Predicting Customer Churn in a Telco Customer Dataset.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "e50b3c66",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Removes Warnings\n",
    "import warnings\n",
    "warnings.filterwarnings('ignore')\n",
    "#import the necessary packages\n",
    "import pandas as pd\n",
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "import matplotlib\n",
    "from matplotlib import colors\n",
    "import seaborn as sns\n",
    "from matplotlib.colors import ListedColormap"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "5907e1ce",
   "metadata": {},
   "outputs": [],
   "source": [
    "#Setting up colors prefrences\n",
    "sns.set(rc={\"axes.facecolor\":\"#FFF9ED\",\"figure.facecolor\":\"#FFF9ED\"})\n",
    "pallet = [\"#682F2F\",\"#9E726F\", \"#D6B2B1\", \"#B9C0C9\", \"#9F8A78\", \"#F3AB60\"]\n",
    "cmap = colors.ListedColormap([\"#682F2F\", \"#9E726F\", \"#D6B2B1\", \"#B9C0C9\", \"#9F8A78\", \"#F3AB60\",\"#31598C\"])\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "a2d41497",
   "metadata": {},
   "outputs": [],
   "source": [
    "df = pd.read_csv('Telco_Churn_Data.csv')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "72e3e417",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "        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>Target Churn</th>\n",
       "      <th>Target Code</th>\n",
       "      <th>Current Bill Amt</th>\n",
       "      <th>Avg Calls</th>\n",
       "      <th>Avg Calls Weekdays</th>\n",
       "      <th>Account Age</th>\n",
       "      <th>Percent Increase MOM</th>\n",
       "      <th>Acct Plan Subtype</th>\n",
       "      <th>Complaint Code</th>\n",
       "      <th>Avg Days Delinquent</th>\n",
       "      <th>Current TechSupComplaints</th>\n",
       "      <th>Current Days OpenWorkOrders</th>\n",
       "      <th>Equipment Age</th>\n",
       "      <th>Condition of Current Handset</th>\n",
       "      <th>Avg Hours WorkOrderOpenned</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>No Churn</td>\n",
       "      <td>0</td>\n",
       "      <td>14210</td>\n",
       "      <td>17950.000000</td>\n",
       "      <td>30297.0000</td>\n",
       "      <td>24</td>\n",
       "      <td>-0.334193</td>\n",
       "      <td>Gold</td>\n",
       "      <td>Billing Problem</td>\n",
       "      <td>6.2</td>\n",
       "      <td>0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>8</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Churn</td>\n",
       "      <td>1</td>\n",
       "      <td>14407</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.0000</td>\n",
       "      <td>28</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>Silver</td>\n",
       "      <td>Moving</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>17</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Churn</td>\n",
       "      <td>1</td>\n",
       "      <td>12712</td>\n",
       "      <td>204.666667</td>\n",
       "      <td>10393.6667</td>\n",
       "      <td>23</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>Gold</td>\n",
       "      <td>Billing Problem</td>\n",
       "      <td>17.6</td>\n",
       "      <td>0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>23</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>No Churn</td>\n",
       "      <td>0</td>\n",
       "      <td>13807</td>\n",
       "      <td>15490.333300</td>\n",
       "      <td>41256.3333</td>\n",
       "      <td>39</td>\n",
       "      <td>0.148986</td>\n",
       "      <td>Silver</td>\n",
       "      <td>Billing Problem</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>17</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>No Churn</td>\n",
       "      <td>0</td>\n",
       "      <td>3805</td>\n",
       "      <td>5075.000000</td>\n",
       "      <td>12333.3333</td>\n",
       "      <td>23</td>\n",
       "      <td>-0.686047</td>\n",
       "      <td>Gold</td>\n",
       "      <td>Billing Problem</td>\n",
       "      <td>3.8</td>\n",
       "      <td>0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>10</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "  Target Churn  Target Code  Current Bill Amt     Avg Calls  \\\n",
       "0     No Churn            0             14210  17950.000000   \n",
       "1        Churn            1             14407      0.000000   \n",
       "2        Churn            1             12712    204.666667   \n",
       "3     No Churn            0             13807  15490.333300   \n",
       "4     No Churn            0              3805   5075.000000   \n",
       "\n",
       "   Avg Calls Weekdays  Account Age  Percent Increase MOM Acct Plan Subtype  \\\n",
       "0          30297.0000           24             -0.334193              Gold   \n",
       "1              0.0000           28              0.000000            Silver   \n",
       "2          10393.6667           23              0.000000              Gold   \n",
       "3          41256.3333           39              0.148986            Silver   \n",
       "4          12333.3333           23             -0.686047              Gold   \n",
       "\n",
       "    Complaint Code  Avg Days Delinquent  Current TechSupComplaints  \\\n",
       "0  Billing Problem                  6.2                          0   \n",
       "1           Moving                  1.0                          0   \n",
       "2  Billing Problem                 17.6                          0   \n",
       "3  Billing Problem                  0.0                          0   \n",
       "4  Billing Problem                  3.8                          0   \n",
       "\n",
       "   Current Days OpenWorkOrders  Equipment Age  Condition of Current Handset  \\\n",
       "0                          0.0              8                           1.0   \n",
       "1                          0.0             17                           1.0   \n",
       "2                          0.0             23                           1.0   \n",
       "3                          0.0             17                           1.0   \n",
       "4                          0.0             10                           1.0   \n",
       "\n",
       "   Avg Hours WorkOrderOpenned  \n",
       "0                         0.0  \n",
       "1                         0.0  \n",
       "2                         0.0  \n",
       "3                         0.0  \n",
       "4                         0.0  "
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "991e3518",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Index(['Target Churn', 'Target Code', 'Current Bill Amt', 'Avg Calls',\n",
       "       'Avg Calls Weekdays', 'Account Age', 'Percent Increase MOM',\n",
       "       'Acct Plan Subtype', 'Complaint Code', 'Avg Days Delinquent',\n",
       "       'Current TechSupComplaints', 'Current Days OpenWorkOrders',\n",
       "       'Equipment Age', 'Condition of Current Handset',\n",
       "       'Avg Hours WorkOrderOpenned'],\n",
       "      dtype='object')"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.columns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "8bf818bb",
   "metadata": {},
   "outputs": [],
   "source": [
    "df = df[['Target Churn','Current Bill Amt', 'Avg Calls',\\\n",
    "       'Avg Calls Weekdays', 'Account Age', 'Percent Increase MOM',\\\n",
    "       'Acct Plan Subtype', 'Complaint Code', 'Avg Days Delinquent',\\\n",
    "       'Current TechSupComplaints', 'Current Days OpenWorkOrders',\\\n",
    "       'Equipment Age', 'Condition of Current Handset',\\\n",
    "       'Avg Hours WorkOrderOpenned','Target Code']]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "24fc05b6",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "    }\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>Target Churn</th>\n",
       "      <th>Current Bill Amt</th>\n",
       "      <th>Avg Calls</th>\n",
       "      <th>Avg Calls Weekdays</th>\n",
       "      <th>Account Age</th>\n",
       "      <th>Percent Increase MOM</th>\n",
       "      <th>Acct Plan Subtype</th>\n",
       "      <th>Complaint Code</th>\n",
       "      <th>Avg Days Delinquent</th>\n",
       "      <th>Current TechSupComplaints</th>\n",
       "      <th>Current Days OpenWorkOrders</th>\n",
       "      <th>Equipment Age</th>\n",
       "      <th>Condition of Current Handset</th>\n",
       "      <th>Avg Hours WorkOrderOpenned</th>\n",
       "      <th>Target Code</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>No Churn</td>\n",
       "      <td>14210</td>\n",
       "      <td>17950.000000</td>\n",
       "      <td>30297.0000</td>\n",
       "      <td>24</td>\n",
       "      <td>-0.334193</td>\n",
       "      <td>Gold</td>\n",
       "      <td>Billing Problem</td>\n",
       "      <td>6.2</td>\n",
       "      <td>0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>8</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Churn</td>\n",
       "      <td>14407</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.0000</td>\n",
       "      <td>28</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>Silver</td>\n",
       "      <td>Moving</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>17</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Churn</td>\n",
       "      <td>12712</td>\n",
       "      <td>204.666667</td>\n",
       "      <td>10393.6667</td>\n",
       "      <td>23</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>Gold</td>\n",
       "      <td>Billing Problem</td>\n",
       "      <td>17.6</td>\n",
       "      <td>0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>23</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>No Churn</td>\n",
       "      <td>13807</td>\n",
       "      <td>15490.333300</td>\n",
       "      <td>41256.3333</td>\n",
       "      <td>39</td>\n",
       "      <td>0.148986</td>\n",
       "      <td>Silver</td>\n",
       "      <td>Billing Problem</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>17</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>No Churn</td>\n",
       "      <td>3805</td>\n",
       "      <td>5075.000000</td>\n",
       "      <td>12333.3333</td>\n",
       "      <td>23</td>\n",
       "      <td>-0.686047</td>\n",
       "      <td>Gold</td>\n",
       "      <td>Billing Problem</td>\n",
       "      <td>3.8</td>\n",
       "      <td>0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>10</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "  Target Churn  Current Bill Amt     Avg Calls  Avg Calls Weekdays  \\\n",
       "0     No Churn             14210  17950.000000          30297.0000   \n",
       "1        Churn             14407      0.000000              0.0000   \n",
       "2        Churn             12712    204.666667          10393.6667   \n",
       "3     No Churn             13807  15490.333300          41256.3333   \n",
       "4     No Churn              3805   5075.000000          12333.3333   \n",
       "\n",
       "   Account Age  Percent Increase MOM Acct Plan Subtype   Complaint Code  \\\n",
       "0           24             -0.334193              Gold  Billing Problem   \n",
       "1           28              0.000000            Silver           Moving   \n",
       "2           23              0.000000              Gold  Billing Problem   \n",
       "3           39              0.148986            Silver  Billing Problem   \n",
       "4           23             -0.686047              Gold  Billing Problem   \n",
       "\n",
       "   Avg Days Delinquent  Current TechSupComplaints  \\\n",
       "0                  6.2                          0   \n",
       "1                  1.0                          0   \n",
       "2                 17.6                          0   \n",
       "3                  0.0                          0   \n",
       "4                  3.8                          0   \n",
       "\n",
       "   Current Days OpenWorkOrders  Equipment Age  Condition of Current Handset  \\\n",
       "0                          0.0              8                           1.0   \n",
       "1                          0.0             17                           1.0   \n",
       "2                          0.0             23                           1.0   \n",
       "3                          0.0             17                           1.0   \n",
       "4                          0.0             10                           1.0   \n",
       "\n",
       "   Avg Hours WorkOrderOpenned  Target Code  \n",
       "0                         0.0            0  \n",
       "1                         0.0            1  \n",
       "2                         0.0            1  \n",
       "3                         0.0            0  \n",
       "4                         0.0            0  "
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "1aec6487",
   "metadata": {},
   "source": [
    "### Unique Values"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "347cd861",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(4708, 15)"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "eec447b6",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Target Churn 2\n",
      "Current Bill Amt 3478\n",
      "Avg Calls 4385\n",
      "Avg Calls Weekdays 4560\n",
      "Account Age 26\n",
      "Percent Increase MOM 3904\n",
      "Acct Plan Subtype 2\n",
      "Complaint Code 7\n",
      "Avg Days Delinquent 393\n",
      "Current TechSupComplaints 11\n",
      "Current Days OpenWorkOrders 96\n",
      "Equipment Age 40\n",
      "Condition of Current Handset 4\n",
      "Avg Hours WorkOrderOpenned 91\n",
      "Target Code 2\n"
     ]
    }
   ],
   "source": [
    "for x in df.columns:\n",
    "   print(x, len(df[x].unique()))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "e9c86889",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Index(['Target Churn', 'Current Bill Amt', 'Avg Calls', 'Avg Calls Weekdays',\n",
       "       'Account Age', 'Percent Increase MOM', 'Acct Plan Subtype',\n",
       "       'Complaint Code', 'Avg Days Delinquent', 'Current TechSupComplaints',\n",
       "       'Current Days OpenWorkOrders', 'Equipment Age',\n",
       "       'Condition of Current Handset', 'Avg Hours WorkOrderOpenned',\n",
       "       'Target Code'],\n",
       "      dtype='object')"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.columns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "03221881",
   "metadata": {},
   "outputs": [],
   "source": [
    "## Bonus method for renaming the columns\n",
    "df.columns = df.columns.str.replace(' ','_')\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "6dc31f90",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Index(['Target_Churn', 'Current_Bill_Amt', 'Avg_Calls', 'Avg_Calls_Weekdays',\n",
       "       'Account_Age', 'Percent_Increase_MOM', 'Acct_Plan_Subtype',\n",
       "       'Complaint_Code', 'Avg_Days_Delinquent', 'Current_TechSupComplaints',\n",
       "       'Current_Days_OpenWorkOrders', 'Equipment_Age',\n",
       "       'Condition_of_Current_Handset', 'Avg_Hours_WorkOrderOpenned',\n",
       "       'Target_Code'],\n",
       "      dtype='object')"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.columns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "id": "0112c25b",
   "metadata": {},
   "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>Current_Bill_Amt</th>\n",
       "      <th>Avg_Calls</th>\n",
       "      <th>Avg_Calls_Weekdays</th>\n",
       "      <th>Account_Age</th>\n",
       "      <th>Percent_Increase_MOM</th>\n",
       "      <th>Avg_Days_Delinquent</th>\n",
       "      <th>Current_TechSupComplaints</th>\n",
       "      <th>Current_Days_OpenWorkOrders</th>\n",
       "      <th>Equipment_Age</th>\n",
       "      <th>Condition_of_Current_Handset</th>\n",
       "      <th>Avg_Hours_WorkOrderOpenned</th>\n",
       "      <th>Target_Code</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>4708.000000</td>\n",
       "      <td>4708.000000</td>\n",
       "      <td>4708.000000</td>\n",
       "      <td>4708.000000</td>\n",
       "      <td>4708.000000</td>\n",
       "      <td>4708.000000</td>\n",
       "      <td>4708.000000</td>\n",
       "      <td>4708.000000</td>\n",
       "      <td>4708.000000</td>\n",
       "      <td>4264.000000</td>\n",
       "      <td>4708.000000</td>\n",
       "      <td>4708.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>19828.815845</td>\n",
       "      <td>9269.690314</td>\n",
       "      <td>38127.994973</td>\n",
       "      <td>26.079652</td>\n",
       "      <td>-0.005127</td>\n",
       "      <td>13.968564</td>\n",
       "      <td>0.414826</td>\n",
       "      <td>2.438828</td>\n",
       "      <td>10.811810</td>\n",
       "      <td>1.019231</td>\n",
       "      <td>0.936501</td>\n",
       "      <td>0.485769</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>17204.510108</td>\n",
       "      <td>10437.339850</td>\n",
       "      <td>39172.244943</td>\n",
       "      <td>7.149933</td>\n",
       "      <td>4.481852</td>\n",
       "      <td>10.789062</td>\n",
       "      <td>0.954549</td>\n",
       "      <td>20.197292</td>\n",
       "      <td>8.710548</td>\n",
       "      <td>0.144021</td>\n",
       "      <td>8.926752</td>\n",
       "      <td>0.499851</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>-690.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>15.000000</td>\n",
       "      <td>-1.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>12288.000000</td>\n",
       "      <td>2602.250002</td>\n",
       "      <td>13031.250025</td>\n",
       "      <td>20.000000</td>\n",
       "      <td>-0.574084</td>\n",
       "      <td>6.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>4.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>15254.000000</td>\n",
       "      <td>6396.333330</td>\n",
       "      <td>26765.833350</td>\n",
       "      <td>25.000000</td>\n",
       "      <td>-0.185280</td>\n",
       "      <td>12.400000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>9.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>22799.000000</td>\n",
       "      <td>12250.499975</td>\n",
       "      <td>50061.416675</td>\n",
       "      <td>32.000000</td>\n",
       "      <td>0.121823</td>\n",
       "      <td>19.300000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>17.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>325127.000000</td>\n",
       "      <td>181786.000000</td>\n",
       "      <td>374457.667000</td>\n",
       "      <td>40.000000</td>\n",
       "      <td>210.100000</td>\n",
       "      <td>126.000000</td>\n",
       "      <td>11.000000</td>\n",
       "      <td>368.000000</td>\n",
       "      <td>39.000000</td>\n",
       "      <td>3.000000</td>\n",
       "      <td>297.500000</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "       Current_Bill_Amt      Avg_Calls  Avg_Calls_Weekdays  Account_Age  \\\n",
       "count       4708.000000    4708.000000         4708.000000  4708.000000   \n",
       "mean       19828.815845    9269.690314        38127.994973    26.079652   \n",
       "std        17204.510108   10437.339850        39172.244943     7.149933   \n",
       "min         -690.000000       0.000000            0.000000    15.000000   \n",
       "25%        12288.000000    2602.250002        13031.250025    20.000000   \n",
       "50%        15254.000000    6396.333330        26765.833350    25.000000   \n",
       "75%        22799.000000   12250.499975        50061.416675    32.000000   \n",
       "max       325127.000000  181786.000000       374457.667000    40.000000   \n",
       "\n",
       "       Percent_Increase_MOM  Avg_Days_Delinquent  Current_TechSupComplaints  \\\n",
       "count           4708.000000          4708.000000                4708.000000   \n",
       "mean              -0.005127            13.968564                   0.414826   \n",
       "std                4.481852            10.789062                   0.954549   \n",
       "min               -1.000000             0.000000                   0.000000   \n",
       "25%               -0.574084             6.000000                   0.000000   \n",
       "50%               -0.185280            12.400000                   0.000000   \n",
       "75%                0.121823            19.300000                   0.000000   \n",
       "max              210.100000           126.000000                  11.000000   \n",
       "\n",
       "       Current_Days_OpenWorkOrders  Equipment_Age  \\\n",
       "count                  4708.000000    4708.000000   \n",
       "mean                      2.438828      10.811810   \n",
       "std                      20.197292       8.710548   \n",
       "min                       0.000000       0.000000   \n",
       "25%                       0.000000       4.000000   \n",
       "50%                       0.000000       9.000000   \n",
       "75%                       0.000000      17.000000   \n",
       "max                     368.000000      39.000000   \n",
       "\n",
       "       Condition_of_Current_Handset  Avg_Hours_WorkOrderOpenned  Target_Code  \n",
       "count                   4264.000000                 4708.000000  4708.000000  \n",
       "mean                       1.019231                    0.936501     0.485769  \n",
       "std                        0.144021                    8.926752     0.499851  \n",
       "min                        1.000000                    0.000000     0.000000  \n",
       "25%                        1.000000                    0.000000     0.000000  \n",
       "50%                        1.000000                    0.000000     0.000000  \n",
       "75%                        1.000000                    0.000000     1.000000  \n",
       "max                        3.000000                  297.500000     1.000000  "
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "id": "d53343ef",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 4708 entries, 0 to 4707\n",
      "Data columns (total 15 columns):\n",
      " #   Column                        Non-Null Count  Dtype  \n",
      "---  ------                        --------------  -----  \n",
      " 0   Target_Churn                  4708 non-null   object \n",
      " 1   Current_Bill_Amt              4708 non-null   int64  \n",
      " 2   Avg_Calls                     4708 non-null   float64\n",
      " 3   Avg_Calls_Weekdays            4708 non-null   float64\n",
      " 4   Account_Age                   4708 non-null   int64  \n",
      " 5   Percent_Increase_MOM          4708 non-null   float64\n",
      " 6   Acct_Plan_Subtype             4708 non-null   object \n",
      " 7   Complaint_Code                4701 non-null   object \n",
      " 8   Avg_Days_Delinquent           4708 non-null   float64\n",
      " 9   Current_TechSupComplaints     4708 non-null   int64  \n",
      " 10  Current_Days_OpenWorkOrders   4708 non-null   float64\n",
      " 11  Equipment_Age                 4708 non-null   int64  \n",
      " 12  Condition_of_Current_Handset  4264 non-null   float64\n",
      " 13  Avg_Hours_WorkOrderOpenned    4708 non-null   float64\n",
      " 14  Target_Code                   4708 non-null   int64  \n",
      "dtypes: float64(7), int64(5), object(3)\n",
      "memory usage: 551.8+ KB\n"
     ]
    }
   ],
   "source": [
    "df.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "id": "831064f0",
   "metadata": {},
   "outputs": [],
   "source": [
    "df['Condition_of_Current_Handset'] = df['Condition_of_Current_Handset'].fillna(df['Condition_of_Current_Handset'].mode()[0])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "id": "b0f785d9",
   "metadata": {},
   "outputs": [],
   "source": [
    "df['Complaint_Code'] = df['Complaint_Code'].fillna(df['Complaint_Code'].mode()[0])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "id": "a187fc3b",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Target_Churn                    0\n",
       "Current_Bill_Amt                0\n",
       "Avg_Calls                       0\n",
       "Avg_Calls_Weekdays              0\n",
       "Account_Age                     0\n",
       "Percent_Increase_MOM            0\n",
       "Acct_Plan_Subtype               0\n",
       "Complaint_Code                  0\n",
       "Avg_Days_Delinquent             0\n",
       "Current_TechSupComplaints       0\n",
       "Current_Days_OpenWorkOrders     0\n",
       "Equipment_Age                   0\n",
       "Condition_of_Current_Handset    0\n",
       "Avg_Hours_WorkOrderOpenned      0\n",
       "Target_Code                     0\n",
       "dtype: int64"
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.isna().sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "id": "decfe94e",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "No Churn    2421\n",
       "Churn       2287\n",
       "Name: Target_Churn, dtype: int64"
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df['Target_Churn'].value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "id": "6ee83031",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "No Churn    51.42311\n",
       "Churn       48.57689\n",
       "Name: Target_Churn, dtype: float64"
      ]
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df['Target_Churn'].value_counts(1) *100"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "id": "ba1979e8",
   "metadata": {},
   "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>Target_Code</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>Current_Bill_Amt</th>\n",
       "      <td>0.019995</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Avg_Calls</th>\n",
       "      <td>0.007375</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Avg_Calls_Weekdays</th>\n",
       "      <td>-0.014987</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Account_Age</th>\n",
       "      <td>-0.089890</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Percent_Increase_MOM</th>\n",
       "      <td>-0.059899</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Avg_Days_Delinquent</th>\n",
       "      <td>0.460092</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Current_TechSupComplaints</th>\n",
       "      <td>0.045102</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Current_Days_OpenWorkOrders</th>\n",
       "      <td>0.002891</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Equipment_Age</th>\n",
       "      <td>0.042373</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Condition_of_Current_Handset</th>\n",
       "      <td>-0.005680</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Avg_Hours_WorkOrderOpenned</th>\n",
       "      <td>0.002611</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Target_Code</th>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                              Target_Code\n",
       "Current_Bill_Amt                 0.019995\n",
       "Avg_Calls                        0.007375\n",
       "Avg_Calls_Weekdays              -0.014987\n",
       "Account_Age                     -0.089890\n",
       "Percent_Increase_MOM            -0.059899\n",
       "Avg_Days_Delinquent              0.460092\n",
       "Current_TechSupComplaints        0.045102\n",
       "Current_Days_OpenWorkOrders      0.002891\n",
       "Equipment_Age                    0.042373\n",
       "Condition_of_Current_Handset    -0.005680\n",
       "Avg_Hours_WorkOrderOpenned       0.002611\n",
       "Target_Code                      1.000000"
      ]
     },
     "execution_count": 20,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.corr()[['Target_Code']] "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 110,
   "id": "991d17b1",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 600x1000 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(6, 10))\n",
    "heatmap = sns.heatmap(df.corr()[['Target_Code']],vmin=-1, vmax=1, annot=True, cmap = cmap)\n",
    "heatmap.set_title('Features Correlating with Churn', fontdict={'fontsize':12}, pad=12);"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "id": "e17af113",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Create an array with the colors you want to use\n",
    "colors = [\"#682F2F\", \"#D6B2B1\"]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "id": "14847e12",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Set your custom color palette\n",
    "sns.set_palette(sns.color_palette(colors))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "id": "58809b59",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0.5, 1.0, 'Customer Complaint Code Distribution')"
      ]
     },
     "execution_count": 24,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<Figure size 1400x1000 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(14,10)) \n",
    "p=sns.countplot(y=\"Complaint_Code\", hue='Target_Churn',data=df) \n",
    "legend = p.get_legend() \n",
    "legend_txt = legend.texts \n",
    "legend_txt[0].set_text(\"No Churn\") \n",
    "legend_txt[1].set_text(\"Churn\") \n",
    "p.set_title('Customer Complaint Code Distribution')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "id": "3030f09b",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0.5, 1.0, 'By Account Plan Subtype')"
      ]
     },
     "execution_count": 25,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<Figure size 1500x400 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(15,4)) \n",
    "p=sns.countplot(y=\"Acct_Plan_Subtype\", hue='Target_Churn',data=df) \n",
    "legend = p.get_legend() \n",
    "legend_txt = legend.texts \n",
    "legend_txt[0].set_text(\"No Churn\") \n",
    "legend_txt[1].set_text(\"Churn\") \n",
    "p.set_title('By Account Plan Subtype')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "id": "2424390a",
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0.5, 1.0, 'By Current Tech Support Complaints Typees')"
      ]
     },
     "execution_count": 26,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<Figure size 1500x800 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(15,8)) \n",
    "p=sns.countplot(y=\"Current_TechSupComplaints\", hue='Target_Churn',data=df) \n",
    "legend = p.get_legend() \n",
    "legend_txt = legend.texts \n",
    "legend_txt[0].set_text(\"No Churn\") \n",
    "legend_txt[1].set_text(\"Churn\") \n",
    "p.set_title('By Current Tech Support Complaints Typees')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "id": "ab32d9d5",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.legend.Legend at 0x7fa4c8325d60>"
      ]
     },
     "execution_count": 27,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1500x400 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(15,4)) \n",
    "\n",
    "ax=sns.kdeplot(df.loc[(df['Target_Code'] == 0), \\\n",
    "                        'Avg_Days_Delinquent'] , \\\n",
    "               shade=True,label='no churn') \n",
    "\n",
    "\n",
    "ax=sns.kdeplot(df.loc[(df['Target_Code'] == 1),\\\n",
    "                        'Avg_Days_Delinquent'] , \\\n",
    "               shade=True, label='churn') \n",
    "\n",
    "ax.set(xlabel='Average No of Days Deliquent/Defalulted \\\n",
    "from paying', ylabel='Frequency') \n",
    "\n",
    "plt.title('Average No of Days Deliquent/Defalulted from \\\n",
    "paying - churn vs no churn')\n",
    "\n",
    "plt.legend()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "id": "c0571b92",
   "metadata": {},
   "outputs": [],
   "source": [
    "churn_by_account_age = df.groupby(\n",
    "    by='Account_Age'\n",
    ")['Target_Code'].sum() / df.groupby(\n",
    "    by='Account_Age'\n",
    ")['Target_Code'].count() *100"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "id": "8eca0830",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<Figure size 1500x800 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "ax = churn_by_account_age.head(24).plot(\n",
    "    grid=True,\n",
    "    figsize=(15, 8),\n",
    "    title='Churn Rate by Account Age'\n",
    ")\n",
    "\n",
    "ax.set_xlabel('Account Age')\n",
    "ax.set_ylabel('Churn Rate (%)')\n",
    "\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "id": "82c7d587",
   "metadata": {},
   "outputs": [],
   "source": [
    "duration_df = pd.concat([\n",
    "    df.loc[df['Target_Code'] == 1, 'Avg_Days_Delinquent'].reset_index(drop=True), \n",
    "    df.loc[df['Target_Code'] == 0, 'Avg_Days_Delinquent'].reset_index(drop=True)\n",
    "], axis=1)\n",
    "\n",
    "duration_df.columns = ['Churn', 'No Churn']\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "id": "d973a090",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1000x800 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "ax = duration_df.plot(\n",
    "    kind='box',\n",
    "    grid=True,\n",
    "    figsize=(10, 8),\n",
    ")\n",
    "\n",
    "ax.set_ylabel('last contact duration (hours)')\n",
    "ax.set_title('Last Contact Duration')\n",
    "\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "id": "d710b84e",
   "metadata": {},
   "outputs": [],
   "source": [
    "#Ordinal Encoder"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "id": "af48175d",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Silver    3914\n",
       "Gold       794\n",
       "Name: Acct_Plan_Subtype, dtype: int64"
      ]
     },
     "execution_count": 33,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df['Acct_Plan_Subtype'].value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "id": "864fa5f1",
   "metadata": {},
   "outputs": [],
   "source": [
    "catcols = ['Acct_Plan_Subtype','Complaint_Code']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "id": "bf959f72",
   "metadata": {},
   "outputs": [],
   "source": [
    "for col in df[catcols].columns:\n",
    "    df[col] = df[col].astype('category')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "id": "8edd4c6c",
   "metadata": {},
   "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>Target_Churn</th>\n",
       "      <th>Current_Bill_Amt</th>\n",
       "      <th>Avg_Calls</th>\n",
       "      <th>Avg_Calls_Weekdays</th>\n",
       "      <th>Account_Age</th>\n",
       "      <th>Percent_Increase_MOM</th>\n",
       "      <th>Acct_Plan_Subtype</th>\n",
       "      <th>Complaint_Code</th>\n",
       "      <th>Avg_Days_Delinquent</th>\n",
       "      <th>Current_TechSupComplaints</th>\n",
       "      <th>Current_Days_OpenWorkOrders</th>\n",
       "      <th>Equipment_Age</th>\n",
       "      <th>Condition_of_Current_Handset</th>\n",
       "      <th>Avg_Hours_WorkOrderOpenned</th>\n",
       "      <th>Target_Code</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>No Churn</td>\n",
       "      <td>14210</td>\n",
       "      <td>17950.000000</td>\n",
       "      <td>30297.0000</td>\n",
       "      <td>24</td>\n",
       "      <td>-0.334193</td>\n",
       "      <td>Gold</td>\n",
       "      <td>Billing Problem</td>\n",
       "      <td>6.2</td>\n",
       "      <td>0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>8</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Churn</td>\n",
       "      <td>14407</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.0000</td>\n",
       "      <td>28</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>Silver</td>\n",
       "      <td>Moving</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>17</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Churn</td>\n",
       "      <td>12712</td>\n",
       "      <td>204.666667</td>\n",
       "      <td>10393.6667</td>\n",
       "      <td>23</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>Gold</td>\n",
       "      <td>Billing Problem</td>\n",
       "      <td>17.6</td>\n",
       "      <td>0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>23</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>No Churn</td>\n",
       "      <td>13807</td>\n",
       "      <td>15490.333300</td>\n",
       "      <td>41256.3333</td>\n",
       "      <td>39</td>\n",
       "      <td>0.148986</td>\n",
       "      <td>Silver</td>\n",
       "      <td>Billing Problem</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>17</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>No Churn</td>\n",
       "      <td>3805</td>\n",
       "      <td>5075.000000</td>\n",
       "      <td>12333.3333</td>\n",
       "      <td>23</td>\n",
       "      <td>-0.686047</td>\n",
       "      <td>Gold</td>\n",
       "      <td>Billing Problem</td>\n",
       "      <td>3.8</td>\n",
       "      <td>0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>10</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "  Target_Churn  Current_Bill_Amt     Avg_Calls  Avg_Calls_Weekdays  \\\n",
       "0     No Churn             14210  17950.000000          30297.0000   \n",
       "1        Churn             14407      0.000000              0.0000   \n",
       "2        Churn             12712    204.666667          10393.6667   \n",
       "3     No Churn             13807  15490.333300          41256.3333   \n",
       "4     No Churn              3805   5075.000000          12333.3333   \n",
       "\n",
       "   Account_Age  Percent_Increase_MOM Acct_Plan_Subtype   Complaint_Code  \\\n",
       "0           24             -0.334193              Gold  Billing Problem   \n",
       "1           28              0.000000            Silver           Moving   \n",
       "2           23              0.000000              Gold  Billing Problem   \n",
       "3           39              0.148986            Silver  Billing Problem   \n",
       "4           23             -0.686047              Gold  Billing Problem   \n",
       "\n",
       "   Avg_Days_Delinquent  Current_TechSupComplaints  \\\n",
       "0                  6.2                          0   \n",
       "1                  1.0                          0   \n",
       "2                 17.6                          0   \n",
       "3                  0.0                          0   \n",
       "4                  3.8                          0   \n",
       "\n",
       "   Current_Days_OpenWorkOrders  Equipment_Age  Condition_of_Current_Handset  \\\n",
       "0                          0.0              8                           1.0   \n",
       "1                          0.0             17                           1.0   \n",
       "2                          0.0             23                           1.0   \n",
       "3                          0.0             17                           1.0   \n",
       "4                          0.0             10                           1.0   \n",
       "\n",
       "   Avg_Hours_WorkOrderOpenned  Target_Code  \n",
       "0                         0.0            0  \n",
       "1                         0.0            1  \n",
       "2                         0.0            1  \n",
       "3                         0.0            0  \n",
       "4                         0.0            0  "
      ]
     },
     "execution_count": 36,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "id": "601db422",
   "metadata": {},
   "outputs": [],
   "source": [
    "df['Acct_Plan_Subtype'] = df['Acct_Plan_Subtype'].apply(lambda x: 1 if x == 'Gold' else 0)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "id": "64860bbb",
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "      <th></th>\n",
       "      <th>Target_Churn</th>\n",
       "      <th>Current_Bill_Amt</th>\n",
       "      <th>Avg_Calls</th>\n",
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       "      <th>Avg_Hours_WorkOrderOpenned</th>\n",
       "      <th>Target_Code</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>No Churn</td>\n",
       "      <td>14210</td>\n",
       "      <td>17950.000000</td>\n",
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       "      <td>24</td>\n",
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       "      <td>1</td>\n",
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       "      <td>0.0</td>\n",
       "      <td>0</td>\n",
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       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Churn</td>\n",
       "      <td>14407</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.0000</td>\n",
       "      <td>28</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0</td>\n",
       "      <td>Moving</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>17</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Churn</td>\n",
       "      <td>12712</td>\n",
       "      <td>204.666667</td>\n",
       "      <td>10393.6667</td>\n",
       "      <td>23</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>1</td>\n",
       "      <td>Billing Problem</td>\n",
       "      <td>17.6</td>\n",
       "      <td>0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>23</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>No Churn</td>\n",
       "      <td>13807</td>\n",
       "      <td>15490.333300</td>\n",
       "      <td>41256.3333</td>\n",
       "      <td>39</td>\n",
       "      <td>0.148986</td>\n",
       "      <td>0</td>\n",
       "      <td>Billing Problem</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>17</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>No Churn</td>\n",
       "      <td>3805</td>\n",
       "      <td>5075.000000</td>\n",
       "      <td>12333.3333</td>\n",
       "      <td>23</td>\n",
       "      <td>-0.686047</td>\n",
       "      <td>1</td>\n",
       "      <td>Billing Problem</td>\n",
       "      <td>3.8</td>\n",
       "      <td>0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>10</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0</td>\n",
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       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "  Target_Churn  Current_Bill_Amt     Avg_Calls  Avg_Calls_Weekdays  \\\n",
       "0     No Churn             14210  17950.000000          30297.0000   \n",
       "1        Churn             14407      0.000000              0.0000   \n",
       "2        Churn             12712    204.666667          10393.6667   \n",
       "3     No Churn             13807  15490.333300          41256.3333   \n",
       "4     No Churn              3805   5075.000000          12333.3333   \n",
       "\n",
       "   Account_Age  Percent_Increase_MOM Acct_Plan_Subtype   Complaint_Code  \\\n",
       "0           24             -0.334193                 1  Billing Problem   \n",
       "1           28              0.000000                 0           Moving   \n",
       "2           23              0.000000                 1  Billing Problem   \n",
       "3           39              0.148986                 0  Billing Problem   \n",
       "4           23             -0.686047                 1  Billing Problem   \n",
       "\n",
       "   Avg_Days_Delinquent  Current_TechSupComplaints  \\\n",
       "0                  6.2                          0   \n",
       "1                  1.0                          0   \n",
       "2                 17.6                          0   \n",
       "3                  0.0                          0   \n",
       "4                  3.8                          0   \n",
       "\n",
       "   Current_Days_OpenWorkOrders  Equipment_Age  Condition_of_Current_Handset  \\\n",
       "0                          0.0              8                           1.0   \n",
       "1                          0.0             17                           1.0   \n",
       "2                          0.0             23                           1.0   \n",
       "3                          0.0             17                           1.0   \n",
       "4                          0.0             10                           1.0   \n",
       "\n",
       "   Avg_Hours_WorkOrderOpenned  Target_Code  \n",
       "0                         0.0            0  \n",
       "1                         0.0            1  \n",
       "2                         0.0            1  \n",
       "3                         0.0            0  \n",
       "4                         0.0            0  "
      ]
     },
     "execution_count": 38,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "id": "26ae00a3",
   "metadata": {},
   "outputs": [],
   "source": [
    "Complaint_Code_Encoded = pd.get_dummies(df['Complaint_Code'])\n",
    "Complaint_Code_Encoded.columns = ['Complaint_Code_%s' % x for x in Complaint_Code_Encoded.columns]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "id": "569a2677",
   "metadata": {},
   "outputs": [
    {
     "data": {
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      ],
      "text/plain": [
       "   Complaint_Code_Billing Problem  Complaint_Code_Call Quality  \\\n",
       "0                               1                            0   \n",
       "1                               0                            0   \n",
       "2                               1                            0   \n",
       "3                               1                            0   \n",
       "4                               1                            0   \n",
       "\n",
       "   Complaint_Code_Check Account  Complaint_Code_Inaccurate Sales Inf  \\\n",
       "0                             0                                    0   \n",
       "1                             0                                    0   \n",
       "2                             0                                    0   \n",
       "3                             0                                    0   \n",
       "4                             0                                    0   \n",
       "\n",
       "   Complaint_Code_Moving  Complaint_Code_Pricing  \n",
       "0                      0                       0  \n",
       "1                      1                       0  \n",
       "2                      0                       0  \n",
       "3                      0                       0  \n",
       "4                      0                       0  "
      ]
     },
     "execution_count": 40,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "Complaint_Code_Encoded.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "id": "1afa258a",
   "metadata": {},
   "outputs": [],
   "source": [
    "df = pd.concat([df, Complaint_Code_Encoded], axis=1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "id": "81184b60",
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "      <td>0.0000</td>\n",
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       "      <td>0.000000</td>\n",
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       "      <td>1.0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>17</td>\n",
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       "      <th>2</th>\n",
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       "      <td>10393.6667</td>\n",
       "      <td>23</td>\n",
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       "      <td>17.6</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>23</td>\n",
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       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>No Churn</td>\n",
       "      <td>13807</td>\n",
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       "      <td>41256.3333</td>\n",
       "      <td>39</td>\n",
       "      <td>0.148986</td>\n",
       "      <td>0</td>\n",
       "      <td>Billing Problem</td>\n",
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       "      <td>0</td>\n",
       "      <td>...</td>\n",
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       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>No Churn</td>\n",
       "      <td>3805</td>\n",
       "      <td>5075.000000</td>\n",
       "      <td>12333.3333</td>\n",
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       "      <td>Billing Problem</td>\n",
       "      <td>3.8</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>10</td>\n",
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       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 21 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "  Target_Churn  Current_Bill_Amt     Avg_Calls  Avg_Calls_Weekdays  \\\n",
       "0     No Churn             14210  17950.000000          30297.0000   \n",
       "1        Churn             14407      0.000000              0.0000   \n",
       "2        Churn             12712    204.666667          10393.6667   \n",
       "3     No Churn             13807  15490.333300          41256.3333   \n",
       "4     No Churn              3805   5075.000000          12333.3333   \n",
       "\n",
       "   Account_Age  Percent_Increase_MOM Acct_Plan_Subtype   Complaint_Code  \\\n",
       "0           24             -0.334193                 1  Billing Problem   \n",
       "1           28              0.000000                 0           Moving   \n",
       "2           23              0.000000                 1  Billing Problem   \n",
       "3           39              0.148986                 0  Billing Problem   \n",
       "4           23             -0.686047                 1  Billing Problem   \n",
       "\n",
       "   Avg_Days_Delinquent  Current_TechSupComplaints  ...  Equipment_Age  \\\n",
       "0                  6.2                          0  ...              8   \n",
       "1                  1.0                          0  ...             17   \n",
       "2                 17.6                          0  ...             23   \n",
       "3                  0.0                          0  ...             17   \n",
       "4                  3.8                          0  ...             10   \n",
       "\n",
       "   Condition_of_Current_Handset  Avg_Hours_WorkOrderOpenned  Target_Code  \\\n",
       "0                           1.0                         0.0            0   \n",
       "1                           1.0                         0.0            1   \n",
       "2                           1.0                         0.0            1   \n",
       "3                           1.0                         0.0            0   \n",
       "4                           1.0                         0.0            0   \n",
       "\n",
       "   Complaint_Code_Billing Problem  Complaint_Code_Call Quality  \\\n",
       "0                               1                            0   \n",
       "1                               0                            0   \n",
       "2                               1                            0   \n",
       "3                               1                            0   \n",
       "4                               1                            0   \n",
       "\n",
       "   Complaint_Code_Check Account  Complaint_Code_Inaccurate Sales Inf  \\\n",
       "0                             0                                    0   \n",
       "1                             0                                    0   \n",
       "2                             0                                    0   \n",
       "3                             0                                    0   \n",
       "4                             0                                    0   \n",
       "\n",
       "   Complaint_Code_Moving  Complaint_Code_Pricing  \n",
       "0                      0                       0  \n",
       "1                      1                       0  \n",
       "2                      0                       0  \n",
       "3                      0                       0  \n",
       "4                      0                       0  \n",
       "\n",
       "[5 rows x 21 columns]"
      ]
     },
     "execution_count": 42,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "id": "e9774467",
   "metadata": {},
   "outputs": [],
   "source": [
    "feature_cols = df.columns.tolist()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "id": "103d056e",
   "metadata": {},
   "outputs": [],
   "source": [
    "items_to_remove =['Target_Churn','Complaint_Code']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "id": "3c7fda4b",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "['Current_Bill_Amt',\n",
       " 'Avg_Calls',\n",
       " 'Avg_Calls_Weekdays',\n",
       " 'Account_Age',\n",
       " 'Percent_Increase_MOM',\n",
       " 'Acct_Plan_Subtype',\n",
       " 'Avg_Days_Delinquent',\n",
       " 'Current_TechSupComplaints',\n",
       " 'Current_Days_OpenWorkOrders',\n",
       " 'Equipment_Age',\n",
       " 'Condition_of_Current_Handset',\n",
       " 'Avg_Hours_WorkOrderOpenned',\n",
       " 'Target_Code',\n",
       " 'Complaint_Code_Billing Problem',\n",
       " 'Complaint_Code_Call Quality',\n",
       " 'Complaint_Code_Check Account',\n",
       " 'Complaint_Code_Inaccurate Sales Inf',\n",
       " 'Complaint_Code_Moving',\n",
       " 'Complaint_Code_Pricing']"
      ]
     },
     "execution_count": 45,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "feature_cols = [item for item in feature_cols if item not in items_to_remove]\n",
    "feature_cols"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "95799e4e",
   "metadata": {},
   "source": [
    "### Feature Selection"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "id": "a177b633",
   "metadata": {},
   "outputs": [],
   "source": [
    "from feature_engine.encoding import OneHotEncoder\n",
    "from sklearn.model_selection import train_test_split\n",
    "from sklearn.preprocessing import StandardScaler\n",
    "from sklearn.ensemble import RandomForestClassifier\n",
    "from boruta import BorutaPy\n",
    "from sklearn.metrics import accuracy_score"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "id": "fb824f68",
   "metadata": {},
   "outputs": [],
   "source": [
    "X_train, X_test, y_train, y_test =  \\\n",
    "  train_test_split(df[feature_cols],\\\n",
    "  df[['Target_Code']], test_size=0.3,random_state=0)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "id": "31687355",
   "metadata": {},
   "outputs": [],
   "source": [
    "scaler = StandardScaler()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "id": "c950e2ce",
   "metadata": {},
   "outputs": [],
   "source": [
    "standcols = X_train.iloc[:,:-7].columns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 50,
   "id": "cffaa889",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<style>#sk-container-id-1 {color: black;background-color: white;}#sk-container-id-1 pre{padding: 0;}#sk-container-id-1 div.sk-toggleable {background-color: white;}#sk-container-id-1 label.sk-toggleable__label {cursor: pointer;display: block;width: 100%;margin-bottom: 0;padding: 0.3em;box-sizing: border-box;text-align: center;}#sk-container-id-1 label.sk-toggleable__label-arrow:before {content: \"▸\";float: left;margin-right: 0.25em;color: #696969;}#sk-container-id-1 label.sk-toggleable__label-arrow:hover:before {color: black;}#sk-container-id-1 div.sk-estimator:hover label.sk-toggleable__label-arrow:before {color: black;}#sk-container-id-1 div.sk-toggleable__content {max-height: 0;max-width: 0;overflow: hidden;text-align: left;background-color: #f0f8ff;}#sk-container-id-1 div.sk-toggleable__content pre {margin: 0.2em;color: black;border-radius: 0.25em;background-color: #f0f8ff;}#sk-container-id-1 input.sk-toggleable__control:checked~div.sk-toggleable__content {max-height: 200px;max-width: 100%;overflow: auto;}#sk-container-id-1 input.sk-toggleable__control:checked~label.sk-toggleable__label-arrow:before {content: \"▾\";}#sk-container-id-1 div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-1 div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-1 input.sk-hidden--visually {border: 0;clip: rect(1px 1px 1px 1px);clip: rect(1px, 1px, 1px, 1px);height: 1px;margin: -1px;overflow: hidden;padding: 0;position: absolute;width: 1px;}#sk-container-id-1 div.sk-estimator {font-family: monospace;background-color: #f0f8ff;border: 1px dotted black;border-radius: 0.25em;box-sizing: border-box;margin-bottom: 0.5em;}#sk-container-id-1 div.sk-estimator:hover {background-color: #d4ebff;}#sk-container-id-1 div.sk-parallel-item::after {content: \"\";width: 100%;border-bottom: 1px solid gray;flex-grow: 1;}#sk-container-id-1 div.sk-label:hover label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-1 div.sk-serial::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: 0;}#sk-container-id-1 div.sk-serial {display: flex;flex-direction: column;align-items: center;background-color: white;padding-right: 0.2em;padding-left: 0.2em;position: relative;}#sk-container-id-1 div.sk-item {position: relative;z-index: 1;}#sk-container-id-1 div.sk-parallel {display: flex;align-items: stretch;justify-content: center;background-color: white;position: relative;}#sk-container-id-1 div.sk-item::before, #sk-container-id-1 div.sk-parallel-item::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: -1;}#sk-container-id-1 div.sk-parallel-item {display: flex;flex-direction: column;z-index: 1;position: relative;background-color: white;}#sk-container-id-1 div.sk-parallel-item:first-child::after {align-self: flex-end;width: 50%;}#sk-container-id-1 div.sk-parallel-item:last-child::after {align-self: flex-start;width: 50%;}#sk-container-id-1 div.sk-parallel-item:only-child::after {width: 0;}#sk-container-id-1 div.sk-dashed-wrapped {border: 1px dashed gray;margin: 0 0.4em 0.5em 0.4em;box-sizing: border-box;padding-bottom: 0.4em;background-color: white;}#sk-container-id-1 div.sk-label label {font-family: monospace;font-weight: bold;display: inline-block;line-height: 1.2em;}#sk-container-id-1 div.sk-label-container {text-align: center;}#sk-container-id-1 div.sk-container {/* jupyter's `normalize.less` sets `[hidden] { display: none; }` but bootstrap.min.css set `[hidden] { display: none !important; }` so we also need the `!important` here to be able to override the default hidden behavior on the sphinx rendered scikit-learn.org. See: https://github.com/scikit-learn/scikit-learn/issues/21755 */display: inline-block !important;position: relative;}#sk-container-id-1 div.sk-text-repr-fallback {display: none;}</style><div id=\"sk-container-id-1\" class=\"sk-top-container\"><div class=\"sk-text-repr-fallback\"><pre>StandardScaler()</pre><b>In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook. <br />On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org.</b></div><div class=\"sk-container\" hidden><div class=\"sk-item\"><div class=\"sk-estimator sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-1\" type=\"checkbox\" checked><label for=\"sk-estimator-id-1\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">StandardScaler</label><div class=\"sk-toggleable__content\"><pre>StandardScaler()</pre></div></div></div></div></div>"
      ],
      "text/plain": [
       "StandardScaler()"
      ]
     },
     "execution_count": 50,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "scaler.fit(X_train[standcols])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 51,
   "id": "29484cb8",
   "metadata": {},
   "outputs": [],
   "source": [
    "X_train_enc = \\\n",
    "  pd.DataFrame(scaler.transform(X_train[standcols]),\\\n",
    " columns=standcols, index=X_train.index).\\\n",
    "  join(X_train[['Complaint_Code_Billing Problem','Complaint_Code_Call Quality','Complaint_Code_Check Account',\\\n",
    " 'Complaint_Code_Inaccurate Sales Inf','Complaint_Code_Moving','Complaint_Code_Pricing']])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 52,
   "id": "b07fe9ba",
   "metadata": {},
   "outputs": [],
   "source": [
    "X_test_enc = \\\n",
    "  pd.DataFrame(scaler.transform(X_test[standcols]),\\\n",
    " columns=standcols, index=X_test.index).\\\n",
    "  join(X_test[['Complaint_Code_Billing Problem','Complaint_Code_Call Quality','Complaint_Code_Check Account',\\\n",
    " 'Complaint_Code_Inaccurate Sales Inf','Complaint_Code_Moving','Complaint_Code_Pricing']])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 53,
   "id": "1f94a3ac",
   "metadata": {},
   "outputs": [],
   "source": [
    "rfc = RandomForestClassifier(n_estimators=100,\\\n",
    "  max_depth=2, n_jobs=-1, random_state=0)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 54,
   "id": "3b110107",
   "metadata": {},
   "outputs": [],
   "source": [
    "borsel = BorutaPy(rfc, random_state=0, verbose=2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 55,
   "id": "c8795950",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Iteration: \t1 / 100\n",
      "Confirmed: \t0\n",
      "Tentative: \t18\n",
      "Rejected: \t0\n",
      "Iteration: \t2 / 100\n",
      "Confirmed: \t0\n",
      "Tentative: \t18\n",
      "Rejected: \t0\n",
      "Iteration: \t3 / 100\n",
      "Confirmed: \t0\n",
      "Tentative: \t18\n",
      "Rejected: \t0\n",
      "Iteration: \t4 / 100\n",
      "Confirmed: \t0\n",
      "Tentative: \t18\n",
      "Rejected: \t0\n",
      "Iteration: \t5 / 100\n",
      "Confirmed: \t0\n",
      "Tentative: \t18\n",
      "Rejected: \t0\n",
      "Iteration: \t6 / 100\n",
      "Confirmed: \t0\n",
      "Tentative: \t18\n",
      "Rejected: \t0\n",
      "Iteration: \t7 / 100\n",
      "Confirmed: \t0\n",
      "Tentative: \t18\n",
      "Rejected: \t0\n",
      "Iteration: \t8 / 100\n",
      "Confirmed: \t13\n",
      "Tentative: \t0\n",
      "Rejected: \t5\n",
      "\n",
      "\n",
      "BorutaPy finished running.\n",
      "\n",
      "Iteration: \t9 / 100\n",
      "Confirmed: \t13\n",
      "Tentative: \t0\n",
      "Rejected: \t5\n"
     ]
    },
    {
     "data": {
      "text/html": [
       "<style>#sk-container-id-2 {color: black;background-color: white;}#sk-container-id-2 pre{padding: 0;}#sk-container-id-2 div.sk-toggleable {background-color: white;}#sk-container-id-2 label.sk-toggleable__label {cursor: pointer;display: block;width: 100%;margin-bottom: 0;padding: 0.3em;box-sizing: border-box;text-align: center;}#sk-container-id-2 label.sk-toggleable__label-arrow:before {content: \"▸\";float: left;margin-right: 0.25em;color: #696969;}#sk-container-id-2 label.sk-toggleable__label-arrow:hover:before {color: black;}#sk-container-id-2 div.sk-estimator:hover label.sk-toggleable__label-arrow:before {color: black;}#sk-container-id-2 div.sk-toggleable__content {max-height: 0;max-width: 0;overflow: hidden;text-align: left;background-color: #f0f8ff;}#sk-container-id-2 div.sk-toggleable__content pre {margin: 0.2em;color: black;border-radius: 0.25em;background-color: #f0f8ff;}#sk-container-id-2 input.sk-toggleable__control:checked~div.sk-toggleable__content {max-height: 200px;max-width: 100%;overflow: auto;}#sk-container-id-2 input.sk-toggleable__control:checked~label.sk-toggleable__label-arrow:before {content: \"▾\";}#sk-container-id-2 div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-2 div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-2 input.sk-hidden--visually {border: 0;clip: rect(1px 1px 1px 1px);clip: rect(1px, 1px, 1px, 1px);height: 1px;margin: -1px;overflow: hidden;padding: 0;position: absolute;width: 1px;}#sk-container-id-2 div.sk-estimator {font-family: monospace;background-color: #f0f8ff;border: 1px dotted black;border-radius: 0.25em;box-sizing: border-box;margin-bottom: 0.5em;}#sk-container-id-2 div.sk-estimator:hover {background-color: #d4ebff;}#sk-container-id-2 div.sk-parallel-item::after {content: \"\";width: 100%;border-bottom: 1px solid gray;flex-grow: 1;}#sk-container-id-2 div.sk-label:hover label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-2 div.sk-serial::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: 0;}#sk-container-id-2 div.sk-serial {display: flex;flex-direction: column;align-items: center;background-color: white;padding-right: 0.2em;padding-left: 0.2em;position: relative;}#sk-container-id-2 div.sk-item {position: relative;z-index: 1;}#sk-container-id-2 div.sk-parallel {display: flex;align-items: stretch;justify-content: center;background-color: white;position: relative;}#sk-container-id-2 div.sk-item::before, #sk-container-id-2 div.sk-parallel-item::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: -1;}#sk-container-id-2 div.sk-parallel-item {display: flex;flex-direction: column;z-index: 1;position: relative;background-color: white;}#sk-container-id-2 div.sk-parallel-item:first-child::after {align-self: flex-end;width: 50%;}#sk-container-id-2 div.sk-parallel-item:last-child::after {align-self: flex-start;width: 50%;}#sk-container-id-2 div.sk-parallel-item:only-child::after {width: 0;}#sk-container-id-2 div.sk-dashed-wrapped {border: 1px dashed gray;margin: 0 0.4em 0.5em 0.4em;box-sizing: border-box;padding-bottom: 0.4em;background-color: white;}#sk-container-id-2 div.sk-label label {font-family: monospace;font-weight: bold;display: inline-block;line-height: 1.2em;}#sk-container-id-2 div.sk-label-container {text-align: center;}#sk-container-id-2 div.sk-container {/* jupyter's `normalize.less` sets `[hidden] { display: none; }` but bootstrap.min.css set `[hidden] { display: none !important; }` so we also need the `!important` here to be able to override the default hidden behavior on the sphinx rendered scikit-learn.org. See: https://github.com/scikit-learn/scikit-learn/issues/21755 */display: inline-block !important;position: relative;}#sk-container-id-2 div.sk-text-repr-fallback {display: none;}</style><div id=\"sk-container-id-2\" class=\"sk-top-container\"><div class=\"sk-text-repr-fallback\"><pre>BorutaPy(estimator=RandomForestClassifier(max_depth=2, n_estimators=1000,\n",
       "                                          n_jobs=-1,\n",
       "                                          random_state=RandomState(MT19937) at 0x7FA4A8356440),\n",
       "         random_state=RandomState(MT19937) at 0x7FA4A8356440, verbose=2)</pre><b>In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook. <br />On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org.</b></div><div class=\"sk-container\" hidden><div class=\"sk-item sk-dashed-wrapped\"><div class=\"sk-label-container\"><div class=\"sk-label sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-2\" type=\"checkbox\" ><label for=\"sk-estimator-id-2\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">BorutaPy</label><div class=\"sk-toggleable__content\"><pre>BorutaPy(estimator=RandomForestClassifier(max_depth=2, n_estimators=1000,\n",
       "                                          n_jobs=-1,\n",
       "                                          random_state=RandomState(MT19937) at 0x7FA4A8356440),\n",
       "         random_state=RandomState(MT19937) at 0x7FA4A8356440, verbose=2)</pre></div></div></div><div class=\"sk-parallel\"><div class=\"sk-parallel-item\"><div class=\"sk-item\"><div class=\"sk-label-container\"><div class=\"sk-label sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-3\" type=\"checkbox\" ><label for=\"sk-estimator-id-3\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">estimator: RandomForestClassifier</label><div class=\"sk-toggleable__content\"><pre>RandomForestClassifier(max_depth=2, n_estimators=1000, n_jobs=-1,\n",
       "                       random_state=RandomState(MT19937) at 0x7FA4A8356440)</pre></div></div></div><div class=\"sk-serial\"><div class=\"sk-item\"><div class=\"sk-estimator sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-4\" type=\"checkbox\" ><label for=\"sk-estimator-id-4\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">RandomForestClassifier</label><div class=\"sk-toggleable__content\"><pre>RandomForestClassifier(max_depth=2, n_estimators=1000, n_jobs=-1,\n",
       "                       random_state=RandomState(MT19937) at 0x7FA4A8356440)</pre></div></div></div></div></div></div></div></div></div></div>"
      ],
      "text/plain": [
       "BorutaPy(estimator=RandomForestClassifier(max_depth=2, n_estimators=1000,\n",
       "                                          n_jobs=-1,\n",
       "                                          random_state=RandomState(MT19937) at 0x7FA4A8356440),\n",
       "         random_state=RandomState(MT19937) at 0x7FA4A8356440, verbose=2)"
      ]
     },
     "execution_count": 55,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "borsel.fit(X_train_enc.values, y_train.values.ravel())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 56,
   "id": "19c85979",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Index(['Current_Bill_Amt', 'Avg_Calls', 'Avg_Calls_Weekdays', 'Account_Age',\n",
       "       'Percent_Increase_MOM', 'Acct_Plan_Subtype', 'Avg_Days_Delinquent',\n",
       "       'Current_TechSupComplaints', 'Equipment_Age',\n",
       "       'Complaint_Code_Billing Problem', 'Complaint_Code_Call Quality',\n",
       "       'Complaint_Code_Check Account', 'Complaint_Code_Moving'],\n",
       "      dtype='object')"
      ]
     },
     "execution_count": 56,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "selcols = X_train_enc.columns[borsel.support_]\n",
    "selcols"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 57,
   "id": "dca683dc",
   "metadata": {},
   "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>feature</th>\n",
       "      <th>ranking</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Current_Bill_Amt</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Avg_Calls</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Avg_Calls_Weekdays</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Account_Age</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Percent_Increase_MOM</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>Acct_Plan_Subtype</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>Avg_Days_Delinquent</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>Current_TechSupComplaints</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16</th>\n",
       "      <td>Complaint_Code_Moving</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>Equipment_Age</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>Complaint_Code_Check Account</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>Complaint_Code_Call Quality</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>Complaint_Code_Billing Problem</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>Current_Days_OpenWorkOrders</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>Avg_Hours_WorkOrderOpenned</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15</th>\n",
       "      <td>Complaint_Code_Inaccurate Sales Inf</td>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>Condition_of_Current_Handset</td>\n",
       "      <td>5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17</th>\n",
       "      <td>Complaint_Code_Pricing</td>\n",
       "      <td>6</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                                feature  ranking\n",
       "0                      Current_Bill_Amt        1\n",
       "1                             Avg_Calls        1\n",
       "2                    Avg_Calls_Weekdays        1\n",
       "3                           Account_Age        1\n",
       "4                  Percent_Increase_MOM        1\n",
       "5                     Acct_Plan_Subtype        1\n",
       "6                   Avg_Days_Delinquent        1\n",
       "7             Current_TechSupComplaints        1\n",
       "16                Complaint_Code_Moving        1\n",
       "9                         Equipment_Age        1\n",
       "14         Complaint_Code_Check Account        1\n",
       "13          Complaint_Code_Call Quality        1\n",
       "12       Complaint_Code_Billing Problem        1\n",
       "8           Current_Days_OpenWorkOrders        2\n",
       "11           Avg_Hours_WorkOrderOpenned        3\n",
       "15  Complaint_Code_Inaccurate Sales Inf        4\n",
       "10         Condition_of_Current_Handset        5\n",
       "17               Complaint_Code_Pricing        6"
      ]
     },
     "execution_count": 57,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pd.DataFrame({'ranking': borsel.ranking_,\\\n",
    "              'feature': X_train_enc.columns},columns=['feature','ranking']).\\\n",
    "sort_values(['ranking'], ascending=True)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "410eae4b",
   "metadata": {},
   "source": [
    "### Fitting Random Forest"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 58,
   "id": "299d7d0c",
   "metadata": {},
   "outputs": [],
   "source": [
    "feature_cols_r = df.columns.tolist()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 59,
   "id": "6268e1ab",
   "metadata": {},
   "outputs": [],
   "source": [
    "items_to_remove_r = ['Target_Churn','Complaint_Code','Current_Days_OpenWorkOrders','Avg_Hours_WorkOrderOpenned',\\\n",
    "                   'Complaint_Code_Inaccurate Sales Inf','Condition_of_Current_Handset','Complaint_Code_Pricing','Target_Code']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 60,
   "id": "96c968cc",
   "metadata": {},
   "outputs": [],
   "source": [
    "feature_cols_r = [item for item in feature_cols_r if item not in items_to_remove_r]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 61,
   "id": "2efe7446",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "['Current_Bill_Amt',\n",
       " 'Avg_Calls',\n",
       " 'Avg_Calls_Weekdays',\n",
       " 'Account_Age',\n",
       " 'Percent_Increase_MOM',\n",
       " 'Acct_Plan_Subtype',\n",
       " 'Avg_Days_Delinquent',\n",
       " 'Current_TechSupComplaints',\n",
       " 'Equipment_Age',\n",
       " 'Complaint_Code_Billing Problem',\n",
       " 'Complaint_Code_Call Quality',\n",
       " 'Complaint_Code_Check Account',\n",
       " 'Complaint_Code_Moving']"
      ]
     },
     "execution_count": 61,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "feature_cols_r"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 62,
   "id": "adafeebf",
   "metadata": {},
   "outputs": [],
   "source": [
    "X_train_r, X_test_r, y_train_r, y_test_r =  \\\n",
    "  train_test_split(df[feature_cols_r],\\\n",
    "  df[['Target_Code']], test_size=0.3,random_state=0)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 63,
   "id": "8ca18b45",
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.model_selection import GridSearchCV"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 64,
   "id": "408cfb26",
   "metadata": {},
   "outputs": [],
   "source": [
    "rf_params = {'max_depth':[3, 6, 9, 12, 15],\n",
    "             'n_estimators':[10, 50, 100, 200, 300]}"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 65,
   "id": "7fa4e379",
   "metadata": {},
   "outputs": [],
   "source": [
    "rf = RandomForestClassifier\\\n",
    "(n_estimators=10, criterion='gini', max_depth=3,\n",
    "min_samples_split=2, min_samples_leaf=1, min_weight_fraction_leaf=0.0,\n",
    "max_features='sqrt', max_leaf_nodes=None, min_impurity_decrease=0.0,\n",
    "bootstrap=True, oob_score=False, n_jobs=None,\n",
    "random_state=4, verbose=0, warm_start=False, class_weight=None)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 66,
   "id": "85177242",
   "metadata": {},
   "outputs": [],
   "source": [
    "cv_rf = GridSearchCV(rf, param_grid=rf_params, scoring='roc_auc',\n",
    "                     n_jobs=-1, refit=True, cv=4, verbose=2,\n",
    "                     error_score=np.nan, return_train_score=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 67,
   "id": "9aa4cb17",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Fitting 4 folds for each of 25 candidates, totalling 100 fits\n"
     ]
    },
    {
     "data": {
      "text/html": [
       "<style>#sk-container-id-3 {color: black;background-color: white;}#sk-container-id-3 pre{padding: 0;}#sk-container-id-3 div.sk-toggleable {background-color: white;}#sk-container-id-3 label.sk-toggleable__label {cursor: pointer;display: block;width: 100%;margin-bottom: 0;padding: 0.3em;box-sizing: border-box;text-align: center;}#sk-container-id-3 label.sk-toggleable__label-arrow:before {content: \"▸\";float: left;margin-right: 0.25em;color: #696969;}#sk-container-id-3 label.sk-toggleable__label-arrow:hover:before {color: black;}#sk-container-id-3 div.sk-estimator:hover label.sk-toggleable__label-arrow:before {color: black;}#sk-container-id-3 div.sk-toggleable__content {max-height: 0;max-width: 0;overflow: hidden;text-align: left;background-color: #f0f8ff;}#sk-container-id-3 div.sk-toggleable__content pre {margin: 0.2em;color: black;border-radius: 0.25em;background-color: #f0f8ff;}#sk-container-id-3 input.sk-toggleable__control:checked~div.sk-toggleable__content {max-height: 200px;max-width: 100%;overflow: auto;}#sk-container-id-3 input.sk-toggleable__control:checked~label.sk-toggleable__label-arrow:before {content: \"▾\";}#sk-container-id-3 div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-3 div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-3 input.sk-hidden--visually {border: 0;clip: rect(1px 1px 1px 1px);clip: rect(1px, 1px, 1px, 1px);height: 1px;margin: -1px;overflow: hidden;padding: 0;position: absolute;width: 1px;}#sk-container-id-3 div.sk-estimator {font-family: monospace;background-color: #f0f8ff;border: 1px dotted black;border-radius: 0.25em;box-sizing: border-box;margin-bottom: 0.5em;}#sk-container-id-3 div.sk-estimator:hover {background-color: #d4ebff;}#sk-container-id-3 div.sk-parallel-item::after {content: \"\";width: 100%;border-bottom: 1px solid gray;flex-grow: 1;}#sk-container-id-3 div.sk-label:hover label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-3 div.sk-serial::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: 0;}#sk-container-id-3 div.sk-serial {display: flex;flex-direction: column;align-items: center;background-color: white;padding-right: 0.2em;padding-left: 0.2em;position: relative;}#sk-container-id-3 div.sk-item {position: relative;z-index: 1;}#sk-container-id-3 div.sk-parallel {display: flex;align-items: stretch;justify-content: center;background-color: white;position: relative;}#sk-container-id-3 div.sk-item::before, #sk-container-id-3 div.sk-parallel-item::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: -1;}#sk-container-id-3 div.sk-parallel-item {display: flex;flex-direction: column;z-index: 1;position: relative;background-color: white;}#sk-container-id-3 div.sk-parallel-item:first-child::after {align-self: flex-end;width: 50%;}#sk-container-id-3 div.sk-parallel-item:last-child::after {align-self: flex-start;width: 50%;}#sk-container-id-3 div.sk-parallel-item:only-child::after {width: 0;}#sk-container-id-3 div.sk-dashed-wrapped {border: 1px dashed gray;margin: 0 0.4em 0.5em 0.4em;box-sizing: border-box;padding-bottom: 0.4em;background-color: white;}#sk-container-id-3 div.sk-label label {font-family: monospace;font-weight: bold;display: inline-block;line-height: 1.2em;}#sk-container-id-3 div.sk-label-container {text-align: center;}#sk-container-id-3 div.sk-container {/* jupyter's `normalize.less` sets `[hidden] { display: none; }` but bootstrap.min.css set `[hidden] { display: none !important; }` so we also need the `!important` here to be able to override the default hidden behavior on the sphinx rendered scikit-learn.org. See: https://github.com/scikit-learn/scikit-learn/issues/21755 */display: inline-block !important;position: relative;}#sk-container-id-3 div.sk-text-repr-fallback {display: none;}</style><div id=\"sk-container-id-3\" class=\"sk-top-container\"><div class=\"sk-text-repr-fallback\"><pre>GridSearchCV(cv=4,\n",
       "             estimator=RandomForestClassifier(max_depth=3, n_estimators=10,\n",
       "                                              random_state=4),\n",
       "             n_jobs=-1,\n",
       "             param_grid={&#x27;max_depth&#x27;: [3, 6, 9, 12, 15],\n",
       "                         &#x27;n_estimators&#x27;: [10, 50, 100, 200, 300]},\n",
       "             return_train_score=True, scoring=&#x27;roc_auc&#x27;, verbose=2)</pre><b>In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook. <br />On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org.</b></div><div class=\"sk-container\" hidden><div class=\"sk-item sk-dashed-wrapped\"><div class=\"sk-label-container\"><div class=\"sk-label sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-5\" type=\"checkbox\" ><label for=\"sk-estimator-id-5\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">GridSearchCV</label><div class=\"sk-toggleable__content\"><pre>GridSearchCV(cv=4,\n",
       "             estimator=RandomForestClassifier(max_depth=3, n_estimators=10,\n",
       "                                              random_state=4),\n",
       "             n_jobs=-1,\n",
       "             param_grid={&#x27;max_depth&#x27;: [3, 6, 9, 12, 15],\n",
       "                         &#x27;n_estimators&#x27;: [10, 50, 100, 200, 300]},\n",
       "             return_train_score=True, scoring=&#x27;roc_auc&#x27;, verbose=2)</pre></div></div></div><div class=\"sk-parallel\"><div class=\"sk-parallel-item\"><div class=\"sk-item\"><div class=\"sk-label-container\"><div class=\"sk-label sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-6\" type=\"checkbox\" ><label for=\"sk-estimator-id-6\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">estimator: RandomForestClassifier</label><div class=\"sk-toggleable__content\"><pre>RandomForestClassifier(max_depth=3, n_estimators=10, random_state=4)</pre></div></div></div><div class=\"sk-serial\"><div class=\"sk-item\"><div class=\"sk-estimator sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-7\" type=\"checkbox\" ><label for=\"sk-estimator-id-7\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">RandomForestClassifier</label><div class=\"sk-toggleable__content\"><pre>RandomForestClassifier(max_depth=3, n_estimators=10, random_state=4)</pre></div></div></div></div></div></div></div></div></div></div>"
      ],
      "text/plain": [
       "GridSearchCV(cv=4,\n",
       "             estimator=RandomForestClassifier(max_depth=3, n_estimators=10,\n",
       "                                              random_state=4),\n",
       "             n_jobs=-1,\n",
       "             param_grid={'max_depth': [3, 6, 9, 12, 15],\n",
       "                         'n_estimators': [10, 50, 100, 200, 300]},\n",
       "             return_train_score=True, scoring='roc_auc', verbose=2)"
      ]
     },
     "execution_count": 67,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "cv_rf.fit(X_train_r.values, y_train_r.values.ravel())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 68,
   "id": "0069e4e9",
   "metadata": {},
   "outputs": [],
   "source": [
    "cv_rf_results_df = pd.DataFrame(cv_rf.cv_results_)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 69,
   "id": "433a4e8b",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
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       "    }\n",
       "\n",
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       "\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>mean_fit_time</th>\n",
       "      <th>std_fit_time</th>\n",
       "      <th>mean_score_time</th>\n",
       "      <th>std_score_time</th>\n",
       "      <th>param_max_depth</th>\n",
       "      <th>param_n_estimators</th>\n",
       "      <th>params</th>\n",
       "      <th>split0_test_score</th>\n",
       "      <th>split1_test_score</th>\n",
       "      <th>split2_test_score</th>\n",
       "      <th>split3_test_score</th>\n",
       "      <th>mean_test_score</th>\n",
       "      <th>std_test_score</th>\n",
       "      <th>rank_test_score</th>\n",
       "      <th>split0_train_score</th>\n",
       "      <th>split1_train_score</th>\n",
       "      <th>split2_train_score</th>\n",
       "      <th>split3_train_score</th>\n",
       "      <th>mean_train_score</th>\n",
       "      <th>std_train_score</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0.020268</td>\n",
       "      <td>0.001674</td>\n",
       "      <td>0.003666</td>\n",
       "      <td>0.000633</td>\n",
       "      <td>3</td>\n",
       "      <td>10</td>\n",
       "      <td>{'max_depth': 3, 'n_estimators': 10}</td>\n",
       "      <td>0.851102</td>\n",
       "      <td>0.860550</td>\n",
       "      <td>0.871572</td>\n",
       "      <td>0.866182</td>\n",
       "      <td>0.862352</td>\n",
       "      <td>0.007574</td>\n",
       "      <td>25</td>\n",
       "      <td>0.876252</td>\n",
       "      <td>0.876508</td>\n",
       "      <td>0.874460</td>\n",
       "      <td>0.874622</td>\n",
       "      <td>0.875461</td>\n",
       "      <td>0.000926</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>0.092480</td>\n",
       "      <td>0.009596</td>\n",
       "      <td>0.005656</td>\n",
       "      <td>0.000217</td>\n",
       "      <td>3</td>\n",
       "      <td>50</td>\n",
       "      <td>{'max_depth': 3, 'n_estimators': 50}</td>\n",
       "      <td>0.864437</td>\n",
       "      <td>0.876427</td>\n",
       "      <td>0.883314</td>\n",
       "      <td>0.875905</td>\n",
       "      <td>0.875021</td>\n",
       "      <td>0.006774</td>\n",
       "      <td>22</td>\n",
       "      <td>0.890848</td>\n",
       "      <td>0.889351</td>\n",
       "      <td>0.883354</td>\n",
       "      <td>0.887090</td>\n",
       "      <td>0.887661</td>\n",
       "      <td>0.002823</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>0.177236</td>\n",
       "      <td>0.019469</td>\n",
       "      <td>0.009966</td>\n",
       "      <td>0.001174</td>\n",
       "      <td>3</td>\n",
       "      <td>100</td>\n",
       "      <td>{'max_depth': 3, 'n_estimators': 100}</td>\n",
       "      <td>0.868171</td>\n",
       "      <td>0.879406</td>\n",
       "      <td>0.889535</td>\n",
       "      <td>0.880745</td>\n",
       "      <td>0.879464</td>\n",
       "      <td>0.007593</td>\n",
       "      <td>19</td>\n",
       "      <td>0.895348</td>\n",
       "      <td>0.891805</td>\n",
       "      <td>0.889273</td>\n",
       "      <td>0.891651</td>\n",
       "      <td>0.892019</td>\n",
       "      <td>0.002168</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>0.308000</td>\n",
       "      <td>0.007835</td>\n",
       "      <td>0.016273</td>\n",
       "      <td>0.000942</td>\n",
       "      <td>3</td>\n",
       "      <td>200</td>\n",
       "      <td>{'max_depth': 3, 'n_estimators': 200}</td>\n",
       "      <td>0.870044</td>\n",
       "      <td>0.881630</td>\n",
       "      <td>0.896602</td>\n",
       "      <td>0.885429</td>\n",
       "      <td>0.883426</td>\n",
       "      <td>0.009486</td>\n",
       "      <td>17</td>\n",
       "      <td>0.898713</td>\n",
       "      <td>0.895430</td>\n",
       "      <td>0.892088</td>\n",
       "      <td>0.894640</td>\n",
       "      <td>0.895218</td>\n",
       "      <td>0.002366</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>0.445491</td>\n",
       "      <td>0.007226</td>\n",
       "      <td>0.032022</td>\n",
       "      <td>0.005452</td>\n",
       "      <td>3</td>\n",
       "      <td>300</td>\n",
       "      <td>{'max_depth': 3, 'n_estimators': 300}</td>\n",
       "      <td>0.869967</td>\n",
       "      <td>0.881730</td>\n",
       "      <td>0.896442</td>\n",
       "      <td>0.885352</td>\n",
       "      <td>0.883373</td>\n",
       "      <td>0.009449</td>\n",
       "      <td>18</td>\n",
       "      <td>0.898650</td>\n",
       "      <td>0.895298</td>\n",
       "      <td>0.891509</td>\n",
       "      <td>0.894222</td>\n",
       "      <td>0.894920</td>\n",
       "      <td>0.002558</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>0.023828</td>\n",
       "      <td>0.002949</td>\n",
       "      <td>0.001842</td>\n",
       "      <td>0.000019</td>\n",
       "      <td>6</td>\n",
       "      <td>10</td>\n",
       "      <td>{'max_depth': 6, 'n_estimators': 10}</td>\n",
       "      <td>0.871563</td>\n",
       "      <td>0.875486</td>\n",
       "      <td>0.883933</td>\n",
       "      <td>0.877848</td>\n",
       "      <td>0.877207</td>\n",
       "      <td>0.004485</td>\n",
       "      <td>21</td>\n",
       "      <td>0.915233</td>\n",
       "      <td>0.918659</td>\n",
       "      <td>0.915395</td>\n",
       "      <td>0.919121</td>\n",
       "      <td>0.917102</td>\n",
       "      <td>0.001796</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>0.102793</td>\n",
       "      <td>0.000533</td>\n",
       "      <td>0.005511</td>\n",
       "      <td>0.000164</td>\n",
       "      <td>6</td>\n",
       "      <td>50</td>\n",
       "      <td>{'max_depth': 6, 'n_estimators': 50}</td>\n",
       "      <td>0.879276</td>\n",
       "      <td>0.885022</td>\n",
       "      <td>0.896454</td>\n",
       "      <td>0.890823</td>\n",
       "      <td>0.887894</td>\n",
       "      <td>0.006410</td>\n",
       "      <td>13</td>\n",
       "      <td>0.927093</td>\n",
       "      <td>0.927223</td>\n",
       "      <td>0.923194</td>\n",
       "      <td>0.927840</td>\n",
       "      <td>0.926338</td>\n",
       "      <td>0.001837</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>0.213030</td>\n",
       "      <td>0.004758</td>\n",
       "      <td>0.011342</td>\n",
       "      <td>0.001548</td>\n",
       "      <td>6</td>\n",
       "      <td>100</td>\n",
       "      <td>{'max_depth': 6, 'n_estimators': 100}</td>\n",
       "      <td>0.878539</td>\n",
       "      <td>0.885553</td>\n",
       "      <td>0.899923</td>\n",
       "      <td>0.892065</td>\n",
       "      <td>0.889020</td>\n",
       "      <td>0.007906</td>\n",
       "      <td>10</td>\n",
       "      <td>0.928965</td>\n",
       "      <td>0.928215</td>\n",
       "      <td>0.924271</td>\n",
       "      <td>0.928717</td>\n",
       "      <td>0.927542</td>\n",
       "      <td>0.001908</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>0.402544</td>\n",
       "      <td>0.006572</td>\n",
       "      <td>0.023441</td>\n",
       "      <td>0.002798</td>\n",
       "      <td>6</td>\n",
       "      <td>200</td>\n",
       "      <td>{'max_depth': 6, 'n_estimators': 200}</td>\n",
       "      <td>0.878739</td>\n",
       "      <td>0.886744</td>\n",
       "      <td>0.901173</td>\n",
       "      <td>0.892088</td>\n",
       "      <td>0.889686</td>\n",
       "      <td>0.008158</td>\n",
       "      <td>6</td>\n",
       "      <td>0.929531</td>\n",
       "      <td>0.928575</td>\n",
       "      <td>0.925058</td>\n",
       "      <td>0.929116</td>\n",
       "      <td>0.928070</td>\n",
       "      <td>0.001772</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>0.652555</td>\n",
       "      <td>0.036184</td>\n",
       "      <td>0.041744</td>\n",
       "      <td>0.005562</td>\n",
       "      <td>6</td>\n",
       "      <td>300</td>\n",
       "      <td>{'max_depth': 6, 'n_estimators': 300}</td>\n",
       "      <td>0.878149</td>\n",
       "      <td>0.886202</td>\n",
       "      <td>0.902330</td>\n",
       "      <td>0.891592</td>\n",
       "      <td>0.889568</td>\n",
       "      <td>0.008785</td>\n",
       "      <td>7</td>\n",
       "      <td>0.929354</td>\n",
       "      <td>0.928884</td>\n",
       "      <td>0.924786</td>\n",
       "      <td>0.928768</td>\n",
       "      <td>0.927948</td>\n",
       "      <td>0.001839</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>0.027463</td>\n",
       "      <td>0.002366</td>\n",
       "      <td>0.001985</td>\n",
       "      <td>0.000013</td>\n",
       "      <td>9</td>\n",
       "      <td>10</td>\n",
       "      <td>{'max_depth': 9, 'n_estimators': 10}</td>\n",
       "      <td>0.871843</td>\n",
       "      <td>0.873725</td>\n",
       "      <td>0.884314</td>\n",
       "      <td>0.887940</td>\n",
       "      <td>0.879455</td>\n",
       "      <td>0.006826</td>\n",
       "      <td>20</td>\n",
       "      <td>0.962087</td>\n",
       "      <td>0.959639</td>\n",
       "      <td>0.958198</td>\n",
       "      <td>0.965865</td>\n",
       "      <td>0.961447</td>\n",
       "      <td>0.002905</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>0.129707</td>\n",
       "      <td>0.001727</td>\n",
       "      <td>0.006142</td>\n",
       "      <td>0.000021</td>\n",
       "      <td>9</td>\n",
       "      <td>50</td>\n",
       "      <td>{'max_depth': 9, 'n_estimators': 50}</td>\n",
       "      <td>0.878256</td>\n",
       "      <td>0.886927</td>\n",
       "      <td>0.899233</td>\n",
       "      <td>0.888256</td>\n",
       "      <td>0.888168</td>\n",
       "      <td>0.007454</td>\n",
       "      <td>12</td>\n",
       "      <td>0.970302</td>\n",
       "      <td>0.969324</td>\n",
       "      <td>0.966985</td>\n",
       "      <td>0.972768</td>\n",
       "      <td>0.969845</td>\n",
       "      <td>0.002074</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>0.280681</td>\n",
       "      <td>0.021941</td>\n",
       "      <td>0.014971</td>\n",
       "      <td>0.004330</td>\n",
       "      <td>9</td>\n",
       "      <td>100</td>\n",
       "      <td>{'max_depth': 9, 'n_estimators': 100}</td>\n",
       "      <td>0.882273</td>\n",
       "      <td>0.886738</td>\n",
       "      <td>0.901462</td>\n",
       "      <td>0.891592</td>\n",
       "      <td>0.890516</td>\n",
       "      <td>0.007127</td>\n",
       "      <td>4</td>\n",
       "      <td>0.970675</td>\n",
       "      <td>0.969847</td>\n",
       "      <td>0.968736</td>\n",
       "      <td>0.972063</td>\n",
       "      <td>0.970330</td>\n",
       "      <td>0.001214</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>0.530904</td>\n",
       "      <td>0.020085</td>\n",
       "      <td>0.027710</td>\n",
       "      <td>0.001933</td>\n",
       "      <td>9</td>\n",
       "      <td>200</td>\n",
       "      <td>{'max_depth': 9, 'n_estimators': 200}</td>\n",
       "      <td>0.882762</td>\n",
       "      <td>0.889063</td>\n",
       "      <td>0.903049</td>\n",
       "      <td>0.891373</td>\n",
       "      <td>0.891562</td>\n",
       "      <td>0.007343</td>\n",
       "      <td>2</td>\n",
       "      <td>0.970782</td>\n",
       "      <td>0.970075</td>\n",
       "      <td>0.969056</td>\n",
       "      <td>0.972251</td>\n",
       "      <td>0.970541</td>\n",
       "      <td>0.001162</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>0.746692</td>\n",
       "      <td>0.012132</td>\n",
       "      <td>0.044403</td>\n",
       "      <td>0.007013</td>\n",
       "      <td>9</td>\n",
       "      <td>300</td>\n",
       "      <td>{'max_depth': 9, 'n_estimators': 300}</td>\n",
       "      <td>0.882332</td>\n",
       "      <td>0.889582</td>\n",
       "      <td>0.903562</td>\n",
       "      <td>0.892307</td>\n",
       "      <td>0.891946</td>\n",
       "      <td>0.007634</td>\n",
       "      <td>1</td>\n",
       "      <td>0.970682</td>\n",
       "      <td>0.970228</td>\n",
       "      <td>0.968896</td>\n",
       "      <td>0.972020</td>\n",
       "      <td>0.970456</td>\n",
       "      <td>0.001116</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15</th>\n",
       "      <td>0.034529</td>\n",
       "      <td>0.004144</td>\n",
       "      <td>0.002118</td>\n",
       "      <td>0.000008</td>\n",
       "      <td>12</td>\n",
       "      <td>10</td>\n",
       "      <td>{'max_depth': 12, 'n_estimators': 10}</td>\n",
       "      <td>0.870224</td>\n",
       "      <td>0.870749</td>\n",
       "      <td>0.881695</td>\n",
       "      <td>0.865499</td>\n",
       "      <td>0.872042</td>\n",
       "      <td>0.005936</td>\n",
       "      <td>23</td>\n",
       "      <td>0.986701</td>\n",
       "      <td>0.986888</td>\n",
       "      <td>0.989280</td>\n",
       "      <td>0.991831</td>\n",
       "      <td>0.988675</td>\n",
       "      <td>0.002087</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16</th>\n",
       "      <td>0.141856</td>\n",
       "      <td>0.002528</td>\n",
       "      <td>0.007666</td>\n",
       "      <td>0.001457</td>\n",
       "      <td>12</td>\n",
       "      <td>50</td>\n",
       "      <td>{'max_depth': 12, 'n_estimators': 50}</td>\n",
       "      <td>0.880128</td>\n",
       "      <td>0.880152</td>\n",
       "      <td>0.900471</td>\n",
       "      <td>0.884950</td>\n",
       "      <td>0.886426</td>\n",
       "      <td>0.008344</td>\n",
       "      <td>15</td>\n",
       "      <td>0.994987</td>\n",
       "      <td>0.993750</td>\n",
       "      <td>0.994662</td>\n",
       "      <td>0.995990</td>\n",
       "      <td>0.994847</td>\n",
       "      <td>0.000801</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17</th>\n",
       "      <td>0.290959</td>\n",
       "      <td>0.018577</td>\n",
       "      <td>0.018507</td>\n",
       "      <td>0.008678</td>\n",
       "      <td>12</td>\n",
       "      <td>100</td>\n",
       "      <td>{'max_depth': 12, 'n_estimators': 100}</td>\n",
       "      <td>0.883470</td>\n",
       "      <td>0.884780</td>\n",
       "      <td>0.900560</td>\n",
       "      <td>0.887570</td>\n",
       "      <td>0.889095</td>\n",
       "      <td>0.006783</td>\n",
       "      <td>9</td>\n",
       "      <td>0.995399</td>\n",
       "      <td>0.994356</td>\n",
       "      <td>0.994847</td>\n",
       "      <td>0.996213</td>\n",
       "      <td>0.995204</td>\n",
       "      <td>0.000690</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18</th>\n",
       "      <td>0.637813</td>\n",
       "      <td>0.009517</td>\n",
       "      <td>0.032047</td>\n",
       "      <td>0.003949</td>\n",
       "      <td>12</td>\n",
       "      <td>200</td>\n",
       "      <td>{'max_depth': 12, 'n_estimators': 200}</td>\n",
       "      <td>0.882532</td>\n",
       "      <td>0.886443</td>\n",
       "      <td>0.903108</td>\n",
       "      <td>0.889161</td>\n",
       "      <td>0.890311</td>\n",
       "      <td>0.007755</td>\n",
       "      <td>5</td>\n",
       "      <td>0.995229</td>\n",
       "      <td>0.994589</td>\n",
       "      <td>0.995020</td>\n",
       "      <td>0.996576</td>\n",
       "      <td>0.995354</td>\n",
       "      <td>0.000742</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19</th>\n",
       "      <td>0.903929</td>\n",
       "      <td>0.031043</td>\n",
       "      <td>0.039782</td>\n",
       "      <td>0.002757</td>\n",
       "      <td>12</td>\n",
       "      <td>300</td>\n",
       "      <td>{'max_depth': 12, 'n_estimators': 300}</td>\n",
       "      <td>0.882355</td>\n",
       "      <td>0.887134</td>\n",
       "      <td>0.903722</td>\n",
       "      <td>0.888901</td>\n",
       "      <td>0.890528</td>\n",
       "      <td>0.007985</td>\n",
       "      <td>3</td>\n",
       "      <td>0.995520</td>\n",
       "      <td>0.994442</td>\n",
       "      <td>0.995170</td>\n",
       "      <td>0.996541</td>\n",
       "      <td>0.995418</td>\n",
       "      <td>0.000756</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20</th>\n",
       "      <td>0.040088</td>\n",
       "      <td>0.004012</td>\n",
       "      <td>0.003501</td>\n",
       "      <td>0.001992</td>\n",
       "      <td>15</td>\n",
       "      <td>10</td>\n",
       "      <td>{'max_depth': 15, 'n_estimators': 10}</td>\n",
       "      <td>0.869584</td>\n",
       "      <td>0.858045</td>\n",
       "      <td>0.867814</td>\n",
       "      <td>0.872590</td>\n",
       "      <td>0.867008</td>\n",
       "      <td>0.005449</td>\n",
       "      <td>24</td>\n",
       "      <td>0.997054</td>\n",
       "      <td>0.996584</td>\n",
       "      <td>0.997066</td>\n",
       "      <td>0.998620</td>\n",
       "      <td>0.997331</td>\n",
       "      <td>0.000769</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>21</th>\n",
       "      <td>0.173430</td>\n",
       "      <td>0.011580</td>\n",
       "      <td>0.007493</td>\n",
       "      <td>0.000484</td>\n",
       "      <td>15</td>\n",
       "      <td>50</td>\n",
       "      <td>{'max_depth': 15, 'n_estimators': 50}</td>\n",
       "      <td>0.879368</td>\n",
       "      <td>0.879952</td>\n",
       "      <td>0.891853</td>\n",
       "      <td>0.883522</td>\n",
       "      <td>0.883673</td>\n",
       "      <td>0.004983</td>\n",
       "      <td>16</td>\n",
       "      <td>0.999861</td>\n",
       "      <td>0.999728</td>\n",
       "      <td>0.999789</td>\n",
       "      <td>0.999970</td>\n",
       "      <td>0.999837</td>\n",
       "      <td>0.000090</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>22</th>\n",
       "      <td>0.300019</td>\n",
       "      <td>0.007485</td>\n",
       "      <td>0.014830</td>\n",
       "      <td>0.002673</td>\n",
       "      <td>15</td>\n",
       "      <td>100</td>\n",
       "      <td>{'max_depth': 15, 'n_estimators': 100}</td>\n",
       "      <td>0.882821</td>\n",
       "      <td>0.882700</td>\n",
       "      <td>0.896734</td>\n",
       "      <td>0.885234</td>\n",
       "      <td>0.886873</td>\n",
       "      <td>0.005783</td>\n",
       "      <td>14</td>\n",
       "      <td>0.999945</td>\n",
       "      <td>0.999772</td>\n",
       "      <td>0.999858</td>\n",
       "      <td>0.999990</td>\n",
       "      <td>0.999891</td>\n",
       "      <td>0.000084</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>23</th>\n",
       "      <td>0.574487</td>\n",
       "      <td>0.013354</td>\n",
       "      <td>0.025400</td>\n",
       "      <td>0.000403</td>\n",
       "      <td>15</td>\n",
       "      <td>200</td>\n",
       "      <td>{'max_depth': 15, 'n_estimators': 200}</td>\n",
       "      <td>0.882078</td>\n",
       "      <td>0.885119</td>\n",
       "      <td>0.899551</td>\n",
       "      <td>0.887186</td>\n",
       "      <td>0.888483</td>\n",
       "      <td>0.006643</td>\n",
       "      <td>11</td>\n",
       "      <td>0.999947</td>\n",
       "      <td>0.999857</td>\n",
       "      <td>0.999908</td>\n",
       "      <td>0.999994</td>\n",
       "      <td>0.999927</td>\n",
       "      <td>0.000050</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>24</th>\n",
       "      <td>0.796480</td>\n",
       "      <td>0.010181</td>\n",
       "      <td>0.039978</td>\n",
       "      <td>0.001047</td>\n",
       "      <td>15</td>\n",
       "      <td>300</td>\n",
       "      <td>{'max_depth': 15, 'n_estimators': 300}</td>\n",
       "      <td>0.882102</td>\n",
       "      <td>0.886113</td>\n",
       "      <td>0.902097</td>\n",
       "      <td>0.887079</td>\n",
       "      <td>0.889348</td>\n",
       "      <td>0.007594</td>\n",
       "      <td>8</td>\n",
       "      <td>0.999956</td>\n",
       "      <td>0.999856</td>\n",
       "      <td>0.999909</td>\n",
       "      <td>0.999989</td>\n",
       "      <td>0.999928</td>\n",
       "      <td>0.000050</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "    mean_fit_time  std_fit_time  mean_score_time  std_score_time  \\\n",
       "0        0.020268      0.001674         0.003666        0.000633   \n",
       "1        0.092480      0.009596         0.005656        0.000217   \n",
       "2        0.177236      0.019469         0.009966        0.001174   \n",
       "3        0.308000      0.007835         0.016273        0.000942   \n",
       "4        0.445491      0.007226         0.032022        0.005452   \n",
       "5        0.023828      0.002949         0.001842        0.000019   \n",
       "6        0.102793      0.000533         0.005511        0.000164   \n",
       "7        0.213030      0.004758         0.011342        0.001548   \n",
       "8        0.402544      0.006572         0.023441        0.002798   \n",
       "9        0.652555      0.036184         0.041744        0.005562   \n",
       "10       0.027463      0.002366         0.001985        0.000013   \n",
       "11       0.129707      0.001727         0.006142        0.000021   \n",
       "12       0.280681      0.021941         0.014971        0.004330   \n",
       "13       0.530904      0.020085         0.027710        0.001933   \n",
       "14       0.746692      0.012132         0.044403        0.007013   \n",
       "15       0.034529      0.004144         0.002118        0.000008   \n",
       "16       0.141856      0.002528         0.007666        0.001457   \n",
       "17       0.290959      0.018577         0.018507        0.008678   \n",
       "18       0.637813      0.009517         0.032047        0.003949   \n",
       "19       0.903929      0.031043         0.039782        0.002757   \n",
       "20       0.040088      0.004012         0.003501        0.001992   \n",
       "21       0.173430      0.011580         0.007493        0.000484   \n",
       "22       0.300019      0.007485         0.014830        0.002673   \n",
       "23       0.574487      0.013354         0.025400        0.000403   \n",
       "24       0.796480      0.010181         0.039978        0.001047   \n",
       "\n",
       "   param_max_depth param_n_estimators                                  params  \\\n",
       "0                3                 10    {'max_depth': 3, 'n_estimators': 10}   \n",
       "1                3                 50    {'max_depth': 3, 'n_estimators': 50}   \n",
       "2                3                100   {'max_depth': 3, 'n_estimators': 100}   \n",
       "3                3                200   {'max_depth': 3, 'n_estimators': 200}   \n",
       "4                3                300   {'max_depth': 3, 'n_estimators': 300}   \n",
       "5                6                 10    {'max_depth': 6, 'n_estimators': 10}   \n",
       "6                6                 50    {'max_depth': 6, 'n_estimators': 50}   \n",
       "7                6                100   {'max_depth': 6, 'n_estimators': 100}   \n",
       "8                6                200   {'max_depth': 6, 'n_estimators': 200}   \n",
       "9                6                300   {'max_depth': 6, 'n_estimators': 300}   \n",
       "10               9                 10    {'max_depth': 9, 'n_estimators': 10}   \n",
       "11               9                 50    {'max_depth': 9, 'n_estimators': 50}   \n",
       "12               9                100   {'max_depth': 9, 'n_estimators': 100}   \n",
       "13               9                200   {'max_depth': 9, 'n_estimators': 200}   \n",
       "14               9                300   {'max_depth': 9, 'n_estimators': 300}   \n",
       "15              12                 10   {'max_depth': 12, 'n_estimators': 10}   \n",
       "16              12                 50   {'max_depth': 12, 'n_estimators': 50}   \n",
       "17              12                100  {'max_depth': 12, 'n_estimators': 100}   \n",
       "18              12                200  {'max_depth': 12, 'n_estimators': 200}   \n",
       "19              12                300  {'max_depth': 12, 'n_estimators': 300}   \n",
       "20              15                 10   {'max_depth': 15, 'n_estimators': 10}   \n",
       "21              15                 50   {'max_depth': 15, 'n_estimators': 50}   \n",
       "22              15                100  {'max_depth': 15, 'n_estimators': 100}   \n",
       "23              15                200  {'max_depth': 15, 'n_estimators': 200}   \n",
       "24              15                300  {'max_depth': 15, 'n_estimators': 300}   \n",
       "\n",
       "    split0_test_score  split1_test_score  split2_test_score  \\\n",
       "0            0.851102           0.860550           0.871572   \n",
       "1            0.864437           0.876427           0.883314   \n",
       "2            0.868171           0.879406           0.889535   \n",
       "3            0.870044           0.881630           0.896602   \n",
       "4            0.869967           0.881730           0.896442   \n",
       "5            0.871563           0.875486           0.883933   \n",
       "6            0.879276           0.885022           0.896454   \n",
       "7            0.878539           0.885553           0.899923   \n",
       "8            0.878739           0.886744           0.901173   \n",
       "9            0.878149           0.886202           0.902330   \n",
       "10           0.871843           0.873725           0.884314   \n",
       "11           0.878256           0.886927           0.899233   \n",
       "12           0.882273           0.886738           0.901462   \n",
       "13           0.882762           0.889063           0.903049   \n",
       "14           0.882332           0.889582           0.903562   \n",
       "15           0.870224           0.870749           0.881695   \n",
       "16           0.880128           0.880152           0.900471   \n",
       "17           0.883470           0.884780           0.900560   \n",
       "18           0.882532           0.886443           0.903108   \n",
       "19           0.882355           0.887134           0.903722   \n",
       "20           0.869584           0.858045           0.867814   \n",
       "21           0.879368           0.879952           0.891853   \n",
       "22           0.882821           0.882700           0.896734   \n",
       "23           0.882078           0.885119           0.899551   \n",
       "24           0.882102           0.886113           0.902097   \n",
       "\n",
       "    split3_test_score  mean_test_score  std_test_score  rank_test_score  \\\n",
       "0            0.866182         0.862352        0.007574               25   \n",
       "1            0.875905         0.875021        0.006774               22   \n",
       "2            0.880745         0.879464        0.007593               19   \n",
       "3            0.885429         0.883426        0.009486               17   \n",
       "4            0.885352         0.883373        0.009449               18   \n",
       "5            0.877848         0.877207        0.004485               21   \n",
       "6            0.890823         0.887894        0.006410               13   \n",
       "7            0.892065         0.889020        0.007906               10   \n",
       "8            0.892088         0.889686        0.008158                6   \n",
       "9            0.891592         0.889568        0.008785                7   \n",
       "10           0.887940         0.879455        0.006826               20   \n",
       "11           0.888256         0.888168        0.007454               12   \n",
       "12           0.891592         0.890516        0.007127                4   \n",
       "13           0.891373         0.891562        0.007343                2   \n",
       "14           0.892307         0.891946        0.007634                1   \n",
       "15           0.865499         0.872042        0.005936               23   \n",
       "16           0.884950         0.886426        0.008344               15   \n",
       "17           0.887570         0.889095        0.006783                9   \n",
       "18           0.889161         0.890311        0.007755                5   \n",
       "19           0.888901         0.890528        0.007985                3   \n",
       "20           0.872590         0.867008        0.005449               24   \n",
       "21           0.883522         0.883673        0.004983               16   \n",
       "22           0.885234         0.886873        0.005783               14   \n",
       "23           0.887186         0.888483        0.006643               11   \n",
       "24           0.887079         0.889348        0.007594                8   \n",
       "\n",
       "    split0_train_score  split1_train_score  split2_train_score  \\\n",
       "0             0.876252            0.876508            0.874460   \n",
       "1             0.890848            0.889351            0.883354   \n",
       "2             0.895348            0.891805            0.889273   \n",
       "3             0.898713            0.895430            0.892088   \n",
       "4             0.898650            0.895298            0.891509   \n",
       "5             0.915233            0.918659            0.915395   \n",
       "6             0.927093            0.927223            0.923194   \n",
       "7             0.928965            0.928215            0.924271   \n",
       "8             0.929531            0.928575            0.925058   \n",
       "9             0.929354            0.928884            0.924786   \n",
       "10            0.962087            0.959639            0.958198   \n",
       "11            0.970302            0.969324            0.966985   \n",
       "12            0.970675            0.969847            0.968736   \n",
       "13            0.970782            0.970075            0.969056   \n",
       "14            0.970682            0.970228            0.968896   \n",
       "15            0.986701            0.986888            0.989280   \n",
       "16            0.994987            0.993750            0.994662   \n",
       "17            0.995399            0.994356            0.994847   \n",
       "18            0.995229            0.994589            0.995020   \n",
       "19            0.995520            0.994442            0.995170   \n",
       "20            0.997054            0.996584            0.997066   \n",
       "21            0.999861            0.999728            0.999789   \n",
       "22            0.999945            0.999772            0.999858   \n",
       "23            0.999947            0.999857            0.999908   \n",
       "24            0.999956            0.999856            0.999909   \n",
       "\n",
       "    split3_train_score  mean_train_score  std_train_score  \n",
       "0             0.874622          0.875461         0.000926  \n",
       "1             0.887090          0.887661         0.002823  \n",
       "2             0.891651          0.892019         0.002168  \n",
       "3             0.894640          0.895218         0.002366  \n",
       "4             0.894222          0.894920         0.002558  \n",
       "5             0.919121          0.917102         0.001796  \n",
       "6             0.927840          0.926338         0.001837  \n",
       "7             0.928717          0.927542         0.001908  \n",
       "8             0.929116          0.928070         0.001772  \n",
       "9             0.928768          0.927948         0.001839  \n",
       "10            0.965865          0.961447         0.002905  \n",
       "11            0.972768          0.969845         0.002074  \n",
       "12            0.972063          0.970330         0.001214  \n",
       "13            0.972251          0.970541         0.001162  \n",
       "14            0.972020          0.970456         0.001116  \n",
       "15            0.991831          0.988675         0.002087  \n",
       "16            0.995990          0.994847         0.000801  \n",
       "17            0.996213          0.995204         0.000690  \n",
       "18            0.996576          0.995354         0.000742  \n",
       "19            0.996541          0.995418         0.000756  \n",
       "20            0.998620          0.997331         0.000769  \n",
       "21            0.999970          0.999837         0.000090  \n",
       "22            0.999990          0.999891         0.000084  \n",
       "23            0.999994          0.999927         0.000050  \n",
       "24            0.999989          0.999928         0.000050  "
      ]
     },
     "execution_count": 69,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "cv_rf_results_df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 70,
   "id": "90f95e5e",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "mean_fit_time         0.903929\n",
       "std_fit_time          0.036184\n",
       "mean_score_time       0.044403\n",
       "std_score_time        0.008678\n",
       "param_max_depth             15\n",
       "param_n_estimators         300\n",
       "split0_test_score      0.88347\n",
       "split1_test_score     0.889582\n",
       "split2_test_score     0.903722\n",
       "split3_test_score     0.892307\n",
       "mean_test_score       0.891946\n",
       "std_test_score        0.009486\n",
       "rank_test_score             25\n",
       "split0_train_score    0.999956\n",
       "split1_train_score    0.999857\n",
       "split2_train_score    0.999909\n",
       "split3_train_score    0.999994\n",
       "mean_train_score      0.999928\n",
       "std_train_score       0.002905\n",
       "dtype: object"
      ]
     },
     "execution_count": 70,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "cv_rf_results_df.max()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 71,
   "id": "b594996e",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'max_depth': 9, 'n_estimators': 300}"
      ]
     },
     "execution_count": 71,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "cv_rf.best_params_"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 72,
   "id": "b4e5f690",
   "metadata": {},
   "outputs": [],
   "source": [
    "feat_imp_df_act = pd.DataFrame({\n",
    "    'Feature name':feature_cols_r,\n",
    "    'Importance':cv_rf.best_estimator_.feature_importances_\n",
    "})"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 73,
   "id": "a8a1cf99",
   "metadata": {},
   "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>Feature name</th>\n",
       "      <th>Importance</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>Avg_Days_Delinquent</td>\n",
       "      <td>0.329056</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Percent_Increase_MOM</td>\n",
       "      <td>0.269084</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>Complaint_Code_Call Quality</td>\n",
       "      <td>0.085989</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Avg_Calls_Weekdays</td>\n",
       "      <td>0.054053</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Avg_Calls</td>\n",
       "      <td>0.046780</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Current_Bill_Amt</td>\n",
       "      <td>0.046630</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Account_Age</td>\n",
       "      <td>0.037833</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>Complaint_Code_Moving</td>\n",
       "      <td>0.035581</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>Equipment_Age</td>\n",
       "      <td>0.033256</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>Complaint_Code_Billing Problem</td>\n",
       "      <td>0.020791</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>Complaint_Code_Check Account</td>\n",
       "      <td>0.016808</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>Current_TechSupComplaints</td>\n",
       "      <td>0.016179</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>Acct_Plan_Subtype</td>\n",
       "      <td>0.007959</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                      Feature name  Importance\n",
       "6              Avg_Days_Delinquent    0.329056\n",
       "4             Percent_Increase_MOM    0.269084\n",
       "10     Complaint_Code_Call Quality    0.085989\n",
       "2               Avg_Calls_Weekdays    0.054053\n",
       "1                        Avg_Calls    0.046780\n",
       "0                 Current_Bill_Amt    0.046630\n",
       "3                      Account_Age    0.037833\n",
       "12           Complaint_Code_Moving    0.035581\n",
       "8                    Equipment_Age    0.033256\n",
       "9   Complaint_Code_Billing Problem    0.020791\n",
       "11    Complaint_Code_Check Account    0.016808\n",
       "7        Current_TechSupComplaints    0.016179\n",
       "5                Acct_Plan_Subtype    0.007959"
      ]
     },
     "execution_count": 73,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "feat_imp_df_act.sort_values('Importance', ascending=False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 74,
   "id": "5d24b3bc",
   "metadata": {},
   "outputs": [],
   "source": [
    "feat_imp_df = pd.DataFrame({\n",
    "    'Importance':cv_rf.best_estimator_.feature_importances_},index=feature_cols_r)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 75,
   "id": "7b24aafb",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1000x700 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "ax = feat_imp_df.sort_values('Importance', ascending=True).plot(\n",
    "    kind='barh',\n",
    "    grid=True,\n",
    "    figsize=(10, 7),\n",
    "    title='Features With Most Impact on Churn Rate'\n",
    ")\n",
    "\n",
    "ax.set_xlabel('Importance')\n",
    "ax.set_ylabel('Features')\n",
    "\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 77,
   "id": "9ac9d38b",
   "metadata": {},
   "outputs": [],
   "source": [
    "xx_rf, yy_rf = np.meshgrid(range(5), range(5))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 92,
   "id": "b2fbf904",
   "metadata": {},
   "outputs": [],
   "source": [
    "cm_rf = plt.cm.twilight_shifted"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 96,
   "id": "24b9af8d",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0, 0.5, 'Maximum depth')"
      ]
     },
     "execution_count": 96,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1200x900 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(12,9)) \n",
    "ax_rf = plt.axes()\n",
    "pcolor_graph = ax_rf.pcolormesh(xx_rf, yy_rf, cv_rf_results_df['mean_test_score'].values.reshape((5,5)),cmap=cm_rf)\n",
    "plt.colorbar(pcolor_graph, label='Average testing ROC AUC')\n",
    "ax_rf.set_aspect('equal')\n",
    "ax_rf.set_xticks([0.5, 1.5, 2.5, 3.5, 4.5])\n",
    "ax_rf.set_yticks([0.5, 1.5, 2.5, 3.5, 4.5])\n",
    "ax_rf.set_xticklabels([str(tick_label) for tick_label in rf_params['n_estimators']])\n",
    "ax_rf.set_yticklabels([str(tick_label) for tick_label in rf_params['max_depth']])\n",
    "ax_rf.set_xlabel('Number of trees')\n",
    "ax_rf.set_ylabel('Maximum depth')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 76,
   "id": "3bfae5f8",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.8802023642556602"
      ]
     },
     "execution_count": 76,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "cv_rf.score(X_test_r, y_test_r)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "d196868e",
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3 (ipykernel)",
   "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": "3.9.15"
  }
 },
 "nbformat": 4,
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