1                    2  ...           23491   \n",
       "\n",
       "       avg_transaction_value  avg_frequency_login_days  points_in_wallet  \\\n",
       "0                      30769                       746             20503   \n",
       "1                       2199                       732             23699   \n",
       "2                       8374                       779              4792   \n",
       "3                      11560                      1637              7111   \n",
       "4                      10936                       743             12218   \n",
       "...                      ...                       ...               ...   \n",
       "36987                  13097                      1637             10708   \n",
       "36988                    814                       809              5785   \n",
       "36989                  21410                      1653             13391   \n",
       "36990                  10433                       774              2601   \n",
       "36991                   9007                      1653             16256   \n",
       "\n",
       "       used_special_discount  offer_application_preference  past_complaint  \\\n",
       "0                          1                             1               0   \n",
       "1                          1                             0               1   \n",
       "2                          0                             1               1   \n",
       "3                          0                             1               1   \n",
       "4                          0                             1               1   \n",
       "...                      ...                           ...             ...   \n",
       "36987                      0                             1               1   \n",
       "36988                      1                             0               0   \n",
       "36989                      0                             1               1   \n",
       "36990                      1                             1               0   \n",
       "36991                      1                             0               0   \n",
       "\n",
       "       complaint_status  feedback  churn_risk_score  \n",
       "0                     1         4                 2  \n",
       "1                     2         5                 1  \n",
       "2                     3         3                 5  \n",
       "3                     4         3                 5  \n",
       "4                     2         3                 5  \n",
       "...                 ...       ...               ...  \n",
       "36987                 0         0                 4  \n",
       "36988                 1         1                 5  \n",
       "36989                 4         3                 4  \n",
       "36990                 1         0                 3  \n",
       "36991                 1         5                 2  \n",
       "\n",
       "[36992 rows x 23 columns]"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Data Visualization"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 297
    },
    "id": "dgG15MbDrxvK",
    "outputId": "6d1511a1-7aca-4ae4-e9b3-7f77019089b0"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:xlabel='churn_risk_score', ylabel='count'>"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# checking the distribution of outcomes\n",
    "sns.countplot(x = 'churn_risk_score', data = df)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Checking Variance"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "6PjV9RjdrZ05",
    "outputId": "5c7e2185-d3f2-4830-9379-fb9256a36531"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Index(['customer_id', 'age', 'gender', 'security_no', 'region_category',\n",
       "       'membership_category', 'joining_date', 'joined_through_referral',\n",
       "       'preferred_offer_types', 'medium_of_operation', 'internet_option',\n",
       "       'last_visit_time', 'days_since_last_login', 'avg_time_spent',\n",
       "       'avg_transaction_value', 'avg_frequency_login_days', 'points_in_wallet',\n",
       "       'used_special_discount', 'offer_application_preference',\n",
       "       'past_complaint', 'complaint_status', 'feedback', 'churn_risk_score'],\n",
       "      dtype='object')"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.columns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {
    "id": "W-IztaS-rThT"
   },
   "outputs": [],
   "source": [
    "# checking variance\n",
    "from statsmodels.stats.outliers_influence import variance_inflation_factor\n",
    "variables = df[['customer_id', 'age', 'gender', 'security_no', 'region_category',\n",
    "       'membership_category', 'joining_date', 'joined_through_referral',\n",
    "       'preferred_offer_types', 'medium_of_operation', 'internet_option',\n",
    "       'last_visit_time', 'days_since_last_login', 'avg_time_spent',\n",
    "       'avg_transaction_value', 'avg_frequency_login_days', 'points_in_wallet',\n",
    "       'used_special_discount', 'offer_application_preference',\n",
    "       'past_complaint', 'complaint_status', 'feedback', 'churn_risk_score']]\n",
    "vif = pd.DataFrame()\n",
    "vif['VIF'] = [variance_inflation_factor(variables.values, i) for i in range(variables.shape[1])]\n",
    "vif['Features'] = variables.columns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 728
    },
    "id": "YSRJPGClrnV6",
    "outputId": "5122f874-ecae-4a09-bbb5-181c96b8ccfd"
   },
   "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>VIF</th>\n",
       "      <th>Features</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>3.910796</td>\n",
       "      <td>customer_id</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>3.776851</td>\n",
       "      <td>age</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>1.973706</td>\n",
       "      <td>gender</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>3.856613</td>\n",
       "      <td>security_no</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>2.071923</td>\n",
       "      <td>region_category</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>2.929538</td>\n",
       "      <td>membership_category</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>3.858486</td>\n",
       "      <td>joining_date</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>4.193386</td>\n",
       "      <td>joined_through_referral</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>2.443468</td>\n",
       "      <td>preferred_offer_types</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>4.428789</td>\n",
       "      <td>medium_of_operation</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>2.469821</td>\n",
       "      <td>internet_option</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>3.863224</td>\n",
       "      <td>last_visit_time</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>2.833812</td>\n",
       "      <td>days_since_last_login</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>4.440880</td>\n",
       "      <td>avg_time_spent</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>3.957072</td>\n",
       "      <td>avg_transaction_value</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15</th>\n",
       "      <td>6.754722</td>\n",
       "      <td>avg_frequency_login_days</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16</th>\n",
       "      <td>5.217857</td>\n",
       "      <td>points_in_wallet</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17</th>\n",
       "      <td>5.659788</td>\n",
       "      <td>used_special_discount</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18</th>\n",
       "      <td>5.677552</td>\n",
       "      <td>offer_application_preference</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19</th>\n",
       "      <td>2.712731</td>\n",
       "      <td>past_complaint</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20</th>\n",
       "      <td>3.764108</td>\n",
       "      <td>complaint_status</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>21</th>\n",
       "      <td>2.573264</td>\n",
       "      <td>feedback</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>22</th>\n",
       "      <td>7.231837</td>\n",
       "      <td>churn_risk_score</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "         VIF                      Features\n",
       "0   3.910796                   customer_id\n",
       "1   3.776851                           age\n",
       "2   1.973706                        gender\n",
       "3   3.856613                   security_no\n",
       "4   2.071923               region_category\n",
       "5   2.929538           membership_category\n",
       "6   3.858486                  joining_date\n",
       "7   4.193386       joined_through_referral\n",
       "8   2.443468         preferred_offer_types\n",
       "9   4.428789           medium_of_operation\n",
       "10  2.469821               internet_option\n",
       "11  3.863224               last_visit_time\n",
       "12  2.833812         days_since_last_login\n",
       "13  4.440880                avg_time_spent\n",
       "14  3.957072         avg_transaction_value\n",
       "15  6.754722      avg_frequency_login_days\n",
       "16  5.217857              points_in_wallet\n",
       "17  5.659788         used_special_discount\n",
       "18  5.677552  offer_application_preference\n",
       "19  2.712731                past_complaint\n",
       "20  3.764108              complaint_status\n",
       "21  2.573264                      feedback\n",
       "22  7.231837              churn_risk_score"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "vif"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### VIF is less than 10 for all the attributes, hence, we can keep them all."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Splitting Data for Training and Testing"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {
    "id": "3YFm1PKqmVOg"
   },
   "outputs": [],
   "source": [
    "data = df.values\n",
    "X, y = data[:,:-1], data[:,-1]\n",
    "X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.20, random_state=0)  # splitting in the ratio 80:20"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Decision Tree Model "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {
    "id": "XI3s3K6UxC2-"
   },
   "outputs": [],
   "source": [
    "clf = DecisionTreeClassifier()\n",
    "clf = clf.fit(X_train,y_train)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Making Predictions"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {
    "id": "7mAVK1_cshZ2"
   },
   "outputs": [],
   "source": [
    "pred = clf.predict(X_test)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Checking Accuracy"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "tsZVZPLnxS7k",
    "outputId": "47cddcbe-78fe-44fb-8db0-ff14c34a8083"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.706176510339235"
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "score = clf.score(X_test, y_test)\n",
    "score"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Predictions are 71% accurate."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Visualizing the Decision Tree"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1440x720 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "s = plt.figure(figsize=(20,10))\n",
    "tree.plot_tree(clf, max_depth=2, filled=True, fontsize=10)\n",
    "plt.title(\"Decision Tree for Customer Churn Data\", fontsize=30)\n",
    "plt.show() "
   ]
  }
 ],
 "metadata": {
  "colab": {
   "collapsed_sections": [],
   "name": "Customer Churn Score Prediction.ipynb",
   "provenance": []
  },
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.8.5"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 1
}