Week4/day 3/Data cleaning and wrangling.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "id": "surgical-version",
   "metadata": {},
   "source": [
    "# Lab | Data cleaning and wrangling\n",
    "\n",
    "For this lab, we will be using the Marketing Customer Value Analysis database from before. Feel free to build on top of your previous notebooks, since there could be overlap with variable names.\n",
    "\n",
    "Instructions:\n",
    "\n",
    "This lab will focus on data cleaning and wrangling, this is a crucial step in the EDA process.\n",
    "\n",
    "1. Remove the outliers in the dataset using one of the methods we've discussed by defining a function and applying it to the dataframe.\n",
    "2. Create a copy of the dataframe for the data wrangling.\n",
    "3. Normalize the continuous variables.\n",
    "4. Encode the categorical variables\n",
    "5. Transform the time variables (day, week and month) to integers.\n",
    "6. Since the model will only accept numerical data, check and make sure that every column is numerical, if some are not, change it using encoding.\n",
    "7. Hint for Categorical Variables\n",
    "\n",
    "Use either encoding or get_dummies to deal with the categorical variables (example below):\n",
    "\n",
    "#### data[\"coverage\"] = data[\"coverage\"].map({\"Basic\" : 0, \"Extended\" : 1, \"Premium\" : 2}) ####\n",
    "\n",
    "given that column \"coverage\" in the dataframe \"data\" has three categories:\n",
    "\"basic\", \"extended\", and \"premium\" and values are to be represented in the same order."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "banned-failure",
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "import pandas as pd\n",
    "import datetime\n",
    "pd.set_option('display.max_columns', None)\n",
    "import warnings\n",
    "warnings.filterwarnings('ignore')\n",
    "import matplotlib.pyplot as plt\n",
    "import seaborn as sns\n",
    "%matplotlib inline"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "vocational-tackle",
   "metadata": {},
   "outputs": [],
   "source": [
    "df = pd.read_csv('marketing_customer_analysis_week_4.csv')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "dynamic-component",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(9134, 25)"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "formal-fraction",
   "metadata": {},
   "outputs": [
    {
     "data": {
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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>customer</th>\n",
       "      <th>state</th>\n",
       "      <th>customer_lifetime_value</th>\n",
       "      <th>response</th>\n",
       "      <th>coverage</th>\n",
       "      <th>education</th>\n",
       "      <th>effective_to_date</th>\n",
       "      <th>employment_status</th>\n",
       "      <th>gender</th>\n",
       "      <th>income</th>\n",
       "      <th>location_code</th>\n",
       "      <th>marital_status</th>\n",
       "      <th>monthly_premium_auto</th>\n",
       "      <th>months_since_last_claim</th>\n",
       "      <th>months_since_policy_inception</th>\n",
       "      <th>number_of_open_complaints</th>\n",
       "      <th>number_of_policies</th>\n",
       "      <th>policy_type</th>\n",
       "      <th>policy</th>\n",
       "      <th>renew_offer_type</th>\n",
       "      <th>sales_channel</th>\n",
       "      <th>total_claim_amount</th>\n",
       "      <th>vehicle_class</th>\n",
       "      <th>vehicle_size</th>\n",
       "      <th>month</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>BU79786</td>\n",
       "      <td>Washington</td>\n",
       "      <td>2763.519279</td>\n",
       "      <td>No</td>\n",
       "      <td>Basic</td>\n",
       "      <td>Bachelor</td>\n",
       "      <td>2/24/11</td>\n",
       "      <td>Employed</td>\n",
       "      <td>F</td>\n",
       "      <td>56274</td>\n",
       "      <td>Suburban</td>\n",
       "      <td>Married</td>\n",
       "      <td>69</td>\n",
       "      <td>32</td>\n",
       "      <td>5</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>Corporate Auto</td>\n",
       "      <td>Corporate L3</td>\n",
       "      <td>Offer1</td>\n",
       "      <td>Agent</td>\n",
       "      <td>384.811147</td>\n",
       "      <td>Two-Door Car</td>\n",
       "      <td>Medsize</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>QZ44356</td>\n",
       "      <td>Arizona</td>\n",
       "      <td>6979.535903</td>\n",
       "      <td>No</td>\n",
       "      <td>Extended</td>\n",
       "      <td>Bachelor</td>\n",
       "      <td>1/31/11</td>\n",
       "      <td>Unemployed</td>\n",
       "      <td>F</td>\n",
       "      <td>0</td>\n",
       "      <td>Suburban</td>\n",
       "      <td>Single</td>\n",
       "      <td>94</td>\n",
       "      <td>13</td>\n",
       "      <td>42</td>\n",
       "      <td>0</td>\n",
       "      <td>8</td>\n",
       "      <td>Personal Auto</td>\n",
       "      <td>Personal L3</td>\n",
       "      <td>Offer3</td>\n",
       "      <td>Agent</td>\n",
       "      <td>1131.464935</td>\n",
       "      <td>Four-Door Car</td>\n",
       "      <td>Medsize</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>AI49188</td>\n",
       "      <td>Nevada</td>\n",
       "      <td>12887.431650</td>\n",
       "      <td>No</td>\n",
       "      <td>Premium</td>\n",
       "      <td>Bachelor</td>\n",
       "      <td>2/19/11</td>\n",
       "      <td>Employed</td>\n",
       "      <td>F</td>\n",
       "      <td>48767</td>\n",
       "      <td>Suburban</td>\n",
       "      <td>Married</td>\n",
       "      <td>108</td>\n",
       "      <td>18</td>\n",
       "      <td>38</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>Personal Auto</td>\n",
       "      <td>Personal L3</td>\n",
       "      <td>Offer1</td>\n",
       "      <td>Agent</td>\n",
       "      <td>566.472247</td>\n",
       "      <td>Two-Door Car</td>\n",
       "      <td>Medsize</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>WW63253</td>\n",
       "      <td>California</td>\n",
       "      <td>7645.861827</td>\n",
       "      <td>No</td>\n",
       "      <td>Basic</td>\n",
       "      <td>Bachelor</td>\n",
       "      <td>1/20/11</td>\n",
       "      <td>Unemployed</td>\n",
       "      <td>M</td>\n",
       "      <td>0</td>\n",
       "      <td>Suburban</td>\n",
       "      <td>Married</td>\n",
       "      <td>106</td>\n",
       "      <td>18</td>\n",
       "      <td>65</td>\n",
       "      <td>0</td>\n",
       "      <td>7</td>\n",
       "      <td>Corporate Auto</td>\n",
       "      <td>Corporate L2</td>\n",
       "      <td>Offer1</td>\n",
       "      <td>Call Center</td>\n",
       "      <td>529.881344</td>\n",
       "      <td>SUV</td>\n",
       "      <td>Medsize</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>HB64268</td>\n",
       "      <td>Washington</td>\n",
       "      <td>2813.692575</td>\n",
       "      <td>No</td>\n",
       "      <td>Basic</td>\n",
       "      <td>Bachelor</td>\n",
       "      <td>2/3/11</td>\n",
       "      <td>Employed</td>\n",
       "      <td>M</td>\n",
       "      <td>43836</td>\n",
       "      <td>Rural</td>\n",
       "      <td>Single</td>\n",
       "      <td>73</td>\n",
       "      <td>12</td>\n",
       "      <td>44</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>Personal Auto</td>\n",
       "      <td>Personal L1</td>\n",
       "      <td>Offer1</td>\n",
       "      <td>Agent</td>\n",
       "      <td>138.130879</td>\n",
       "      <td>Four-Door Car</td>\n",
       "      <td>Medsize</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "  customer       state  customer_lifetime_value response  coverage education  \\\n",
       "0  BU79786  Washington              2763.519279       No     Basic  Bachelor   \n",
       "1  QZ44356     Arizona              6979.535903       No  Extended  Bachelor   \n",
       "2  AI49188      Nevada             12887.431650       No   Premium  Bachelor   \n",
       "3  WW63253  California              7645.861827       No     Basic  Bachelor   \n",
       "4  HB64268  Washington              2813.692575       No     Basic  Bachelor   \n",
       "\n",
       "  effective_to_date employment_status gender  income location_code  \\\n",
       "0           2/24/11          Employed      F   56274      Suburban   \n",
       "1           1/31/11        Unemployed      F       0      Suburban   \n",
       "2           2/19/11          Employed      F   48767      Suburban   \n",
       "3           1/20/11        Unemployed      M       0      Suburban   \n",
       "4            2/3/11          Employed      M   43836         Rural   \n",
       "\n",
       "  marital_status  monthly_premium_auto  months_since_last_claim  \\\n",
       "0        Married                    69                       32   \n",
       "1         Single                    94                       13   \n",
       "2        Married                   108                       18   \n",
       "3        Married                   106                       18   \n",
       "4         Single                    73                       12   \n",
       "\n",
       "   months_since_policy_inception  number_of_open_complaints  \\\n",
       "0                              5                          0   \n",
       "1                             42                          0   \n",
       "2                             38                          0   \n",
       "3                             65                          0   \n",
       "4                             44                          0   \n",
       "\n",
       "   number_of_policies     policy_type        policy renew_offer_type  \\\n",
       "0                   1  Corporate Auto  Corporate L3           Offer1   \n",
       "1                   8   Personal Auto   Personal L3           Offer3   \n",
       "2                   2   Personal Auto   Personal L3           Offer1   \n",
       "3                   7  Corporate Auto  Corporate L2           Offer1   \n",
       "4                   1   Personal Auto   Personal L1           Offer1   \n",
       "\n",
       "  sales_channel  total_claim_amount  vehicle_class vehicle_size  month  \n",
       "0         Agent          384.811147   Two-Door Car      Medsize      2  \n",
       "1         Agent         1131.464935  Four-Door Car      Medsize      1  \n",
       "2         Agent          566.472247   Two-Door Car      Medsize      2  \n",
       "3   Call Center          529.881344            SUV      Medsize      1  \n",
       "4         Agent          138.130879  Four-Door Car      Medsize      2  "
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "united-coordination",
   "metadata": {},
   "outputs": [],
   "source": [
    "df = df.drop(['month'],axis=1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "biblical-classroom",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "    .dataframe thead th {\n",
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>customer_lifetime_value</th>\n",
       "      <th>income</th>\n",
       "      <th>monthly_premium_auto</th>\n",
       "      <th>months_since_last_claim</th>\n",
       "      <th>months_since_policy_inception</th>\n",
       "      <th>number_of_open_complaints</th>\n",
       "      <th>number_of_policies</th>\n",
       "      <th>total_claim_amount</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>2763.519279</td>\n",
       "      <td>56274</td>\n",
       "      <td>69</td>\n",
       "      <td>32</td>\n",
       "      <td>5</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>384.811147</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>6979.535903</td>\n",
       "      <td>0</td>\n",
       "      <td>94</td>\n",
       "      <td>13</td>\n",
       "      <td>42</td>\n",
       "      <td>0</td>\n",
       "      <td>8</td>\n",
       "      <td>1131.464935</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>12887.431650</td>\n",
       "      <td>48767</td>\n",
       "      <td>108</td>\n",
       "      <td>18</td>\n",
       "      <td>38</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>566.472247</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>7645.861827</td>\n",
       "      <td>0</td>\n",
       "      <td>106</td>\n",
       "      <td>18</td>\n",
       "      <td>65</td>\n",
       "      <td>0</td>\n",
       "      <td>7</td>\n",
       "      <td>529.881344</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>2813.692575</td>\n",
       "      <td>43836</td>\n",
       "      <td>73</td>\n",
       "      <td>12</td>\n",
       "      <td>44</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>138.130879</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   customer_lifetime_value  income  monthly_premium_auto  \\\n",
       "0              2763.519279   56274                    69   \n",
       "1              6979.535903       0                    94   \n",
       "2             12887.431650   48767                   108   \n",
       "3              7645.861827       0                   106   \n",
       "4              2813.692575   43836                    73   \n",
       "\n",
       "   months_since_last_claim  months_since_policy_inception  \\\n",
       "0                       32                              5   \n",
       "1                       13                             42   \n",
       "2                       18                             38   \n",
       "3                       18                             65   \n",
       "4                       12                             44   \n",
       "\n",
       "   number_of_open_complaints  number_of_policies  total_claim_amount  \n",
       "0                          0                   1          384.811147  \n",
       "1                          0                   8         1131.464935  \n",
       "2                          0                   2          566.472247  \n",
       "3                          0                   7          529.881344  \n",
       "4                          0                   1          138.130879  "
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "numericals = df.select_dtypes(np.number)\n",
    "numericals.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "extraordinary-things",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "customer_lifetime_value          0.0\n",
       "income                           0.0\n",
       "monthly_premium_auto             0.0\n",
       "months_since_last_claim          0.0\n",
       "months_since_policy_inception    0.0\n",
       "number_of_open_complaints        0.0\n",
       "number_of_policies               0.0\n",
       "total_claim_amount               0.0\n",
       "dtype: float64"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "numericals.isnull().sum()/len(df)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "everyday-lincoln",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.boxplot(x ='customer_lifetime_value', data=df)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "warming-easter",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAWIAAAEHCAYAAACUZUyTAAAAOXRFWHRTb2Z0d2FyZQBNYXRwbG90bGliIHZlcnNpb24zLjMuNCwgaHR0cHM6Ly9tYXRwbG90bGliLm9yZy8QVMy6AAAACXBIWXMAAAsTAAALEwEAmpwYAAATr0lEQVR4nO3df3BV5Z3H8c83BEggroUEaUXWC41Moavrqtvpbjtd3GqLuIJ1cUYXlzDTAdmuFOK0bAu3mli7XWXFYbC2/lg7nQ1TVy3rD4xstcJq7a8BFxRH26YmWn+gELCaKmjw2T/Ouddzw01yk9zkm+S+XzOZnHvuc87zw4ePT86991wLIQgA4KfMuwEAUOoIYgBwRhADgDOCGACcEcQA4Ky8rwfU1NSEVCo1CE0BgNFr165dB0IIU/I91+cgTqVS2rlz58BbBQAlxMxe6O45Lk0AgDOCGACcEcQA4IwgBgBnBDEAOCOIAcAZQQwAzghiAHBGEAOAM4IYAJwRxADgjCAGAGcEMQA4I4gBwBlBDADOCGIAcEYQA4AzghgAnBHEAOCsz99ZN9g2bdqklpaWopzr5ZdfliRNmzatKOfrr9raWq1cudK1DQCGr2EXxC0tLdq991kdnTB5wOca8/YfJEn7jvh1c8zbB93qBjAyDLsglqSjEybrnY/NH/B5Kp9rlqSinGugbQCA7nCNGACcEcQA4IwgBgBnBDEAOCOIAcAZQQwAzghiAHBGEAOAM4IYAJwRxADgjCAGAGcEMQA4I4gBwBlBDADOCGIAcEYQA4AzghgAnBHEAOCMIAYAZwQxADgjiAHAGUEMAM4IYgBwRhADgDOCGACcEcQA4IwgBgBnBDEAOCOIAcAZQQwAzghiAHBGEAOAM4IYAJwRxADgjCAGAGcEMQA4I4gBwBlBDADOCGIAcEYQA4AzghgAnA1pEG/atEmbNm0ayioxgjFfUCrKh7KylpaWoawOIxzzBaWCSxMA4IwgBgBnBDEAOCOIAcAZQQwAzghiAHBGEAOAM4IYAJwRxADgjCAGAGcEMQA4I4gBwBlBDADOCGIAcEYQA4AzghgAnBHEAOCMIAYAZwQxADgjiAHAGUEMAM4IYgBwRhADgDOCGACcEcQA4IwgBgBnBDEAOCOIAcAZQQwAzghiAHBGEAOAM4IYAJwRxADgjCAGAGcEMQA4I4gBwBlBDADOCGIAcFbu3QCgJ/v379fcuXNlZlq8eLGampo0duxYvffeexo/frwuvvhiNTU1qbq6WgcPHlQqlVJra2vOOWbOnKnnn38++7iqqkodHR05Zaqrq9Xe3p59XFFRocOHD+eUmTx5sg4ePJgtf+jQIa1evVrf+973NHXqVFVWVurKK6/UddddpxdeeEHvvvuuKioq9K1vfUvr16/Xvn37JEmXXXaZmpqacs69ZMkSPf7442ptbVVFRYXWrFmj66+/XkeOHNGyZct0++2368ILL9SWLVu0aNEibdmyRatXr9Ytt9yia665Rt/5znfU2tqqq666SnfddZeOHj2qMWPG6Nprr9WhQ4d0xRVX6PDhw7rhhht0/PHHZx9L0sc//nE988wzuvzyy9XU1KSNGzfqxRdf1DXXXKNx48bp5ptv1htvvKE1a9aopqZGBw4c0Pr163XmmWeqvb1dX/3qV7N133333XrzzTf1yiuvZMsktbe3q7GxUVdffbWqq6uz+1taWrRq1Spt3LhRkyZNypbZtm2bbrvtNq1YsULnnnuu0um0zExXXnml0um09u3bpxUrVuiSSy7JW8eWLVu0efNmTZo0SYcPH9Y3v/lN3XHHHQoh6Nprr83+d++uTStXrtT06dO1fPlyfeMb39D06dP17W9/O6dcMVgIoU8HnHXWWWHnzp39qmzVqlWSpI0bN/ZYZtfzr+mdj83vVx1Jlc81S1JRzjWQNpw5c2qPfUZ+q1at0p49e7yb0SMzU/LfUCqVUltbW06ZfMHfm/LycnV2dhZUd/L8XY9buHCh9uzZk21TVVWVampqjmljUiqV0ksvvZQ9TyqV0oEDB3L6UFVVpa1bt2rDhg26//7789adKZO0YcMGPfDAA1qwYIHq6+uz+5cuXaq2tjalUimddtpp2TL33XdftsyCBQuydXUd5x07duStI3l8pk2ZfixcuFD19fW9tqm74/rKzHaFEM7K9xyXJjBs7d+/37sJveq6kMkXcH0NYUm9hnCy7uT5ux734IMP5rSpo6OjxxCWoj4kz9PW1nZMHzo6OrR9+3Y1Nzd3W3dHR4d27dqVfdze3q5t27YphKBt27Zl/wJpaWnJtqmtrU3Nzc0KIeiBBx7IOV8mhDPlku68885j6ugawpk2ZTz00ENqaWnptU1dj2tubs7566kYhnRFvGjRIr3zzjuqra3ttkxLS4veejfoj6df0m2ZQg2HFfHE3XfquHHWY5+R33BfDZe6QlbtyVXxhg0b1NzcrM7OTpWXl+v8889XfX19zspzIHbs2JFTR2/MTCeffHJ29d+XNvVnVTzgFbGZLTeznWa2cySsUgAMvkLCLrmSfOSRR7LHdHZ26uGHH5aU/6+I/krW0ZsQQs7qvy9typQrloJerAsh3CrpVilaEfe3smnTpkkq7BrxaPF+xZ+olmvE/TJ37lzvJqAHha6IM84555ycFfG5554rKf919f5K1tGbfCviQtuUKVcsXCPGsHXiiSd6N2HEKy8fvDdGrVu3rtfzNzY2Zrfr6upUVhZFzpgxY7RkyRJJUjqdzjkmc85M2UKsWLHimDp6M3bsWKXT6YLa1PW4TLliIYgxbE2ZMsW7Cb0ys5zHqVTqmDLJVWGhCgnQTN3J83c97vzzz89pU1VVVd42JqVSqZzzpFKpY/pQVVWls88+W/Pnf/D6S9e6q6qqct6+Vl1drXnz5snMNG/evOxbwGpra7NtSqVSmj9/vsxMF1xwQc75FixYkNOmpMzb15J1LFy48Ji+Jftx3nnnqba2ttc2dT1u/vz5RX/7GkGMYS2zKjYzXXbZZZKiFYkkjR8/PruvurpaZqYZM2Ycc46ZM2fmPM4XjF3/YVVUVBxTZvLkyTnly8rKVF9frwkTJmjGjBmaM2eO0um0TjnlFI0bNy57nsbGRn34wx/OHptpc9KSJUuyba+oqNDatWtVUVEhM9Py5ctVVlamiy66SFL0onem7okTJ6qxsVEzZ86UmWndunWaPXu2Zs2apdmzZ2vJkiVKp9PZ/jQ2NuY8lqL3EUvS5ZdfrokTJyqdTmvt2rWSpHHjximdTquhoUFlZWU64YQTVFZWll3p1tXV5dQ9Z84cnXTSSTllkurq6nTqqaces6JMp9PZupNlli1bJila8dbV1Wn27NnZcc6MaWY1nK+OxYsXS5ImTZqkyspKNTQ0aM6cOdmx6a1NlZWVmjVrlhoaGrLbxV4NS7yPeNDxPuL+K2S+ACMF7yMGgGGMIAYAZwQxADgjiAHAGUEMAM4IYgBwRhADgDOCGACcEcQA4IwgBgBnBDEAOCOIAcAZQQwAzghiAHBGEAOAM4IYAJwRxADgjCAGAGcEMQA4I4gBwBlBDADOCGIAcEYQA4AzghgAnBHEAOCMIAYAZwQxADgjiAHAGUEMAM4IYgBwRhADgDOCGACcEcQA4IwgBgBnBDEAOCOIAcAZQQwAzghiAHBWPpSV1dbWDmV1GOGYLygVQxrEK1euHMrqMMIxX1AquDQBAM4IYgBwRhADgDOCGACcEcQA4IwgBgBnBDEAOCOIAcAZQQwAzghiAHBGEAOAM4IYAJwRxADgjCAGAGcEMQA4I4gBwBlBDADOCGIAcEYQA4AzghgAnBHEAOCMIAYAZwQxADgjiAHAGUEMAM4IYgBwRhADgDOCGACcEcQA4IwgBgBnBDEAOCOIAcAZQQwAzghiAHBGEAOAM4IYAJwRxADgjCAGAGcEMQA4I4gBwFm5dwPyGfP2QVU+11yE87RLUlHO1f82HJQ01a1+AMPfsAvi2traop3r5Zc7JUnTpnkG4dSi9gnA6DPsgnjlypXeTQCAIcU1YgBwRhADgDOCGACcEcQA4IwgBgBnBDEAOCOIAcAZQQwAzghiAHBGEAOAM4IYAJwRxADgjCAGAGcEMQA4I4gBwBlBDADOCGIAcEYQA4AzghgAnBHEAODMQgh9O8Bsv6QXJNVIOjAYjRoh6D/9p/+lra9jcHIIYUq+J/ocxNkDzXaGEM7q18GjAP2n//S/dPsvFXcMuDQBAM4IYgBwNpAgvrVorRiZ6H9po/8o2hj0+xoxAKA4uDQBAM4IYgBwVlAQm1mbmT1tZrvNbGe8b7KZPWxmv41/Txrcpg4dM7vDzF43s72Jfd3218y+bmYtZvZrM/u8T6uLq5sxaDCzl+N5sNvM5ieeGzVjYGbTzWy7mT1rZs+Y2ap4f8nMgR7GoFTmQIWZ/crM9sT9b4z3D84cCCH0+iOpTVJNl33XS/pavP01SdcVcq6R8CPpM5LOkLS3t/5KmiNpj6TxkmZI+p2kMd59GKQxaJD0lTxlR9UYSPqIpDPi7eMk/SbuY8nMgR7GoFTmgEmqirfHSvqlpE8O1hwYyKWJhZJ+EG//QNKFAzjXsBJCeEzSwS67u+vvQkl3hhCOhBBaJbVI+sRQtHMwdTMG3RlVYxBCeDWE8GS8/ZakZyVNUwnNgR7GoDujagxCpCN+ODb+CRqkOVBoEAdJPzazXWa2PN43NYTwatzoVyWdUGilI1R3/Z0m6feJci+p5wk70l1hZk/Fly4yf5aN2jEws5Skv1C0IirJOdBlDKQSmQNmNsbMdkt6XdLDIYRBmwOFBvGnQghnSDpP0j+b2WcKraAEWJ59o/U9gd+V9FFJp0t6VdIN8f5ROQZmViXpR5JWhxDe7Klonn0jvv9S3jEomTkQQjgaQjhd0kmSPmFmf9ZD8QH1v6AgDiG8Ev9+XdJ/K1pyv2ZmH5Gk+PfrhVY6QnXX35ckTU+UO0nSK0PctiERQngtnpzvS7pNH/zpNerGwMzGKgqgzSGELfHukpoD+caglOZARgjhDUk7JM3TIM2BXoPYzCaa2XGZbUmfk7RX0v2S6uJidZLuK7TSEaq7/t4v6RIzG29mMySdIulXDu0bdJkJGPuConkgjbIxMDOT9B+Sng0hbEg8VTJzoLsxKKE5MMXMPhRvV0o6R9JzGqw5UMCrhzMVvRq4R9IzktbF+6sl/UTSb+Pfk71f6SziK6Y/VPRn13uK/k/3xZ76K2mdoldJfy3pPO/2D+IY/KekpyU9FU+8j4zGMZD0aUV/Vj4laXf8M7+U5kAPY1Aqc+A0Sf8X93OvpKvi/YMyB/iIMwA445N1AOCMIAYAZwQxADgjiAHAGUEMAM4IYgBwRhCj38zsQ2b2pcTjuWa2tZuyO8xsWH7rr5mdaGb3eLcjycxWm9kE73ZgaBDEGIgPSfpSb4UGm5mNGcjxIYRXQgiLitWeIlktiSAuEQRxiTCzlJk9Z2a3m9leM9tsZueY2RPxTa4/Ed/0+t74zlq/MLPT4mMb4jtt7TCz583sy/Fp/03SR+MbhK+P91WZ2T1xXZvjj8om2/FFM7sx8XiZmSU/RpyvzT+I23RPZpVo0ZcVXGVmP5V0sZl9zsx+bmZPmtnd8c1qMuX+NX5up5mdYWb/Y2a/M7MViXr2xttLzeymRBu2mtnceLvDzK6L70L4SDxmmTFZ0MvYPx637Ukz++t4f85fEGZ2U1z/lyWdKGm7mW2Pn7vUoi9n2Gtm1/X8XxsjjvdHCfkZmh9JKUmdkk5V9D/gXZLuUHTXqIWS7pW0SdLVcfm/lbQ73m6Q9DNFN72ukdSu6P6sKeXeOH6upD8ouuFJmaSfS/p0/NwOSWdJmqjoY6Bj4/0/k3RqD20Oiu7+p7i9X4m32yStibdrJD0maWL8+F/0wUdS2yT9U7x9o6KPrB4naYqk1xP17I23l0q6KdGGrZLmxttB8UdXFd386sfxOPx5Zqy66ccESRXx9imSdibGa2ui3E2SlibaXRNvnyjpxbjN5ZIelXSh95zip3g/5UIpaQ0hPC1JZvaMpJ+EEIKZPa0ojE6W9PeSFEJ41Myqzez4+NgHQwhHJB0xs9clTe2mjl+FEF6K69gdn/enmSdDCH80s0cl/Z2ZPasokJ/uoc2/DyE8EW83SfqypH+PH/9X/PuTir4h4Yl4AT5O0f8EMu6Pfz+t6FsX3pL0lpkdztzYpUDvStqWONeREMJ7ifHrzlhJN5nZ6ZKOSprVhzol6S8l7Qgh7JckM9us6BtU7u3jeTBMEcSl5Uhi+/3E4/cVzYXOPMdkbkaSPPaoup87hZS7XdJaRXez+n7PTT7mnq7Jx3+Mf5uiG3df2kubkn3OPO7avk7lXrKrSGy/F0LI1J89VwjhfTPr6d9SvaTXFK2cyyQdLqCupHz3usUowjViJD0mabEUXb+UdCD0fEP0txT9md8nIfqmg+mS/kHRXd568qdm9lfx9qVKrK4TfiHpU2ZWK0lmNsHM+rrqzGiTdLqZlZnZdBXn636Ol/RqiO7h+4+SMi8uviBpTnzrxOMlfTZxTHJsfynpb8ysJn5h8lJJ/1uEdmGYYEWMpAZJ3zezpyS9rQ/uu5pXCKE9frFvr6SHJD3Yh7ruknR6COFQL+WelVRnZrcouvXgd/O0Y7+ZLZX0QzMbH+9OK/rCy756QlKroksPeyU92Y9zdHWzpB+Z2cWStiteyYcQfm9mdym6bv1bRbddzLhV0kNm9moI4Wwz+3p8rElqDiGM9vt/lxRugwkX8bsFbgwh/KSHMilFL2b19BU1wIjHpQkMKYs+BPIbSe/0FMJAKWFFDHdmlvnWg64+G0JoH+r29JeZfV5S1/f4toYQvuDRHowcBDEAOOPSBAA4I4gBwBlBDADOCGIAcPb/bL8QbaylSiYAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.boxplot(x ='monthly_premium_auto', data=df)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "third-aggregate",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.boxplot(x ='number_of_policies', data=df)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "textile-channels",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.boxplot(x ='total_claim_amount', data=df)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "induced-growing",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Dealing with outliers \n",
    "iqr = np.percentile(df['customer_lifetime_value'],75) - np.percentile(df['customer_lifetime_value'],25)\n",
    "upper_limit = np.percentile(df['customer_lifetime_value'],75) + 1.5*iqr\n",
    "lower_limit = np.percentile(df['customer_lifetime_value'],25) - 1.5*iqr\n",
    "# Normalization\n",
    "df = df[(df['customer_lifetime_value']>lower_limit) & (df['customer_lifetime_value']<upper_limit)]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "id": "blond-leadership",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Dealing with outliers\n",
    "iqr = np.percentile(df['monthly_premium_auto'],75) - np.percentile(df['monthly_premium_auto'],25)\n",
    "upper_limit = np.percentile(df['monthly_premium_auto'],75) + 1.5*iqr\n",
    "lower_limit = np.percentile(df['monthly_premium_auto'],25) - 1.5*iqr\n",
    "# Normalization\n",
    "df = df[(df['monthly_premium_auto']>lower_limit) & (df['monthly_premium_auto']<upper_limit)]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "id": "grateful-tolerance",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Dealing with outliers\n",
    "iqr = np.percentile(df['number_of_policies'],75) - np.percentile(df['number_of_policies'],25)\n",
    "upper_limit = np.percentile(df['number_of_policies'],75) + 1.5*iqr\n",
    "lower_limit = np.percentile(df['number_of_policies'],25) - 1.5*iqr\n",
    "# Normalization\n",
    "df = df[(df['number_of_policies']>lower_limit) & (df['number_of_policies']<upper_limit)]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "id": "looking-exhibition",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Dealing with outliers\n",
    "iqr = np.percentile(df['total_claim_amount'],75) - np.percentile(df['total_claim_amount'],25)\n",
    "upper_limit = np.percentile(df['total_claim_amount'],75) + 1.5*iqr\n",
    "lower_limit = np.percentile(df['total_claim_amount'],25) - 1.5*iqr\n",
    "# Normalization\n",
    "df = df[(df['total_claim_amount']>lower_limit) & (df['total_claim_amount']<upper_limit)]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "id": "cardiovascular-brisbane",
   "metadata": {},
   "outputs": [],
   "source": [
    "df_new = df"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "minor-orleans",
   "metadata": {},
   "source": [
    "#### Normalization"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "id": "cardiovascular-mongolia",
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.preprocessing import Normalizer"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "id": "distant-senator",
   "metadata": {},
   "outputs": [],
   "source": [
    "X = df_new.select_dtypes(include=np.number)\n",
    "transformer = Normalizer().fit(X)\n",
    "X_normalised= transformer.transform(X)\n",
    "X_normalised = pd.DataFrame(X_normalised)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "cognitive-gathering",
   "metadata": {},
   "source": [
    "#### Transforming time variables (day, week and month) to integers."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "id": "alien-norman",
   "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>customer</th>\n",
       "      <th>state</th>\n",
       "      <th>customer_lifetime_value</th>\n",
       "      <th>response</th>\n",
       "      <th>coverage</th>\n",
       "      <th>education</th>\n",
       "      <th>effective_to_date</th>\n",
       "      <th>employment_status</th>\n",
       "      <th>gender</th>\n",
       "      <th>income</th>\n",
       "      <th>location_code</th>\n",
       "      <th>marital_status</th>\n",
       "      <th>monthly_premium_auto</th>\n",
       "      <th>months_since_last_claim</th>\n",
       "      <th>months_since_policy_inception</th>\n",
       "      <th>number_of_open_complaints</th>\n",
       "      <th>number_of_policies</th>\n",
       "      <th>policy_type</th>\n",
       "      <th>policy</th>\n",
       "      <th>renew_offer_type</th>\n",
       "      <th>sales_channel</th>\n",
       "      <th>total_claim_amount</th>\n",
       "      <th>vehicle_class</th>\n",
       "      <th>vehicle_size</th>\n",
       "      <th>day</th>\n",
       "      <th>month</th>\n",
       "      <th>year</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>BU79786</td>\n",
       "      <td>Washington</td>\n",
       "      <td>2763.519279</td>\n",
       "      <td>No</td>\n",
       "      <td>Basic</td>\n",
       "      <td>Bachelor</td>\n",
       "      <td>2/24/11</td>\n",
       "      <td>Employed</td>\n",
       "      <td>F</td>\n",
       "      <td>56274</td>\n",
       "      <td>Suburban</td>\n",
       "      <td>Married</td>\n",
       "      <td>69</td>\n",
       "      <td>32</td>\n",
       "      <td>5</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>Corporate Auto</td>\n",
       "      <td>Corporate L3</td>\n",
       "      <td>Offer1</td>\n",
       "      <td>Agent</td>\n",
       "      <td>384.811147</td>\n",
       "      <td>Two-Door Car</td>\n",
       "      <td>Medsize</td>\n",
       "      <td>24</td>\n",
       "      <td>2</td>\n",
       "      <td>2011</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>AI49188</td>\n",
       "      <td>Nevada</td>\n",
       "      <td>12887.431650</td>\n",
       "      <td>No</td>\n",
       "      <td>Premium</td>\n",
       "      <td>Bachelor</td>\n",
       "      <td>2/19/11</td>\n",
       "      <td>Employed</td>\n",
       "      <td>F</td>\n",
       "      <td>48767</td>\n",
       "      <td>Suburban</td>\n",
       "      <td>Married</td>\n",
       "      <td>108</td>\n",
       "      <td>18</td>\n",
       "      <td>38</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>Personal Auto</td>\n",
       "      <td>Personal L3</td>\n",
       "      <td>Offer1</td>\n",
       "      <td>Agent</td>\n",
       "      <td>566.472247</td>\n",
       "      <td>Two-Door Car</td>\n",
       "      <td>Medsize</td>\n",
       "      <td>19</td>\n",
       "      <td>2</td>\n",
       "      <td>2011</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>WW63253</td>\n",
       "      <td>California</td>\n",
       "      <td>7645.861827</td>\n",
       "      <td>No</td>\n",
       "      <td>Basic</td>\n",
       "      <td>Bachelor</td>\n",
       "      <td>1/20/11</td>\n",
       "      <td>Unemployed</td>\n",
       "      <td>M</td>\n",
       "      <td>0</td>\n",
       "      <td>Suburban</td>\n",
       "      <td>Married</td>\n",
       "      <td>106</td>\n",
       "      <td>18</td>\n",
       "      <td>65</td>\n",
       "      <td>0</td>\n",
       "      <td>7</td>\n",
       "      <td>Corporate Auto</td>\n",
       "      <td>Corporate L2</td>\n",
       "      <td>Offer1</td>\n",
       "      <td>Call Center</td>\n",
       "      <td>529.881344</td>\n",
       "      <td>SUV</td>\n",
       "      <td>Medsize</td>\n",
       "      <td>20</td>\n",
       "      <td>1</td>\n",
       "      <td>2011</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>HB64268</td>\n",
       "      <td>Washington</td>\n",
       "      <td>2813.692575</td>\n",
       "      <td>No</td>\n",
       "      <td>Basic</td>\n",
       "      <td>Bachelor</td>\n",
       "      <td>2/3/11</td>\n",
       "      <td>Employed</td>\n",
       "      <td>M</td>\n",
       "      <td>43836</td>\n",
       "      <td>Rural</td>\n",
       "      <td>Single</td>\n",
       "      <td>73</td>\n",
       "      <td>12</td>\n",
       "      <td>44</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>Personal Auto</td>\n",
       "      <td>Personal L1</td>\n",
       "      <td>Offer1</td>\n",
       "      <td>Agent</td>\n",
       "      <td>138.130879</td>\n",
       "      <td>Four-Door Car</td>\n",
       "      <td>Medsize</td>\n",
       "      <td>3</td>\n",
       "      <td>2</td>\n",
       "      <td>2011</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>OC83172</td>\n",
       "      <td>Oregon</td>\n",
       "      <td>8256.297800</td>\n",
       "      <td>Yes</td>\n",
       "      <td>Basic</td>\n",
       "      <td>Bachelor</td>\n",
       "      <td>1/25/11</td>\n",
       "      <td>Employed</td>\n",
       "      <td>F</td>\n",
       "      <td>62902</td>\n",
       "      <td>Rural</td>\n",
       "      <td>Married</td>\n",
       "      <td>69</td>\n",
       "      <td>14</td>\n",
       "      <td>94</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>Personal Auto</td>\n",
       "      <td>Personal L3</td>\n",
       "      <td>Offer2</td>\n",
       "      <td>Web</td>\n",
       "      <td>159.383042</td>\n",
       "      <td>Two-Door Car</td>\n",
       "      <td>Medsize</td>\n",
       "      <td>25</td>\n",
       "      <td>1</td>\n",
       "      <td>2011</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "  customer       state  customer_lifetime_value response coverage education  \\\n",
       "0  BU79786  Washington              2763.519279       No    Basic  Bachelor   \n",
       "2  AI49188      Nevada             12887.431650       No  Premium  Bachelor   \n",
       "3  WW63253  California              7645.861827       No    Basic  Bachelor   \n",
       "4  HB64268  Washington              2813.692575       No    Basic  Bachelor   \n",
       "5  OC83172      Oregon              8256.297800      Yes    Basic  Bachelor   \n",
       "\n",
       "  effective_to_date employment_status gender  income location_code  \\\n",
       "0           2/24/11          Employed      F   56274      Suburban   \n",
       "2           2/19/11          Employed      F   48767      Suburban   \n",
       "3           1/20/11        Unemployed      M       0      Suburban   \n",
       "4            2/3/11          Employed      M   43836         Rural   \n",
       "5           1/25/11          Employed      F   62902         Rural   \n",
       "\n",
       "  marital_status  monthly_premium_auto  months_since_last_claim  \\\n",
       "0        Married                    69                       32   \n",
       "2        Married                   108                       18   \n",
       "3        Married                   106                       18   \n",
       "4         Single                    73                       12   \n",
       "5        Married                    69                       14   \n",
       "\n",
       "   months_since_policy_inception  number_of_open_complaints  \\\n",
       "0                              5                          0   \n",
       "2                             38                          0   \n",
       "3                             65                          0   \n",
       "4                             44                          0   \n",
       "5                             94                          0   \n",
       "\n",
       "   number_of_policies     policy_type        policy renew_offer_type  \\\n",
       "0                   1  Corporate Auto  Corporate L3           Offer1   \n",
       "2                   2   Personal Auto   Personal L3           Offer1   \n",
       "3                   7  Corporate Auto  Corporate L2           Offer1   \n",
       "4                   1   Personal Auto   Personal L1           Offer1   \n",
       "5                   2   Personal Auto   Personal L3           Offer2   \n",
       "\n",
       "  sales_channel  total_claim_amount  vehicle_class vehicle_size  day  month  \\\n",
       "0         Agent          384.811147   Two-Door Car      Medsize   24      2   \n",
       "2         Agent          566.472247   Two-Door Car      Medsize   19      2   \n",
       "3   Call Center          529.881344            SUV      Medsize   20      1   \n",
       "4         Agent          138.130879  Four-Door Car      Medsize    3      2   \n",
       "5           Web          159.383042   Two-Door Car      Medsize   25      1   \n",
       "\n",
       "   year  \n",
       "0  2011  \n",
       "2  2011  \n",
       "3  2011  \n",
       "4  2011  \n",
       "5  2011  "
      ]
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_new['day'] = pd.DatetimeIndex(df_new['effective_to_date']).day\n",
    "df_new['month'] = pd.DatetimeIndex(df_new['effective_to_date']).month\n",
    "df_new['year'] = pd.DatetimeIndex(df_new['effective_to_date']).year\n",
    "df_new.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "id": "governmental-seafood",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Droping effective_to_date column\n",
    "df_new = df_new.drop(['effective_to_date'], axis=1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "id": "linear-gardening",
   "metadata": {},
   "outputs": [],
   "source": [
    "# df_new['effective_to_date'] = df_new['day'].map(str) + df_new['month'].map(str) + df_new['year'].map(str)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "id": "decreased-navigator",
   "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>customer</th>\n",
       "      <th>state</th>\n",
       "      <th>customer_lifetime_value</th>\n",
       "      <th>response</th>\n",
       "      <th>coverage</th>\n",
       "      <th>education</th>\n",
       "      <th>employment_status</th>\n",
       "      <th>gender</th>\n",
       "      <th>income</th>\n",
       "      <th>location_code</th>\n",
       "      <th>marital_status</th>\n",
       "      <th>monthly_premium_auto</th>\n",
       "      <th>months_since_last_claim</th>\n",
       "      <th>months_since_policy_inception</th>\n",
       "      <th>number_of_open_complaints</th>\n",
       "      <th>number_of_policies</th>\n",
       "      <th>policy_type</th>\n",
       "      <th>policy</th>\n",
       "      <th>renew_offer_type</th>\n",
       "      <th>sales_channel</th>\n",
       "      <th>total_claim_amount</th>\n",
       "      <th>vehicle_class</th>\n",
       "      <th>vehicle_size</th>\n",
       "      <th>day</th>\n",
       "      <th>month</th>\n",
       "      <th>year</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>BU79786</td>\n",
       "      <td>Washington</td>\n",
       "      <td>2763.519279</td>\n",
       "      <td>No</td>\n",
       "      <td>Basic</td>\n",
       "      <td>Bachelor</td>\n",
       "      <td>Employed</td>\n",
       "      <td>F</td>\n",
       "      <td>56274</td>\n",
       "      <td>Suburban</td>\n",
       "      <td>Married</td>\n",
       "      <td>69</td>\n",
       "      <td>32</td>\n",
       "      <td>5</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>Corporate Auto</td>\n",
       "      <td>Corporate L3</td>\n",
       "      <td>Offer1</td>\n",
       "      <td>Agent</td>\n",
       "      <td>384.811147</td>\n",
       "      <td>Two-Door Car</td>\n",
       "      <td>Medsize</td>\n",
       "      <td>24</td>\n",
       "      <td>2</td>\n",
       "      <td>2011</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>AI49188</td>\n",
       "      <td>Nevada</td>\n",
       "      <td>12887.431650</td>\n",
       "      <td>No</td>\n",
       "      <td>Premium</td>\n",
       "      <td>Bachelor</td>\n",
       "      <td>Employed</td>\n",
       "      <td>F</td>\n",
       "      <td>48767</td>\n",
       "      <td>Suburban</td>\n",
       "      <td>Married</td>\n",
       "      <td>108</td>\n",
       "      <td>18</td>\n",
       "      <td>38</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>Personal Auto</td>\n",
       "      <td>Personal L3</td>\n",
       "      <td>Offer1</td>\n",
       "      <td>Agent</td>\n",
       "      <td>566.472247</td>\n",
       "      <td>Two-Door Car</td>\n",
       "      <td>Medsize</td>\n",
       "      <td>19</td>\n",
       "      <td>2</td>\n",
       "      <td>2011</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>WW63253</td>\n",
       "      <td>California</td>\n",
       "      <td>7645.861827</td>\n",
       "      <td>No</td>\n",
       "      <td>Basic</td>\n",
       "      <td>Bachelor</td>\n",
       "      <td>Unemployed</td>\n",
       "      <td>M</td>\n",
       "      <td>0</td>\n",
       "      <td>Suburban</td>\n",
       "      <td>Married</td>\n",
       "      <td>106</td>\n",
       "      <td>18</td>\n",
       "      <td>65</td>\n",
       "      <td>0</td>\n",
       "      <td>7</td>\n",
       "      <td>Corporate Auto</td>\n",
       "      <td>Corporate L2</td>\n",
       "      <td>Offer1</td>\n",
       "      <td>Call Center</td>\n",
       "      <td>529.881344</td>\n",
       "      <td>SUV</td>\n",
       "      <td>Medsize</td>\n",
       "      <td>20</td>\n",
       "      <td>1</td>\n",
       "      <td>2011</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>HB64268</td>\n",
       "      <td>Washington</td>\n",
       "      <td>2813.692575</td>\n",
       "      <td>No</td>\n",
       "      <td>Basic</td>\n",
       "      <td>Bachelor</td>\n",
       "      <td>Employed</td>\n",
       "      <td>M</td>\n",
       "      <td>43836</td>\n",
       "      <td>Rural</td>\n",
       "      <td>Single</td>\n",
       "      <td>73</td>\n",
       "      <td>12</td>\n",
       "      <td>44</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>Personal Auto</td>\n",
       "      <td>Personal L1</td>\n",
       "      <td>Offer1</td>\n",
       "      <td>Agent</td>\n",
       "      <td>138.130879</td>\n",
       "      <td>Four-Door Car</td>\n",
       "      <td>Medsize</td>\n",
       "      <td>3</td>\n",
       "      <td>2</td>\n",
       "      <td>2011</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>OC83172</td>\n",
       "      <td>Oregon</td>\n",
       "      <td>8256.297800</td>\n",
       "      <td>Yes</td>\n",
       "      <td>Basic</td>\n",
       "      <td>Bachelor</td>\n",
       "      <td>Employed</td>\n",
       "      <td>F</td>\n",
       "      <td>62902</td>\n",
       "      <td>Rural</td>\n",
       "      <td>Married</td>\n",
       "      <td>69</td>\n",
       "      <td>14</td>\n",
       "      <td>94</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>Personal Auto</td>\n",
       "      <td>Personal L3</td>\n",
       "      <td>Offer2</td>\n",
       "      <td>Web</td>\n",
       "      <td>159.383042</td>\n",
       "      <td>Two-Door Car</td>\n",
       "      <td>Medsize</td>\n",
       "      <td>25</td>\n",
       "      <td>1</td>\n",
       "      <td>2011</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9128</th>\n",
       "      <td>YM19146</td>\n",
       "      <td>California</td>\n",
       "      <td>4100.398533</td>\n",
       "      <td>No</td>\n",
       "      <td>Premium</td>\n",
       "      <td>College</td>\n",
       "      <td>Employed</td>\n",
       "      <td>F</td>\n",
       "      <td>47761</td>\n",
       "      <td>Suburban</td>\n",
       "      <td>Single</td>\n",
       "      <td>104</td>\n",
       "      <td>16</td>\n",
       "      <td>58</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>Personal Auto</td>\n",
       "      <td>Personal L2</td>\n",
       "      <td>Offer1</td>\n",
       "      <td>Branch</td>\n",
       "      <td>541.282007</td>\n",
       "      <td>Four-Door Car</td>\n",
       "      <td>Large</td>\n",
       "      <td>6</td>\n",
       "      <td>1</td>\n",
       "      <td>2011</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9130</th>\n",
       "      <td>PK87824</td>\n",
       "      <td>California</td>\n",
       "      <td>3096.511217</td>\n",
       "      <td>Yes</td>\n",
       "      <td>Extended</td>\n",
       "      <td>College</td>\n",
       "      <td>Employed</td>\n",
       "      <td>F</td>\n",
       "      <td>21604</td>\n",
       "      <td>Suburban</td>\n",
       "      <td>Divorced</td>\n",
       "      <td>79</td>\n",
       "      <td>14</td>\n",
       "      <td>28</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>Corporate Auto</td>\n",
       "      <td>Corporate L3</td>\n",
       "      <td>Offer1</td>\n",
       "      <td>Branch</td>\n",
       "      <td>379.200000</td>\n",
       "      <td>Four-Door Car</td>\n",
       "      <td>Medsize</td>\n",
       "      <td>12</td>\n",
       "      <td>2</td>\n",
       "      <td>2011</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9131</th>\n",
       "      <td>TD14365</td>\n",
       "      <td>California</td>\n",
       "      <td>8163.890428</td>\n",
       "      <td>No</td>\n",
       "      <td>Extended</td>\n",
       "      <td>Bachelor</td>\n",
       "      <td>Unemployed</td>\n",
       "      <td>M</td>\n",
       "      <td>0</td>\n",
       "      <td>Suburban</td>\n",
       "      <td>Single</td>\n",
       "      <td>85</td>\n",
       "      <td>9</td>\n",
       "      <td>37</td>\n",
       "      <td>3</td>\n",
       "      <td>2</td>\n",
       "      <td>Corporate Auto</td>\n",
       "      <td>Corporate L2</td>\n",
       "      <td>Offer1</td>\n",
       "      <td>Branch</td>\n",
       "      <td>790.784983</td>\n",
       "      <td>Four-Door Car</td>\n",
       "      <td>Medsize</td>\n",
       "      <td>6</td>\n",
       "      <td>2</td>\n",
       "      <td>2011</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9132</th>\n",
       "      <td>UP19263</td>\n",
       "      <td>California</td>\n",
       "      <td>7524.442436</td>\n",
       "      <td>No</td>\n",
       "      <td>Extended</td>\n",
       "      <td>College</td>\n",
       "      <td>Employed</td>\n",
       "      <td>M</td>\n",
       "      <td>21941</td>\n",
       "      <td>Suburban</td>\n",
       "      <td>Married</td>\n",
       "      <td>96</td>\n",
       "      <td>34</td>\n",
       "      <td>3</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>Personal Auto</td>\n",
       "      <td>Personal L2</td>\n",
       "      <td>Offer3</td>\n",
       "      <td>Branch</td>\n",
       "      <td>691.200000</td>\n",
       "      <td>Four-Door Car</td>\n",
       "      <td>Large</td>\n",
       "      <td>3</td>\n",
       "      <td>2</td>\n",
       "      <td>2011</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9133</th>\n",
       "      <td>Y167826</td>\n",
       "      <td>California</td>\n",
       "      <td>2611.836866</td>\n",
       "      <td>No</td>\n",
       "      <td>Extended</td>\n",
       "      <td>College</td>\n",
       "      <td>Unemployed</td>\n",
       "      <td>M</td>\n",
       "      <td>0</td>\n",
       "      <td>Suburban</td>\n",
       "      <td>Single</td>\n",
       "      <td>77</td>\n",
       "      <td>3</td>\n",
       "      <td>90</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>Corporate Auto</td>\n",
       "      <td>Corporate L3</td>\n",
       "      <td>Offer4</td>\n",
       "      <td>Call Center</td>\n",
       "      <td>369.600000</td>\n",
       "      <td>Two-Door Car</td>\n",
       "      <td>Medsize</td>\n",
       "      <td>14</td>\n",
       "      <td>2</td>\n",
       "      <td>2011</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>7399 rows × 26 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "     customer       state  customer_lifetime_value response  coverage  \\\n",
       "0     BU79786  Washington              2763.519279       No     Basic   \n",
       "2     AI49188      Nevada             12887.431650       No   Premium   \n",
       "3     WW63253  California              7645.861827       No     Basic   \n",
       "4     HB64268  Washington              2813.692575       No     Basic   \n",
       "5     OC83172      Oregon              8256.297800      Yes     Basic   \n",
       "...       ...         ...                      ...      ...       ...   \n",
       "9128  YM19146  California              4100.398533       No   Premium   \n",
       "9130  PK87824  California              3096.511217      Yes  Extended   \n",
       "9131  TD14365  California              8163.890428       No  Extended   \n",
       "9132  UP19263  California              7524.442436       No  Extended   \n",
       "9133  Y167826  California              2611.836866       No  Extended   \n",
       "\n",
       "     education employment_status gender  income location_code marital_status  \\\n",
       "0     Bachelor          Employed      F   56274      Suburban        Married   \n",
       "2     Bachelor          Employed      F   48767      Suburban        Married   \n",
       "3     Bachelor        Unemployed      M       0      Suburban        Married   \n",
       "4     Bachelor          Employed      M   43836         Rural         Single   \n",
       "5     Bachelor          Employed      F   62902         Rural        Married   \n",
       "...        ...               ...    ...     ...           ...            ...   \n",
       "9128   College          Employed      F   47761      Suburban         Single   \n",
       "9130   College          Employed      F   21604      Suburban       Divorced   \n",
       "9131  Bachelor        Unemployed      M       0      Suburban         Single   \n",
       "9132   College          Employed      M   21941      Suburban        Married   \n",
       "9133   College        Unemployed      M       0      Suburban         Single   \n",
       "\n",
       "      monthly_premium_auto  months_since_last_claim  \\\n",
       "0                       69                       32   \n",
       "2                      108                       18   \n",
       "3                      106                       18   \n",
       "4                       73                       12   \n",
       "5                       69                       14   \n",
       "...                    ...                      ...   \n",
       "9128                   104                       16   \n",
       "9130                    79                       14   \n",
       "9131                    85                        9   \n",
       "9132                    96                       34   \n",
       "9133                    77                        3   \n",
       "\n",
       "      months_since_policy_inception  number_of_open_complaints  \\\n",
       "0                                 5                          0   \n",
       "2                                38                          0   \n",
       "3                                65                          0   \n",
       "4                                44                          0   \n",
       "5                                94                          0   \n",
       "...                             ...                        ...   \n",
       "9128                             58                          0   \n",
       "9130                             28                          0   \n",
       "9131                             37                          3   \n",
       "9132                              3                          0   \n",
       "9133                             90                          0   \n",
       "\n",
       "      number_of_policies     policy_type        policy renew_offer_type  \\\n",
       "0                      1  Corporate Auto  Corporate L3           Offer1   \n",
       "2                      2   Personal Auto   Personal L3           Offer1   \n",
       "3                      7  Corporate Auto  Corporate L2           Offer1   \n",
       "4                      1   Personal Auto   Personal L1           Offer1   \n",
       "5                      2   Personal Auto   Personal L3           Offer2   \n",
       "...                  ...             ...           ...              ...   \n",
       "9128                   1   Personal Auto   Personal L2           Offer1   \n",
       "9130                   1  Corporate Auto  Corporate L3           Offer1   \n",
       "9131                   2  Corporate Auto  Corporate L2           Offer1   \n",
       "9132                   3   Personal Auto   Personal L2           Offer3   \n",
       "9133                   1  Corporate Auto  Corporate L3           Offer4   \n",
       "\n",
       "     sales_channel  total_claim_amount  vehicle_class vehicle_size  day  \\\n",
       "0            Agent          384.811147   Two-Door Car      Medsize   24   \n",
       "2            Agent          566.472247   Two-Door Car      Medsize   19   \n",
       "3      Call Center          529.881344            SUV      Medsize   20   \n",
       "4            Agent          138.130879  Four-Door Car      Medsize    3   \n",
       "5              Web          159.383042   Two-Door Car      Medsize   25   \n",
       "...            ...                 ...            ...          ...  ...   \n",
       "9128        Branch          541.282007  Four-Door Car        Large    6   \n",
       "9130        Branch          379.200000  Four-Door Car      Medsize   12   \n",
       "9131        Branch          790.784983  Four-Door Car      Medsize    6   \n",
       "9132        Branch          691.200000  Four-Door Car        Large    3   \n",
       "9133   Call Center          369.600000   Two-Door Car      Medsize   14   \n",
       "\n",
       "      month  year  \n",
       "0         2  2011  \n",
       "2         2  2011  \n",
       "3         1  2011  \n",
       "4         2  2011  \n",
       "5         1  2011  \n",
       "...     ...   ...  \n",
       "9128      1  2011  \n",
       "9130      2  2011  \n",
       "9131      2  2011  \n",
       "9132      2  2011  \n",
       "9133      2  2011  \n",
       "\n",
       "[7399 rows x 26 columns]"
      ]
     },
     "execution_count": 22,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_new"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "id": "civic-operation",
   "metadata": {},
   "outputs": [],
   "source": [
    "# df_new = df_new.drop(['day'], axis=1)\n",
    "# df_new = df_new.drop(['month'], axis=1)\n",
    "# df_new = df_new.drop(['year'], axis=1)\n",
    "# df_new = df_new.drop(['customer'], axis=1) # customer id is not a value"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "id": "abandoned-heavy",
   "metadata": {},
   "outputs": [],
   "source": [
    "# df_new['effective_to_date'] = df_new['effective_to_date'].astype('int')\n",
    "df_new['day'] = df_new['day'].astype('int')\n",
    "df_new['month'] = df_new['month'].astype('int')\n",
    "df_new['month'] = df_new['month'].astype('int')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "id": "breathing-senior",
   "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>customer</th>\n",
       "      <th>state</th>\n",
       "      <th>customer_lifetime_value</th>\n",
       "      <th>response</th>\n",
       "      <th>coverage</th>\n",
       "      <th>education</th>\n",
       "      <th>employment_status</th>\n",
       "      <th>gender</th>\n",
       "      <th>income</th>\n",
       "      <th>location_code</th>\n",
       "      <th>marital_status</th>\n",
       "      <th>monthly_premium_auto</th>\n",
       "      <th>months_since_last_claim</th>\n",
       "      <th>months_since_policy_inception</th>\n",
       "      <th>number_of_open_complaints</th>\n",
       "      <th>number_of_policies</th>\n",
       "      <th>policy_type</th>\n",
       "      <th>policy</th>\n",
       "      <th>renew_offer_type</th>\n",
       "      <th>sales_channel</th>\n",
       "      <th>total_claim_amount</th>\n",
       "      <th>vehicle_class</th>\n",
       "      <th>vehicle_size</th>\n",
       "      <th>day</th>\n",
       "      <th>month</th>\n",
       "      <th>year</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>BU79786</td>\n",
       "      <td>Washington</td>\n",
       "      <td>2763.519279</td>\n",
       "      <td>No</td>\n",
       "      <td>Basic</td>\n",
       "      <td>Bachelor</td>\n",
       "      <td>Employed</td>\n",
       "      <td>F</td>\n",
       "      <td>56274</td>\n",
       "      <td>Suburban</td>\n",
       "      <td>Married</td>\n",
       "      <td>69</td>\n",
       "      <td>32</td>\n",
       "      <td>5</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>Corporate Auto</td>\n",
       "      <td>Corporate L3</td>\n",
       "      <td>Offer1</td>\n",
       "      <td>Agent</td>\n",
       "      <td>384.811147</td>\n",
       "      <td>Two-Door Car</td>\n",
       "      <td>Medsize</td>\n",
       "      <td>24</td>\n",
       "      <td>2</td>\n",
       "      <td>2011</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>AI49188</td>\n",
       "      <td>Nevada</td>\n",
       "      <td>12887.431650</td>\n",
       "      <td>No</td>\n",
       "      <td>Premium</td>\n",
       "      <td>Bachelor</td>\n",
       "      <td>Employed</td>\n",
       "      <td>F</td>\n",
       "      <td>48767</td>\n",
       "      <td>Suburban</td>\n",
       "      <td>Married</td>\n",
       "      <td>108</td>\n",
       "      <td>18</td>\n",
       "      <td>38</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>Personal Auto</td>\n",
       "      <td>Personal L3</td>\n",
       "      <td>Offer1</td>\n",
       "      <td>Agent</td>\n",
       "      <td>566.472247</td>\n",
       "      <td>Two-Door Car</td>\n",
       "      <td>Medsize</td>\n",
       "      <td>19</td>\n",
       "      <td>2</td>\n",
       "      <td>2011</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>WW63253</td>\n",
       "      <td>California</td>\n",
       "      <td>7645.861827</td>\n",
       "      <td>No</td>\n",
       "      <td>Basic</td>\n",
       "      <td>Bachelor</td>\n",
       "      <td>Unemployed</td>\n",
       "      <td>M</td>\n",
       "      <td>0</td>\n",
       "      <td>Suburban</td>\n",
       "      <td>Married</td>\n",
       "      <td>106</td>\n",
       "      <td>18</td>\n",
       "      <td>65</td>\n",
       "      <td>0</td>\n",
       "      <td>7</td>\n",
       "      <td>Corporate Auto</td>\n",
       "      <td>Corporate L2</td>\n",
       "      <td>Offer1</td>\n",
       "      <td>Call Center</td>\n",
       "      <td>529.881344</td>\n",
       "      <td>SUV</td>\n",
       "      <td>Medsize</td>\n",
       "      <td>20</td>\n",
       "      <td>1</td>\n",
       "      <td>2011</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>HB64268</td>\n",
       "      <td>Washington</td>\n",
       "      <td>2813.692575</td>\n",
       "      <td>No</td>\n",
       "      <td>Basic</td>\n",
       "      <td>Bachelor</td>\n",
       "      <td>Employed</td>\n",
       "      <td>M</td>\n",
       "      <td>43836</td>\n",
       "      <td>Rural</td>\n",
       "      <td>Single</td>\n",
       "      <td>73</td>\n",
       "      <td>12</td>\n",
       "      <td>44</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>Personal Auto</td>\n",
       "      <td>Personal L1</td>\n",
       "      <td>Offer1</td>\n",
       "      <td>Agent</td>\n",
       "      <td>138.130879</td>\n",
       "      <td>Four-Door Car</td>\n",
       "      <td>Medsize</td>\n",
       "      <td>3</td>\n",
       "      <td>2</td>\n",
       "      <td>2011</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>OC83172</td>\n",
       "      <td>Oregon</td>\n",
       "      <td>8256.297800</td>\n",
       "      <td>Yes</td>\n",
       "      <td>Basic</td>\n",
       "      <td>Bachelor</td>\n",
       "      <td>Employed</td>\n",
       "      <td>F</td>\n",
       "      <td>62902</td>\n",
       "      <td>Rural</td>\n",
       "      <td>Married</td>\n",
       "      <td>69</td>\n",
       "      <td>14</td>\n",
       "      <td>94</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>Personal Auto</td>\n",
       "      <td>Personal L3</td>\n",
       "      <td>Offer2</td>\n",
       "      <td>Web</td>\n",
       "      <td>159.383042</td>\n",
       "      <td>Two-Door Car</td>\n",
       "      <td>Medsize</td>\n",
       "      <td>25</td>\n",
       "      <td>1</td>\n",
       "      <td>2011</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9128</th>\n",
       "      <td>YM19146</td>\n",
       "      <td>California</td>\n",
       "      <td>4100.398533</td>\n",
       "      <td>No</td>\n",
       "      <td>Premium</td>\n",
       "      <td>College</td>\n",
       "      <td>Employed</td>\n",
       "      <td>F</td>\n",
       "      <td>47761</td>\n",
       "      <td>Suburban</td>\n",
       "      <td>Single</td>\n",
       "      <td>104</td>\n",
       "      <td>16</td>\n",
       "      <td>58</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>Personal Auto</td>\n",
       "      <td>Personal L2</td>\n",
       "      <td>Offer1</td>\n",
       "      <td>Branch</td>\n",
       "      <td>541.282007</td>\n",
       "      <td>Four-Door Car</td>\n",
       "      <td>Large</td>\n",
       "      <td>6</td>\n",
       "      <td>1</td>\n",
       "      <td>2011</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9130</th>\n",
       "      <td>PK87824</td>\n",
       "      <td>California</td>\n",
       "      <td>3096.511217</td>\n",
       "      <td>Yes</td>\n",
       "      <td>Extended</td>\n",
       "      <td>College</td>\n",
       "      <td>Employed</td>\n",
       "      <td>F</td>\n",
       "      <td>21604</td>\n",
       "      <td>Suburban</td>\n",
       "      <td>Divorced</td>\n",
       "      <td>79</td>\n",
       "      <td>14</td>\n",
       "      <td>28</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>Corporate Auto</td>\n",
       "      <td>Corporate L3</td>\n",
       "      <td>Offer1</td>\n",
       "      <td>Branch</td>\n",
       "      <td>379.200000</td>\n",
       "      <td>Four-Door Car</td>\n",
       "      <td>Medsize</td>\n",
       "      <td>12</td>\n",
       "      <td>2</td>\n",
       "      <td>2011</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9131</th>\n",
       "      <td>TD14365</td>\n",
       "      <td>California</td>\n",
       "      <td>8163.890428</td>\n",
       "      <td>No</td>\n",
       "      <td>Extended</td>\n",
       "      <td>Bachelor</td>\n",
       "      <td>Unemployed</td>\n",
       "      <td>M</td>\n",
       "      <td>0</td>\n",
       "      <td>Suburban</td>\n",
       "      <td>Single</td>\n",
       "      <td>85</td>\n",
       "      <td>9</td>\n",
       "      <td>37</td>\n",
       "      <td>3</td>\n",
       "      <td>2</td>\n",
       "      <td>Corporate Auto</td>\n",
       "      <td>Corporate L2</td>\n",
       "      <td>Offer1</td>\n",
       "      <td>Branch</td>\n",
       "      <td>790.784983</td>\n",
       "      <td>Four-Door Car</td>\n",
       "      <td>Medsize</td>\n",
       "      <td>6</td>\n",
       "      <td>2</td>\n",
       "      <td>2011</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9132</th>\n",
       "      <td>UP19263</td>\n",
       "      <td>California</td>\n",
       "      <td>7524.442436</td>\n",
       "      <td>No</td>\n",
       "      <td>Extended</td>\n",
       "      <td>College</td>\n",
       "      <td>Employed</td>\n",
       "      <td>M</td>\n",
       "      <td>21941</td>\n",
       "      <td>Suburban</td>\n",
       "      <td>Married</td>\n",
       "      <td>96</td>\n",
       "      <td>34</td>\n",
       "      <td>3</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>Personal Auto</td>\n",
       "      <td>Personal L2</td>\n",
       "      <td>Offer3</td>\n",
       "      <td>Branch</td>\n",
       "      <td>691.200000</td>\n",
       "      <td>Four-Door Car</td>\n",
       "      <td>Large</td>\n",
       "      <td>3</td>\n",
       "      <td>2</td>\n",
       "      <td>2011</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9133</th>\n",
       "      <td>Y167826</td>\n",
       "      <td>California</td>\n",
       "      <td>2611.836866</td>\n",
       "      <td>No</td>\n",
       "      <td>Extended</td>\n",
       "      <td>College</td>\n",
       "      <td>Unemployed</td>\n",
       "      <td>M</td>\n",
       "      <td>0</td>\n",
       "      <td>Suburban</td>\n",
       "      <td>Single</td>\n",
       "      <td>77</td>\n",
       "      <td>3</td>\n",
       "      <td>90</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>Corporate Auto</td>\n",
       "      <td>Corporate L3</td>\n",
       "      <td>Offer4</td>\n",
       "      <td>Call Center</td>\n",
       "      <td>369.600000</td>\n",
       "      <td>Two-Door Car</td>\n",
       "      <td>Medsize</td>\n",
       "      <td>14</td>\n",
       "      <td>2</td>\n",
       "      <td>2011</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>7399 rows × 26 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "     customer       state  customer_lifetime_value response  coverage  \\\n",
       "0     BU79786  Washington              2763.519279       No     Basic   \n",
       "2     AI49188      Nevada             12887.431650       No   Premium   \n",
       "3     WW63253  California              7645.861827       No     Basic   \n",
       "4     HB64268  Washington              2813.692575       No     Basic   \n",
       "5     OC83172      Oregon              8256.297800      Yes     Basic   \n",
       "...       ...         ...                      ...      ...       ...   \n",
       "9128  YM19146  California              4100.398533       No   Premium   \n",
       "9130  PK87824  California              3096.511217      Yes  Extended   \n",
       "9131  TD14365  California              8163.890428       No  Extended   \n",
       "9132  UP19263  California              7524.442436       No  Extended   \n",
       "9133  Y167826  California              2611.836866       No  Extended   \n",
       "\n",
       "     education employment_status gender  income location_code marital_status  \\\n",
       "0     Bachelor          Employed      F   56274      Suburban        Married   \n",
       "2     Bachelor          Employed      F   48767      Suburban        Married   \n",
       "3     Bachelor        Unemployed      M       0      Suburban        Married   \n",
       "4     Bachelor          Employed      M   43836         Rural         Single   \n",
       "5     Bachelor          Employed      F   62902         Rural        Married   \n",
       "...        ...               ...    ...     ...           ...            ...   \n",
       "9128   College          Employed      F   47761      Suburban         Single   \n",
       "9130   College          Employed      F   21604      Suburban       Divorced   \n",
       "9131  Bachelor        Unemployed      M       0      Suburban         Single   \n",
       "9132   College          Employed      M   21941      Suburban        Married   \n",
       "9133   College        Unemployed      M       0      Suburban         Single   \n",
       "\n",
       "      monthly_premium_auto  months_since_last_claim  \\\n",
       "0                       69                       32   \n",
       "2                      108                       18   \n",
       "3                      106                       18   \n",
       "4                       73                       12   \n",
       "5                       69                       14   \n",
       "...                    ...                      ...   \n",
       "9128                   104                       16   \n",
       "9130                    79                       14   \n",
       "9131                    85                        9   \n",
       "9132                    96                       34   \n",
       "9133                    77                        3   \n",
       "\n",
       "      months_since_policy_inception  number_of_open_complaints  \\\n",
       "0                                 5                          0   \n",
       "2                                38                          0   \n",
       "3                                65                          0   \n",
       "4                                44                          0   \n",
       "5                                94                          0   \n",
       "...                             ...                        ...   \n",
       "9128                             58                          0   \n",
       "9130                             28                          0   \n",
       "9131                             37                          3   \n",
       "9132                              3                          0   \n",
       "9133                             90                          0   \n",
       "\n",
       "      number_of_policies     policy_type        policy renew_offer_type  \\\n",
       "0                      1  Corporate Auto  Corporate L3           Offer1   \n",
       "2                      2   Personal Auto   Personal L3           Offer1   \n",
       "3                      7  Corporate Auto  Corporate L2           Offer1   \n",
       "4                      1   Personal Auto   Personal L1           Offer1   \n",
       "5                      2   Personal Auto   Personal L3           Offer2   \n",
       "...                  ...             ...           ...              ...   \n",
       "9128                   1   Personal Auto   Personal L2           Offer1   \n",
       "9130                   1  Corporate Auto  Corporate L3           Offer1   \n",
       "9131                   2  Corporate Auto  Corporate L2           Offer1   \n",
       "9132                   3   Personal Auto   Personal L2           Offer3   \n",
       "9133                   1  Corporate Auto  Corporate L3           Offer4   \n",
       "\n",
       "     sales_channel  total_claim_amount  vehicle_class vehicle_size  day  \\\n",
       "0            Agent          384.811147   Two-Door Car      Medsize   24   \n",
       "2            Agent          566.472247   Two-Door Car      Medsize   19   \n",
       "3      Call Center          529.881344            SUV      Medsize   20   \n",
       "4            Agent          138.130879  Four-Door Car      Medsize    3   \n",
       "5              Web          159.383042   Two-Door Car      Medsize   25   \n",
       "...            ...                 ...            ...          ...  ...   \n",
       "9128        Branch          541.282007  Four-Door Car        Large    6   \n",
       "9130        Branch          379.200000  Four-Door Car      Medsize   12   \n",
       "9131        Branch          790.784983  Four-Door Car      Medsize    6   \n",
       "9132        Branch          691.200000  Four-Door Car        Large    3   \n",
       "9133   Call Center          369.600000   Two-Door Car      Medsize   14   \n",
       "\n",
       "      month  year  \n",
       "0         2  2011  \n",
       "2         2  2011  \n",
       "3         1  2011  \n",
       "4         2  2011  \n",
       "5         1  2011  \n",
       "...     ...   ...  \n",
       "9128      1  2011  \n",
       "9130      2  2011  \n",
       "9131      2  2011  \n",
       "9132      2  2011  \n",
       "9133      2  2011  \n",
       "\n",
       "[7399 rows x 26 columns]"
      ]
     },
     "execution_count": 25,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_new"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "id": "chubby-agreement",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "Int64Index: 7399 entries, 0 to 9133\n",
      "Data columns (total 26 columns):\n",
      " #   Column                         Non-Null Count  Dtype  \n",
      "---  ------                         --------------  -----  \n",
      " 0   customer                       7399 non-null   object \n",
      " 1   state                          7399 non-null   object \n",
      " 2   customer_lifetime_value        7399 non-null   float64\n",
      " 3   response                       7399 non-null   object \n",
      " 4   coverage                       7399 non-null   object \n",
      " 5   education                      7399 non-null   object \n",
      " 6   employment_status              7399 non-null   object \n",
      " 7   gender                         7399 non-null   object \n",
      " 8   income                         7399 non-null   int64  \n",
      " 9   location_code                  7399 non-null   object \n",
      " 10  marital_status                 7399 non-null   object \n",
      " 11  monthly_premium_auto           7399 non-null   int64  \n",
      " 12  months_since_last_claim        7399 non-null   int64  \n",
      " 13  months_since_policy_inception  7399 non-null   int64  \n",
      " 14  number_of_open_complaints      7399 non-null   int64  \n",
      " 15  number_of_policies             7399 non-null   int64  \n",
      " 16  policy_type                    7399 non-null   object \n",
      " 17  policy                         7399 non-null   object \n",
      " 18  renew_offer_type               7399 non-null   object \n",
      " 19  sales_channel                  7399 non-null   object \n",
      " 20  total_claim_amount             7399 non-null   float64\n",
      " 21  vehicle_class                  7399 non-null   object \n",
      " 22  vehicle_size                   7399 non-null   object \n",
      " 23  day                            7399 non-null   int64  \n",
      " 24  month                          7399 non-null   int64  \n",
      " 25  year                           7399 non-null   int64  \n",
      "dtypes: float64(2), int64(9), object(15)\n",
      "memory usage: 1.5+ MB\n"
     ]
    }
   ],
   "source": [
    "df_new.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "neither-native",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "markdown",
   "id": "alternate-closure",
   "metadata": {},
   "source": [
    "#### Since the model will only accept numerical data, check and make sure that every column is numerical, if some are not, change it using encoding."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "id": "cleared-optimization",
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
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       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>customer</th>\n",
       "      <th>state</th>\n",
       "      <th>response</th>\n",
       "      <th>coverage</th>\n",
       "      <th>education</th>\n",
       "      <th>employment_status</th>\n",
       "      <th>gender</th>\n",
       "      <th>location_code</th>\n",
       "      <th>marital_status</th>\n",
       "      <th>policy_type</th>\n",
       "      <th>policy</th>\n",
       "      <th>renew_offer_type</th>\n",
       "      <th>sales_channel</th>\n",
       "      <th>vehicle_class</th>\n",
       "      <th>vehicle_size</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>BU79786</td>\n",
       "      <td>Washington</td>\n",
       "      <td>No</td>\n",
       "      <td>Basic</td>\n",
       "      <td>Bachelor</td>\n",
       "      <td>Employed</td>\n",
       "      <td>F</td>\n",
       "      <td>Suburban</td>\n",
       "      <td>Married</td>\n",
       "      <td>Corporate Auto</td>\n",
       "      <td>Corporate L3</td>\n",
       "      <td>Offer1</td>\n",
       "      <td>Agent</td>\n",
       "      <td>Two-Door Car</td>\n",
       "      <td>Medsize</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>AI49188</td>\n",
       "      <td>Nevada</td>\n",
       "      <td>No</td>\n",
       "      <td>Premium</td>\n",
       "      <td>Bachelor</td>\n",
       "      <td>Employed</td>\n",
       "      <td>F</td>\n",
       "      <td>Suburban</td>\n",
       "      <td>Married</td>\n",
       "      <td>Personal Auto</td>\n",
       "      <td>Personal L3</td>\n",
       "      <td>Offer1</td>\n",
       "      <td>Agent</td>\n",
       "      <td>Two-Door Car</td>\n",
       "      <td>Medsize</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>WW63253</td>\n",
       "      <td>California</td>\n",
       "      <td>No</td>\n",
       "      <td>Basic</td>\n",
       "      <td>Bachelor</td>\n",
       "      <td>Unemployed</td>\n",
       "      <td>M</td>\n",
       "      <td>Suburban</td>\n",
       "      <td>Married</td>\n",
       "      <td>Corporate Auto</td>\n",
       "      <td>Corporate L2</td>\n",
       "      <td>Offer1</td>\n",
       "      <td>Call Center</td>\n",
       "      <td>SUV</td>\n",
       "      <td>Medsize</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>HB64268</td>\n",
       "      <td>Washington</td>\n",
       "      <td>No</td>\n",
       "      <td>Basic</td>\n",
       "      <td>Bachelor</td>\n",
       "      <td>Employed</td>\n",
       "      <td>M</td>\n",
       "      <td>Rural</td>\n",
       "      <td>Single</td>\n",
       "      <td>Personal Auto</td>\n",
       "      <td>Personal L1</td>\n",
       "      <td>Offer1</td>\n",
       "      <td>Agent</td>\n",
       "      <td>Four-Door Car</td>\n",
       "      <td>Medsize</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>OC83172</td>\n",
       "      <td>Oregon</td>\n",
       "      <td>Yes</td>\n",
       "      <td>Basic</td>\n",
       "      <td>Bachelor</td>\n",
       "      <td>Employed</td>\n",
       "      <td>F</td>\n",
       "      <td>Rural</td>\n",
       "      <td>Married</td>\n",
       "      <td>Personal Auto</td>\n",
       "      <td>Personal L3</td>\n",
       "      <td>Offer2</td>\n",
       "      <td>Web</td>\n",
       "      <td>Two-Door Car</td>\n",
       "      <td>Medsize</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9128</th>\n",
       "      <td>YM19146</td>\n",
       "      <td>California</td>\n",
       "      <td>No</td>\n",
       "      <td>Premium</td>\n",
       "      <td>College</td>\n",
       "      <td>Employed</td>\n",
       "      <td>F</td>\n",
       "      <td>Suburban</td>\n",
       "      <td>Single</td>\n",
       "      <td>Personal Auto</td>\n",
       "      <td>Personal L2</td>\n",
       "      <td>Offer1</td>\n",
       "      <td>Branch</td>\n",
       "      <td>Four-Door Car</td>\n",
       "      <td>Large</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9130</th>\n",
       "      <td>PK87824</td>\n",
       "      <td>California</td>\n",
       "      <td>Yes</td>\n",
       "      <td>Extended</td>\n",
       "      <td>College</td>\n",
       "      <td>Employed</td>\n",
       "      <td>F</td>\n",
       "      <td>Suburban</td>\n",
       "      <td>Divorced</td>\n",
       "      <td>Corporate Auto</td>\n",
       "      <td>Corporate L3</td>\n",
       "      <td>Offer1</td>\n",
       "      <td>Branch</td>\n",
       "      <td>Four-Door Car</td>\n",
       "      <td>Medsize</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9131</th>\n",
       "      <td>TD14365</td>\n",
       "      <td>California</td>\n",
       "      <td>No</td>\n",
       "      <td>Extended</td>\n",
       "      <td>Bachelor</td>\n",
       "      <td>Unemployed</td>\n",
       "      <td>M</td>\n",
       "      <td>Suburban</td>\n",
       "      <td>Single</td>\n",
       "      <td>Corporate Auto</td>\n",
       "      <td>Corporate L2</td>\n",
       "      <td>Offer1</td>\n",
       "      <td>Branch</td>\n",
       "      <td>Four-Door Car</td>\n",
       "      <td>Medsize</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9132</th>\n",
       "      <td>UP19263</td>\n",
       "      <td>California</td>\n",
       "      <td>No</td>\n",
       "      <td>Extended</td>\n",
       "      <td>College</td>\n",
       "      <td>Employed</td>\n",
       "      <td>M</td>\n",
       "      <td>Suburban</td>\n",
       "      <td>Married</td>\n",
       "      <td>Personal Auto</td>\n",
       "      <td>Personal L2</td>\n",
       "      <td>Offer3</td>\n",
       "      <td>Branch</td>\n",
       "      <td>Four-Door Car</td>\n",
       "      <td>Large</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9133</th>\n",
       "      <td>Y167826</td>\n",
       "      <td>California</td>\n",
       "      <td>No</td>\n",
       "      <td>Extended</td>\n",
       "      <td>College</td>\n",
       "      <td>Unemployed</td>\n",
       "      <td>M</td>\n",
       "      <td>Suburban</td>\n",
       "      <td>Single</td>\n",
       "      <td>Corporate Auto</td>\n",
       "      <td>Corporate L3</td>\n",
       "      <td>Offer4</td>\n",
       "      <td>Call Center</td>\n",
       "      <td>Two-Door Car</td>\n",
       "      <td>Medsize</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>7399 rows × 15 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "     customer       state response  coverage education employment_status  \\\n",
       "0     BU79786  Washington       No     Basic  Bachelor          Employed   \n",
       "2     AI49188      Nevada       No   Premium  Bachelor          Employed   \n",
       "3     WW63253  California       No     Basic  Bachelor        Unemployed   \n",
       "4     HB64268  Washington       No     Basic  Bachelor          Employed   \n",
       "5     OC83172      Oregon      Yes     Basic  Bachelor          Employed   \n",
       "...       ...         ...      ...       ...       ...               ...   \n",
       "9128  YM19146  California       No   Premium   College          Employed   \n",
       "9130  PK87824  California      Yes  Extended   College          Employed   \n",
       "9131  TD14365  California       No  Extended  Bachelor        Unemployed   \n",
       "9132  UP19263  California       No  Extended   College          Employed   \n",
       "9133  Y167826  California       No  Extended   College        Unemployed   \n",
       "\n",
       "     gender location_code marital_status     policy_type        policy  \\\n",
       "0         F      Suburban        Married  Corporate Auto  Corporate L3   \n",
       "2         F      Suburban        Married   Personal Auto   Personal L3   \n",
       "3         M      Suburban        Married  Corporate Auto  Corporate L2   \n",
       "4         M         Rural         Single   Personal Auto   Personal L1   \n",
       "5         F         Rural        Married   Personal Auto   Personal L3   \n",
       "...     ...           ...            ...             ...           ...   \n",
       "9128      F      Suburban         Single   Personal Auto   Personal L2   \n",
       "9130      F      Suburban       Divorced  Corporate Auto  Corporate L3   \n",
       "9131      M      Suburban         Single  Corporate Auto  Corporate L2   \n",
       "9132      M      Suburban        Married   Personal Auto   Personal L2   \n",
       "9133      M      Suburban         Single  Corporate Auto  Corporate L3   \n",
       "\n",
       "     renew_offer_type sales_channel  vehicle_class vehicle_size  \n",
       "0              Offer1         Agent   Two-Door Car      Medsize  \n",
       "2              Offer1         Agent   Two-Door Car      Medsize  \n",
       "3              Offer1   Call Center            SUV      Medsize  \n",
       "4              Offer1         Agent  Four-Door Car      Medsize  \n",
       "5              Offer2           Web   Two-Door Car      Medsize  \n",
       "...               ...           ...            ...          ...  \n",
       "9128           Offer1        Branch  Four-Door Car        Large  \n",
       "9130           Offer1        Branch  Four-Door Car      Medsize  \n",
       "9131           Offer1        Branch  Four-Door Car      Medsize  \n",
       "9132           Offer3        Branch  Four-Door Car        Large  \n",
       "9133           Offer4   Call Center   Two-Door Car      Medsize  \n",
       "\n",
       "[7399 rows x 15 columns]"
      ]
     },
     "execution_count": 27,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "cat = df_new.select_dtypes(include = np.object)\n",
    "cat"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "id": "bright-claim",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Index(['customer', 'state', 'response', 'coverage', 'education',\n",
       "       'employment_status', 'gender', 'location_code', 'marital_status',\n",
       "       'policy_type', 'policy', 'renew_offer_type', 'sales_channel',\n",
       "       'vehicle_class', 'vehicle_size'],\n",
       "      dtype='object')"
      ]
     },
     "execution_count": 28,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "col = cat.columns\n",
    "col"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "id": "appropriate-directive",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>customer</th>\n",
       "      <th>state_Arizona</th>\n",
       "      <th>state_California</th>\n",
       "      <th>state_Nevada</th>\n",
       "      <th>state_Oregon</th>\n",
       "      <th>state_Washington</th>\n",
       "      <th>response_No</th>\n",
       "      <th>response_Yes</th>\n",
       "      <th>coverage_Basic</th>\n",
       "      <th>coverage_Extended</th>\n",
       "      <th>coverage_Premium</th>\n",
       "      <th>education_Bachelor</th>\n",
       "      <th>education_College</th>\n",
       "      <th>education_Doctor</th>\n",
       "      <th>education_High School or Below</th>\n",
       "      <th>education_Master</th>\n",
       "      <th>employment_status_Disabled</th>\n",
       "      <th>employment_status_Employed</th>\n",
       "      <th>employment_status_Medical Leave</th>\n",
       "      <th>employment_status_Retired</th>\n",
       "      <th>employment_status_Unemployed</th>\n",
       "      <th>gender_F</th>\n",
       "      <th>gender_M</th>\n",
       "      <th>location_code_Rural</th>\n",
       "      <th>location_code_Suburban</th>\n",
       "      <th>location_code_Urban</th>\n",
       "      <th>marital_status_Divorced</th>\n",
       "      <th>marital_status_Married</th>\n",
       "      <th>marital_status_Single</th>\n",
       "      <th>policy_type_Corporate Auto</th>\n",
       "      <th>policy_type_Personal Auto</th>\n",
       "      <th>policy_type_Special Auto</th>\n",
       "      <th>policy_Corporate L1</th>\n",
       "      <th>policy_Corporate L2</th>\n",
       "      <th>policy_Corporate L3</th>\n",
       "      <th>policy_Personal L1</th>\n",
       "      <th>policy_Personal L2</th>\n",
       "      <th>policy_Personal L3</th>\n",
       "      <th>policy_Special L1</th>\n",
       "      <th>policy_Special L2</th>\n",
       "      <th>policy_Special L3</th>\n",
       "      <th>renew_offer_type_Offer1</th>\n",
       "      <th>renew_offer_type_Offer2</th>\n",
       "      <th>renew_offer_type_Offer3</th>\n",
       "      <th>renew_offer_type_Offer4</th>\n",
       "      <th>sales_channel_Agent</th>\n",
       "      <th>sales_channel_Branch</th>\n",
       "      <th>sales_channel_Call Center</th>\n",
       "      <th>sales_channel_Web</th>\n",
       "      <th>vehicle_class_Four-Door Car</th>\n",
       "      <th>vehicle_class_SUV</th>\n",
       "      <th>vehicle_class_Sports Car</th>\n",
       "      <th>vehicle_class_Two-Door Car</th>\n",
       "      <th>vehicle_size_Large</th>\n",
       "      <th>vehicle_size_Medsize</th>\n",
       "      <th>vehicle_size_Small</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>BU79786</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <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",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <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>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <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",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>AI49188</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</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",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <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",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <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",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>WW63253</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <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",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <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",
       "      <td>0</td>\n",
       "      <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",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>HB64268</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <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",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <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>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <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>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>OC83172</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <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",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <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",
       "      <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>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "  customer  state_Arizona  state_California  state_Nevada  state_Oregon  \\\n",
       "0  BU79786              0                 0             0             0   \n",
       "2  AI49188              0                 0             1             0   \n",
       "3  WW63253              0                 1             0             0   \n",
       "4  HB64268              0                 0             0             0   \n",
       "5  OC83172              0                 0             0             1   \n",
       "\n",
       "   state_Washington  response_No  response_Yes  coverage_Basic  \\\n",
       "0                 1            1             0               1   \n",
       "2                 0            1             0               0   \n",
       "3                 0            1             0               1   \n",
       "4                 1            1             0               1   \n",
       "5                 0            0             1               1   \n",
       "\n",
       "   coverage_Extended  coverage_Premium  education_Bachelor  education_College  \\\n",
       "0                  0                 0                   1                  0   \n",
       "2                  0                 1                   1                  0   \n",
       "3                  0                 0                   1                  0   \n",
       "4                  0                 0                   1                  0   \n",
       "5                  0                 0                   1                  0   \n",
       "\n",
       "   education_Doctor  education_High School or Below  education_Master  \\\n",
       "0                 0                               0                 0   \n",
       "2                 0                               0                 0   \n",
       "3                 0                               0                 0   \n",
       "4                 0                               0                 0   \n",
       "5                 0                               0                 0   \n",
       "\n",
       "   employment_status_Disabled  employment_status_Employed  \\\n",
       "0                           0                           1   \n",
       "2                           0                           1   \n",
       "3                           0                           0   \n",
       "4                           0                           1   \n",
       "5                           0                           1   \n",
       "\n",
       "   employment_status_Medical Leave  employment_status_Retired  \\\n",
       "0                                0                          0   \n",
       "2                                0                          0   \n",
       "3                                0                          0   \n",
       "4                                0                          0   \n",
       "5                                0                          0   \n",
       "\n",
       "   employment_status_Unemployed  gender_F  gender_M  location_code_Rural  \\\n",
       "0                             0         1         0                    0   \n",
       "2                             0         1         0                    0   \n",
       "3                             1         0         1                    0   \n",
       "4                             0         0         1                    1   \n",
       "5                             0         1         0                    1   \n",
       "\n",
       "   location_code_Suburban  location_code_Urban  marital_status_Divorced  \\\n",
       "0                       1                    0                        0   \n",
       "2                       1                    0                        0   \n",
       "3                       1                    0                        0   \n",
       "4                       0                    0                        0   \n",
       "5                       0                    0                        0   \n",
       "\n",
       "   marital_status_Married  marital_status_Single  policy_type_Corporate Auto  \\\n",
       "0                       1                      0                           1   \n",
       "2                       1                      0                           0   \n",
       "3                       1                      0                           1   \n",
       "4                       0                      1                           0   \n",
       "5                       1                      0                           0   \n",
       "\n",
       "   policy_type_Personal Auto  policy_type_Special Auto  policy_Corporate L1  \\\n",
       "0                          0                         0                    0   \n",
       "2                          1                         0                    0   \n",
       "3                          0                         0                    0   \n",
       "4                          1                         0                    0   \n",
       "5                          1                         0                    0   \n",
       "\n",
       "   policy_Corporate L2  policy_Corporate L3  policy_Personal L1  \\\n",
       "0                    0                    1                   0   \n",
       "2                    0                    0                   0   \n",
       "3                    1                    0                   0   \n",
       "4                    0                    0                   1   \n",
       "5                    0                    0                   0   \n",
       "\n",
       "   policy_Personal L2  policy_Personal L3  policy_Special L1  \\\n",
       "0                   0                   0                  0   \n",
       "2                   0                   1                  0   \n",
       "3                   0                   0                  0   \n",
       "4                   0                   0                  0   \n",
       "5                   0                   1                  0   \n",
       "\n",
       "   policy_Special L2  policy_Special L3  renew_offer_type_Offer1  \\\n",
       "0                  0                  0                        1   \n",
       "2                  0                  0                        1   \n",
       "3                  0                  0                        1   \n",
       "4                  0                  0                        1   \n",
       "5                  0                  0                        0   \n",
       "\n",
       "   renew_offer_type_Offer2  renew_offer_type_Offer3  renew_offer_type_Offer4  \\\n",
       "0                        0                        0                        0   \n",
       "2                        0                        0                        0   \n",
       "3                        0                        0                        0   \n",
       "4                        0                        0                        0   \n",
       "5                        1                        0                        0   \n",
       "\n",
       "   sales_channel_Agent  sales_channel_Branch  sales_channel_Call Center  \\\n",
       "0                    1                     0                          0   \n",
       "2                    1                     0                          0   \n",
       "3                    0                     0                          1   \n",
       "4                    1                     0                          0   \n",
       "5                    0                     0                          0   \n",
       "\n",
       "   sales_channel_Web  vehicle_class_Four-Door Car  vehicle_class_SUV  \\\n",
       "0                  0                            0                  0   \n",
       "2                  0                            0                  0   \n",
       "3                  0                            0                  1   \n",
       "4                  0                            1                  0   \n",
       "5                  1                            0                  0   \n",
       "\n",
       "   vehicle_class_Sports Car  vehicle_class_Two-Door Car  vehicle_size_Large  \\\n",
       "0                         0                           1                   0   \n",
       "2                         0                           1                   0   \n",
       "3                         0                           0                   0   \n",
       "4                         0                           0                   0   \n",
       "5                         0                           1                   0   \n",
       "\n",
       "   vehicle_size_Medsize  vehicle_size_Small  \n",
       "0                     1                   0  \n",
       "2                     1                   0  \n",
       "3                     1                   0  \n",
       "4                     1                   0  \n",
       "5                     1                   0  "
      ]
     },
     "execution_count": 29,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "categorical=pd.get_dummies(cat, columns =['state', 'response', 'coverage', 'education',\n",
    "        'employment_status', 'gender', 'location_code',\n",
    "       'marital_status', 'policy_type', 'policy', 'renew_offer_type',\n",
    "       'sales_channel', 'vehicle_class', 'vehicle_size'],drop_first=False)\n",
    "categorical.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "liable-thing",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "markdown",
   "id": "composite-hostel",
   "metadata": {},
   "source": [
    "#### Hint for Categorical Variables"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "informational-location",
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
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