lab-customer-analysis-round-2.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {
    "tags": []
   },
   "source": [
    "# Imports"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import numpy as np\n",
    "import statistics as stats\n",
    "import matplotlib.mlab as mlab\n",
    "import matplotlib.pyplot as plt\n",
    "import seaborn as sns\n",
    "from array import *\n",
    "from sklearn import linear_model\n",
    "from sklearn.metrics import mean_squared_error\n",
    "from sklearn.preprocessing import MinMaxScaler # do not use the function Normalise() - it does something entirely different\n",
    "from sklearn.preprocessing import StandardScaler\n",
    "from sklearn.preprocessing import OneHotEncoder\n",
    "from sklearn.model_selection import train_test_split\n",
    "from sklearn.metrics import r2_score\n",
    "from sklearn.metrics import mean_squared_error\n",
    "from sklearn.metrics import mean_absolute_error\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "tags": []
   },
   "source": [
    "# Round 2"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "df = pd.read_csv('files_for_lab/csv_files/marketing_customer_analysis.csv')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 1. Show the dataframe shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(10910, 26)"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.shape"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 2. Standardize header names."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "df.drop(['Unnamed: 0'], axis=1, inplace=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [],
   "source": [
    "df.columns= df.columns.str.lower().str.replace(' ', '_')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "tags": []
   },
   "source": [
    "## 3. Which columns are numerical?"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "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_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>4809.216960</td>\n",
       "      <td>48029</td>\n",
       "      <td>61</td>\n",
       "      <td>7.0</td>\n",
       "      <td>52</td>\n",
       "      <td>0.0</td>\n",
       "      <td>9</td>\n",
       "      <td>292.800000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2228.525238</td>\n",
       "      <td>0</td>\n",
       "      <td>64</td>\n",
       "      <td>3.0</td>\n",
       "      <td>26</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1</td>\n",
       "      <td>744.924331</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>14947.917300</td>\n",
       "      <td>22139</td>\n",
       "      <td>100</td>\n",
       "      <td>34.0</td>\n",
       "      <td>31</td>\n",
       "      <td>0.0</td>\n",
       "      <td>2</td>\n",
       "      <td>480.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>22332.439460</td>\n",
       "      <td>49078</td>\n",
       "      <td>97</td>\n",
       "      <td>10.0</td>\n",
       "      <td>3</td>\n",
       "      <td>0.0</td>\n",
       "      <td>2</td>\n",
       "      <td>484.013411</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>9025.067525</td>\n",
       "      <td>23675</td>\n",
       "      <td>117</td>\n",
       "      <td>NaN</td>\n",
       "      <td>31</td>\n",
       "      <td>NaN</td>\n",
       "      <td>7</td>\n",
       "      <td>707.925645</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",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10905</th>\n",
       "      <td>15563.369440</td>\n",
       "      <td>0</td>\n",
       "      <td>253</td>\n",
       "      <td>NaN</td>\n",
       "      <td>40</td>\n",
       "      <td>NaN</td>\n",
       "      <td>7</td>\n",
       "      <td>1214.400000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10906</th>\n",
       "      <td>5259.444853</td>\n",
       "      <td>61146</td>\n",
       "      <td>65</td>\n",
       "      <td>7.0</td>\n",
       "      <td>68</td>\n",
       "      <td>0.0</td>\n",
       "      <td>6</td>\n",
       "      <td>273.018929</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10907</th>\n",
       "      <td>23893.304100</td>\n",
       "      <td>39837</td>\n",
       "      <td>201</td>\n",
       "      <td>11.0</td>\n",
       "      <td>63</td>\n",
       "      <td>0.0</td>\n",
       "      <td>2</td>\n",
       "      <td>381.306996</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10908</th>\n",
       "      <td>11971.977650</td>\n",
       "      <td>64195</td>\n",
       "      <td>158</td>\n",
       "      <td>0.0</td>\n",
       "      <td>27</td>\n",
       "      <td>4.0</td>\n",
       "      <td>6</td>\n",
       "      <td>618.288849</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10909</th>\n",
       "      <td>6857.519928</td>\n",
       "      <td>0</td>\n",
       "      <td>101</td>\n",
       "      <td>31.0</td>\n",
       "      <td>1</td>\n",
       "      <td>0.0</td>\n",
       "      <td>3</td>\n",
       "      <td>1021.719397</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>10910 rows × 8 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "       customer_lifetime_value  income  monthly_premium_auto  \\\n",
       "0                  4809.216960   48029                    61   \n",
       "1                  2228.525238       0                    64   \n",
       "2                 14947.917300   22139                   100   \n",
       "3                 22332.439460   49078                    97   \n",
       "4                  9025.067525   23675                   117   \n",
       "...                        ...     ...                   ...   \n",
       "10905             15563.369440       0                   253   \n",
       "10906              5259.444853   61146                    65   \n",
       "10907             23893.304100   39837                   201   \n",
       "10908             11971.977650   64195                   158   \n",
       "10909              6857.519928       0                   101   \n",
       "\n",
       "       months_since_last_claim  months_since_policy_inception  \\\n",
       "0                          7.0                             52   \n",
       "1                          3.0                             26   \n",
       "2                         34.0                             31   \n",
       "3                         10.0                              3   \n",
       "4                          NaN                             31   \n",
       "...                        ...                            ...   \n",
       "10905                      NaN                             40   \n",
       "10906                      7.0                             68   \n",
       "10907                     11.0                             63   \n",
       "10908                      0.0                             27   \n",
       "10909                     31.0                              1   \n",
       "\n",
       "       number_of_open_complaints  number_of_policies  total_claim_amount  \n",
       "0                            0.0                   9          292.800000  \n",
       "1                            0.0                   1          744.924331  \n",
       "2                            0.0                   2          480.000000  \n",
       "3                            0.0                   2          484.013411  \n",
       "4                            NaN                   7          707.925645  \n",
       "...                          ...                 ...                 ...  \n",
       "10905                        NaN                   7         1214.400000  \n",
       "10906                        0.0                   6          273.018929  \n",
       "10907                        0.0                   2          381.306996  \n",
       "10908                        4.0                   6          618.288849  \n",
       "10909                        0.0                   3         1021.719397  \n",
       "\n",
       "[10910 rows x 8 columns]"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# df._get_numeric_data()\n",
    "df.select_dtypes('number')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 4. Which columns are categorical?"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "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>response</th>\n",
       "      <th>coverage</th>\n",
       "      <th>education</th>\n",
       "      <th>effective_to_date</th>\n",
       "      <th>employmentstatus</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",
       "      <th>vehicle_type</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>DK49336</td>\n",
       "      <td>Arizona</td>\n",
       "      <td>No</td>\n",
       "      <td>Basic</td>\n",
       "      <td>College</td>\n",
       "      <td>2/18/11</td>\n",
       "      <td>Employed</td>\n",
       "      <td>M</td>\n",
       "      <td>Suburban</td>\n",
       "      <td>Married</td>\n",
       "      <td>Corporate Auto</td>\n",
       "      <td>Corporate L3</td>\n",
       "      <td>Offer3</td>\n",
       "      <td>Agent</td>\n",
       "      <td>Four-Door Car</td>\n",
       "      <td>Medsize</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>KX64629</td>\n",
       "      <td>California</td>\n",
       "      <td>No</td>\n",
       "      <td>Basic</td>\n",
       "      <td>College</td>\n",
       "      <td>1/18/11</td>\n",
       "      <td>Unemployed</td>\n",
       "      <td>F</td>\n",
       "      <td>Suburban</td>\n",
       "      <td>Single</td>\n",
       "      <td>Personal Auto</td>\n",
       "      <td>Personal L3</td>\n",
       "      <td>Offer4</td>\n",
       "      <td>Call Center</td>\n",
       "      <td>Four-Door Car</td>\n",
       "      <td>Medsize</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>LZ68649</td>\n",
       "      <td>Washington</td>\n",
       "      <td>No</td>\n",
       "      <td>Basic</td>\n",
       "      <td>Bachelor</td>\n",
       "      <td>2/10/11</td>\n",
       "      <td>Employed</td>\n",
       "      <td>M</td>\n",
       "      <td>Suburban</td>\n",
       "      <td>Single</td>\n",
       "      <td>Personal Auto</td>\n",
       "      <td>Personal L3</td>\n",
       "      <td>Offer3</td>\n",
       "      <td>Call Center</td>\n",
       "      <td>SUV</td>\n",
       "      <td>Medsize</td>\n",
       "      <td>A</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>XL78013</td>\n",
       "      <td>Oregon</td>\n",
       "      <td>Yes</td>\n",
       "      <td>Extended</td>\n",
       "      <td>College</td>\n",
       "      <td>1/11/11</td>\n",
       "      <td>Employed</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>Offer2</td>\n",
       "      <td>Branch</td>\n",
       "      <td>Four-Door Car</td>\n",
       "      <td>Medsize</td>\n",
       "      <td>A</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>QA50777</td>\n",
       "      <td>Oregon</td>\n",
       "      <td>No</td>\n",
       "      <td>Premium</td>\n",
       "      <td>Bachelor</td>\n",
       "      <td>1/17/11</td>\n",
       "      <td>Medical Leave</td>\n",
       "      <td>F</td>\n",
       "      <td>Suburban</td>\n",
       "      <td>Married</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>Medsize</td>\n",
       "      <td>NaN</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",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10905</th>\n",
       "      <td>FE99816</td>\n",
       "      <td>Nevada</td>\n",
       "      <td>No</td>\n",
       "      <td>Premium</td>\n",
       "      <td>Bachelor</td>\n",
       "      <td>1/19/11</td>\n",
       "      <td>Unemployed</td>\n",
       "      <td>F</td>\n",
       "      <td>Suburban</td>\n",
       "      <td>Married</td>\n",
       "      <td>Personal Auto</td>\n",
       "      <td>Personal L1</td>\n",
       "      <td>Offer3</td>\n",
       "      <td>Web</td>\n",
       "      <td>Luxury Car</td>\n",
       "      <td>Medsize</td>\n",
       "      <td>A</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10906</th>\n",
       "      <td>KX53892</td>\n",
       "      <td>Oregon</td>\n",
       "      <td>No</td>\n",
       "      <td>Basic</td>\n",
       "      <td>College</td>\n",
       "      <td>1/6/11</td>\n",
       "      <td>Employed</td>\n",
       "      <td>F</td>\n",
       "      <td>Urban</td>\n",
       "      <td>Married</td>\n",
       "      <td>Personal Auto</td>\n",
       "      <td>Personal L3</td>\n",
       "      <td>Offer2</td>\n",
       "      <td>Branch</td>\n",
       "      <td>Four-Door Car</td>\n",
       "      <td>Medsize</td>\n",
       "      <td>A</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10907</th>\n",
       "      <td>TL39050</td>\n",
       "      <td>Arizona</td>\n",
       "      <td>No</td>\n",
       "      <td>Extended</td>\n",
       "      <td>Bachelor</td>\n",
       "      <td>2/6/11</td>\n",
       "      <td>Employed</td>\n",
       "      <td>F</td>\n",
       "      <td>Rural</td>\n",
       "      <td>Married</td>\n",
       "      <td>Corporate Auto</td>\n",
       "      <td>Corporate L3</td>\n",
       "      <td>Offer1</td>\n",
       "      <td>Web</td>\n",
       "      <td>Luxury SUV</td>\n",
       "      <td>Medsize</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10908</th>\n",
       "      <td>WA60547</td>\n",
       "      <td>California</td>\n",
       "      <td>No</td>\n",
       "      <td>Premium</td>\n",
       "      <td>College</td>\n",
       "      <td>2/13/11</td>\n",
       "      <td>Employed</td>\n",
       "      <td>F</td>\n",
       "      <td>Urban</td>\n",
       "      <td>Divorced</td>\n",
       "      <td>Personal Auto</td>\n",
       "      <td>Personal L1</td>\n",
       "      <td>Offer1</td>\n",
       "      <td>Branch</td>\n",
       "      <td>SUV</td>\n",
       "      <td>Medsize</td>\n",
       "      <td>A</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10909</th>\n",
       "      <td>IV32877</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Basic</td>\n",
       "      <td>Bachelor</td>\n",
       "      <td>1/8/11</td>\n",
       "      <td>Unemployed</td>\n",
       "      <td>M</td>\n",
       "      <td>Suburban</td>\n",
       "      <td>Single</td>\n",
       "      <td>Personal Auto</td>\n",
       "      <td>Personal L1</td>\n",
       "      <td>Offer4</td>\n",
       "      <td>Web</td>\n",
       "      <td>SUV</td>\n",
       "      <td>Medsize</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>10910 rows × 17 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "      customer       state response  coverage education effective_to_date  \\\n",
       "0      DK49336     Arizona       No     Basic   College           2/18/11   \n",
       "1      KX64629  California       No     Basic   College           1/18/11   \n",
       "2      LZ68649  Washington       No     Basic  Bachelor           2/10/11   \n",
       "3      XL78013      Oregon      Yes  Extended   College           1/11/11   \n",
       "4      QA50777      Oregon       No   Premium  Bachelor           1/17/11   \n",
       "...        ...         ...      ...       ...       ...               ...   \n",
       "10905  FE99816      Nevada       No   Premium  Bachelor           1/19/11   \n",
       "10906  KX53892      Oregon       No     Basic   College            1/6/11   \n",
       "10907  TL39050     Arizona       No  Extended  Bachelor            2/6/11   \n",
       "10908  WA60547  California       No   Premium   College           2/13/11   \n",
       "10909  IV32877         NaN      NaN     Basic  Bachelor            1/8/11   \n",
       "\n",
       "      employmentstatus gender location_code marital_status     policy_type  \\\n",
       "0             Employed      M      Suburban        Married  Corporate Auto   \n",
       "1           Unemployed      F      Suburban         Single   Personal Auto   \n",
       "2             Employed      M      Suburban         Single   Personal Auto   \n",
       "3             Employed      M      Suburban         Single  Corporate Auto   \n",
       "4        Medical Leave      F      Suburban        Married   Personal Auto   \n",
       "...                ...    ...           ...            ...             ...   \n",
       "10905       Unemployed      F      Suburban        Married   Personal Auto   \n",
       "10906         Employed      F         Urban        Married   Personal Auto   \n",
       "10907         Employed      F         Rural        Married  Corporate Auto   \n",
       "10908         Employed      F         Urban       Divorced   Personal Auto   \n",
       "10909       Unemployed      M      Suburban         Single   Personal Auto   \n",
       "\n",
       "             policy renew_offer_type sales_channel  vehicle_class  \\\n",
       "0      Corporate L3           Offer3         Agent  Four-Door Car   \n",
       "1       Personal L3           Offer4   Call Center  Four-Door Car   \n",
       "2       Personal L3           Offer3   Call Center            SUV   \n",
       "3      Corporate L3           Offer2        Branch  Four-Door Car   \n",
       "4       Personal L2           Offer1        Branch  Four-Door Car   \n",
       "...             ...              ...           ...            ...   \n",
       "10905   Personal L1           Offer3           Web     Luxury Car   \n",
       "10906   Personal L3           Offer2        Branch  Four-Door Car   \n",
       "10907  Corporate L3           Offer1           Web     Luxury SUV   \n",
       "10908   Personal L1           Offer1        Branch            SUV   \n",
       "10909   Personal L1           Offer4           Web            SUV   \n",
       "\n",
       "      vehicle_size vehicle_type  \n",
       "0          Medsize          NaN  \n",
       "1          Medsize          NaN  \n",
       "2          Medsize            A  \n",
       "3          Medsize            A  \n",
       "4          Medsize          NaN  \n",
       "...            ...          ...  \n",
       "10905      Medsize            A  \n",
       "10906      Medsize            A  \n",
       "10907      Medsize          NaN  \n",
       "10908      Medsize            A  \n",
       "10909      Medsize          NaN  \n",
       "\n",
       "[10910 rows x 17 columns]"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.select_dtypes('object')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 5. Check and deal with `NaN` values."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "customer                            0\n",
       "state                             631\n",
       "customer_lifetime_value             0\n",
       "response                          631\n",
       "coverage                            0\n",
       "education                           0\n",
       "effective_to_date                   0\n",
       "employmentstatus                    0\n",
       "gender                              0\n",
       "income                              0\n",
       "location_code                       0\n",
       "marital_status                      0\n",
       "monthly_premium_auto                0\n",
       "months_since_last_claim           633\n",
       "months_since_policy_inception       0\n",
       "number_of_open_complaints         633\n",
       "number_of_policies                  0\n",
       "policy_type                         0\n",
       "policy                              0\n",
       "renew_offer_type                    0\n",
       "sales_channel                       0\n",
       "total_claim_amount                  0\n",
       "vehicle_class                     622\n",
       "vehicle_size                      622\n",
       "vehicle_type                     5482\n",
       "dtype: int64"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.isna().sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'California'"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "stats.mode(df['state'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [],
   "source": [
    "df['state'].fillna(stats.mode(df['state']), inplace=True)\n",
    "df['response'].fillna(stats.mode(df['response']), inplace=True)\n",
    "df['vehicle_class'].fillna(stats.mode(df['vehicle_class']), inplace=True)\n",
    "df['vehicle_size'].fillna(stats.mode(df['vehicle_size']), inplace=True)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Use Mean or Median to fill months_since_last_claim?"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "15.149070740488469"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "np.mean(df['months_since_last_claim'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "nan"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "np.median(df['months_since_last_claim'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [],
   "source": [
    "df['months_since_last_claim'].fillna(np.mean(df['months_since_last_claim']), inplace=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "nan"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "np.median(df['number_of_open_complaints'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.38425610586747105"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "np.mean(df['number_of_open_complaints'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [],
   "source": [
    "# the majority of the values are zero therefore we add the median which is zero\n",
    "df['number_of_open_complaints'].fillna(np.mean(df['number_of_open_complaints']), inplace=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [],
   "source": [
    "# half of them have no values, the others are Type A so we categorized the missing ones as unknown\n",
    "df['vehicle_type'].fillna('unknown', inplace=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "customer                         0\n",
       "state                            0\n",
       "customer_lifetime_value          0\n",
       "response                         0\n",
       "coverage                         0\n",
       "education                        0\n",
       "effective_to_date                0\n",
       "employmentstatus                 0\n",
       "gender                           0\n",
       "income                           0\n",
       "location_code                    0\n",
       "marital_status                   0\n",
       "monthly_premium_auto             0\n",
       "months_since_last_claim          0\n",
       "months_since_policy_inception    0\n",
       "number_of_open_complaints        0\n",
       "number_of_policies               0\n",
       "policy_type                      0\n",
       "policy                           0\n",
       "renew_offer_type                 0\n",
       "sales_channel                    0\n",
       "total_claim_amount               0\n",
       "vehicle_class                    0\n",
       "vehicle_size                     0\n",
       "vehicle_type                     0\n",
       "dtype: int64"
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.isna().sum()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 6 Datetime format"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Extract the months from the dataset and store in a separate column. Then filter the data to show only the information for the first quarter , ie. January, February and March. _Hint_: If data from March does not exist, consider only January and February."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [],
   "source": [
    "df['effective_to_date'] = pd.to_datetime(df['effective_to_date'], errors='coerce')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {
    "scrolled": true
   },
   "outputs": [],
   "source": [
    "# df['month'] = pd.DatetimeIndex(df['effective_to_date']).month\n",
    "df['month'] = df['effective_to_date'].dt.month"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "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>employmentstatus</th>\n",
       "      <th>gender</th>\n",
       "      <th>income</th>\n",
       "      <th>...</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>vehicle_type</th>\n",
       "      <th>month</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>DK49336</td>\n",
       "      <td>Arizona</td>\n",
       "      <td>4809.216960</td>\n",
       "      <td>No</td>\n",
       "      <td>Basic</td>\n",
       "      <td>College</td>\n",
       "      <td>2011-02-18</td>\n",
       "      <td>Employed</td>\n",
       "      <td>M</td>\n",
       "      <td>48029</td>\n",
       "      <td>...</td>\n",
       "      <td>9</td>\n",
       "      <td>Corporate Auto</td>\n",
       "      <td>Corporate L3</td>\n",
       "      <td>Offer3</td>\n",
       "      <td>Agent</td>\n",
       "      <td>292.800000</td>\n",
       "      <td>Four-Door Car</td>\n",
       "      <td>Medsize</td>\n",
       "      <td>unknown</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>KX64629</td>\n",
       "      <td>California</td>\n",
       "      <td>2228.525238</td>\n",
       "      <td>No</td>\n",
       "      <td>Basic</td>\n",
       "      <td>College</td>\n",
       "      <td>2011-01-18</td>\n",
       "      <td>Unemployed</td>\n",
       "      <td>F</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>1</td>\n",
       "      <td>Personal Auto</td>\n",
       "      <td>Personal L3</td>\n",
       "      <td>Offer4</td>\n",
       "      <td>Call Center</td>\n",
       "      <td>744.924331</td>\n",
       "      <td>Four-Door Car</td>\n",
       "      <td>Medsize</td>\n",
       "      <td>unknown</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>LZ68649</td>\n",
       "      <td>Washington</td>\n",
       "      <td>14947.917300</td>\n",
       "      <td>No</td>\n",
       "      <td>Basic</td>\n",
       "      <td>Bachelor</td>\n",
       "      <td>2011-02-10</td>\n",
       "      <td>Employed</td>\n",
       "      <td>M</td>\n",
       "      <td>22139</td>\n",
       "      <td>...</td>\n",
       "      <td>2</td>\n",
       "      <td>Personal Auto</td>\n",
       "      <td>Personal L3</td>\n",
       "      <td>Offer3</td>\n",
       "      <td>Call Center</td>\n",
       "      <td>480.000000</td>\n",
       "      <td>SUV</td>\n",
       "      <td>Medsize</td>\n",
       "      <td>A</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>XL78013</td>\n",
       "      <td>Oregon</td>\n",
       "      <td>22332.439460</td>\n",
       "      <td>Yes</td>\n",
       "      <td>Extended</td>\n",
       "      <td>College</td>\n",
       "      <td>2011-01-11</td>\n",
       "      <td>Employed</td>\n",
       "      <td>M</td>\n",
       "      <td>49078</td>\n",
       "      <td>...</td>\n",
       "      <td>2</td>\n",
       "      <td>Corporate Auto</td>\n",
       "      <td>Corporate L3</td>\n",
       "      <td>Offer2</td>\n",
       "      <td>Branch</td>\n",
       "      <td>484.013411</td>\n",
       "      <td>Four-Door Car</td>\n",
       "      <td>Medsize</td>\n",
       "      <td>A</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>QA50777</td>\n",
       "      <td>Oregon</td>\n",
       "      <td>9025.067525</td>\n",
       "      <td>No</td>\n",
       "      <td>Premium</td>\n",
       "      <td>Bachelor</td>\n",
       "      <td>2011-01-17</td>\n",
       "      <td>Medical Leave</td>\n",
       "      <td>F</td>\n",
       "      <td>23675</td>\n",
       "      <td>...</td>\n",
       "      <td>7</td>\n",
       "      <td>Personal Auto</td>\n",
       "      <td>Personal L2</td>\n",
       "      <td>Offer1</td>\n",
       "      <td>Branch</td>\n",
       "      <td>707.925645</td>\n",
       "      <td>Four-Door Car</td>\n",
       "      <td>Medsize</td>\n",
       "      <td>unknown</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10905</th>\n",
       "      <td>FE99816</td>\n",
       "      <td>Nevada</td>\n",
       "      <td>15563.369440</td>\n",
       "      <td>No</td>\n",
       "      <td>Premium</td>\n",
       "      <td>Bachelor</td>\n",
       "      <td>2011-01-19</td>\n",
       "      <td>Unemployed</td>\n",
       "      <td>F</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>7</td>\n",
       "      <td>Personal Auto</td>\n",
       "      <td>Personal L1</td>\n",
       "      <td>Offer3</td>\n",
       "      <td>Web</td>\n",
       "      <td>1214.400000</td>\n",
       "      <td>Luxury Car</td>\n",
       "      <td>Medsize</td>\n",
       "      <td>A</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10906</th>\n",
       "      <td>KX53892</td>\n",
       "      <td>Oregon</td>\n",
       "      <td>5259.444853</td>\n",
       "      <td>No</td>\n",
       "      <td>Basic</td>\n",
       "      <td>College</td>\n",
       "      <td>2011-01-06</td>\n",
       "      <td>Employed</td>\n",
       "      <td>F</td>\n",
       "      <td>61146</td>\n",
       "      <td>...</td>\n",
       "      <td>6</td>\n",
       "      <td>Personal Auto</td>\n",
       "      <td>Personal L3</td>\n",
       "      <td>Offer2</td>\n",
       "      <td>Branch</td>\n",
       "      <td>273.018929</td>\n",
       "      <td>Four-Door Car</td>\n",
       "      <td>Medsize</td>\n",
       "      <td>A</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10907</th>\n",
       "      <td>TL39050</td>\n",
       "      <td>Arizona</td>\n",
       "      <td>23893.304100</td>\n",
       "      <td>No</td>\n",
       "      <td>Extended</td>\n",
       "      <td>Bachelor</td>\n",
       "      <td>2011-02-06</td>\n",
       "      <td>Employed</td>\n",
       "      <td>F</td>\n",
       "      <td>39837</td>\n",
       "      <td>...</td>\n",
       "      <td>2</td>\n",
       "      <td>Corporate Auto</td>\n",
       "      <td>Corporate L3</td>\n",
       "      <td>Offer1</td>\n",
       "      <td>Web</td>\n",
       "      <td>381.306996</td>\n",
       "      <td>Luxury SUV</td>\n",
       "      <td>Medsize</td>\n",
       "      <td>unknown</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10908</th>\n",
       "      <td>WA60547</td>\n",
       "      <td>California</td>\n",
       "      <td>11971.977650</td>\n",
       "      <td>No</td>\n",
       "      <td>Premium</td>\n",
       "      <td>College</td>\n",
       "      <td>2011-02-13</td>\n",
       "      <td>Employed</td>\n",
       "      <td>F</td>\n",
       "      <td>64195</td>\n",
       "      <td>...</td>\n",
       "      <td>6</td>\n",
       "      <td>Personal Auto</td>\n",
       "      <td>Personal L1</td>\n",
       "      <td>Offer1</td>\n",
       "      <td>Branch</td>\n",
       "      <td>618.288849</td>\n",
       "      <td>SUV</td>\n",
       "      <td>Medsize</td>\n",
       "      <td>A</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10909</th>\n",
       "      <td>IV32877</td>\n",
       "      <td>California</td>\n",
       "      <td>6857.519928</td>\n",
       "      <td>No</td>\n",
       "      <td>Basic</td>\n",
       "      <td>Bachelor</td>\n",
       "      <td>2011-01-08</td>\n",
       "      <td>Unemployed</td>\n",
       "      <td>M</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>3</td>\n",
       "      <td>Personal Auto</td>\n",
       "      <td>Personal L1</td>\n",
       "      <td>Offer4</td>\n",
       "      <td>Web</td>\n",
       "      <td>1021.719397</td>\n",
       "      <td>SUV</td>\n",
       "      <td>Medsize</td>\n",
       "      <td>unknown</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>10910 rows × 26 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "      customer       state  customer_lifetime_value response  coverage  \\\n",
       "0      DK49336     Arizona              4809.216960       No     Basic   \n",
       "1      KX64629  California              2228.525238       No     Basic   \n",
       "2      LZ68649  Washington             14947.917300       No     Basic   \n",
       "3      XL78013      Oregon             22332.439460      Yes  Extended   \n",
       "4      QA50777      Oregon              9025.067525       No   Premium   \n",
       "...        ...         ...                      ...      ...       ...   \n",
       "10905  FE99816      Nevada             15563.369440       No   Premium   \n",
       "10906  KX53892      Oregon              5259.444853       No     Basic   \n",
       "10907  TL39050     Arizona             23893.304100       No  Extended   \n",
       "10908  WA60547  California             11971.977650       No   Premium   \n",
       "10909  IV32877  California              6857.519928       No     Basic   \n",
       "\n",
       "      education effective_to_date employmentstatus gender  income  ...  \\\n",
       "0       College        2011-02-18         Employed      M   48029  ...   \n",
       "1       College        2011-01-18       Unemployed      F       0  ...   \n",
       "2      Bachelor        2011-02-10         Employed      M   22139  ...   \n",
       "3       College        2011-01-11         Employed      M   49078  ...   \n",
       "4      Bachelor        2011-01-17    Medical Leave      F   23675  ...   \n",
       "...         ...               ...              ...    ...     ...  ...   \n",
       "10905  Bachelor        2011-01-19       Unemployed      F       0  ...   \n",
       "10906   College        2011-01-06         Employed      F   61146  ...   \n",
       "10907  Bachelor        2011-02-06         Employed      F   39837  ...   \n",
       "10908   College        2011-02-13         Employed      F   64195  ...   \n",
       "10909  Bachelor        2011-01-08       Unemployed      M       0  ...   \n",
       "\n",
       "      number_of_policies     policy_type        policy  renew_offer_type  \\\n",
       "0                      9  Corporate Auto  Corporate L3            Offer3   \n",
       "1                      1   Personal Auto   Personal L3            Offer4   \n",
       "2                      2   Personal Auto   Personal L3            Offer3   \n",
       "3                      2  Corporate Auto  Corporate L3            Offer2   \n",
       "4                      7   Personal Auto   Personal L2            Offer1   \n",
       "...                  ...             ...           ...               ...   \n",
       "10905                  7   Personal Auto   Personal L1            Offer3   \n",
       "10906                  6   Personal Auto   Personal L3            Offer2   \n",
       "10907                  2  Corporate Auto  Corporate L3            Offer1   \n",
       "10908                  6   Personal Auto   Personal L1            Offer1   \n",
       "10909                  3   Personal Auto   Personal L1            Offer4   \n",
       "\n",
       "       sales_channel  total_claim_amount  vehicle_class vehicle_size  \\\n",
       "0              Agent          292.800000  Four-Door Car      Medsize   \n",
       "1        Call Center          744.924331  Four-Door Car      Medsize   \n",
       "2        Call Center          480.000000            SUV      Medsize   \n",
       "3             Branch          484.013411  Four-Door Car      Medsize   \n",
       "4             Branch          707.925645  Four-Door Car      Medsize   \n",
       "...              ...                 ...            ...          ...   \n",
       "10905            Web         1214.400000     Luxury Car      Medsize   \n",
       "10906         Branch          273.018929  Four-Door Car      Medsize   \n",
       "10907            Web          381.306996     Luxury SUV      Medsize   \n",
       "10908         Branch          618.288849            SUV      Medsize   \n",
       "10909            Web         1021.719397            SUV      Medsize   \n",
       "\n",
       "      vehicle_type month  \n",
       "0          unknown     2  \n",
       "1          unknown     1  \n",
       "2                A     2  \n",
       "3                A     1  \n",
       "4          unknown     1  \n",
       "...            ...   ...  \n",
       "10905            A     1  \n",
       "10906            A     1  \n",
       "10907      unknown     2  \n",
       "10908            A     2  \n",
       "10909      unknown     1  \n",
       "\n",
       "[10910 rows x 26 columns]"
      ]
     },
     "execution_count": 21,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df[df['month'].isin([1,2])]\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 7. BONUS: Put all the previously mentioned data transformations into a function."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [],
   "source": [
    "#fill NA\n",
    "def fillna(table, col, method):\n",
    "    if method == 'mean':\n",
    "        table[col].fillna(np.mean(table[col]), inplace=True)\n",
    "    elif method== 'med':\n",
    "        table[col].fillna(np.median(table[col]), inplace=True)\n",
    "    elif method == 'mod':\n",
    "        table[col].fillna(stats.mode(table[col]), inplace=True)    \n",
    "    else:\n",
    "        print('Method not defined')\n",
    "        \n",
    "#change Type to Datetime\n",
    "def col_to_datetime(table, col):\n",
    "    table[col] = pd.to_datetime(table[col], errors='coerce')\n",
    "    \n",
    "#Clean Table\n",
    "def clean_table(table):  \n",
    "    table.drop(['Unnamed: 0'], axis=1, inplace=True)\n",
    "    table.columns= table.columns.str.lower().str.replace(' ', '_')\n",
    "    col_to_datetime(table, 'effective_to_date')\n",
    "    fillna(table, 'state', 'mod')\n",
    "    fillna(table, 'response', 'mod')\n",
    "    fillna(table, 'vehicle_class', 'mod')\n",
    "    fillna(table, 'vehicle_size', 'mod')\n",
    "    fillna(table, 'months_since_last_claim', 'mean')\n",
    "    fillna(table, 'number_of_open_complaints', 'mean')\n",
    "    table['vehicle_type'].fillna('Unknown', inplace=True)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "customer                         0\n",
       "state                            0\n",
       "customer_lifetime_value          0\n",
       "response                         0\n",
       "coverage                         0\n",
       "education                        0\n",
       "effective_to_date                0\n",
       "employmentstatus                 0\n",
       "gender                           0\n",
       "income                           0\n",
       "location_code                    0\n",
       "marital_status                   0\n",
       "monthly_premium_auto             0\n",
       "months_since_last_claim          0\n",
       "months_since_policy_inception    0\n",
       "number_of_open_complaints        0\n",
       "number_of_policies               0\n",
       "policy_type                      0\n",
       "policy                           0\n",
       "renew_offer_type                 0\n",
       "sales_channel                    0\n",
       "total_claim_amount               0\n",
       "vehicle_class                    0\n",
       "vehicle_size                     0\n",
       "vehicle_type                     0\n",
       "dtype: int64"
      ]
     },
     "execution_count": 23,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df2 = pd.read_csv('files_for_lab/csv_files/marketing_customer_analysis.csv')\n",
    "clean_table(df2)\n",
    "df2.isna().sum()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "tags": []
   },
   "source": [
    "# Round 3"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 1. Show DataFrame info."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 10910 entries, 0 to 10909\n",
      "Data columns (total 26 columns):\n",
      " #   Column                         Non-Null Count  Dtype         \n",
      "---  ------                         --------------  -----         \n",
      " 0   customer                       10910 non-null  object        \n",
      " 1   state                          10910 non-null  object        \n",
      " 2   customer_lifetime_value        10910 non-null  float64       \n",
      " 3   response                       10910 non-null  object        \n",
      " 4   coverage                       10910 non-null  object        \n",
      " 5   education                      10910 non-null  object        \n",
      " 6   effective_to_date              10910 non-null  datetime64[ns]\n",
      " 7   employmentstatus               10910 non-null  object        \n",
      " 8   gender                         10910 non-null  object        \n",
      " 9   income                         10910 non-null  int64         \n",
      " 10  location_code                  10910 non-null  object        \n",
      " 11  marital_status                 10910 non-null  object        \n",
      " 12  monthly_premium_auto           10910 non-null  int64         \n",
      " 13  months_since_last_claim        10910 non-null  float64       \n",
      " 14  months_since_policy_inception  10910 non-null  int64         \n",
      " 15  number_of_open_complaints      10910 non-null  float64       \n",
      " 16  number_of_policies             10910 non-null  int64         \n",
      " 17  policy_type                    10910 non-null  object        \n",
      " 18  policy                         10910 non-null  object        \n",
      " 19  renew_offer_type               10910 non-null  object        \n",
      " 20  sales_channel                  10910 non-null  object        \n",
      " 21  total_claim_amount             10910 non-null  float64       \n",
      " 22  vehicle_class                  10910 non-null  object        \n",
      " 23  vehicle_size                   10910 non-null  object        \n",
      " 24  vehicle_type                   10910 non-null  object        \n",
      " 25  month                          10910 non-null  int64         \n",
      "dtypes: datetime64[ns](1), float64(4), int64(5), object(16)\n",
      "memory usage: 2.2+ MB\n"
     ]
    }
   ],
   "source": [
    "df.info()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 2. Describe DataFrame."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "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>count</th>\n",
       "      <th>mean</th>\n",
       "      <th>std</th>\n",
       "      <th>min</th>\n",
       "      <th>25%</th>\n",
       "      <th>50%</th>\n",
       "      <th>75%</th>\n",
       "      <th>max</th>\n",
       "      <th>iqr</th>\n",
       "      <th>range</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>customer_lifetime_value</th>\n",
       "      <td>10910.0</td>\n",
       "      <td>8018.24</td>\n",
       "      <td>6885.08</td>\n",
       "      <td>1898.01</td>\n",
       "      <td>4014.45</td>\n",
       "      <td>5771.15</td>\n",
       "      <td>8992.78</td>\n",
       "      <td>83325.38</td>\n",
       "      <td>4978.33</td>\n",
       "      <td>81427.37</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>income</th>\n",
       "      <td>10910.0</td>\n",
       "      <td>37536.28</td>\n",
       "      <td>30359.20</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>33813.50</td>\n",
       "      <td>62250.75</td>\n",
       "      <td>99981.00</td>\n",
       "      <td>62250.75</td>\n",
       "      <td>99981.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>monthly_premium_auto</th>\n",
       "      <td>10910.0</td>\n",
       "      <td>93.20</td>\n",
       "      <td>34.44</td>\n",
       "      <td>61.00</td>\n",
       "      <td>68.00</td>\n",
       "      <td>83.00</td>\n",
       "      <td>109.00</td>\n",
       "      <td>298.00</td>\n",
       "      <td>41.00</td>\n",
       "      <td>237.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>months_since_last_claim</th>\n",
       "      <td>10910.0</td>\n",
       "      <td>15.15</td>\n",
       "      <td>9.78</td>\n",
       "      <td>0.00</td>\n",
       "      <td>7.00</td>\n",
       "      <td>15.00</td>\n",
       "      <td>23.00</td>\n",
       "      <td>35.00</td>\n",
       "      <td>16.00</td>\n",
       "      <td>35.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>months_since_policy_inception</th>\n",
       "      <td>10910.0</td>\n",
       "      <td>48.09</td>\n",
       "      <td>27.94</td>\n",
       "      <td>0.00</td>\n",
       "      <td>24.00</td>\n",
       "      <td>48.00</td>\n",
       "      <td>71.00</td>\n",
       "      <td>99.00</td>\n",
       "      <td>47.00</td>\n",
       "      <td>99.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>number_of_open_complaints</th>\n",
       "      <td>10910.0</td>\n",
       "      <td>0.38</td>\n",
       "      <td>0.89</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.38</td>\n",
       "      <td>5.00</td>\n",
       "      <td>0.38</td>\n",
       "      <td>5.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>number_of_policies</th>\n",
       "      <td>10910.0</td>\n",
       "      <td>2.98</td>\n",
       "      <td>2.40</td>\n",
       "      <td>1.00</td>\n",
       "      <td>1.00</td>\n",
       "      <td>2.00</td>\n",
       "      <td>4.00</td>\n",
       "      <td>9.00</td>\n",
       "      <td>3.00</td>\n",
       "      <td>8.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>total_claim_amount</th>\n",
       "      <td>10910.0</td>\n",
       "      <td>434.89</td>\n",
       "      <td>292.18</td>\n",
       "      <td>0.10</td>\n",
       "      <td>271.08</td>\n",
       "      <td>382.56</td>\n",
       "      <td>547.20</td>\n",
       "      <td>2893.24</td>\n",
       "      <td>276.12</td>\n",
       "      <td>2893.14</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>month</th>\n",
       "      <td>10910.0</td>\n",
       "      <td>1.47</td>\n",
       "      <td>0.50</td>\n",
       "      <td>1.00</td>\n",
       "      <td>1.00</td>\n",
       "      <td>1.00</td>\n",
       "      <td>2.00</td>\n",
       "      <td>2.00</td>\n",
       "      <td>1.00</td>\n",
       "      <td>1.00</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                                 count      mean       std      min      25%  \\\n",
       "customer_lifetime_value        10910.0   8018.24   6885.08  1898.01  4014.45   \n",
       "income                         10910.0  37536.28  30359.20     0.00     0.00   \n",
       "monthly_premium_auto           10910.0     93.20     34.44    61.00    68.00   \n",
       "months_since_last_claim        10910.0     15.15      9.78     0.00     7.00   \n",
       "months_since_policy_inception  10910.0     48.09     27.94     0.00    24.00   \n",
       "number_of_open_complaints      10910.0      0.38      0.89     0.00     0.00   \n",
       "number_of_policies             10910.0      2.98      2.40     1.00     1.00   \n",
       "total_claim_amount             10910.0    434.89    292.18     0.10   271.08   \n",
       "month                          10910.0      1.47      0.50     1.00     1.00   \n",
       "\n",
       "                                    50%       75%       max       iqr  \\\n",
       "customer_lifetime_value         5771.15   8992.78  83325.38   4978.33   \n",
       "income                         33813.50  62250.75  99981.00  62250.75   \n",
       "monthly_premium_auto              83.00    109.00    298.00     41.00   \n",
       "months_since_last_claim           15.00     23.00     35.00     16.00   \n",
       "months_since_policy_inception     48.00     71.00     99.00     47.00   \n",
       "number_of_open_complaints          0.00      0.38      5.00      0.38   \n",
       "number_of_policies                 2.00      4.00      9.00      3.00   \n",
       "total_claim_amount               382.56    547.20   2893.24    276.12   \n",
       "month                              1.00      2.00      2.00      1.00   \n",
       "\n",
       "                                  range  \n",
       "customer_lifetime_value        81427.37  \n",
       "income                         99981.00  \n",
       "monthly_premium_auto             237.00  \n",
       "months_since_last_claim           35.00  \n",
       "months_since_policy_inception     99.00  \n",
       "number_of_open_complaints          5.00  \n",
       "number_of_policies                 8.00  \n",
       "total_claim_amount              2893.14  \n",
       "month                              1.00  "
      ]
     },
     "execution_count": 25,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "summary = df.describe().T\n",
    "summary['iqr'] = summary['75%']-summary['25%'] # adds Interquartile Range\n",
    "summary['range'] = summary['max'] - summary['min'] # adds range\n",
    "for col in summary.columns: # rounds all values in summary\n",
    "    summary[col] = summary[col].apply(lambda x : round(x, 2))\n",
    "summary"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 3. Show a plot of the total number of responses."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[1466, 9444]"
      ]
     },
     "execution_count": 26,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df[(df['response']=='Yes')].shape[0]\n",
    "\n",
    "amount = [df[(df['response']=='Yes')].shape[0],df[(df['response']=='No')].shape[0]] \n",
    "amount"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:xlabel='response', ylabel='count'>"
      ]
     },
     "execution_count": 27,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAYsAAAEGCAYAAACUzrmNAAAAOXRFWHRTb2Z0d2FyZQBNYXRwbG90bGliIHZlcnNpb24zLjUuMSwgaHR0cHM6Ly9tYXRwbG90bGliLm9yZy/YYfK9AAAACXBIWXMAAAsTAAALEwEAmpwYAAAROUlEQVR4nO3df+xddX3H8efLooAwIoTCoEWLs1MBfyANQ51mGUbqj1niBOuGdMrWjTB/bXPCdJNpGl1EozghYyq0wiQdOkEXRFLnjBPFL8L41TEaUeioUNQpOIOi7/1xP42X9tt+Ltr7vd/2+3wkJ+ec9z3n9H2bC6+ec+753FQVkiTtyKMm3YAkafYzLCRJXYaFJKnLsJAkdRkWkqSuPSbdwLgceOCBtWjRokm3IUm7lOuuu+6+qpq/dX23DYtFixYxNTU16TYkaZeS5FvT1b0MJUnqMiwkSV2GhSSpy7CQJHUZFpKkLsNCktRlWEiSugwLSVKXYSFJ6tptn+D+ZR3z5jWTbkGz0HXvOXXSLUgT4ZmFJKnLsJAkdRkWkqQuw0KS1GVYSJK6DAtJUpdhIUnqMiwkSV2GhSSpy7CQJHUZFpKkLsNCktRlWEiSugwLSVKXYSFJ6jIsJEldhoUkqcuwkCR1GRaSpC7DQpLUZVhIkroMC0lSl2EhSeoyLCRJXYaFJKnLsJAkdRkWkqSusYZFkjcluSXJzUk+nmSvJAckuTrJ7W2+/9D2ZyXZkOS2JCcM1Y9JclN77dwkGWffkqSHG1tYJFkAvB5YUlVHAfOA5cCZwLqqWgysa+skOaK9fiSwFDgvybx2uPOBlcDiNi0dV9+SpG2N+zLUHsDeSfYAHgvcDSwDVrfXVwMntuVlwKVV9WBV3QFsAI5NcgiwX1VdU1UFrBnaR5I0A8YWFlX1P8A5wJ3AJuD7VfU54OCq2tS22QQc1HZZANw1dIiNrbagLW9d30aSlUmmkkxt3rx5Z74dSZrTxnkZan8GZwuHA4cC+yQ5ZUe7TFOrHdS3LVZdUFVLqmrJ/PnzH2nLkqTtGOdlqBcAd1TV5qr6CfBJ4DnAPe3SEm1+b9t+I3DY0P4LGVy22tiWt65LkmbIOMPiTuC4JI9t3146HlgPXAGsaNusAC5vy1cAy5PsmeRwBjeyr22Xqu5Pclw7zqlD+0iSZsAe4zpwVX01yWXA14GHgOuBC4B9gbVJTmMQKCe17W9Jsha4tW1/RlX9tB3udOAiYG/gyjZJkmbI2MICoKreDrx9q/KDDM4yptt+FbBqmvoUcNROb1CSNBKf4JYkdRkWkqQuw0KS1GVYSJK6DAtJUpdhIUnqMiwkSV2GhSSpy7CQJHUZFpKkLsNCktRlWEiSugwLSVKXYSFJ6jIsJEldhoUkqcuwkCR1GRaSpC7DQpLUZVhIkroMC0lSl2EhSeoyLCRJXYaFJKnLsJAkdRkWkqQuw0KS1GVYSJK6DAtJUpdhIUnqMiwkSV2GhSSpy7CQJHUZFpKkLsNCktQ11rBI8rgklyX5ryTrkzw7yQFJrk5ye5vvP7T9WUk2JLktyQlD9WOS3NReOzdJxtm3JOnhxn1m8QHgs1X1FOAZwHrgTGBdVS0G1rV1khwBLAeOBJYC5yWZ145zPrASWNympWPuW5I0ZGxhkWQ/4PnARwCq6sdV9b/AMmB122w1cGJbXgZcWlUPVtUdwAbg2CSHAPtV1TVVVcCaoX0kSTNgnGcWTwQ2AxcmuT7Jh5PsAxxcVZsA2vygtv0C4K6h/Te22oK2vHV9G0lWJplKMrV58+ad+24kaQ4bZ1jsATwLOL+qjgZ+SLvktB3T3YeoHdS3LVZdUFVLqmrJ/PnzH2m/kqTtGGdYbAQ2VtVX2/plDMLjnnZpiTa/d2j7w4b2Xwjc3eoLp6lLkmbI2MKiqr4N3JXkya10PHArcAWwotVWAJe35SuA5Un2THI4gxvZ17ZLVfcnOa59C+rUoX0kSTNgjzEf/3XAJUkeA3wDeA2DgFqb5DTgTuAkgKq6JclaBoHyEHBGVf20Hed04CJgb+DKNkmSZshYw6KqbgCWTPPS8dvZfhWwapr6FHDUTm1OkjQyn+CWJHUZFpKkLsNCktRlWEiSugwLSVKXYSFJ6hopLJKsG6UmSdo97fA5iyR7AY8FDmy/O7FlnKb9gEPH3JskaZboPZT3x8AbGQTDdfw8LH4AfGh8bUmSZpMdhkVVfQD4QJLXVdUHZ6gnSdIsM9JwH1X1wSTPARYN71NVa8bUlyRpFhkpLJJ8DPg14AZgy+B+W361TpK0mxt1IMElwBHtZ00lSXPMqM9Z3Az86jgbkSTNXqOeWRwI3JrkWuDBLcWqetlYupIkzSqjhsXZ42xCkjS7jfptqH8fdyOSpNlr1G9D3c/g208AjwEeDfywqvYbV2OSpNlj1DOLXxleT3IicOw4GpIkzT6/0KizVfUp4Ld3biuSpNlq1MtQLx9afRSD5y585kKS5ohRvw31O0PLDwHfBJbt9G4kSbPSqPcsXjPuRiRJs9eoP360MMm/JLk3yT1JPpFk4bibkyTNDqPe4L4QuILB71osAD7dapKkOWDUsJhfVRdW1UNtugiYP8a+JEmzyKhhcV+SU5LMa9MpwHfG2ZgkafYYNSxeC5wMfBvYBLwC8Ka3JM0Ro3519p3Aiqr6HkCSA4BzGISIJGk3N+qZxdO3BAVAVX0XOHo8LUmSZptRw+JRSfbfstLOLEY9K5Ek7eJG/R/+e4EvJ7mMwTAfJwOrxtaVJGlWGfUJ7jVJphgMHhjg5VV161g7kyTNGiNfSmrhYEBI0hz0Cw1RLkmaW8YeFu0hvuuTfKatH5Dk6iS3t/nwjfOzkmxIcluSE4bqxyS5qb12bpKMu29J0s/NxJnFG4D1Q+tnAuuqajGwrq2T5AhgOXAksBQ4L8m8ts/5wEpgcZuWzkDfkqRmrGHRRqZ9CfDhofIyYHVbXg2cOFS/tKoerKo7gA3AsUkOAfarqmuqqoA1Q/tIkmbAuM8s3g/8JfCzodrBVbUJoM0PavUFwF1D221stQVteev6NpKsTDKVZGrz5s075Q1IksYYFkleCtxbVdeNuss0tdpBfdti1QVVtaSqlsyf76C4krSzjPMp7OcCL0vyYmAvYL8kFwP3JDmkqja1S0z3tu03AocN7b8QuLvVF05TlyTNkLGdWVTVWVW1sKoWMbhx/fmqOoXBjyitaJutAC5vy1cAy5PsmeRwBjeyr22Xqu5Pclz7FtSpQ/tIkmbAJMZ3ejewNslpwJ3ASQBVdUuStQwe/HsIOKOqftr2OR24CNgbuLJNkqQZMiNhUVVfAL7Qlr8DHL+d7VYxzZhTVTUFHDW+DiVJO+IT3JKkLsNCktRlWEiSugwLSVKXYSFJ6jIsJEldhoUkqcuwkCR1GRaSpC7DQpLUZVhIkroMC0lSl2EhSeoyLCRJXYaFJKnLsJAkdRkWkqQuw0KS1GVYSJK6DAtJUpdhIUnqMiwkSV2GhSSpy7CQJHUZFpKkLsNCktRlWEiSugwLSVKXYSFJ6jIsJEldhoUkqcuwkCR1GRaSpC7DQpLUZVhIkrrGFhZJDkvyb0nWJ7klyRta/YAkVye5vc33H9rnrCQbktyW5ISh+jFJbmqvnZsk4+pbkrStcZ5ZPAT8eVU9FTgOOCPJEcCZwLqqWgysa+u015YDRwJLgfOSzGvHOh9YCSxu09Ix9i1J2srYwqKqNlXV19vy/cB6YAGwDFjdNlsNnNiWlwGXVtWDVXUHsAE4NskhwH5VdU1VFbBmaB9J0gyYkXsWSRYBRwNfBQ6uqk0wCBTgoLbZAuCuod02ttqCtrx1fbo/Z2WSqSRTmzdv3qnvQZLmsrGHRZJ9gU8Ab6yqH+xo02lqtYP6tsWqC6pqSVUtmT9//iNvVpI0rbGGRZJHMwiKS6rqk618T7u0RJvf2+obgcOGdl8I3N3qC6epS5JmyDi/DRXgI8D6qnrf0EtXACva8grg8qH68iR7JjmcwY3sa9ulqvuTHNeOeerQPpKkGbDHGI/9XODVwE1Jbmi1vwLeDaxNchpwJ3ASQFXdkmQtcCuDb1KdUVU/bfudDlwE7A1c2SZJ0gwZW1hU1ZeY/n4DwPHb2WcVsGqa+hRw1M7rTpL0SPgEtySpy7CQJHUZFpKkLsNCktRlWEiSugwLSVLXOJ+zkDQmd77jaZNuQbPQ4//mprEd2zMLSVKXYSFJ6jIsJEldhoUkqcuwkCR1GRaSpC7DQpLUZVhIkroMC0lSl2EhSeoyLCRJXYaFJKnLsJAkdRkWkqQuw0KS1GVYSJK6DAtJUpdhIUnqMiwkSV2GhSSpy7CQJHUZFpKkLsNCktRlWEiSugwLSVKXYSFJ6jIsJEldhoUkqWuXCYskS5PclmRDkjMn3Y8kzSW7RFgkmQd8CHgRcATwqiRHTLYrSZo7domwAI4FNlTVN6rqx8ClwLIJ9yRJc8Yek25gRAuAu4bWNwK/sfVGSVYCK9vqA0lum4He5oIDgfsm3cRskHNWTLoFbcvP5xZvz844yhOmK+4qYTHd30BtU6i6ALhg/O3MLUmmqmrJpPuQpuPnc2bsKpehNgKHDa0vBO6eUC+SNOfsKmHxNWBxksOTPAZYDlwx4Z4kac7YJS5DVdVDSf4UuAqYB3y0qm6ZcFtziZf2NJv5+ZwBqdrm0r8kSQ+zq1yGkiRNkGEhSeoyLPQwSSrJe4fW/yLJ2RNsSXNcBr6U5EVDtZOTfHaSfc01hoW29iDw8iQHTroRCaAGN1b/BHhfkr2S7AOsAs6YbGdzi2GhrT3E4Nslb9r6hSRPSLIuyY1t/viZb09zUVXdDHwaeAvwduBi4K1Jvpbk+iTLAJIcmeTaJDe0z+niCba9W/HbUHqYJA8AhwI3As8A/gjYt6rOTvJp4LKqWp3ktcDLqurEyXWruaSdUXwd+DHwGeCWqro4yeOAa4GjgXcDX6mqS9ozWfOq6keT6nl3YljoYZI8UFX7JnkH8BPgR/w8LO4DDqmqnyR5NLCpqrxcpRnTPpcPACcDezE4EwY4ADiBQWC8FVgDfLKqbp9En7ujXeKhPE3E+xn8K+7CHWzjvzQ0037WpgC/W1VbDxa6PslXgZcAVyX5w6r6/Ew3uTvynoWmVVXfBdYCpw2Vv8xgqBWA3we+NNN9Sc1VwOuSBCDJ0W3+ROAbVXUugyGBnj65FncvhoV25L0Mhn/e4vXAa5LcCLwaeMNEupLgncCjgRuT3NzWAV4J3JzkBuApDC5HaSfwnoUkqcszC0lSl2EhSeoyLCRJXYaFJKnLsJAkdRkWkqQuw0IaQRsm2/9eNGf54Ze2I8miJOuTnMdg6JO/bqOc3pjkb9s2+yT51yT/meTmJK9s9W8m+bs2Auq1SZ7U6tOO3JvkoiTnJvlykm8keUWrH5Lki20U1ZuTPK/VX5jkmiRfT/LPSfadxN+R5g7DQtqxJzN4CvgtwALgWOCZwDFJng8sBe6uqmdU1VHA8A/y/KCqjgX+nsFYW7TlNVX1dOAS4Nyh7Q8BfhN4KYPRUwF+D7iqqp7JYBTgG9pvjbwNeEFVPQuYAv5sJ75naRuGhbRj36qqrwAvbNP1DM4yngIsBm4CXtDOIp5XVd8f2vfjQ/Nnt+VnA//Ulj/GIBy2+FRV/ayqbgUObrWvMRhi5WzgaVV1P3AccATwH21YixXAE3bS+5Wm5aiz0o79sM0DvKuq/mHrDZIcA7wYeFeSz1XVO9pLw2PpbG9cneH6g8OHBaiqL7YzmJcAH0vyHuB7wNVV9apH/G6kX5BnFtJorgJeu+XeQJIFSQ5Kcijwf1V1MXAO8KyhfV45NL+mLT+ikXuTPAG4t6r+EfhIO/5XgOcO3Qd5bJJf/2XfoLQjnllII6iqzyV5KnBNGxX7AeAU4EnAe5L8jMGPRZ0+tNue7bcVHgVsOQt4PfDRJG8GNgOv6fzRvwW8OclP2p95alVtTvIHwMeT7Nm2exvw37/cu5S2z1FnpTFI8k1gSVXdN+lepJ3By1CSpC7PLCRJXZ5ZSJK6DAtJUpdhIUnqMiwkSV2GhSSp6/8BATNFGozGYckAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# via countplot\n",
    "sns.countplot(x='response',data=df)\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 4 Show a plot of the response rate by the sales channel."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [],
   "source": [
    "### Adding a new column: response_rate"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
   "outputs": [],
   "source": [
    "df['response_rate'] = np.where(df.response=='Yes', 1, 0)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:xlabel='sales_channel', ylabel='response_rate'>"
      ]
     },
     "execution_count": 30,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.barplot(x='sales_channel',y='response_rate',data=df)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {},
   "outputs": [],
   "source": [
    "### Storing the response_rate in a list"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[Text(0.5, 0, 'Response Rate'), Text(0, 0.5, 'Sales Channel')]"
      ]
     },
     "execution_count": 32,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "amount = []\n",
    "for val in df['sales_channel'].unique():\n",
    "    amount.append(round(df[(df['sales_channel']==val) & (df['response']=='Yes')].shape[0] / df[df['sales_channel']==val].shape[0],2))      \n",
    "    \n",
    "\n",
    "data = {'Response': df['sales_channel'].unique().tolist(), 'Amount':amount}\n",
    "rr = sns.barplot(x='Response', y=\"Amount\", data=data)\n",
    "rr.set(xlabel=\"Response Rate\", ylabel=\"Sales Channel\") "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {},
   "outputs": [],
   "source": [
    "# same plot but y-axis goes up to 100%\n",
    "# rr = sns.barplot(x='Response', y=\"Amount\", data=data)\n",
    "# rr.set(ylim= (0, 1))  "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 5. Show a plot of the response rate by the total claim amount."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "count    10910.000000\n",
       "mean       434.888330\n",
       "std        292.180556\n",
       "min          0.099007\n",
       "25%        271.082527\n",
       "50%        382.564630\n",
       "75%        547.200000\n",
       "max       2893.239678\n",
       "Name: total_claim_amount, dtype: float64"
      ]
     },
     "execution_count": 34,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df['total_claim_amount'].describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {},
   "outputs": [],
   "source": [
    "step = 200\n",
    "amount = []\n",
    "tca_steps = []\n",
    "for val in range(0, df['total_claim_amount'].astype(int).max()+step, step):\n",
    "    tca_steps.append(str(val) + '-' + str(val+step))\n",
    "    if (df[(df['total_claim_amount']>=val) &(df['total_claim_amount']<val+step) &  (df['response']=='Yes')].shape[0]) == 0:\n",
    "        amount.append(0)\n",
    "    else:\n",
    "        amount.append((df[(df['total_claim_amount']>=val) &(df['total_claim_amount']<val+step) &  (df['response']=='Yes')].shape[0])/(df[(df['total_claim_amount']<val+step) & (df['total_claim_amount'] >=val) ].shape[0]))                  \n",
    "        "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[Text(0.5, 0, 'Total Claim Amount'), Text(0, 0.5, 'Response Rate')]"
      ]
     },
     "execution_count": 36,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1296x432 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "data = {'Response': tca_steps, 'Amount':amount}\n",
    "plt.figure(figsize=(18,6))\n",
    "rr = sns.barplot(x='Response', y=\"Amount\", data=data)\n",
    "rr.set(xlabel=\"Total Claim Amount\", ylabel=\"Response Rate\")\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 6. Show a plot of the response rate by income."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "count    10910.000000\n",
       "mean     37536.284785\n",
       "std      30359.195670\n",
       "min          0.000000\n",
       "25%          0.000000\n",
       "50%      33813.500000\n",
       "75%      62250.750000\n",
       "max      99981.000000\n",
       "Name: income, dtype: float64"
      ]
     },
     "execution_count": 37,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df['income'].describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "metadata": {},
   "outputs": [],
   "source": [
    "step = 15000\n",
    "amount = []\n",
    "tca_steps = []\n",
    "for value in range(0, df['income'].astype(int).max()+step, step):\n",
    "    tca_steps.append(str(value) + '-' + str(value+step))\n",
    "    if (df[(df['income']>=value) &(df['income']<value+step) &  (df['response']=='Yes')].shape[0]) == 0:\n",
    "        amount.append(0)\n",
    "    else:\n",
    "        amount.append(df[(df['income']<value+step) & (df['income'] >=value) ].shape[0] / (df[(df['income']>=value) &(df['income']<value+step) &  (df['response']=='Yes')].shape[0]))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[Text(0.5, 0, 'Income'), Text(0, 0.5, 'Response Rate')]"
      ]
     },
     "execution_count": 39,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 864x432 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "data = {'Response': tca_steps, 'Amount':amount}\n",
    "plt.figure(figsize=(12,6))\n",
    "rr = sns.barplot(x='Response', y=\"Amount\", data=data)\n",
    "rr.set(xlabel=\"Income\", ylabel=\"Response Rate\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "tags": []
   },
   "source": [
    "# Round 4"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 1. Check the data types of the columns"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Get the numeric data into dataframe called `numerical` and categorical columns in a dataframe called `categoricals`."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Index(['customer_lifetime_value', 'income', 'monthly_premium_auto',\n",
       "       'months_since_last_claim', 'months_since_policy_inception',\n",
       "       'number_of_open_complaints', 'number_of_policies', 'total_claim_amount',\n",
       "       'month', 'response_rate'],\n",
       "      dtype='object')"
      ]
     },
     "execution_count": 40,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "numerical = df._get_numeric_data()\n",
    "numerical.columns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "metadata": {},
   "outputs": [],
   "source": [
    "categoricals = df.select_dtypes(object)\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Now we will try to check the normality of the numerical variables visually\n",
    "Use seaborn library to construct distribution plots for the numerical variables"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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ycQCbmWXiADYzy8QBbGaWiQPYzCwTB7CZWSYOYDOzTBzAZmaZOIDNzDJxAJuZZeIANjPLxAFsZpaJA9jMLBMHsJlZJg5gM7NMHMBmZpk4gM3MMnEAm5ll4gA2M8skWwBLapD0gKQ70vMTJK2T9Fj62lzadpmkrZK2SDovV81mZoMpZw/4w8AjpeeXA+sjYi6wPj1H0jxgEXAqsAC4RlJDxbWamQ26LAEsqQX4M+D6UvNCYGVaXglcWGq/NSL2REQ7sBWYX1GpZmZ1k6sH/AXgk8C+Utu0iNgOkL5OTe0zgW2l7TpT2yEkLZG0QdKGXbt2DXrRZmaDqfIAlvQOYGdE/HSgu9Roi1obRsSKiGiLiLYpU6YccY1mZlUYk+GYbwIukPR2oBE4VtJXgackzYiI7ZJmADvT9p3ArNL+LcCTlVZsZlYHlfeAI2JZRLRExGyKD9e+FxHvA9YAi9Nmi4Hb0/IaYJGkcZJagbnAfRWXbWY26HL0gHvzWWCVpEuBJ4B3AkTEZkmrgIeBvcDSiOjKV6aZ2eDIGsAR8QPgB2n5GeDcXrZbDiyvrDAzswr4Sjgzs0wcwGZmmTiAzcwycQCbmWXiADYzy8QBbGaWiQPYzCwTB7CZWSYOYDOzTBzAZmaZOIDNzDJxAJuZZTKUZkMb0WLfPjo6OvY/b21tpaHBt7YzG80cwBXZ/dxOrly9jeYZz7L7mR1ct/R85syZk7ssM8vIAVyhpuZpTJrakrsMMxsiPAZsZpaJA9jMLBMHsJlZJg5gM7NMHMBmZpk4gM3MMnEAm5ll4gA2M8vEAWxmlokD2MwsEwewmVkmngsig/LMaJ4VzWz0cg84g2JmtAe47Oq1tLe35y7HzDJxDziTpuZpNDY25i7DzDJyD9jMLBMHsJlZJg5gM7NMHMBmZpk4gM3MMqk8gCXNkvR9SY9I2izpw6n9BEnrJD2WvjaX9lkmaaukLZLOq7pmM7N6yNED3gv8TUT8PnAWsFTSPOByYH1EzAXWp+ekdYuAU4EFwDWSfOWCmQ17lQdwRGyPiPvT8gvAI8BMYCGwMm22ErgwLS8Ebo2IPRHRDmwF5ldatJlZHWQdA5Y0Gzgd+DEwLSK2QxHSwNS02UxgW2m3ztRmZjasZbsSTtJE4BvARyLieUm9blqjLXp5zSXAEoCTTjppMMqsXFdX10GXJ3uuCLORK0sASzqGInxvjojVqfkpSTMiYrukGcDO1N4JzCrt3gI8Wet1I2IFsAKgra2tZkgPde3t7Vx29VrGT57O7md2cN3S85kzZ07ussysDnKcBSHgy8AjEfHPpVVrgMVpeTFwe6l9kaRxklqBucB9VdWbw/jJ05k0tYXxk6fnLsXM6ihHD/hNwPuBjZIeTG2fBj4LrJJ0KfAE8E6AiNgsaRXwMMUZFEsjoqvyquugPC0lFMMNZjZ6VB7AEfF/qT2uC3BuL/ssB5bXrahMimkpt9E849n9ww1mNnp4OsrMmpqnMWlqS+4yzCwDB/AQUR6OiGH58aGZHS4H8BDRPRzRtft5Js6cC9QeI/YpaWYjhwN4CGlqnkbX2HH7n9caI/YpaWYjhwN4iPMYsdnI5ekozcwycQCbmWXiADYzy8QBbGaWiQPYzCwTB7CZWSY+DW2Y6L4oo6urmIeo+4IMX5xhNnw5gIeJA1fK3U3D+GNpnnGyL84wG+YcwMNI95VyDROOZ9LUFl+qbDbMOYCHMV+qbDa8OYCHOV+qbDZ8+SwIM7NMHMBmZpk4gM3MMnEAm5ll4gA2M8vEZ0GMEOVzgn0+sNnw4AAeIbrPCR439uE+zwfu6uqivb19/3OHtVk+DuARpKl5GuPGju3z6rj29nYuu3ot4ydP98UbZpk5gEeYgVwdN37y9EMu3ij3jN0rNquGA3gE6r46rjwu3D2LWmdnJxHFduX1HR0d/MMdD8O+fVx5wWmcfPLJgMPYrJ4cwCNYuTf89OMbaRh/LF27n2fizLk110+cOZeuF3/Nlasf8PwSZhVwAI9w3b3hF5/ZQcOE4+kaO67X9T3b+uMP9MyOjgPYBqTWGLE/0DM7Og5g61WtMWLBQUFb6wM9MxsYB7D1qtYYcWNjY+6yzEYMB7D1qecYcc9ecfcZFd08Lmw2cA5gOyy1esVwIJi7hyom9DIu3F9AO8BtNHEA22GrdebEgZuGFqe59XahR38B3d8Hew5oG0kcwDZoum8a2q3Wh3gv/XrX/oDuXt99kUhDQwMdHR00nTD9kAtJap158eKuJ7nygtNoaWnZv39524FyqFsuwyaAJS0A/gVoAK6PiM9mLsn6UWu4oqnG+q7dd9Mw/liaZ5x80LBGbxMMdZ958eIzO7hy9QMH7V/uNQ/08ureet2+PNvqbVgEsKQG4GrgT4BO4CeS1kTEw3krs/7UGq7oub5r7DgaJhxfc7ueEwz1/OCv5/79XV5d7m2XL8+u1euudepdrd4ysL+t/Prd63sb4+7ettvh9OB767XX+k/D4+5D17AIYGA+sDUifgEg6VZgITCoAbw7/eN/6de7aHh5D127n6fh5T280Ni4v628XI/1OY6Ze31/+/x29/N8/JGXOHbKq3lu28+Z+OrXINX+OT3TvvmQbbt2P8/Hr//O/raGxokHLXf97rf7X7PW/uPGjjsolJfd9D2ajjuRl37zNP/14rcC7G8rv373+u55NXruXz5+X/vUUquOk08+eX87cEhbz237ey071GBfaKToeR7RECTpImBBRHwgPX8/8IaI+Ose2y0BlqSnrwW29PKSJwJP16ncgch9fNcwNI4/FGrIffyhUEMVx386Ihb0bBwuPWDVaDvkf46IWAGs6PfFpA0R0TYYhR2J3Md3DUPj+EOhhtzHHwo15Dz+cLknXCcwq/S8BXgyUy1mZoNiuATwT4C5kloljQUWAWsy12RmdlSGxRBEROyV9NfAnRSnod0QEZuP4iX7Haaos9zHB9cwFI4P+WvIfXzIX0O24w+LD+HMzEai4TIEYWY24jiAzcxyiYhR8wAWUJwbvBW4fBBe7wZgJ7Cp1HYCsA54LH1tLq1blo69BTiv1H4msDGtu4oDQ0PjgK+l9h8Ds3scfxbwfeARYDPw4SprABqB+4CfpeP/fdXvQWn/BuAB4I4cNQC/TPs+CGzI8LtwPHAb8Gj6fTi74uO/Nn3v3Y/ngY9UXMNHKX4PNwG3UPx+Vv67eFgZUlX45X5Q/AN9HDgFGEsRGvOO8jXfApzBwQH830jhDlwO/FNanpeOOQ5oTbU0pHX3pX8wAtYC56f2vwK+lJYXAV/rcfwZwBlpeRLw83ScSmpI205My8ekX8qzqnwPSrV8DPhfHAjgSmugCOATe7RV+buwEvhAWh5LEciV/xxK/9Z2ACdXVQMwE2gHmtLzVcAlud6DAWdIvQJvqD3SG3pn6fkyYNkgvO5sDg7gLcCMtDwD2FLreBRndJydtnm01P4e4H+Wt0nLYyiu1lEftdxOMV9G5TUA44H7gTdUfXyK88LXA2/lQABXXcMvOTSAK6kBOJYifHrWlOV3EfhT4IcVvwczgW0UPd4xwB2pjmz/HgfyGE1jwN0/oG6dqW2wTYuI7QDp69R+jj8zLdeqa/8+EbEX+A0wudZBJc0GTqfohVZWg6QGSQ9SDMWsi4hKj598AfgksK/UVnUNAdwl6afpkvgqazgF2AX8q6QHJF0vaUKG96DbIoohgMreg4j4FfB54AlgO/CbiLgr43swIKMpgAd0OXOG4/dV14BqljQR+AbwkYh4vsoaIqIrIl5P0QudL+m0Ko8v6R3Azoj4aR/HrWsNyZsi4gzgfGCppLdUWMMYiqGwayPidOBFij+3qzr+gRcuLpS6APh6H8cf9BokNVNM0NUKvBqYIOl9VR3/SI2mAK7qcuanJM0ASF939nP8zrRcq679+0gaAxwHPFs+mKRjKML35ohYnaMGgIj4NfADig86qzz+m4ALJP0SuBV4q6SvVv0eRMST6etO4JsUM/hVVUMn0Jn++oDiw7gzqn4PkvOB+yPiqfS8qhreBrRHxK6IeAVYDbwx03swYKMpgKu6nHkNsDgtL6YYl+1uXyRpnKRWYC5wX/qz6AVJZ0kScHGPfbpf6yLge5EGoADS9l8GHomIf666BklTJB2flpso/hE8WuV7EBHLIqIlImZT/Ey/FxHvq/jnMEHSpO5lirHHTVXVEBE7gG2SXpvWn0sxVWtl70HJezgw/NBzv3rW8ARwlqTxab9zKc4GyfEeDNzRDCAPtwfwdoozBR4HrhiE17uFYrzpFYr/HS+lGBNaT3Hay3rghNL2V6RjbyF9spra2yj+wT4OfJEDHyw0Uvwpt5Xik9lTehz/zRR/Aj3EgdN/3l5VDcDrKE79eijt+3epvbL3oMf7cQ4HPoSr8udwCsUn6t2n412RoYbXAxvSz+JbQHPVPweKD2KfAY4rtVX5Hvw9RQdgE/AVijMcsvwuDvThS5HNzDIZTUMQZmZDigPYzCwTB7CZWSYOYDOzTBzAZmaZOIDNzDJxAFulJH06dw1QzJ0haVNabpN0VVoeJ+m7kh6U9O4+9r9E0qtLz6+XNK/+lfev/L3Z0DYs7glnI8qngX+s6mCSxkQxcUqvImIDxUUMUExodEwU81v05RKKk/W7L0H+wNFVaqORe8B2WCRdLOkhST+T9BVJN0q6qLT+t+nrDEn3pJ7kJkl/LOmzQFNquzlt97G0fpOkj6S22ZIeTb3KTZJulvQ2ST+U9Jik+Wm7CZJukPQTFbOALUztl0j6uqRvA3cN4Hs6R9IdkqYCXwVen2p8jaQzJd2tYpazO9P3dRHF1VI3p+2aJP1AUlv3eyDpn9I+35U0P63/haQL0jYNkj6Xan9I0n/qo76vSXp76fmNkv4ivU//Jun+9HhjjX0vkfTF0vM7JJ2Tlv9U0r1p36+rmNTJqlSPS379GJkP4FSKyzZPTM9PAG4ELipt89v09W84cEluAzCpvD4td995YAIwkeIy3tMp5ljeC/wBRSfhpxR3HxHFjFffSvv/I/C+tHw8xWXmEyh6p52ULjut8b3MJs3jzMGXMJeXjwH+HzAlPX83xR25oZh4qK30evufU1we3j2J9zcp/hM4BvhD4MHUvgT427Q8jqIH3tpLrX8OrEzLYymmRGyiuPS3MbXP5cCdOMrf2yXAF0uvdUf6Hk8E7gEmpPZPkS4l96O6h4cg7HC8FbgtIp4GiIhni/lKavoJcIOK2dq+FREP1tjmzcA3I+JFAEmrgT+mmPSkPSI2pvbNwPqICEkbKQIGiklvLpD08fS8ETgpLa+LiKOaqYriNjunAevS99lAMfdHf14GvpOWNwJ7IuKVGrW/rvTXw3EUIdpe4/XWAldJGkcx29w9EfGSpOOAL0p6PdAF/N5hfG9nUdwV4ofpexsL3HsY+9sgcADb4RCHzn+6lzSUlWaPGgsQEfeomBP3z4CvSPpcRNxU4/V6s6e0vK/0fB8Hfm8F/EVEbDnoRaU3UMyJe7QEbI6Isw9zv1cidSsp1R4R+1RMY9j92h+KiDv7e7GI+J2kHwDnUfTCu2cb+yjwFEXP+lXA72rsvv/nkzSWjr8uIt4z0G/KBp/HgO1wrAfeJWkygKQTKG7Fc2Zav5DiT20knUwxUfp1FFNmnpG2eSX1iqH4E/hCFVMITqD4U/vfDqOeO4EPpeBH0ulH+o31YgswRdLZ6fWPkXRqWvcCxX34jtSdwAe73wtJv5feg97cCvwlxV8I3aF9HLA9IvYB76fooff0S4ox7VdJmkUxTzHAj4A3SZqTjj9e0uH0oG0QuAdsAxYRmyUtB+6W1EUxFeWngNsl3UcR0N09z3OAT0h6BfgtxbyqACuAhyTdHxHvlXQjxdR+ANdHxAMqbq80EP9AcTuih1II/xJ4x5F/hweLiJfTEMFV6c/9Mel4mynGvr8k6SWKe4kdrusphiPuT7XvAi7sY/u7gJuANRHxcmq7BviGpHdS3B27Vq//hxTDGhspztq4P31vuyRdAtyShjYA/pZiHN0q4ukozcwy8RCEmVkmHoKwEU3SH1DcHaFsT0S8IUc9fRlOtdrg8BCEmVkmHoIwM8vEAWxmlokD2MwsEwewmVkm/x9tAxUsxuc79gAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<Figure size 360x360 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 360x360 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 360x360 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 360x360 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 360x360 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 360x360 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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A70hyR5uiOLDVFgMPDq2zqdUWt+Xt65I07407gC8CXgYcB2wG/rTVp5rXrWnqU0qyKsm6JOu2bt26k61K0miNNYCr6uGqmqyqp4APA8vbU5uAw4ZWXQI81OpLpqg/2/YvrqplVbVs4cKFs9u8JM2ysQZwm9Pd5k3AtjMk1gIrk+yVZCmDN9tuqarNwKNJTmxnP5wBXDXOniVpVPYY1YaTXA68CnhJkk3AHwKvSnIcg2mE+4C3A1TVhiRrgLuAJ4Fzq2qybeocBmdU7ANc026SNO+NLICr6s1TlD86zfqrgdVT1NcBx85ia5I0J/hJOEnqxACWpE4MYEnqZGRzwJqfJicnmZiYePrx0qVLWbBgwTQjJD1fBrB+wMTEBGdfeA37HnQojz/yHT587us58sgje7cl7ZIMYD3Dvgcdyv4HL9nxipJ2inPAktSJASxJnRjAktSJASxJnRjAktSJASxJnRjAktSJASxJnRjAktSJASxJnRjAktSJASxJnRjAktSJASxJnRjAktSJASxJnRjAktSJASxJnRjAktSJASxJnRjAktSJASxJnRjAktTJyAI4yceSbEly51DtxUmuS/LNdn/g0HPnJ9mY5N4kpwzVT0iyvj13QZKMqmdJGqdRHgFfAqzYrnYecH1VHQVc3x6T5GhgJXBMG/OhJAvamIuAVcBR7bb9NiVpXhpZAFfVV4B/2K58KnBpW74UOG2ofkVVPVFVE8BGYHmSRcABVXVjVRVw2dAYSZrXxj0HfEhVbQZo9we3+mLgwaH1NrXa4ra8fX1KSVYlWZdk3datW2e1cUmabXPlTbip5nVrmvqUquriqlpWVcsWLlw4a81J0iiMO4AfbtMKtPstrb4JOGxovSXAQ62+ZIq6JM174w7gtcCZbflM4Kqh+sokeyVZyuDNtlvaNMWjSU5sZz+cMTRGkua1PUa14SSXA68CXpJkE/CHwHuBNUnOAh4ATgeoqg1J1gB3AU8C51bVZNvUOQzOqNgHuKbdJGneG1kAV9Wbn+Wpk59l/dXA6inq64BjZ7E1SZoT5sqbcJK02zGAJakTA1iSOjGAJakTA1iSOjGAJakTA1iSOjGAJakTA1iSOjGAJakTA1iSOjGAJakTA1iSOjGAJakTA1iSOjGAJakTA1iSOjGAJakTA1iSOjGAJakTA1iSOjGAJakTA1iSOjGAJakTA1iSOjGAJakTA1iSOjGAJakTA1iSOtmjx06T3Ac8CkwCT1bVsiQvBv4HcARwH/CrVfWPbf3zgbPa+v+5qj7XoW1ptzQ5OcnExMTTj5cuXcqCBQs6drTr6BLAzaur6rtDj88Drq+q9yY5rz3+vSRHAyuBY4CXAl9I8vKqmhx/y9LuZ2JigrMvvIZ9DzqUxx/5Dh8+9/UceeSRvdvaJcylKYhTgUvb8qXAaUP1K6rqiaqaADYCy8ffnnY3k5OTbNy4kY0bNzI5uXv/f7/vQYey/8FL2PegQ3u3Mnaj/DvodQRcwOeTFPAXVXUxcEhVbQaoqs1JDm7rLgZuGhq7qdWeIckqYBXA4YcfPqreNUJz6eXutiM/wKO+3dgo/w56BfBJVfVQC9nrktwzzbqZolZTrdiC/GKAZcuWTbmO5ra59nJ3dzzi0zON6u+gSwBX1UPtfkuSKxlMKTycZFE7+l0EbGmrbwIOGxq+BHhorA1rrLa93JXgB18V7WpvAI59DjjJfkn237YMvA64E1gLnNlWOxO4qi2vBVYm2SvJUuAo4Jbxdi2pl22vis6+8JofmJ7aFfQ4Aj4EuDLJtv3/TVVdm+RrwJokZwEPAKcDVNWGJGuAu4AngXM9A0K7urk0Fz4X7KpTQWMP4Kr6FvCTU9QfAU5+ljGrgdUjbk2aM+baXLhGo+d5wJKm4Vz4rm8unQcsSbsVA1iSOjGAJakTA1iSOjGAJakTA1iSOjGAJakTA1iSOjGAJakTA1iSOjGAJakTA1iSOjGAJakTA1iSOjGAJakTA1iSOjGAJakTA1iSOjGAJakTA1iSOjGAJakTA1iSOjGAJakTA1iSOjGAJakTA1iSOjGAJakTA1iSOpk3AZxkRZJ7k2xMcl7vfiRpZ82LAE6yALgQeD1wNPDmJEf37UqSds4evRuYoeXAxqr6FkCSK4BTgbtmcyePP/Kdp+/vv//+2dz0Dt1///1P73/c+362Pnr/Hnrs/9l68fcwN/4mevwehvc/21JVI9nwbEryK8CKqvoP7fFbgZ+pqndst94qYFV7+KPAvc9xVy8BvruT7e4se5g7PcDc6MMe5n8P362qFdsX58sRcKaoPeN/jqq6GLj4ee8kWVdVy57v+NlgD3Onh7nShz3suj3MizlgYBNw2NDjJcBDnXqRpFkxXwL4a8BRSZYm+SFgJbC2c0+StFPmxRREVT2Z5B3A54AFwMeqasMIdvW8py9mkT0MzIUeYG70YQ8Du1wP8+JNOEnaFc2XKQhJ2uUYwJLUiQEMJPlYki1J7uzYw2FJbkhyd5INSd7ZoYe9k9yS5Buthz8adw9DvSxI8vUkn+20//uSrE9ye5J1nXp4UZJPJrmn/V38bIcefrT9Drbdvp/kXR36+K32N3lnksuT7N2hh3e2/W+Yrd+Bc8BAklcCjwGXVdWxnXpYBCyqqtuS7A/cCpxWVbP6ab8d9BBgv6p6LMmewFeBd1bVTePqYaiXdwPLgAOq6hc77P8+YFlVdTvxP8mlwN9V1Ufa2T/7VtU/dexnAfBtBh+CGttH0pIsZvC3eHRV/d8ka4Crq+qSMfZwLHAFg0/l/jNwLXBOVX1zZ7brETBQVV8B/qFzD5ur6ra2/ChwN7B4zD1UVT3WHu7ZbmP/HzrJEuAXgI+Me99zRZIDgFcCHwWoqn/uGb7NycD/Hmf4DtkD2CfJHsC+jP9zAD8O3FRVj1fVk8CXgTft7EYN4DkoyRHA8cDNHfa9IMntwBbguqoaew/AB4HfBZ7qsO9tCvh8klvbR9zH7UeArcBftqmYjyTZr0Mfw1YCl497p1X1beD9wAPAZuB7VfX5MbdxJ/DKJAcl2Rd4Az/44bDnxQCeY5K8EPgU8K6q+v64919Vk1V1HINPGy5vL73GJskvAluq6tZx7ncKJ1XVTzG4At+5bZpqnPYAfgq4qKqOB/4P0O0yrG0K5I3AJzrs+0AGF99aCrwU2C/JW8bZQ1XdDfwJcB2D6YdvAE/u7HYN4Dmkzbt+Cvh4VX26Zy/t5e6XgGdcQGTETgLe2OZgrwBek+Svx9wDVfVQu98CXMlg7m+cNgGbhl6BfJJBIPfyeuC2qnq4w75fC0xU1daq+hfg08Arxt1EVX20qn6qql7JYMpyp+Z/wQCeM9obYB8F7q6qP+vUw8IkL2rL+zD4w79nnD1U1flVtaSqjmDwkveLVTXWo50k+7U3Qmkv+1/H4CXo2FTVd4AHk/xoK53MLF9+9Tl6Mx2mH5oHgBOT7Nv+nZzM4D2SsUpycLs/HPglZuH3MS8+ijxqSS4HXgW8JMkm4A+r6qNjbuMk4K3A+jYHC/D7VXX1GHtYBFza3u1+AbCmqrqcBtbZIcCVg3/r7AH8TVVd26GP3wQ+3l7+fwv49x16oM15/jvg7T32X1U3J/kkcBuDl/1fp8/Hkj+V5CDgX4Bzq+ofd3aDnoYmSZ04BSFJnRjAktSJASxJnRjAktSJASxJnRjAktSJAax5KcmXkoztG3KTvK9dhvB9s7S9V2271GaSNyaZ9mPGSf5+NvarucUPYmi3k2SPdkWr5+LtwMKqemK2+6mqtezgS2arauwfvdXoeQSskUpyRLuY+IfbEeTnk+wzfASb5CXt2g8keVuS/5nkM0kmkrwjybvbFcFuSvLioc2/Jcnft4tkL2/j98vgAvtfa2NOHdruJ5J8BpjySloZeF/b3vokv9bqa4H9gJu31aYYe0mSP0/yd0n+V7uo0LaL3P9l297Xk7x6irFvS/Lf2/IhSa7M4KL430jyilZ/bGj932k/3x1pF81vP/fftjF3Plufmls8AtY4HAW8uarOzuBi2r+8g/WPZXA5zr2BjcDvVdXxST4AnMHgcpUwuHj8K9qVyj7Wxv0Bg+tH/Ea7rsUtSb7Q1v9Z4N9U1bNd+/mXgOOAnwReAnwtyVeq6o1JHmtXiZvOEcC/BV4G3JDkSOBcgKr6iSQ/xuASly+fZhsXAF+uqje1j4S/cPjJJK9j8PtcDgRY237+hcBDVfULbb0f3kGvmgM8AtY4TFTV7W35VgZBNZ0bqurRqtoKfA/4TKuv327s5fD0BfUPaIH7OuC8dj2NLzEI8cPb+tdNE74APwdc3i7J+TCDi27/9A56Hbamqp5q35LwLeDH2jb/qvV5D3A/MF0Avwa4qK0/WVXf2+7517Xb1xlcG+HHGATyeuC1Sf4kyc9PMU5zkEfAGofhedNJYB8GF1XZdgCw/fd7Da//1NDjp/jBv9ntL2RSDI4Kf7mq7h1+IsnPMLim7nSyg+d35Nn6mU0B/riq/uIZTyQnMLhQ+B8n+XxV/ZdZ3rdmmUfA6uU+4IS2/CvPcxvb5mh/jsG3JHwP+Bzwm+2yhSQ5/jls7yvAr2XwrSALGXwl0C3PYfzpSV6Q5GUMvtHi3rbNX2+9vJzB0fi9z74JrgfOaesvyOCriYZ9DviNDC7cT5LFSQ5O8lLg8ar6awbfHtHz2sGaIY+A1cv7gTVJ3gp88Xlu4x/b6VkHAL/Rav+VwRzxHS2E7wNm+qWeVzKYJ/4Gg6PX323X5Z2pexlMWxwC/Meq+n9JPgT8eZL1DI7631ZVT7T/H6byTuDiJGcxeLVwDnDjtier6vNJfhy4sW3jMeAtwJHA+5I8xeByiec8h77ViZejlGZBkkuAz1bVJ3v3ovnDKQhJ6sQpCO12kvwE7cyEIU9U1c/MYOwfAKdvV/5EVb1tltrTbsQpCEnqxCkISerEAJakTgxgSerEAJakTv4/CoDBnjZrbHYAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<Figure size 360x360 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 360x360 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 360x360 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 360x360 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "for col in numerical:\n",
    "    sns.displot(numerical[col], bins=100)\n",
    "# sns.pairplot(numerical) # takes a long time to load, therefore I deactivated it   "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Use Matplotlib to construct histograms"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "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"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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okAAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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8F9KQNR9m9H82JLkYeB3wtYnOcrIWvF3L/ky/TvLVDkl+vT3/UUZ/zXENcAj4H0ZnC8vSwPX+PvDDwG3tzPdYLeNvKBy45rPKkDVX1cEknwW+AnwPuKOq5vzTv+Vg4L/zHwB3JtnP6NLHB6pq2X7lcpJPAW8HLkwyA3wIeBksXrv8GgZJ6sjZcHlHkjSQ0Zekjhh9SeqI0Zekjhh9SeqI0Zekjhh9SerI/wF6P/x3P+he4AAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "CPU times: total: 828 ms\n",
      "Wall time: 832 ms\n"
     ]
    }
   ],
   "source": [
    "%%time\n",
    "\n",
    "for col in numerical:\n",
    "    plt.hist(numerical[col])\n",
    "    plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 2. Check the multicollinearity"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "For the numerical variables, check the multicollinearity between the features. Please note that we will use the column `total_claim_amount` later as the target variable. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "metadata": {},
   "outputs": [],
   "source": [
    "# ???"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 3. Correlation Matrix & Seaborn Heatmap"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Drop one of the two features that show a high correlation between them (greater than 0.9). Write code for both the correlation matrix and for seaborn heatmap. If there is no pair of features that have a high correlation, then do not drop any features"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "CPU times: total: 375 ms\n",
      "Wall time: 387 ms\n"
     ]
    }
   ],
   "source": [
    "%%time\n",
    "correlations_matrix = round(numerical.corr(),2)\n",
    "sns.heatmap(correlations_matrix, annot=True)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "tags": []
   },
   "source": [
    "# Round 5"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "tags": []
   },
   "source": [
    "## 1a. Removing Outlies of total_claim_amount"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\TgMiles\\anaconda3\\lib\\site-packages\\seaborn\\_decorators.py:36: FutureWarning: Pass the following variable as a keyword arg: x. From version 0.12, the only valid positional argument will be `data`, and passing other arguments without an explicit keyword will result in an error or misinterpretation.\n",
      "  warnings.warn(\n"
     ]
    },
    {
     "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": [
    "tca = df['total_claim_amount']\n",
    "sns.boxplot(tca)\n",
    "plt.show()\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 360x360 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.displot(tca)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "metadata": {},
   "outputs": [],
   "source": [
    "\n",
    "iqr = np.percentile(tca,75) - np.percentile(tca,25)\n",
    "upper_limit = np.percentile(tca,75) + 1.5*iqr\n",
    "lower_limit = np.percentile(tca,25) - 1.5*iqr"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 360x360 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "tca = tca[(tca>lower_limit) & (tca<upper_limit)]\n",
    "sns.displot(tca)\n",
    "plt.show()\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 1b. Removing Outliers of monthly_premium_auto"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 50,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 360x360 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "mpa = df['monthly_premium_auto']\n",
    "sns.displot(mpa)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 51,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\TgMiles\\anaconda3\\lib\\site-packages\\seaborn\\_decorators.py:36: FutureWarning: Pass the following variable as a keyword arg: x. From version 0.12, the only valid positional argument will be `data`, and passing other arguments without an explicit keyword will result in an error or misinterpretation.\n",
      "  warnings.warn(\n"
     ]
    },
    {
     "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(mpa)\n",
    "plt.show()\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 52,
   "metadata": {},
   "outputs": [],
   "source": [
    "iqr = np.percentile(mpa,75) - np.percentile(mpa,25)\n",
    "upper_limit = np.percentile(mpa,75) + 1.5*iqr\n",
    "lower_limit = np.percentile(mpa,25) - 1.5*iqr"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 53,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 360x360 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "mpa = mpa[(mpa>lower_limit) & (mpa<upper_limit)]\n",
    "sns.displot(mpa)\n",
    "plt.show()\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 54,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 360x360 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "def log_transfom_clean1(x):\n",
    "    x = np.log10(x)\n",
    "    if np.isfinite(x):\n",
    "        return x\n",
    "    else:\n",
    "        return 0\n",
    "\n",
    "mpa = mpa.apply(lambda x :log_transfom_clean1(x))\n",
    "\n",
    "\n",
    "\n",
    "sns.displot(mpa)\n",
    "plt.show()\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "tags": []
   },
   "source": [
    "## 2. X_y split & Normalizing the data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 55,
   "metadata": {},
   "outputs": [],
   "source": [
    "y = df['total_claim_amount']\n",
    "X = df.drop(['total_claim_amount'], axis=1)\n",
    "X_num = X._get_numeric_data()\n",
    "X_cat = X.select_dtypes('object')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 56,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(10910, 9)\n"
     ]
    }
   ],
   "source": [
    "# Normalizing data: make data range from 0 - 1, instead of from min to max\n",
    "transformer = MinMaxScaler().fit(X_num)\n",
    "x_normalized = transformer.transform(X_num)\n",
    "print(x_normalized.shape)\n",
    "x_normalized #this is an array\n",
    "X_num = pd.DataFrame(x_normalized, columns=X_num.columns) #now we convert the array to a dataframe"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Round 6"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## One Hot/Label Encoding (categorical).\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 57,
   "metadata": {},
   "outputs": [],
   "source": [
    "X_cat = X_cat.drop(['customer'], axis=1)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 58,
   "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>California</th>\n",
       "      <th>Nevada</th>\n",
       "      <th>Oregon</th>\n",
       "      <th>Washington</th>\n",
       "      <th>Yes</th>\n",
       "      <th>Extended</th>\n",
       "      <th>Premium</th>\n",
       "      <th>College</th>\n",
       "      <th>Doctor</th>\n",
       "      <th>High School or Below</th>\n",
       "      <th>...</th>\n",
       "      <th>Call Center</th>\n",
       "      <th>Web</th>\n",
       "      <th>Luxury Car</th>\n",
       "      <th>Luxury SUV</th>\n",
       "      <th>SUV</th>\n",
       "      <th>Sports Car</th>\n",
       "      <th>Two-Door Car</th>\n",
       "      <th>Medsize</th>\n",
       "      <th>Small</th>\n",
       "      <th>unknown</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>...</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
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       "      <td>1.0</td>\n",
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       "    <tr>\n",
       "      <th>2</th>\n",
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       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
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       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>...</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
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       "    <tr>\n",
       "      <th>4</th>\n",
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       "      <td>1.0</td>\n",
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       "      <td>1.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 44 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "   California  Nevada  Oregon  Washington  Yes  Extended  Premium  College  \\\n",
       "0         0.0     0.0     0.0         0.0  0.0       0.0      0.0      1.0   \n",
       "1         1.0     0.0     0.0         0.0  0.0       0.0      0.0      1.0   \n",
       "2         0.0     0.0     0.0         1.0  0.0       0.0      0.0      0.0   \n",
       "3         0.0     0.0     1.0         0.0  1.0       1.0      0.0      1.0   \n",
       "4         0.0     0.0     1.0         0.0  0.0       0.0      1.0      0.0   \n",
       "\n",
       "   Doctor  High School or Below  ...  Call Center  Web  Luxury Car  \\\n",
       "0     0.0                   0.0  ...          0.0  0.0         0.0   \n",
       "1     0.0                   0.0  ...          1.0  0.0         0.0   \n",
       "2     0.0                   0.0  ...          1.0  0.0         0.0   \n",
       "3     0.0                   0.0  ...          0.0  0.0         0.0   \n",
       "4     0.0                   0.0  ...          0.0  0.0         0.0   \n",
       "\n",
       "   Luxury SUV  SUV  Sports Car  Two-Door Car  Medsize  Small  unknown  \n",
       "0         0.0  0.0         0.0           0.0      1.0    0.0      1.0  \n",
       "1         0.0  0.0         0.0           0.0      1.0    0.0      1.0  \n",
       "2         0.0  1.0         0.0           0.0      1.0    0.0      0.0  \n",
       "3         0.0  0.0         0.0           0.0      1.0    0.0      0.0  \n",
       "4         0.0  0.0         0.0           0.0      1.0    0.0      1.0  \n",
       "\n",
       "[5 rows x 44 columns]"
      ]
     },
     "execution_count": 58,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "encoder = OneHotEncoder().fit(X_cat)\n",
    "encoded_for_p = encoder.transform(X_cat).toarray()\n",
    "encoded_for_p\n",
    "encoder.categories_\n",
    "\n",
    "cols=[colname for row in encoder.categories_ for colname in row]\n",
    "cols\n",
    "onehot_encoded_for_p = pd.DataFrame(encoded_for_p,columns=cols)\n",
    "cols_to_drop=[row[0] for row in encoder.categories_]\n",
    "cols_to_drop\n",
    "onehot_encoded_for_p = onehot_encoded_for_p.drop(cols_to_drop,axis=1)\n",
    "onehot_encoded_for_p.head()\n",
    "# print(encoded_for_p)\n",
    "# print(cols)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 59,
   "metadata": {},
   "outputs": [],
   "source": [
    "onehot_encoded_for_p = onehot_encoded_for_p.drop(['unknown'], axis=1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 60,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "  </tbody>\n",
       "</table>\n",
       "<p>10910 rows × 43 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "       California  Nevada  Oregon  Washington  Yes  Extended  Premium  \\\n",
       "0             0.0     0.0     0.0         0.0  0.0       0.0      0.0   \n",
       "1             1.0     0.0     0.0         0.0  0.0       0.0      0.0   \n",
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       "10909         1.0     0.0     0.0         0.0  0.0       0.0      0.0   \n",
       "\n",
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       "\n",
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       "0             0.0         0.0  0.0         0.0           0.0      1.0    0.0  \n",
       "1             0.0         0.0  0.0         0.0           0.0      1.0    0.0  \n",
       "2             0.0         0.0  1.0         0.0           0.0      1.0    0.0  \n",
       "3             0.0         0.0  0.0         0.0           0.0      1.0    0.0  \n",
       "4             0.0         0.0  0.0         0.0           0.0      1.0    0.0  \n",
       "...           ...         ...  ...         ...           ...      ...    ...  \n",
       "10905         1.0         0.0  0.0         0.0           0.0      1.0    0.0  \n",
       "10906         0.0         0.0  0.0         0.0           0.0      1.0    0.0  \n",
       "10907         0.0         1.0  0.0         0.0           0.0      1.0    0.0  \n",
       "10908         0.0         0.0  1.0         0.0           0.0      1.0    0.0  \n",
       "10909         0.0         0.0  1.0         0.0           0.0      1.0    0.0  \n",
       "\n",
       "[10910 rows x 43 columns]"
      ]
     },
     "execution_count": 60,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "onehot_encoded_for_p"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 61,
   "metadata": {},
   "outputs": [],
   "source": [
    "X = pd.concat([X_num, onehot_encoded_for_p], axis=1)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## train/split & Linear Regression"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 92,
   "metadata": {
    "tags": []
   },
   "outputs": [],
   "source": [
    "# train test split is the way ML generates its claim to fame: \n",
    "# we build the model on a portion of the data but we then validate it in \n",
    "# another \"fresh\" portion\n",
    "# our model has no opportunity to \"cheat\": it must accurately guess the values \n",
    "# in the \"fresh\" dataset that it never saw before\n",
    "X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.02, random_state=35)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 93,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "LinearRegression()"
      ]
     },
     "execution_count": 93,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#we train/fit our model like yesterday\n",
    "lm = linear_model.LinearRegression()\n",
    "lm.fit(X_train,y_train)\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## R2 Score"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "the best r2-score I got is 0.83257. It is in round 7 after I removed some numerical columns. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 94,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(0.8325739188202834,)"
      ]
     },
     "execution_count": 94,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "predictions = lm.predict(X_test)\n",
    "\n",
    "scores = []\n",
    "scores = r2_score(y_test, predictions),\n",
    "scores\n",
    "\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## MSE"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 65,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "114.86070505453965"
      ]
     },
     "execution_count": 65,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "\n",
    "np.sqrt(mean_squared_error(y_test,predictions))\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 66,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "13192.98156562595"
      ]
     },
     "execution_count": 66,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "mse=mean_squared_error(y_test,predictions)\n",
    "mse\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## RMSE"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 67,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "114.86070505453965\n"
     ]
    }
   ],
   "source": [
    "mse=mean_squared_error(y_test,predictions)\n",
    "rmse = np.sqrt(mse)\n",
    "print(rmse)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## MAE"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 68,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "82.47396358887435\n"
     ]
    }
   ],
   "source": [
    "mae = mean_absolute_error(y_test, predictions)\n",
    "print(mae)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "tags": []
   },
   "source": [
    "# Round 7"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "##  Use a different ratio of train test split"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 69,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.7664840056119686"
      ]
     },
     "execution_count": 69,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.12, random_state=35)\n",
    "lm = linear_model.LinearRegression()\n",
    "lm.fit(X_train,y_train)\n",
    "\n",
    "predictions = lm.predict(X_test)\n",
    "r2_score(y_test, predictions)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 70,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0.02\n",
      "0.04\n",
      "0.06\n",
      "0.08\n",
      "0.1\n",
      "0.12\n",
      "0.14\n",
      "0.16\n",
      "0.18\n",
      "0.2\n"
     ]
    }
   ],
   "source": [
    "for i in range(2,21,2):      \n",
    "    print(i/100)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 71,
   "metadata": {},
   "outputs": [],
   "source": [
    "# I ran this multiple times with different values, 0.02 got the best results\n",
    "scores =  []\n",
    "highest_value = 0\n",
    "for i in range(2, 10, 1):    \n",
    "    X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=i/200, random_state=35)\n",
    "    lm.fit(X_train,y_train)\n",
    "    \n",
    "    predictions = lm.predict(X_test)\n",
    "    score = r2_score(y_test, predictions)\n",
    "    # print('test_size: ' + str(i/100) + ', Score: ' + str(score))\n",
    "    scores.append(score)\n",
    "    if score > highest_value:\n",
    "        highest_value = score\n",
    "        highest_i = i/200  \n",
    "# scores"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 72,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Highest R2Score for testsize = 0.02\n",
      "Max(score) = 0.8342481902937723\n"
     ]
    }
   ],
   "source": [
    "print('Highest R2Score for testsize = ' + str(highest_i))\n",
    "print('Max(score) = ' + str(max(scores))) "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## use the concept of multicollinearity and remove insignificant variables"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 73,
   "metadata": {},
   "outputs": [],
   "source": [
    "# result: \n",
    "# Dropping some categorical columns that had a correlation smaller 0.03 slighty decreases the R2score\n",
    "# Dropping all numerical columns but monthly_premium_autoa increases the r2score"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 74,
   "metadata": {},
   "outputs": [],
   "source": [
    "y = df['total_claim_amount']\n",
    "X = df.drop(['total_claim_amount'], axis=1)\n",
    "X_num = X._get_numeric_data()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 75,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(10910, 9)\n"
     ]
    }
   ],
   "source": [
    "# Normalizing data: make data range from 0 - 1, instead of from min to max\n",
    "transformer = MinMaxScaler().fit(X_num)\n",
    "x_normalized = transformer.transform(X_num)\n",
    "print(x_normalized.shape)\n",
    "x_normalized #this is an array\n",
    "X_num = pd.DataFrame(x_normalized, columns=X_num.columns) #now we convert the array to a dataframe"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 76,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "CPU times: total: 375 ms\n",
      "Wall time: 379 ms\n"
     ]
    }
   ],
   "source": [
    "%%time\n",
    "correlations_matrix = round(df.corr(),2)\n",
    "sns.heatmap(correlations_matrix, annot=True)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 77,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Dropping all numerical columns but mpa increases the r2score\n",
    "# X_num = X_num.drop(['months_since_last_claim', 'months_since_policy_inception', 'number_of_open_complaints', 'number_of_policies', 'month', 'response_rate'], axis=1)\n",
    "# X_num = X_num.drop(['customer_lifetime_value'], axis=1)\n",
    "X_num = X_num['monthly_premium_auto']\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 78,
   "metadata": {},
   "outputs": [],
   "source": [
    "X = pd.concat([X_num, onehot_encoded_for_p], axis=1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 79,
   "metadata": {},
   "outputs": [],
   "source": [
    "# %%time\n",
    "# df_onehotencoded = pd.concat([X, y,], axis=1)\n",
    "# correlations_matrix = round(df_onehotencoded.corr(),2)\n",
    "# plt.subplots(figsize=(55,40))\n",
    "# sns.heatmap(correlations_matrix, annot=True)\n",
    "# plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 80,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Dropping some categorical columns that had a correlation smaller 0.03 slighty decreases the R2score\n",
    "# X = X.drop(['Yes', 'Extended', 'College', 'Retired', 'M', 'Personal Auto', 'Special Auto', 'Special L2', 'Special L3', 'Offer2', 'Offer3', 'Web', 'Luxury SUV', 'Medsize'], axis = 1) \n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 81,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0.83257\n"
     ]
    }
   ],
   "source": [
    "scores =  []\n",
    "\n",
    "X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.02, random_state = 35)\n",
    "lm = linear_model.LinearRegression()\n",
    "lm.fit(X_train,y_train)\n",
    "\n",
    "predictions = lm.predict(X_test)\n",
    "score = round(r2_score(y_test, predictions), 5)\n",
    "print(score)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## use a different method of scaling the numerical variables"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 82,
   "metadata": {},
   "outputs": [],
   "source": [
    "# here I use the standardscaler instead of maxmin\n",
    "# result: It makes hardly any difference"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 83,
   "metadata": {},
   "outputs": [],
   "source": [
    "y = df['total_claim_amount']\n",
    "X = df.drop(['total_claim_amount'], axis=1)\n",
    "X_num = X._get_numeric_data()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 84,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(10910, 9)\n"
     ]
    },
    {
     "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_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>month</th>\n",
       "      <th>response_rate</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>-0.466105</td>\n",
       "      <td>0.345635</td>\n",
       "      <td>-0.934819</td>\n",
       "      <td>-0.832977</td>\n",
       "      <td>0.139877</td>\n",
       "      <td>-0.433919</td>\n",
       "      <td>2.509455</td>\n",
       "      <td>1.068914</td>\n",
       "      <td>-0.393993</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>-0.840946</td>\n",
       "      <td>-1.236462</td>\n",
       "      <td>-0.847713</td>\n",
       "      <td>-1.241846</td>\n",
       "      <td>-0.790709</td>\n",
       "      <td>-0.433919</td>\n",
       "      <td>-0.824922</td>\n",
       "      <td>-0.935529</td>\n",
       "      <td>-0.393993</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>1.006523</td>\n",
       "      <td>-0.507194</td>\n",
       "      <td>0.197554</td>\n",
       "      <td>1.926893</td>\n",
       "      <td>-0.611750</td>\n",
       "      <td>-0.433919</td>\n",
       "      <td>-0.408125</td>\n",
       "      <td>1.068914</td>\n",
       "      <td>-0.393993</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>2.079112</td>\n",
       "      <td>0.380189</td>\n",
       "      <td>0.110448</td>\n",
       "      <td>-0.526325</td>\n",
       "      <td>-1.613919</td>\n",
       "      <td>-0.433919</td>\n",
       "      <td>-0.408125</td>\n",
       "      <td>-0.935529</td>\n",
       "      <td>2.538113</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>0.146240</td>\n",
       "      <td>-0.456597</td>\n",
       "      <td>0.691152</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>-0.611750</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>1.675860</td>\n",
       "      <td>-0.935529</td>\n",
       "      <td>-0.393993</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",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10905</th>\n",
       "      <td>1.095916</td>\n",
       "      <td>-1.236462</td>\n",
       "      <td>4.639940</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>-0.289625</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>1.675860</td>\n",
       "      <td>-0.935529</td>\n",
       "      <td>-0.393993</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10906</th>\n",
       "      <td>-0.400710</td>\n",
       "      <td>0.777715</td>\n",
       "      <td>-0.818678</td>\n",
       "      <td>-0.832977</td>\n",
       "      <td>0.712545</td>\n",
       "      <td>-0.433919</td>\n",
       "      <td>1.259063</td>\n",
       "      <td>-0.935529</td>\n",
       "      <td>-0.393993</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10907</th>\n",
       "      <td>2.305825</td>\n",
       "      <td>0.075787</td>\n",
       "      <td>3.130109</td>\n",
       "      <td>-0.424107</td>\n",
       "      <td>0.533586</td>\n",
       "      <td>-0.433919</td>\n",
       "      <td>-0.408125</td>\n",
       "      <td>1.068914</td>\n",
       "      <td>-0.393993</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10908</th>\n",
       "      <td>0.574273</td>\n",
       "      <td>0.878150</td>\n",
       "      <td>1.881595</td>\n",
       "      <td>-1.548498</td>\n",
       "      <td>-0.754917</td>\n",
       "      <td>4.083057</td>\n",
       "      <td>1.259063</td>\n",
       "      <td>1.068914</td>\n",
       "      <td>-0.393993</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10909</th>\n",
       "      <td>-0.168593</td>\n",
       "      <td>-1.236462</td>\n",
       "      <td>0.226589</td>\n",
       "      <td>1.620241</td>\n",
       "      <td>-1.685503</td>\n",
       "      <td>-0.433919</td>\n",
       "      <td>0.008672</td>\n",
       "      <td>-0.935529</td>\n",
       "      <td>-0.393993</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>10910 rows × 9 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "       customer_lifetime_value    income  monthly_premium_auto  \\\n",
       "0                    -0.466105  0.345635             -0.934819   \n",
       "1                    -0.840946 -1.236462             -0.847713   \n",
       "2                     1.006523 -0.507194              0.197554   \n",
       "3                     2.079112  0.380189              0.110448   \n",
       "4                     0.146240 -0.456597              0.691152   \n",
       "...                        ...       ...                   ...   \n",
       "10905                 1.095916 -1.236462              4.639940   \n",
       "10906                -0.400710  0.777715             -0.818678   \n",
       "10907                 2.305825  0.075787              3.130109   \n",
       "10908                 0.574273  0.878150              1.881595   \n",
       "10909                -0.168593 -1.236462              0.226589   \n",
       "\n",
       "       months_since_last_claim  months_since_policy_inception  \\\n",
       "0                    -0.832977                       0.139877   \n",
       "1                    -1.241846                      -0.790709   \n",
       "2                     1.926893                      -0.611750   \n",
       "3                    -0.526325                      -1.613919   \n",
       "4                     0.000000                      -0.611750   \n",
       "...                        ...                            ...   \n",
       "10905                 0.000000                      -0.289625   \n",
       "10906                -0.832977                       0.712545   \n",
       "10907                -0.424107                       0.533586   \n",
       "10908                -1.548498                      -0.754917   \n",
       "10909                 1.620241                      -1.685503   \n",
       "\n",
       "       number_of_open_complaints  number_of_policies     month  response_rate  \n",
       "0                      -0.433919            2.509455  1.068914      -0.393993  \n",
       "1                      -0.433919           -0.824922 -0.935529      -0.393993  \n",
       "2                      -0.433919           -0.408125  1.068914      -0.393993  \n",
       "3                      -0.433919           -0.408125 -0.935529       2.538113  \n",
       "4                       0.000000            1.675860 -0.935529      -0.393993  \n",
       "...                          ...                 ...       ...            ...  \n",
       "10905                   0.000000            1.675860 -0.935529      -0.393993  \n",
       "10906                  -0.433919            1.259063 -0.935529      -0.393993  \n",
       "10907                  -0.433919           -0.408125  1.068914      -0.393993  \n",
       "10908                   4.083057            1.259063  1.068914      -0.393993  \n",
       "10909                  -0.433919            0.008672 -0.935529      -0.393993  \n",
       "\n",
       "[10910 rows x 9 columns]"
      ]
     },
     "execution_count": 84,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# scaling standard scaler: make data distributed with mean=0 and std=1\n",
    "transformer = StandardScaler().fit(X_num)\n",
    "x_standardized = transformer.transform(X_num)\n",
    "print(x_standardized.shape)\n",
    "pd.DataFrame(x_standardized, columns=X_num.columns)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 85,
   "metadata": {},
   "outputs": [],
   "source": [
    "X_num = X_num['monthly_premium_auto']\n",
    "X = pd.concat([X_num, onehot_encoded_for_p], axis=1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 86,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0.83257\n"
     ]
    }
   ],
   "source": [
    "scores =  []\n",
    "\n",
    "X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.02, random_state = 35)\n",
    "lm = linear_model.LinearRegression()\n",
    "lm.fit(X_train,y_train)\n",
    "\n",
    "predictions = lm.predict(X_test)\n",
    "score = round(r2_score(y_test, predictions), 5)\n",
    "print(score)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Calculating average of r2 score"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "scores =  []\n",
    "highest_value = 0\n",
    "# for i in range(2, 1001, 2):    \n",
    "for r in range(1, 21, 1):\n",
    "    X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.02, random_state=r)\n",
    "    lm = linear_model.LinearRegression()\n",
    "    lm.fit(X_train,y_train)\n",
    "    predictions = lm.predict(X_test)                 \n",
    "    score = r2_score(y_test, predictions)\n",
    "    scores.append(score)     \n",
    "    # print('i = ' + str(i))\n",
    "    # print('r = ' + str(r))\n",
    "    # print(r2_score(y_test, predictions))\n",
    "    if score > highest_value:\n",
    "        highest_value = score\n",
    "        highest_r = r\n",
    "        # highest_i = i  \n",
    "    \n",
    "# print('Highest R2Score for testsize = ' + str(highest_i) + ' and random_state = ' + str(highest_r))\n",
    "print('Highest R2Score for random_state = ' + str(highest_r))\n",
    "print('Min(score) = ' + str(round(min(scores), 5)))\n",
    "print('Max(score) = ' + str(round(max(scores), 5))) \n",
    "print('Mean(score) = ' + str(round(stats.mean(scores), 5)))\n"
   ]
  }
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