EDA.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Importing the required libraries\n",
    "\n",
    "import pandas as pd\n",
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "import seaborn as sns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(381109, 12)"
      ]
     },
     "execution_count": 2,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Importing the dataset\n",
    "\n",
    "train=pd.read_csv(\"train.csv\")\n",
    "test=pd.read_csv(\"test.csv\")\n",
    "\n",
    "train.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "id                      0\n",
       "Gender                  0\n",
       "Age                     0\n",
       "Driving_License         0\n",
       "Region_Code             0\n",
       "Previously_Insured      0\n",
       "Vehicle_Age             0\n",
       "Vehicle_Damage          0\n",
       "Annual_Premium          0\n",
       "Policy_Sales_Channel    0\n",
       "Vintage                 0\n",
       "Response                0\n",
       "dtype: int64"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Checking for Missing Values\n",
    "\n",
    "train.isnull().sum()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**There are No Missing Values in our dataset**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>id</th>\n",
       "      <th>Gender</th>\n",
       "      <th>Age</th>\n",
       "      <th>Driving_License</th>\n",
       "      <th>Region_Code</th>\n",
       "      <th>Previously_Insured</th>\n",
       "      <th>Vehicle_Age</th>\n",
       "      <th>Vehicle_Damage</th>\n",
       "      <th>Annual_Premium</th>\n",
       "      <th>Policy_Sales_Channel</th>\n",
       "      <th>Vintage</th>\n",
       "      <th>Response</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1</td>\n",
       "      <td>Male</td>\n",
       "      <td>44</td>\n",
       "      <td>1</td>\n",
       "      <td>28.0</td>\n",
       "      <td>0</td>\n",
       "      <td>&gt; 2 Years</td>\n",
       "      <td>Yes</td>\n",
       "      <td>40454.0</td>\n",
       "      <td>26.0</td>\n",
       "      <td>217</td>\n",
       "      <td>1</td>\n",
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       "      <td>76</td>\n",
       "      <td>1</td>\n",
       "      <td>3.0</td>\n",
       "      <td>0</td>\n",
       "      <td>1-2 Year</td>\n",
       "      <td>No</td>\n",
       "      <td>33536.0</td>\n",
       "      <td>26.0</td>\n",
       "      <td>183</td>\n",
       "      <td>0</td>\n",
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       "      <th>2</th>\n",
       "      <td>3</td>\n",
       "      <td>Male</td>\n",
       "      <td>47</td>\n",
       "      <td>1</td>\n",
       "      <td>28.0</td>\n",
       "      <td>0</td>\n",
       "      <td>&gt; 2 Years</td>\n",
       "      <td>Yes</td>\n",
       "      <td>38294.0</td>\n",
       "      <td>26.0</td>\n",
       "      <td>27</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>4</td>\n",
       "      <td>Male</td>\n",
       "      <td>21</td>\n",
       "      <td>1</td>\n",
       "      <td>11.0</td>\n",
       "      <td>1</td>\n",
       "      <td>&lt; 1 Year</td>\n",
       "      <td>No</td>\n",
       "      <td>28619.0</td>\n",
       "      <td>152.0</td>\n",
       "      <td>203</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>5</td>\n",
       "      <td>Female</td>\n",
       "      <td>29</td>\n",
       "      <td>1</td>\n",
       "      <td>41.0</td>\n",
       "      <td>1</td>\n",
       "      <td>&lt; 1 Year</td>\n",
       "      <td>No</td>\n",
       "      <td>27496.0</td>\n",
       "      <td>152.0</td>\n",
       "      <td>39</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>6</td>\n",
       "      <td>Female</td>\n",
       "      <td>24</td>\n",
       "      <td>1</td>\n",
       "      <td>33.0</td>\n",
       "      <td>0</td>\n",
       "      <td>&lt; 1 Year</td>\n",
       "      <td>Yes</td>\n",
       "      <td>2630.0</td>\n",
       "      <td>160.0</td>\n",
       "      <td>176</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>7</td>\n",
       "      <td>Male</td>\n",
       "      <td>23</td>\n",
       "      <td>1</td>\n",
       "      <td>11.0</td>\n",
       "      <td>0</td>\n",
       "      <td>&lt; 1 Year</td>\n",
       "      <td>Yes</td>\n",
       "      <td>23367.0</td>\n",
       "      <td>152.0</td>\n",
       "      <td>249</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>8</td>\n",
       "      <td>Female</td>\n",
       "      <td>56</td>\n",
       "      <td>1</td>\n",
       "      <td>28.0</td>\n",
       "      <td>0</td>\n",
       "      <td>1-2 Year</td>\n",
       "      <td>Yes</td>\n",
       "      <td>32031.0</td>\n",
       "      <td>26.0</td>\n",
       "      <td>72</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>9</td>\n",
       "      <td>Female</td>\n",
       "      <td>24</td>\n",
       "      <td>1</td>\n",
       "      <td>3.0</td>\n",
       "      <td>1</td>\n",
       "      <td>&lt; 1 Year</td>\n",
       "      <td>No</td>\n",
       "      <td>27619.0</td>\n",
       "      <td>152.0</td>\n",
       "      <td>28</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>10</td>\n",
       "      <td>Female</td>\n",
       "      <td>32</td>\n",
       "      <td>1</td>\n",
       "      <td>6.0</td>\n",
       "      <td>1</td>\n",
       "      <td>&lt; 1 Year</td>\n",
       "      <td>No</td>\n",
       "      <td>28771.0</td>\n",
       "      <td>152.0</td>\n",
       "      <td>80</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
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      ],
      "text/plain": [
       "   id  Gender  Age  Driving_License  Region_Code  Previously_Insured  \\\n",
       "0   1    Male   44                1         28.0                   0   \n",
       "1   2    Male   76                1          3.0                   0   \n",
       "2   3    Male   47                1         28.0                   0   \n",
       "3   4    Male   21                1         11.0                   1   \n",
       "4   5  Female   29                1         41.0                   1   \n",
       "5   6  Female   24                1         33.0                   0   \n",
       "6   7    Male   23                1         11.0                   0   \n",
       "7   8  Female   56                1         28.0                   0   \n",
       "8   9  Female   24                1          3.0                   1   \n",
       "9  10  Female   32                1          6.0                   1   \n",
       "\n",
       "  Vehicle_Age Vehicle_Damage  Annual_Premium  Policy_Sales_Channel  Vintage  \\\n",
       "0   > 2 Years            Yes         40454.0                  26.0      217   \n",
       "1    1-2 Year             No         33536.0                  26.0      183   \n",
       "2   > 2 Years            Yes         38294.0                  26.0       27   \n",
       "3    < 1 Year             No         28619.0                 152.0      203   \n",
       "4    < 1 Year             No         27496.0                 152.0       39   \n",
       "5    < 1 Year            Yes          2630.0                 160.0      176   \n",
       "6    < 1 Year            Yes         23367.0                 152.0      249   \n",
       "7    1-2 Year            Yes         32031.0                  26.0       72   \n",
       "8    < 1 Year             No         27619.0                 152.0       28   \n",
       "9    < 1 Year             No         28771.0                 152.0       80   \n",
       "\n",
       "   Response  \n",
       "0         1  \n",
       "1         0  \n",
       "2         1  \n",
       "3         0  \n",
       "4         0  \n",
       "5         0  \n",
       "6         0  \n",
       "7         1  \n",
       "8         0  \n",
       "9         0  "
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "train.head(10)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "  <thead>\n",
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       "      <th></th>\n",
       "      <th>id</th>\n",
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       "      <td>1</td>\n",
       "      <td>28.0</td>\n",
       "      <td>0</td>\n",
       "      <td>&gt; 2 Years</td>\n",
       "      <td>Yes</td>\n",
       "      <td>38294.0</td>\n",
       "      <td>26.0</td>\n",
       "      <td>27</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>4</td>\n",
       "      <td>Male</td>\n",
       "      <td>21</td>\n",
       "      <td>1</td>\n",
       "      <td>11.0</td>\n",
       "      <td>1</td>\n",
       "      <td>&lt; 1 Year</td>\n",
       "      <td>No</td>\n",
       "      <td>28619.0</td>\n",
       "      <td>152.0</td>\n",
       "      <td>203</td>\n",
       "      <td>0</td>\n",
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       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>5</td>\n",
       "      <td>Female</td>\n",
       "      <td>29</td>\n",
       "      <td>1</td>\n",
       "      <td>41.0</td>\n",
       "      <td>1</td>\n",
       "      <td>&lt; 1 Year</td>\n",
       "      <td>No</td>\n",
       "      <td>27496.0</td>\n",
       "      <td>152.0</td>\n",
       "      <td>39</td>\n",
       "      <td>0</td>\n",
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       "    <tr>\n",
       "      <th>381104</th>\n",
       "      <td>381105</td>\n",
       "      <td>Male</td>\n",
       "      <td>74</td>\n",
       "      <td>1</td>\n",
       "      <td>26.0</td>\n",
       "      <td>1</td>\n",
       "      <td>1-2 Year</td>\n",
       "      <td>No</td>\n",
       "      <td>30170.0</td>\n",
       "      <td>26.0</td>\n",
       "      <td>88</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>381105</th>\n",
       "      <td>381106</td>\n",
       "      <td>Male</td>\n",
       "      <td>30</td>\n",
       "      <td>1</td>\n",
       "      <td>37.0</td>\n",
       "      <td>1</td>\n",
       "      <td>&lt; 1 Year</td>\n",
       "      <td>No</td>\n",
       "      <td>40016.0</td>\n",
       "      <td>152.0</td>\n",
       "      <td>131</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>381106</th>\n",
       "      <td>381107</td>\n",
       "      <td>Male</td>\n",
       "      <td>21</td>\n",
       "      <td>1</td>\n",
       "      <td>30.0</td>\n",
       "      <td>1</td>\n",
       "      <td>&lt; 1 Year</td>\n",
       "      <td>No</td>\n",
       "      <td>35118.0</td>\n",
       "      <td>160.0</td>\n",
       "      <td>161</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>381107</th>\n",
       "      <td>381108</td>\n",
       "      <td>Female</td>\n",
       "      <td>68</td>\n",
       "      <td>1</td>\n",
       "      <td>14.0</td>\n",
       "      <td>0</td>\n",
       "      <td>&gt; 2 Years</td>\n",
       "      <td>Yes</td>\n",
       "      <td>44617.0</td>\n",
       "      <td>124.0</td>\n",
       "      <td>74</td>\n",
       "      <td>0</td>\n",
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       "      <td>381109</td>\n",
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       "      <td>46</td>\n",
       "      <td>1</td>\n",
       "      <td>29.0</td>\n",
       "      <td>0</td>\n",
       "      <td>1-2 Year</td>\n",
       "      <td>No</td>\n",
       "      <td>41777.0</td>\n",
       "      <td>26.0</td>\n",
       "      <td>237</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>381109 rows × 12 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "            id  Gender  Age  Driving_License  Region_Code  Previously_Insured  \\\n",
       "0            1    Male   44                1         28.0                   0   \n",
       "1            2    Male   76                1          3.0                   0   \n",
       "2            3    Male   47                1         28.0                   0   \n",
       "3            4    Male   21                1         11.0                   1   \n",
       "4            5  Female   29                1         41.0                   1   \n",
       "...        ...     ...  ...              ...          ...                 ...   \n",
       "381104  381105    Male   74                1         26.0                   1   \n",
       "381105  381106    Male   30                1         37.0                   1   \n",
       "381106  381107    Male   21                1         30.0                   1   \n",
       "381107  381108  Female   68                1         14.0                   0   \n",
       "381108  381109    Male   46                1         29.0                   0   \n",
       "\n",
       "       Vehicle_Age Vehicle_Damage  Annual_Premium  Policy_Sales_Channel  \\\n",
       "0        > 2 Years            Yes         40454.0                  26.0   \n",
       "1         1-2 Year             No         33536.0                  26.0   \n",
       "2        > 2 Years            Yes         38294.0                  26.0   \n",
       "3         < 1 Year             No         28619.0                 152.0   \n",
       "4         < 1 Year             No         27496.0                 152.0   \n",
       "...            ...            ...             ...                   ...   \n",
       "381104    1-2 Year             No         30170.0                  26.0   \n",
       "381105    < 1 Year             No         40016.0                 152.0   \n",
       "381106    < 1 Year             No         35118.0                 160.0   \n",
       "381107   > 2 Years            Yes         44617.0                 124.0   \n",
       "381108    1-2 Year             No         41777.0                  26.0   \n",
       "\n",
       "        Vintage  Response  \n",
       "0           217         1  \n",
       "1           183         0  \n",
       "2            27         1  \n",
       "3           203         0  \n",
       "4            39         0  \n",
       "...         ...       ...  \n",
       "381104       88         0  \n",
       "381105      131         0  \n",
       "381106      161         0  \n",
       "381107       74         0  \n",
       "381108      237         0  \n",
       "\n",
       "[381109 rows x 12 columns]"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "train.drop_duplicates()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**There are no duplicate values in our dataset as well**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "id                        int64\n",
       "Gender                   object\n",
       "Age                       int64\n",
       "Driving_License           int64\n",
       "Region_Code             float64\n",
       "Previously_Insured        int64\n",
       "Vehicle_Age              object\n",
       "Vehicle_Damage           object\n",
       "Annual_Premium          float64\n",
       "Policy_Sales_Channel    float64\n",
       "Vintage                   int64\n",
       "Response                  int64\n",
       "dtype: object"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "train.dtypes"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Categorical Variables"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Gender"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Male      0.540761\n",
       "Female    0.459239\n",
       "Name: Gender, dtype: float64"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "train.Gender.value_counts(normalize=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [],
   "source": [
    "train[\"Gender\"]=train[\"Gender\"].astype(\"category\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Driving License"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "1    0.997869\n",
       "0    0.002131\n",
       "Name: Driving_License, dtype: float64"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "train.Driving_License.value_counts(normalize=True)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Region Code"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {
    "collapsed": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "28.0    0.279225\n",
       "8.0     0.088891\n",
       "46.0    0.051820\n",
       "41.0    0.047921\n",
       "15.0    0.034919\n",
       "30.0    0.031988\n",
       "29.0    0.029034\n",
       "50.0    0.026877\n",
       "3.0     0.024274\n",
       "11.0    0.024224\n",
       "36.0    0.023083\n",
       "33.0    0.020083\n",
       "47.0    0.019511\n",
       "35.0    0.018215\n",
       "6.0     0.016478\n",
       "45.0    0.014707\n",
       "37.0    0.014434\n",
       "18.0    0.013521\n",
       "48.0    0.012283\n",
       "14.0    0.012275\n",
       "39.0    0.012185\n",
       "10.0    0.011477\n",
       "21.0    0.011194\n",
       "2.0     0.010595\n",
       "13.0    0.010590\n",
       "7.0     0.008604\n",
       "12.0    0.008391\n",
       "9.0     0.008137\n",
       "27.0    0.007407\n",
       "32.0    0.007313\n",
       "43.0    0.006925\n",
       "17.0    0.006867\n",
       "26.0    0.006788\n",
       "25.0    0.006568\n",
       "24.0    0.006337\n",
       "38.0    0.005316\n",
       "0.0     0.005303\n",
       "16.0    0.005266\n",
       "31.0    0.005143\n",
       "23.0    0.005143\n",
       "20.0    0.005077\n",
       "49.0    0.004807\n",
       "4.0     0.004726\n",
       "34.0    0.004366\n",
       "19.0    0.004028\n",
       "22.0    0.003435\n",
       "40.0    0.003398\n",
       "5.0     0.003356\n",
       "1.0     0.002645\n",
       "44.0    0.002120\n",
       "42.0    0.001551\n",
       "52.0    0.000701\n",
       "51.0    0.000480\n",
       "Name: Region_Code, dtype: float64"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "train.Region_Code.value_counts(normalize=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [],
   "source": [
    "train[\"Region_Code\"]=train.Region_Code.astype(\"int\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {
    "collapsed": true
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
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       "    .dataframe tbody tr th {\n",
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       "    .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>index</th>\n",
       "      <th>Region_Code</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0</td>\n",
       "      <td>0.005303</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1</td>\n",
       "      <td>0.002645</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2</td>\n",
       "      <td>0.010595</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>3</td>\n",
       "      <td>0.024274</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>4</td>\n",
       "      <td>0.004726</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>5</td>\n",
       "      <td>0.003356</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>6</td>\n",
       "      <td>0.016478</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>7</td>\n",
       "      <td>0.008604</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>8</td>\n",
       "      <td>0.088891</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>9</td>\n",
       "      <td>0.008137</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>10</td>\n",
       "      <td>0.011477</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>11</td>\n",
       "      <td>0.024224</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>12</td>\n",
       "      <td>0.008391</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>13</td>\n",
       "      <td>0.010590</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>14</td>\n",
       "      <td>0.012275</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15</th>\n",
       "      <td>15</td>\n",
       "      <td>0.034919</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16</th>\n",
       "      <td>16</td>\n",
       "      <td>0.005266</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17</th>\n",
       "      <td>17</td>\n",
       "      <td>0.006867</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18</th>\n",
       "      <td>18</td>\n",
       "      <td>0.013521</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19</th>\n",
       "      <td>19</td>\n",
       "      <td>0.004028</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20</th>\n",
       "      <td>20</td>\n",
       "      <td>0.005077</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>21</th>\n",
       "      <td>21</td>\n",
       "      <td>0.011194</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>22</th>\n",
       "      <td>22</td>\n",
       "      <td>0.003435</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>23</th>\n",
       "      <td>23</td>\n",
       "      <td>0.005143</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>24</th>\n",
       "      <td>24</td>\n",
       "      <td>0.006337</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25</th>\n",
       "      <td>25</td>\n",
       "      <td>0.006568</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>26</th>\n",
       "      <td>26</td>\n",
       "      <td>0.006788</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>27</th>\n",
       "      <td>27</td>\n",
       "      <td>0.007407</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>28</th>\n",
       "      <td>28</td>\n",
       "      <td>0.279225</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>29</th>\n",
       "      <td>29</td>\n",
       "      <td>0.029034</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>30</th>\n",
       "      <td>30</td>\n",
       "      <td>0.031988</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>31</th>\n",
       "      <td>31</td>\n",
       "      <td>0.005143</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>32</th>\n",
       "      <td>32</td>\n",
       "      <td>0.007313</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>33</th>\n",
       "      <td>33</td>\n",
       "      <td>0.020083</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>34</th>\n",
       "      <td>34</td>\n",
       "      <td>0.004366</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>35</th>\n",
       "      <td>35</td>\n",
       "      <td>0.018215</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>36</th>\n",
       "      <td>36</td>\n",
       "      <td>0.023083</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>37</th>\n",
       "      <td>37</td>\n",
       "      <td>0.014434</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>38</th>\n",
       "      <td>38</td>\n",
       "      <td>0.005316</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>39</th>\n",
       "      <td>39</td>\n",
       "      <td>0.012185</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>40</th>\n",
       "      <td>40</td>\n",
       "      <td>0.003398</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>41</th>\n",
       "      <td>41</td>\n",
       "      <td>0.047921</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>42</th>\n",
       "      <td>42</td>\n",
       "      <td>0.001551</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>43</th>\n",
       "      <td>43</td>\n",
       "      <td>0.006925</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>44</th>\n",
       "      <td>44</td>\n",
       "      <td>0.002120</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>45</th>\n",
       "      <td>45</td>\n",
       "      <td>0.014707</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>46</th>\n",
       "      <td>46</td>\n",
       "      <td>0.051820</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>47</th>\n",
       "      <td>47</td>\n",
       "      <td>0.019511</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>48</th>\n",
       "      <td>48</td>\n",
       "      <td>0.012283</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>49</th>\n",
       "      <td>49</td>\n",
       "      <td>0.004807</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50</th>\n",
       "      <td>50</td>\n",
       "      <td>0.026877</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>51</th>\n",
       "      <td>51</td>\n",
       "      <td>0.000480</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>52</th>\n",
       "      <td>52</td>\n",
       "      <td>0.000701</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "    index  Region_Code\n",
       "0       0     0.005303\n",
       "1       1     0.002645\n",
       "2       2     0.010595\n",
       "3       3     0.024274\n",
       "4       4     0.004726\n",
       "5       5     0.003356\n",
       "6       6     0.016478\n",
       "7       7     0.008604\n",
       "8       8     0.088891\n",
       "9       9     0.008137\n",
       "10     10     0.011477\n",
       "11     11     0.024224\n",
       "12     12     0.008391\n",
       "13     13     0.010590\n",
       "14     14     0.012275\n",
       "15     15     0.034919\n",
       "16     16     0.005266\n",
       "17     17     0.006867\n",
       "18     18     0.013521\n",
       "19     19     0.004028\n",
       "20     20     0.005077\n",
       "21     21     0.011194\n",
       "22     22     0.003435\n",
       "23     23     0.005143\n",
       "24     24     0.006337\n",
       "25     25     0.006568\n",
       "26     26     0.006788\n",
       "27     27     0.007407\n",
       "28     28     0.279225\n",
       "29     29     0.029034\n",
       "30     30     0.031988\n",
       "31     31     0.005143\n",
       "32     32     0.007313\n",
       "33     33     0.020083\n",
       "34     34     0.004366\n",
       "35     35     0.018215\n",
       "36     36     0.023083\n",
       "37     37     0.014434\n",
       "38     38     0.005316\n",
       "39     39     0.012185\n",
       "40     40     0.003398\n",
       "41     41     0.047921\n",
       "42     42     0.001551\n",
       "43     43     0.006925\n",
       "44     44     0.002120\n",
       "45     45     0.014707\n",
       "46     46     0.051820\n",
       "47     47     0.019511\n",
       "48     48     0.012283\n",
       "49     49     0.004807\n",
       "50     50     0.026877\n",
       "51     51     0.000480\n",
       "52     52     0.000701"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "Region_Code=pd.DataFrame(train.Region_Code.value_counts(normalize=True))\n",
    "Region_Code.reset_index().sort_values(\"index\").reset_index().drop(columns=\"level_0\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**53 Region Codes are there (0-52)**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {
    "collapsed": true
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x720 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(10,10))\n",
    "sns.countplot(y=train[\"Region_Code\"],color=\"lightgreen\",order=train[\"Region_Code\"].value_counts(ascending=False).index)\n",
    "plt.tight_layout()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Region 28 has highest number of people, followed by 8,46,41,15**"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Previously Insured"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0    0.54179\n",
       "1    0.45821\n",
       "Name: Previously_Insured, dtype: float64"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "train.Previously_Insured.value_counts(normalize=True)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Vehicle Age"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "1-2 Year     0.525613\n",
       "< 1 Year     0.432385\n",
       "> 2 Years    0.042001\n",
       "Name: Vehicle_Age, dtype: float64"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "train.Vehicle_Age.value_counts(normalize=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [],
   "source": [
    "train.Vehicle_Age=train.Vehicle_Age.astype(\"category\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Vehicle Damage"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Yes    0.504877\n",
       "No     0.495123\n",
       "Name: Vehicle_Damage, dtype: float64"
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "train.Vehicle_Damage.value_counts(normalize=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [],
   "source": [
    "train.Vehicle_Damage=train.Vehicle_Damage.astype('category')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Policy Sales Channel"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 55,
   "metadata": {},
   "outputs": [],
   "source": [
    "train.Policy_Sales_Channel=train.Policy_Sales_Channel.astype('int')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "152    0.353663\n",
       "26     0.209127\n",
       "124    0.194157\n",
       "160    0.057146\n",
       "156    0.027974\n",
       "122    0.026056\n",
       "157    0.017538\n",
       "154    0.015725\n",
       "151    0.010194\n",
       "163    0.007591\n",
       "Name: Policy_Sales_Channel, dtype: float64"
      ]
     },
     "execution_count": 20,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "train.Policy_Sales_Channel.value_counts(normalize=True).head(10)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Response"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0    0.877437\n",
       "1    0.122563\n",
       "Name: Response, dtype: float64"
      ]
     },
     "execution_count": 21,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "train.Response.value_counts(normalize=True)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Continuous Variables"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Age"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "count    381109.000000\n",
       "mean         38.822584\n",
       "std          15.511611\n",
       "min          20.000000\n",
       "25%          25.000000\n",
       "50%          36.000000\n",
       "75%          49.000000\n",
       "max          85.000000\n",
       "Name: Age, dtype: float64"
      ]
     },
     "execution_count": 22,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "train.Age.describe()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "- People Above Age 20 are buying insurance\n",
    "- Aged people will be less likely to get an insurance because they don't drive\n",
    "- People above Age 25 are more likely to have a car of their own\n",
    "- Mostly, we have people between Age 25 and 49"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "24    0.068117\n",
       "23    0.063646\n",
       "22    0.055008\n",
       "25    0.054147\n",
       "21    0.043182\n",
       "26    0.035515\n",
       "27    0.028233\n",
       "28    0.023547\n",
       "43    0.022138\n",
       "44    0.021928\n",
       "Name: Age, dtype: float64"
      ]
     },
     "execution_count": 23,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "train.Age.value_counts(normalize=True).head(10)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Annual Premium"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "count    381109.000000\n",
       "mean      30564.389581\n",
       "std       17213.155057\n",
       "min        2630.000000\n",
       "25%       24405.000000\n",
       "50%       31669.000000\n",
       "75%       39400.000000\n",
       "max      540165.000000\n",
       "Name: Annual_Premium, dtype: float64"
      ]
     },
     "execution_count": 24,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "train.Annual_Premium.describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x28b8e890b48>"
      ]
     },
     "execution_count": 25,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAZgAAADrCAYAAABU+oCOAAAABHNCSVQICAgIfAhkiAAAAAlwSFlzAAALEgAACxIB0t1+/AAAADh0RVh0U29mdHdhcmUAbWF0cGxvdGxpYiB2ZXJzaW9uMy4xLjMsIGh0dHA6Ly9tYXRwbG90bGliLm9yZy+AADFEAAAYGUlEQVR4nO3df5Bd5X3f8fdHWkByMQYtsspoRSV7lSaYVq61xpq4jX4gycsPITyNG0hSbT10lLrYqHGdGkNnJOyAncwkDngcKjUoXsVOME1LEAFL6GfSH8Zm1RCJH2a4wdgsqCBWmJ8GstK3f9xnxd3V3bvnyjr3nLv6vGbu3PN8z3Pu+WoG9NVzznOeo4jAzMzsZJtSdAJmZjY5ucCYmVkuXGDMzCwXLjBmZpYLFxgzM8uFC4yZmeWio+gEyuTcc8+NuXPnFp2GmVlb2bdv34sRMXNs3AWmxty5cxkYGCg6DTOztiLpR/XivkRmZma5cIExM7NcuMCYmVkuXGDMzCwXLjBmJbd7926WLFnCnj17ik7FrCkuMGYld8sttwBw8803F5yJWXNcYMxKbPfu3QwPDwMwPDzsUYy1FRcYsxIbGb2M8CjG2okLjFmJjYxexmublZkLjFmJdXR0NGyblZkLjFmJ3XDDDaPaN954Y0GZmDXPBcasxJYtW3Zs1NLR0cHSpUsLzsgsOxcYs5IbGcV49GLtxhd0zUpu2bJlLFu2rOg0zJrmEYyZmeXCBcbMzHLhAmNWckNDQ1x33XUMDQ0VnYpZU1xgzEquv7+fAwcOsGXLlqJTMWuKC4xZiQ0NDbFt2zYigm3btnkUY20l9wIj6WlJByQ9LGkgxWZI2iHpyfR9TopL0m2SKpL2S/pQze/0pf5PSuqriS9Mv19Jx6rROczaSX9/P0ePHgXgyJEjHsVYW2nVCGZpRHwwInpS+3pgV0TMB3alNsAlwPz0WQvcDtViAawHPgJcBKyvKRi3p74jx/VOcA6ztrFz585Rqynv2LGj4IzMsivqEtlqoD9t9wNX1sS3RNWDwNmSzgM+BuyIiMMR8RKwA+hN+86KiO9GRABbxvxWvXOYtY3ly5eTBuVIYsWKFQVnZJZdKwpMAA9I2idpbYrNioiDAOn7vSk+G3im5tjBFGsUH6wTb3QOs7ZxxRVXUP23E0QEq1atKjgjs+xaUWA+GhEfonr561pJv9Sgr+rE4gTimUlaK2lA0sChQ4eaOdQsd1u3bh3VvvfeewvKxKx5uReYiHgufb8A3E31Hsrz6fIW6fuF1H0QmFNzeBfw3ATxrjpxGpxjbH6bIqInInpmzpx5on9Ms1yMvefywAMPFJSJWfNyLTCS/oGkd49sAyuBR4CtwMhMsD7gnrS9FViTZpMtAl5Ol7e2AyslnZNu7q8Etqd9r0palGaPrRnzW/XOYdY2Zs2a1bBtVmZ5L3Y5C7g73aTsAP40IrZJegi4S9I1wI+BT6T+9wOXAhXgDeCTABFxWNKXgIdSvy9GxOG0/SngG8B04DvpA/CVcc5h1jaef/75hm2zMsu1wETEU8CCOvEh4OI68QCuHee3NgOb68QHgAuznsOsnaxYsYJ7772XiEASK1euLDols8z8JL9ZifX19R174dhpp53GmjVrCs7ILDsXGLMS6+zspKen+nzywoUL6ezsLDgjs+xcYMxKbv/+/aO+zdqFC4xZiQ0MDPD6668D8Prrr7Nv376CMzLLzgXGrMQ2bNgwqr1+/fpiEjE7AS4wZiX22muvNWyblZkLjFmJnXnmmQ3bZmXmAmNWYmMvkd10003FJGJ2AlxgzEqsp6fn2KjlzDPPZOHChQVnZJadC4xZyW3YsIEpU6Z49GJtxwXGrOTmzZvHhRdeyNy5c4tOxawpLjBmJdff38+BAwfYsmVL0amYNcUFxqzEhoaG2LZtGxHBtm3bGBoaKjols8xcYMxKrL+/n6NHjwJw5MgRj2KsrbjAmJXYzp07GR4eBmB4ePi4N1yalZkLjFmJLV++/Nhy/R0dHaxYsaLgjMyyc4ExK7G+vj6mTKn+bzp16lS/D8baiguMWYl1dnbS29uLJHp7e/0+GGsrub4y2cx+dn19fTz99NMevVjbcYExK7nOzk5uu+22otMwa5ovkZmZWS5cYMzMLBcuMGZmlgsXGDMzy4ULjJmZ5cIFxszMcuECY1ZyQ0NDXHfddV5J2dqOC4xZyW3cuJH9+/ezadOmolMxa0pLCoykqZL+RtJfpvY8Sd+T9KSkb0s6PcXPSO1K2j+35je+kOJPSPpYTbw3xSqSrq+J1z2HWTsZGho6toLyAw884FGMtZVWjWDWAY/XtH8H+GpEzAdeAq5J8WuAlyKiG/hq6oekC4CrgA8AvcAfpqI1Ffg6cAlwAXB16tvoHGZtY+PGjUQEABHhUYy1ldwLjKQu4DLgj1JbwDLgz1OXfuDKtL06tUn7L079VwN3RsRbEfFDoAJclD6ViHgqIt4G7gRWT3AOs7axa9euUe2dO3cWlIlZ81oxgvkD4D8BR1O7E/hJRAyn9iAwO23PBp4BSPtfTv2PxcccM1680TlGkbRW0oCkgUOHDp3on9EsF0eOHGnYNiuzXAuMpMuBFyJiX224TteYYN/Jih8fjNgUET0R0TNz5sx6XczM7ATkPYL5KHCFpKepXr5aRnVEc7akkZWcu4Dn0vYgMAcg7X8PcLg2PuaY8eIvNjiHWdvo6upq2DYrs1wLTER8ISK6ImIu1Zv0uyPi14A9wC+nbn3APWl7a2qT9u+O6h3OrcBVaZbZPGA+8H3gIWB+mjF2ejrH1nTMeOcwaxsbNmxo2DYrs6Keg/k88FlJFar3S+5I8TuAzhT/LHA9QEQ8CtwFPAZsA66NiCPpHsunge1UZ6ndlfo2OodZ2+ju7j42aunq6qK7u7vgjMyy08gUSIOenp4YGBgoOg2zUSqVCuvWrePWW291gbFSkrQvInrGxv1GS7OS6+7u5r777is6DbOmeakYMzPLRaYCI+nytNTLYUmvSHpV0it5J2dmsHv3bpYsWcKePXuKTsWsKVlHMH9AdSZWZ0ScFRHvjoizcszLzJJbbrkFgJtvvrngTMyak7XAPAM8Ep4RYNZSu3fvZni4uiDF8PCwRzHWVjLNIpP0YeBLwF8Bb43EI+L380ut9TyLzMpm+fLlxwoMQEdHh9cjs9L5WWeR3Qy8BkwDvOy9WYvUFpd6bbMyy1pgZkTEylwzMbPjdHR0HDeCMWsXWe/B7JTkAmPWYjfccMOo9o033lhQJmbNy1pgrgW2Sfqppymbtc6yZcuOjVo6OjpYunRpwRmZZZepwKRpyVMiYrqnKZu11hVXXAHAxz/+8YIzMWtO1gctf6neJ+/kzAy2bt0KwN13311wJmbNyXrH8LdqtqdRfVXxPqrvdzGznNR7DsaXyaxdnNBqypLmAL8bEVef/JSK4+dgrGz8HIy1g/GegznRxS4HgQt/tpTMbCJ+DsbaWaZLZJK+xjvvtJ8CfBD427ySMjOz9pf1HkztdaNh4M8i4n/nkI+ZmU0SmQpMRPTnnYiZmU0uDQuMpLsi4l9JOsA7l8gABERE/NNcszMzs7Y10QhmXfq+PO9EzMxscmlYYCLiYPr+EYCksyY6xsxOHi92ae0s65P8vyHpeWA/1Qcs9zH6xr+Z5eDSSy8d1b7ssssKysSseVmfg/kc8IGImBsR89LnfXkmZmbvrEM2YtWqVQVlYta8rAXm74A38kzEzI73zW9+s2HbrMyyXtD9AvB/JH2P0a9Mvi6XrMwMgL179zZsm5VZ1gKzEdgNHACO5peOmZlNFlkLzHBEfDbXTMzMbFLJeg9mj6S1ks6TNGPkM9FBkqZJ+r6kv5X0qKSbUnyepO9JelLStyWdnuJnpHYl7Z9b81tfSPEnJH2sJt6bYhVJ19fE657DzMxaI2uB+VXSfRiam6b8FrAsIhZQXSCzV9Ii4HeAr0bEfOAl4JrU/xrgpYjoBr6a+iHpAuAq4ANAL/CHkqZKmgp8HbgEuAC4OvWlwTnMzKwFsr4yeV6dz4TTlKPqtdQ8LX2C6ovK/jzF+4Er0/bq1Cbtv1iSUvzOiHgrIn4IVKi+9OwioBIRT0XE28CdwOp0zHjnMGsbZ5xxRsO2WZllfdDyXZL+s6RNqT1fUqblY9JI42HgBWAH1SnPP4mIkceTB4HZaXs28AxA2v8y0FkbH3PMePHOBucwaxtvvfVWw7ZZmWW9RPbHwNvAL6b2IPDbWQ6MiCMR8UGgi+qI4xfqdUvfGmffyYofJ91bGpA0cOjQoXpdzMzsBGQtMO+PiN8F/h4gIn5K/b/ExxURPwH2AouAsyWNzGDrAp5L24PAHIC0/z3A4dr4mGPGi7/Y4Bxj89oUET0R0TNz5sxm/khmZtZA1gLztqTppFGApPdT88DleCTNlHR22p4OLAceB/YAv5y69QH3pO2tqU3avzsiIsWvSrPM5gHzge8DDwHz04yx06lOBNiajhnvHGZm1gJZn4NZD2wD5kj6FvBR4N9kOO48oD/N9poC3BURfynpMeBOSb8N/A1wR+p/B/AnkipURy5XAUTEo5LuAh6j+kbNayPiCICkTwPbganA5oh4NP3W58c5h5mZtYCq/9hv0KE6I6uL6lpki6heGnswIl7MP73W6unpiYEBLxJt5bFkyZLjYl4uxspG0r6I6Bkbn3AEExEh6S8iYiFwXy7ZmVldfh+MtbOs92AelPThXDMxs+P4fTDWzrIWmKVUi8zfSdov6YCk/XkmZmbQ19dH9So1SGLNmjUFZ2SWXdbx9iW5ZmFmdXV2drJ48WL27t3L4sWL6ezsLDols8waFhhJ04B/B3RTXar/jpqn482sBZ544olR32btYqJLZP1AD9Xicgnwe7lnZGbHVCoVDh48CMDBgwepVCoFZ2SW3UQF5oKI+PWI2Ej1ocV/0YKczCzZsGFDw7ZZmU1UYP5+ZMOXxsxab3BwsGHbrMwmusm/QNIraVvA9NQW1Udkzso1O7NTnCRqH4YemVFm1g4ajmAiYmpEnJU+746IjprtY8VF0jn5p2p26lm8eHHDtlmZZX0OZiK7TtLvmFmNyy8f/dqlVatWFZSJWfNOVoHxuN0sB1/+8pcbts3K7GQVmMYrZprZCRkaGhrVfvHFSbfGrE1iJ6vAmJmZjeJLZGZmlouJloqZ0Wh/RBxOmxeftIzMzGxSmOg5mH1U76/UG6EE8D4YVWjMzMyACQpMRMxrVSJmZja5ZH49XnqYcj4wbSQWEX+dR1JmZtb+MhUYSf8WWAd0AQ8Di4DvAsvyS83MzNpZ1llk64APAz+KiKXAPwMO5ZaVmZm1vawF5s2IeBNA0hkR8QPgH+eXlpmZtbus92AGJZ0N/AWwQ9JLwHP5pWVmZu0uU4GJiI+nzQ2S9gDvAbbllpWZAccv1z9lihffsPaR9Sb/+TXNH6bvfwj8+KRnZGbH1BYXgKNHjxaUiVnzsl4iu493HricBswDngA+kFNeZmbW5rJeIvsntW1JHwJ+I5eMzMxsUjihC7oR8X+pTls2MzOrK1OBkfTZms/nJP0pGZ6DkTRH0h5Jj0t6VNK6FJ8haYekJ9P3OSkuSbdJqkjan0ZKI7/Vl/o/KamvJr5Q0oF0zG1KLy0f7xxmZtYaWUcw7675nEH1nszqDMcNA/8xIn6B6tP/10q6ALge2BUR86m+bvn61P8SqsvRzAfWArfDsVWd1wMfAS4C1tcUjNtT35HjelN8vHOYmVkLZL0Hc9OJ/HhEHAQOpu1XJT0OzKZanJakbv3AXuDzKb4lqlNnHpR0tqTzUt8dI6s2S9oB9EraC5wVEd9N8S3AlcB3GpzDrG1MmzaNN998c1TbrF1knab8c8DngLm1x0RE5rXIJM2lusTM94BZqfgQEQclvTd1mw08U3PYYIo1ig/WidPgHGZto7a41GublVnWacr/DfgvwB8BR5o9iaQzgf8O/IeIeCXdJqnbtU6s0ftoxos3k9taqpfYOP/88yfobWZmWWUtMMMRcfuJnEDSaVSLy7ci4n+k8POSzksji/OAF1J8EJhTc3gX1SVpBnnnctdIfG+Kd9Xp3+gco0TEJmATQE9PT1PFyczMxpf1Jv+9kv69pPPS7KwZE71OGaqzwoA7gMcj4vdrdm0FRmaC9QH31MTXpNlki4CX02Wu7cBKSeekm/srge1p36uSFqVzrRnzW/XOYWZmLZB1BDPyF/Vv1cSOvTK5gY8C/xo4IOnhFLsB+Apwl6RrqC4384m0737gUqACvAF8EqqvZJb0JeCh1O+LNa9p/hTwDWA61Zv730nx8c5hZmYtkHUW2Qm9Ojki/hf175MAXFynfwDXjvNbm4HNdeIDwIV14kP1zmFmZq3RzCuTf5HjZ5FtySEnMzObBLJOU/4T4P1UX5c8MossABcYMzOrK+sIpge4IMauHW5mZjaOrLPIHqH6/hczM7NMso5gzgUek/R94K0Ui4jIsh6ZmZmdgrIWmA012wL+OXD1Sc/GzMwmjUyXyCLir4CXgcuoPnNyMdWlY8zMzOpqOIJJi1xeRXW0MgR8G1BELG1BbmZm1sYmukT2A+B/AqsiogIg6Tdzz8rMzNreRJfI/iXw/4A9kv6rpIsZ/8l8MzOzYxoWmIi4OyJ+Bfh5qqsX/yYwS9Ltkla2ID8zM2tTWW/yvx4R34qIy6kuif8wfgWxmZk1kPVBy2Mi4nBEbGzmbZZmZnbqabrAmJmZZeECY2ZmuXCBMTOzXLjAmJlZLlxgzMwsFy4wZmaWCxcYMzPLhQuMmZnlwgXGzMxy4QJjZma5cIExM7NcuMCYmVkuXGDMzCwXLjBmZpYLFxgzM8tFrgVG0mZJL0h6pCY2Q9IOSU+m73NSXJJuk1SRtF/Sh2qO6Uv9n5TUVxNfKOlAOuY2SWp0DjMza528RzDfAHrHxK4HdkXEfGAX77wZ8xJgfvqsBW6HarEA1gMfAS4C1tcUjNtT35Hjeic4h5mZtUiuBSYi/ho4PCa8GuhP2/3AlTXxLVH1IHC2pPOAjwE70ps0XwJ2AL1p31kR8d2ICGDLmN+qdw4zM2uRIu7BzIqIgwDp+70pPht4pqbfYIo1ig/WiTc6h5mZtUiZbvKrTixOIN7cSaW1kgYkDRw6dKjZw83MbBxFFJjn0+Ut0vcLKT4IzKnp1wU8N0G8q0680TmOExGbIqInInpmzpx5wn8oMzMbrYgCsxUYmQnWB9xTE1+TZpMtAl5Ol7e2AyslnZNu7q8Etqd9r0palGaPrRnzW/XOYWZmLdKR549L+jNgCXCupEGqs8G+Atwl6Rrgx8AnUvf7gUuBCvAG8EmAiDgs6UvAQ6nfFyNiZOLAp6jOVJsOfCd9aHAOMzNrkVwLTERcPc6ui+v0DeDacX5nM7C5TnwAuLBOfKjeOczMrHXKdJPfzMwmERcYMzPLhQuMmZnlwgXGzMxy4QJjZma5cIExM7NcuMCYmVkuXGDMzCwXuT5oaXaivva1r1GpVIpOo5TWrVtXdAqF6u7u5jOf+UzRaVgGHsGYmVkuVF2hxQB6enpiYGCg6DTMjlmyZMlxsb1797Y8D7NGJO2LiJ6xcY9gzMwsFy4wZiU2drTi0Yu1ExcYMzPLhQuMWcktWLCABQsWePRibccFxszMcuHnYErEz35YPSP/TZzqz7/Y8cr+TJALTIlUKhUefuRxjrxrRtGpWIlMebv6KMG+p54vOBMrk6lvHJ64U8FcYErmyLtm8NOfv7ToNMys5Kb/4P6iU5iQC0yJPPvss0x94+W2+A/HzIo19Y0hnn12uOg0GvJNfjMzy4ULTInMnj0bUNFpWMlMefMVprz5StFpWOko/Z1RXr5EViLd3d1Fp2AlVKm8CkD3+2YVnImVy6zS/53hAlMiZZ5uaMUZmZ586623FpyJWXN8iczMzHLhAmNmZrlwgTEzs1y4wJiZWS4mdYGR1CvpCUkVSdcXnY+Z2alk0s4ikzQV+DqwAhgEHpK0NSIeKzYzy8ILf77Di12OVvYFHu0dk3kEcxFQiYinIuJt4E5gdcE5mTVt+vTpTJ8+veg0zJo2aUcwwGzgmZr2IPCRsZ0krQXWApx//vmtycwm5H+hmrW/yTyCqbfmShwXiNgUET0R0TNz5swWpGVmdmqYzAVmEJhT0+4CnisoFzOzU85kLjAPAfMlzZN0OnAVsLXgnMzMThmT9h5MRAxL+jSwHZgKbI6IRwtOy8zslDFpCwxARNwP+O1dZmYFmMyXyMzMrEAuMGZmlgsXGDMzy4Uijns05JQl6RDwo6LzMKvjXODFopMwG8c/iojjHiR0gTFrA5IGIqKn6DzMmuFLZGZmlgsXGDMzy4ULjFl72FR0AmbN8j0YMzPLhUcwZmaWCxcYMzPLhQuMmZnlwgXGzMxy4QJjZma5+P++ZNlcx3VqNgAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.boxplot(y=train.Annual_Premium)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Skewness= -1.470648201844564\n",
      "Kurtosis= 0.6573470323138011\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x28b8ae79d08>"
      ]
     },
     "execution_count": 26,
     "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": [
    "log_trans=np.log(train.Annual_Premium)\n",
    "print(\"Skewness= \"+str(log_trans.skew()))\n",
    "print(\"Kurtosis= \"+str(log_trans.kurt()))\n",
    "\n",
    "\n",
    "sns.kdeplot(log_trans)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Follows Lognormal distribution**"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Vintage"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "count    381109.000000\n",
       "mean        154.347397\n",
       "std          83.671304\n",
       "min          10.000000\n",
       "25%          82.000000\n",
       "50%         154.000000\n",
       "75%         227.000000\n",
       "max         299.000000\n",
       "Name: Vintage, dtype: float64"
      ]
     },
     "execution_count": 27,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "train.Vintage.describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x28b8ae83088>"
      ]
     },
     "execution_count": 28,
     "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.boxplot(train.Vintage)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x28b8af517c8>"
      ]
     },
     "execution_count": 29,
     "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.distplot(train.Vintage)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Uniformly Distributed, we have new as well as old customers**"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Analysis"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {},
   "outputs": [],
   "source": [
    "def categorical_biv(data,tar,cat):\n",
    "    \n",
    "    data=data[[cat,tar]]\n",
    "    \n",
    "    \n",
    "    print(\"Contingency Table:-\")\n",
    "    table=pd.crosstab(data[tar],data[cat])\n",
    "    print(table)\n",
    "    \n",
    "    obs_freq= np.array([table.iloc[:,0].values, table.iloc[:,1].values])\n",
    "    \n",
    "    from scipy.stats import chi2_contingency\n",
    "    \n",
    "    chi,p,dof,expected = chi2_contingency(obs_freq)\n",
    "    \n",
    "    if p<0.05:\n",
    "        sig=True\n",
    "    else:\n",
    "        sig=False\n",
    "    \n",
    "    fig,ax=plt.subplots(1,2,figsize=(14,7))\n",
    "    \n",
    "    # Grouped Countplot\n",
    "    sns.set(palette='RdBu')\n",
    "    sns.countplot(x=cat, hue=tar, data=data,ax=ax[0])\n",
    "    ax[0].legend(loc='upper right')\n",
    "    fig.suptitle(f\"p-value = {round(p,3)}\\n difference significant? = {sig}\\n\")\n",
    "    \n",
    "    ax1 = data.groupby(cat)[tar].value_counts(normalize=True).unstack()\n",
    "    ax1.plot(kind='bar',ax=ax[1], stacked='True')\n",
    "    \n",
    "    ax[1].legend(loc='upper right')\n",
    "    plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Contingency Table:-\n",
      "Gender    Female    Male\n",
      "Response                \n",
      "0         156835  177564\n",
      "1          18185   28525\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1008x504 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "categorical_biv(train,\"Response\",\"Gender\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Contingency Table:-\n",
      "Driving_License    0       1\n",
      "Response                    \n",
      "0                771  333628\n",
      "1                 41   46669\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1008x504 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "categorical_biv(train,\"Response\",\"Driving_License\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Contingency Table:-\n",
      "Previously_Insured       0       1\n",
      "Response                          \n",
      "0                   159929  174470\n",
      "1                    46552     158\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1008x504 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "categorical_biv(train,\"Response\",\"Previously_Insured\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Contingency Table:-\n",
      "Vehicle_Age  1-2 Year  < 1 Year  > 2 Years\n",
      "Response                                  \n",
      "0              165510    157584      11305\n",
      "1               34806      7202       4702\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1008x504 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "categorical_biv(train,\"Response\",\"Vehicle_Age\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Contingency Table:-\n",
      "Vehicle_Damage      No     Yes\n",
      "Response                      \n",
      "0               187714  146685\n",
      "1                  982   45728\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1008x504 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "categorical_biv(train,\"Response\",\"Vehicle_Damage\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 53,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Contingency Table:-\n",
      "Region_Code    0    1     2     3     4     5     6     7      8     9   ...  \\\n",
      "Response                                                                 ...   \n",
      "0            1847  899  3751  8070  1516  1131  5841  2847  30620  2848  ...   \n",
      "1             174  109   287  1181   285   148   439   432   3257   253  ...   \n",
      "\n",
      "Region_Code    43   44    45     46    47    48    49    50   51   52  \n",
      "Response                                                               \n",
      "0            2395  775  4990  17717  6784  4204  1695  9601  155  234  \n",
      "1             244   33   615   2032   652   477   137   642   28   33  \n",
      "\n",
      "[2 rows x 53 columns]\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1008x504 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "categorical_biv(train,\"Response\",\"Region_Code\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 54,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Contingency Table:-\n",
      "Policy_Sales_Channel   1    2    3    4    6     7     8    9    10    11   \\\n",
      "Response                                                                     \n",
      "0                     1039    3  364  407    3  1416  1390  152  214  1095   \n",
      "1                       35    1  159  102    0   182   125   17   50   108   \n",
      "\n",
      "Policy_Sales_Channel  ...     152  153   154  155   156   157  158  159  \\\n",
      "Response              ...                                                 \n",
      "0                     ...  130926  571  4519  839  8364  4890  357   50   \n",
      "1                     ...    3858   36  1474  395  2297  1794  135    1   \n",
      "\n",
      "Policy_Sales_Channel    160   163  \n",
      "Response                           \n",
      "0                     21304  2013  \n",
      "1                       475   880  \n",
      "\n",
      "[2 rows x 155 columns]\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1008x504 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "categorical_biv(train,\"Response\",\"Policy_Sales_Channel\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Takeaways-**\n",
    "\n",
    "- Males are indeed more likely to get a vehicle insurance\n",
    "- People who had a DL before are more likely to get it again\n",
    "- People who don't have DL before, are more likely to get one\n",
    "- People with >2 years old Vehicle are more likely to get a car insurance\n",
    "- People with previously Damaged Vehicles are more likely to get a car insurance\n",
    "- Region Code doesn't have much effect on Response\n",
    "- Policy Sales Channel doesn't have much effect on Resonse"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "metadata": {},
   "outputs": [],
   "source": [
    "def numerical_Response(var,data):\n",
    "    \n",
    "    \n",
    "    fig,ax=plt.subplots(1,2,figsize=(14,7))\n",
    "    \n",
    "    sns.barplot(y=var,x=\"Response\",data=train,ax=ax[0])\n",
    "    sns.violinplot(x=\"Response\", y=var,\n",
    "                    data=train,ax=ax[1]) \n",
    "    \n",
    "    \n",
    "    \n",
    "    x1=data[data.Response==1][var]\n",
    "    x2=data[data.Response==0][var]\n",
    "    \n",
    "    xm1=x1.mean()\n",
    "    xm2=x2.mean()\n",
    "    sd1=x1.std()\n",
    "    sd2=x2.std()\n",
    "    n1=len(x1)\n",
    "    n2=len(x2)\n",
    "\n",
    "    sd= (sd1*sd1/n1)+(sd2*sd2/n2)\n",
    "\n",
    "    t_stat=(xm2-xm1)/sd**0.5\n",
    "\n",
    "    from scipy.stats import t\n",
    "\n",
    "    p_val=round(2*(1-t.cdf(abs(t_stat),n1+n2-2)),5)\n",
    "    \n",
    "    print(\"P-Value is \"+str(p_val))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 50,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "P-Value is 0.0\n"
     ]
    },
    {
     "data": {
      "image/png": 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AAOAqmXfBp1J+TNPU4OCgZZmA6Xqv/Fx55ofyYx/KDwAAcJVYbHQJW2CSBx5kSlKcpW9woXg8JsMwFPAVfOhrQW+BPIbBz66NKD8AAMBV4vHRd8H9k3y8f6wkZUoT4CaxWEyFfq88xodv2WsYhkJ+Hz+7NqL8AAAAVxmf+Znk49+b+WHpENwnHo+q8DJL3jIK/V5+dm1E+QEAAK4ynT0/khSN8u453CcWi6nwMkveMgp9Bcz82IjyAwAAXCWz+Xvq5Yd7pcB9YrGoCn0fPukto9DvVYwDD2xD+QEAAK4SjUblN4xJv0jJ3A+IE7PgRvFYVIWXOeY6o9Dv5T4/NqL8AAAAV4lGIwrqw5vDr6RAks8wFI2GrQsFTFM0FlNh4Mrlp8jvo/zYiPIDAABcJRweUNCc2g1LQ4ahSIRlb3CXdDqtWCym4qsceFDk9yoWj3OTXptQfgAAgKuEB/oVmuQ9fjKCaVPh8IA1gYBpisfjMk1z/Gaml1Mc8Mk0TcXjcRuT5a8rz8EBADCLPfnkk/qnf/qn8Y+bm5t1991364477tBjjz2mZDKpO++8Uw8++KCDKfNTODyg+VMtP6ap8EC/NYGAacosxbzqzM9YMYpGwyouLrYlVz6j/AAA8tJnP/tZffazn5UknTt3Tvfff7++9KUv6fOf/7yeeOIJzZ8/X1/+8pd15MgRbd++3eG0+SOdTisajSo0xe8LmVJ7mD0/cJfMUsyS4JXLT8lY+YlEIpo3z5ZYeY1lbwCAvPdf/+t/1YMPPqimpiYtXbpUixcvltfr1e7du3Xo0CGn4+WVeDymtGlOvfxIikQjMs0pThkBFsosxSwJ+q/4mMzXWLZpD2Z+AAB57ejRo0okErrzzjt18OBBVVdXj3+tpqZGHR0dU7peVRXLVmZicLBP0nvHV09WUKOzRqGQoZKSkqznAqbDNIckTTDzM/Y10xxSdTU/u1aj/AAA8trPfvYz/eVf/qWk0RfPhvHeEcumaV7y8WT09ESVTjP7MF0NDW2SNOUDDzIzRRcvtmr+/AXZDQVMU0vL6JsnV9vzk1n21tzcrq4uTiycKY/HuOqbUCx7AwDkraGhIb3xxhu6/fbbJUnz5s1TV1fX+Ne7urpUU1PjVLy8lLnfyVRnfgLmpd8PuMHAQL+KAn55C678krvA41FRwM+yN5tQfgAAeeudd97RNddco8LCQknSxo0bdfHiRTU0NCiVSungwYPatm2bwynzSzQ6Wl4CU/y+TFnKfD/gBgMDAyq9ypK3jNKgTwMDlB87sOwNAJC3mpqaNO99xysFAgF997vf1QMPPKBkMqnt27dr586dDibMP7FYTNLUy0/m8cz8wE0GBvpUepV7/GSUBn3q7++zIREoPwCAvHXXXXfprrvuuuRzW7du1YEDBxxKhMHBuAxN/QVK5iwtbhQJN+nv69Py4iuf9JZRFvSrjvJjC5a9AQAA1xgcHJTfMDS1YyakzHvrg4OD2Y4ETItpmhoID6g0NHH5KQ35NRAe4Kh2G1B+AACAayQSg/JNufpIBZIKZCiRoPzAHeLxuIaHh1V2lXv8ZJQF/RoeHmbm0gaUHwAA4Bqj5Wd6fIahRCKR1TzAdA0M9EuSykIT72DLPCbzPbAO5QcAALhGMpmUd5pLf3yGlExSfuAO/f29kqSySSx7yzwm8z2wDuUHAAC4xkzKj9cc/X7ADfr6Rg8wKJ9E+ck8JvM9sA7lBwAAuEZicPrL3rymycwPXCNzdHVpcPLL3ig/1qP8AAAA10gmBuWb5oFXo+WHmR+4Q19fn0J+n4K+ggkfG/AWKOT3qa+PZW9Wo/wAAADXSCST074JoU9SYpDTsuAOfX29k1ryllEe8rPnxwaUHwAA4BpDQ8kZLHuTkpz2Bpfo6+tR+SSOuc4oD/rV29tjYSJIlB8AAOAS5tiytRnN/FB+4BK9PT0qL5x4v09GeWGA8mMDyg8AAHCF4eFhpU1z+vf5kZQcYs8PnDcyMqKB8IAqplB+KgoDCofDGhkZsTAZKD8AAMAVMrM2Mys/QzKneVQ2kC0DA/0yTXPK5cc0zfFT4mANyg8AAHCFzDHV01325tXo0rmhoaGsZQKmI7N8bSrlp3LssT093ZZkwijKDwAAcIXxmZ9pTtxkZow47hpOyxSYyinO/Ehi34/FKD8AAMAVMjM/M1n2JkmJxGBW8gDT1d09Wn4qCoOT/p7MY3t6KD9WovwAAABXmPGen7EZo0yJApzS29utwsDkbnCaEfQVqCjgU09Pl4XJQPkBAACukI09PxLHXcN53d1dU1ryllFZGGTPj8UoPwAAwBUye3VmuuyNPT9wWk93l6qmVX786ulm5sdKlpaf//E//ofuuusu7dq1Sz/60Y8kSUePHtXu3btVW1urvXv3Wjk8AADIIdk46lpi2RucZZqmenp6VFk0+f0+GZVFQXX3dHNcu4WmO7M8oddff12//e1vdeDAAY2MjOiuu+7S1q1b9dBDD+mJJ57Q/Pnz9eUvf1lHjhzR9u3brYoBAAByxEyXvb134AHlB86JxWJKJBPTWvZWVRRUMplULBZTcXGxBelg2czPzTffrB//+Mfyer3q6elRKpVSOBzW0qVLtXjxYnm9Xu3evVuHDh2yKgIAAMgh2Zr5ofzASd1jy9bmFIem/L1VY7NF3Sx9s4yly958Pp9+8IMfaNeuXdq6das6OztVXV09/vWamhp1dHRYGQEAAOSIwcG4/IYhY5rfnyk/g4McdQ3nZIpLVdF0Zn4Cl1wD2WfZsreMr33ta/rSl76kr3zlK6qvr5dhvPdPmmmal3w8GVVVTAHCOdXVJU5HAGaEn2G42eDgoHyGIWl6+x0KNPqubiIRz2YsYEq6ujolaVp7fqqKQmPXYHLAKpaVnwsXLmhoaEjr1q1TKBRSbW2tDh06pIKC98477+rqUk1NzZSu29MTVTo9s01gPPljurq6Ik5HkMTPMKZvpj/DHo/Bm1CwzODg4Pi9eqbDkOQ3PIrHmfmBc7q6OlTo96nIP/UFnIV+rwoDvvECheyzbNlbc3OzHn74YQ0NDWloaEgvvviiPve5z+nixYtqaGhQKpXSwYMHtW3bNqsiAACAHBKPx+Q30zO6hl+jy+cAp3R2dmhO8dRnfTKqi4Lq7GTmxyqWzfxs375dJ06c0Kc//WkVFBSotrZWu3btUmVlpR544AElk0lt375dO3futCoCAADIIYPxmPwzPOHXb5qKxyk/cE5nR7sWT2PJW8acoqCaOtqzmAjvZ+menwceeEAPPPDAJZ/bunWrDhw4YOWwAAAgB8ViMRXPsPz4TFPxWDQ7gYApGhkZUXdPt7asXTTta1SXhPRWS7NGRkbk9Vq+PT/vWHraGwAAwGTFYzFN/XysSwUkxSg/cEhXV6fS6bTmlkz9mOuMuSUhpdNplr5ZhPIDAAAcZ5qm4oODMy4/fnN0BglwQnt7myRpbmnhtK+R+d6OjrasZMKlKD8AAMBxiURCaTMt/wyvE5DY8wPHtLW1SpJqZjTzM1p+WltbspIJl6L8AAAAx8Xjo7M12Vj2NjwyrKGhoRlnAqaqpaVJZaHAtI65zij0e1UWClB+LEL5AQAAjotGR/fpBGZ44EGmPGXKFGCnlpYmzS+d/qxPxvzSkFpamrKQCB9E+QEAAI7LHFIw/QOCR2XKD/t+YLd0Oq221hbNLyua8bXmlxWptbVF6fTM7nuFD6P8AAAAx2XKz4z3/IzNHEWjkRleCZia7u5OJYeGtDAL5WdhWZGGhobU3d2ZhWR4P8oPAABwXCQyWlZmOvOT+f7MMjrALo2NjZKkheVZKD9j18hcE9lD+QEAAI7LzNRkr/ww8wN7NTU1yJC0IAszPwvKimSMXRPZRfkBAACOi0Yj8huGCmZ4nUz5ycwkAXZpaKhXTWmh/N6Z/hRLfm+BakoL1dBQP/NguATlBwAAOC4SiSggY8bX8UryGoYikfDMQwFT0NBQpyUVxVm73pKKYjU01GXtehhF+QEAAI4Lh8MKmjM853pMyDBY9gZbhcMD6uvry3r56evrUzg8kLVrgvIDAABcIBoZyFr5CaRNlr3BVvX1ozM0SypKsnbNzLUy10Z2UH4AAHnrpZde0p49e3TnnXfq29/+tiTp6NGj2r17t2pra7V3716HE+aP0Zmf7FwraJqKhPuzczFgEi5cOC/DMLSkMoszP5XFMgxDFy6cz9o1QfkBAOSppqYm/c3f/I1++MMf6sCBAzp9+rSOHDmihx56SD/84Q/13HPP6eTJkzpy5IjTUfNCLBad8UlvGUGTAw9grwsXzmlBWZGCPm/Wrhn0ebWgrEjnz7+btWuC8gMAyFO//OUvddddd2nevHny+Xzau3evQqGQli5dqsWLF8vr9Wr37t06dOiQ01FnvaGhIQ0ND2et/ATEUdewTzqdVt2Fc1pWlb0lbxnLqkpUV3de6XQ669fOV9mrpwAA5JCGhgb5fD595StfUVtbmz7+8Y9r1apVqq6uHn9MTU2NOjo6pnTdqqrsLXvJF93d3ZJGS0s2BCUlkklVVITk9fJSB9aqr69XfHBQy6uWZP3ay+eU6pULbRoc7NM111yT9evnI/5FAADkpVQqpWPHjumJJ55QYWGhvvrVryoYDMow3jtu2TTNSz6ejJ6eqNLpLG1eyRNNTe2SpECW/toyJaq+vk1lZeXZuShwBa+9dkyStKqmLOvXXl1dPj5GUVFV1q8/G3k8xlXfhGLZGwAgL82ZM0dbt25VZWWlgsGg7rjjDh09elRdXV3jj+nq6lJNTY2DKfNDLBaTlL2Zn8x1MtcFrPTOO6dVURRUVVG2Fm6+p7IooIqioN5553TWr52vKD8AgLx022236ZVXXlE4HFYqldLLL7+snTt36uLFi2poaFAqldLBgwe1bds2p6POelkvP2MzSPE45QfWMk1T75w9rVVzSqc8SzwZhmFo1ZxSvXP2tMwsHQWf71j2BgDISxs3btR/+A//QV/4whc0PDysj370o/r85z+v5cuX64EHHlAymdT27du1c+dOp6POepmS4s/S9Zj5gV1aWpoVjkS0Zt18y8ZYM7dCrzd0qqWlWYsWLbZsnHxB+QEA5K177rlH99xzzyWf27p1qw4cOOBQovyUKT/ZmvnJlChmfmC1M2dOSpLW1FRYNsaaueXjY1F+Zo5lbwAAwFHxeFxS9mZ+3is/8SxdEbi8U6dOqqo4pKri7O/3yagqCqq6pFCnTp20bIx8QvkBAACOisdjChiGsrVjIjODxMwPrJRKpfTO2VNaa8Epbx+0prpM75w9pVQqZflYsx3lBwAAOCoWi8mfxc3iBZK8hsGeH1iqvr5Og4mE1sy1bslbxpp55RpMJFRfX2f5WLMd5QcAADgqFospkOV7IwVkMPMDS50+PbbfZ67195JaU1N+yZiYPsoPAABwVCwakT/Lx/j6ZSoWi2b1msD7nTl9UgvLi1UazNZutSsrCfq1sLxYZyg/M0b5AQAAjopFo1k76S0jkDYVjVJ+YI3h4WGdO/+uVtuw3ydjdU2Zzp1/V8PDw7aNORtRfgAAgKMikbCCWb5/Y1BSJDyQ3YsCY+rqzmt4eHh8OZod1tSUa3h4WHV1520bczai/AAAAMek02nF4jFl+6DgoClFwuEsXxUYdfr0SRmSVto487OqplyGYbDvZ4YoPwAAwDHRaFSmlP3yIykWjymdTmf5yoB09swpLaksUZHfZ9uYhX6vllQU6+yZU7aNORtRfgAAgGMGBvolSaEsXzckKW2aikSY/UF2JZNJnb9wztb9Phmra8p0/sI5JZNJ28eeLSg/AADAMX19PZKk4izv+SkyM9fvy+6FkffOnXtHqVTKlvv7fNCauRVKpVI6d+6s7WPPFpQfAADgmN7eXklSUZavWzz2a6ZcAdly8uTb8no8WjHH/pmfldVl8no8OnnyhO1jzxaUHwAA4Jje3h4ZkgqzfN1Mmerp6c7ylZHvTrz9llZWlynoK7B97IC3QCury3Ti7bdsH3u2oPwAAADHdHS0q8TwZP0FSUiS1zDU0dGR5Ssjn3V3d6m1rVXr59u/5C1j/fwKtba1qru7y7EMuYzyAwAAHNPe1qoSC05kMySVmVJHR1vWr4389fvfvylJuiyS85wAACAASURBVG5BlWMZMmO/9dabjmXIZZQfAADgCNM01dHRprIsH3aQUZo21d7aYs3FkZd+97ujml9WpHml2V6oOXnzSgu1oLxYr79+1LEMuYzyAwAAHNHf36dEMqlyi65fLqmrp1tDQ0MWjYB80tvbo3Pn3tWNi6udjqIbFs/RuXPvqreXAz2mivIDAAAc0dTUIEmqtGjmp9IcnV1qZfYHWfD667+VJN2wxPnyc+NYhkwmTB7lBwAAOKKpqVGSVGnR9TPXbW5utGgE5AvTNPXyy7/W0spSzXVwyVtGTUmhrqkq1csv/1qmadG7B7MU5QcAADiiqalRxYZHAYuuX6rRE98yM0zAdNXVnVdLS7M+tmKe01HGfXT5PLW0NKuu7rzTUXIK5QcAADiiob5OVRac9JbhkVRhSg31Fy0bA/nhyJFfK+AtGF9u5gY3LqlWwFugI0decjpKTqH8AAAA2yWTCbW3t6nK4hU7c9KmGhousjQI0xaPx/W7376iGxZXK+jzOh1nXNDn1Q2Lq/W7376qeDzudJycQfkBAAC2a2xskClpjsWdpErSYCKhri5udorpefXV3yg5NKRtqxY4HeVDtq1aoOTQkF599TdOR8kZE5afWCymRx99VH/+53+u/v5+PfLII4rFYnZkAwBgQjxP5abGxtF9OFbfKjJTrhoa2PeDqTNNUy+9+LyuqSrV0soSp+N8yNLKEl1TVaqXXnye2c1JmrD8fPvb31Zpaal6enoUCAQUjUb1yCOP2JENAIAJ8TyVmxoa6hU0PCqyeJwKSYakxsZ6i0fCbHT27Gm1tbdp28r5Tke5ou0r56utvU1nzpx0OkpOmLD8nDlzRg8++KC8Xq9CoZAef/xxnTlzxo5sAABMiOep3NTYcFGV6bQMi8fxSqowDMoPpuXFF59XUcCnG5fUOB3lim5YUqOigE8vvfRLp6PkhAnLj8dz6UNSqdSHPgcAgFN4nso9qVRKzc1Nlh92kFGZMtVQX2fPYJg1+vp6dfz4Mf3RsrnyFbj33xRfgUd/tGyujh8/pr6+XqfjuN6E/0/edNNN+tu//VslEgm9/PLLeuCBB/SRj3zEjmwAAEyI56nc09HRrpHUiGU3N/2gSkn9AwOKxaI2jYjZ4PDhF2Wm07p1pfsOOvigW1cukJlO6/DhF52O4noTlp+vf/3rKiwsVElJifbu3as1a9boG9/4hh3ZAACYEM9Tuae5uUmSVGnXzI956bjAREZGRnT48K907fxKVReHnI4zoerikK6dX6nDh3+lkZERp+O42oSHlft8Pt1///26//777cgDAMCU8DyVe1pammRIKrdpvIqxX5uaGrVmzTqbRkUue+utNzUwMKAvXL/e6SiTtm3lfP39y6f01ltv6qabmP2+kgnLz+233y7DeG87omEYCoVCWrVqlb75zW+qpsa9G8AAALMfz1O5p6WlSaWGR16lbRmvSJLfMNTa2mLLeMh9L730giqLgrpuvtWHsWfPdfOrVFkU1EsvvUD5uYoJy88dd9yhWCymP/uzP5PH49FTTz2lWCymNWvW6JFHHtH/+l//y46cAABcFs9Tuae1pVnlaXuKjzR61HW5KbW1UX4wsdbWFp05c0qf2nCNPB6rzyPMHo/H0MeWz9OBP5xSa2uLFixY6HQkV5pwz8+xY8f0ne98R9dee63Wrl2rhx9+WOfOndNf/MVfqKWFf0QAAM7ieSq3pFIpdXR0qNzm+zGWp021sucHk/DSS7+U1+PRR1e4994+V/LRFfPl9Xg49voqJiw/sVhM0eh7p6NEo1ENDg5aGgoAgMnieSq3dHV1KpVO2bbfJ6Nc0kAkrHg8bvPIyCWDg4N65ZXD2rJ4jkqDfqfjTFlp0K8ti+folVcO8+/gFUy47O0zn/mM/uRP/kQ7d+5UOp3WL3/5S332s5/VE088oeXLl9uREQCAK+J5Kre0tbVKkspsnvnJjNfe3qrly1faOzhyxquv/kaJREIfX527B2N8fPVCvd7QqVdf/Y3uuGOH03FcZ8KZn/vuu0//+T//Z0UiESWTSX31q1/Vs88+qy1btug73/mOHRkBALginqdyS3v7aPlxYuZHeq98AR+UTqf1yxee09LKUl1TWeJ0nGm7prJE11SV6pcvPKe0jXvrcsWkbld7/fXXq7q6Ws8++6weffRR3XrrrVq/fr2Ki4utzgcAwIR4nsodbW1tChkeBWwet0SjBx+0t7fZPDJyxdtvv6WOzg59Ys3CS06QzDWGYej21QvV0dmht99+y+k4rnPVZW91dXX6x3/8Rx04cEALFy5UMpnUSy+9pJKS3G3DAIDZg+ep3NPe1qJSB96NLpBUZniY+cEVPf/8QVUUBbVl8RzLxjBNU/2DSSWGU/rN+VbdumK+JUVry+I5euZEUM8/f1CbN9+Q9evnsivO/Nx33336t//238rn8+nHP/6xDh48qKKiIp5QAACuwPNUbmptbVGFzft9MsrSabW1NDszOFzt4sULOnv2jG5btUAFnkktjJqW35xvU1c0oUhyWD89dk6/OW/NTGSBx6PbVi3Q2bNndPHiBUvGyFVX/H/39OnTWr9+vVatWqWlS5dKUk5PAQIAZheep3JPNBpRNBZVmUPjl5lSe2c7+yDwIQcP7leh36dbLT7e+g+tPVf9OJtuXTFfhX6fDh7cb9kYueiKy94OHz6sF154QT/96U/1ne98Rx//+MeVTCbtzAYAwBVl43nqi1/8onp7e+X1jj4d/rf/9t/U2Niov//7v9fIyIj+/M//XH/2Z39mRfy8lFlyZvc9fjLKNXqfoc7ODs2bl3v3cIE1WltbdPz4G9q5brGCvgkPQp6RoZHUVT/OpqDPq+0r5+vQ8Te46en7XHHmx+v16q677tITTzyhp59+WjU1NUomk6qtrdVPf/pTOzMCAPAhM32eMk1T9fX12r9///j/5s2bp7179+onP/mJfvGLX+hf/uVfdP78eRv+NPmhZWzJWYVD42eW27W1cfNbvOfZZ/fL6/HottWzrxzctnqhvB6PDh78hdNRXGNSixpXrlyphx9+WL/5zW/07//9v9e+ffuszgUAwKRN53mqrq5OkvTv/t2/06c+9Sn90z/9k44ePapbbrlF5eXlKiws1I4dO3To0CGr4+eNlpZmeQ1DTp3BlznuuoV9PxjT3t6mo0df1raV81SSgzc1nUhJ0K9tK+frtdde4aTDMVOa2wuFQvrTP/1T/emf/qlVeQAAmLapPE+Fw2Ft3bpV/+W//BcNDw/r3nvv1Z133qnq6urxx9TU1OjEiRNTylBVxfHaV9Ld1aYKc/TIaSf4JRV7POrubld1NQdjQPrHf/z/5CvwqHbdEqejWKZ23WK9fKFNzz9/QF//+tedjuM4axc2AgDgUps3b9bmzZvHP77nnnv02GOP6atf/er450zTnPIhCj09UaXTDm1qcbm6C3WqcfjvpjyV1oXzderqijiaA85ra2vV4cOH9YnVC1U6C2d9MkqDfm1bMV8vHj6s2trdmj9/gdORLOXxGFd9E8q6s/wAAHCxY8eO6bXXXhv/2DRNLVy4UF1dXeOf6+rqUk1NjRPxZp1oNKKBSFiVDueoNEdf9KZS1m00R254+ul/kb/Aoz9et9jpKJb743WL5S/w6Omn/8XpKI6j/AAA8lIkEtH3v/99JZNJRaNRPfPMM/rbv/1bvfbaa+rt7dXg4KBeeOEFbdu2zemos0Jzc5MkTfseP6akmKQ+SaeN0Y+no0LSSGpEHR3t07wCZoO6uvN6443f6Y41i2b1rE9GadCvO9Ys0htv/E51dfl9iAvlBwCQl2677TZt375dn/70p/WZz3xGn/nMZ3TDDTfowQcf1L333qtPf/rT+uQnP6nrr7/e6aizwnj5meb3nzGksCElDOlVz+jH05EpX5k8yD+maWrfvn9WSdCvO9YucjqObe5Yu0glQb/27ftnmWb+Ls1lzw8AIG/91V/9lf7qr/7qks/t3r1bu3fvdijR7NXc3KSAYahomnM2jcaHP752Gpeq0OiBCy0tTZJumVYW5LYTJ36vs2fP6E+2rLT8vj5uEvR5dee1S7Tv+Bm9/fZb2rRpi9ORHMHMDwAAsFxzU4Mq0ua0T3obmeDjyfJKKjUMNTU1TvMKyGUjIyP62U9/rJqSQt26Iv9udHvrivmqKSnUv/zsCY2MTPe/otxG+QEAAJYyTVPNzY3T3u+TbZVpU82N9U7HgAMOH/6V2trbtGfTMnkL8u9lsLfAoz2blqmtvU2HD//K6TiOyL//1wEAgK16erqVSCYdP+kto8KUurq7lEwmnI4CG8ViUf3imSe1Zm65rl9Q5XQcx1y/oEpr5pbrmWeeVCwWdTqO7SwtP//zf/5P7dq1S7t27dL3v/99SdLRo0e1e/du1dbWau/evVYODwAAXCBzuEClW2Z+zNHT4lpbW5yOAhsdOPCMYrGY7tm0Ysr375pNDMPQPZtWKB6L6cCBZ5yOYzvLys/Ro0f1yiuv6JlnntEvfvELnTp1SgcPHtRDDz2kH/7wh3ruued08uRJHTlyxKoIAADABWZ60lu2ZXJw4lv+aG9v069+dUhbl8/Tooor3wAzXyyqKNbW5fP0q18dUnt7m9NxbGVZ+amurtY3v/lN+f1++Xw+rVixQvX19Vq6dKkWL14sr9er3bt369ChQ1ZFAAAALtDc3Khiw6OA00HGlEoqMAw1N3PoQb7Yt++f5fUY+tSGZU5HcY1PbVgmr8fQvn3/7HQUW1lWflatWqVNmzZJkurr6/Wv//qvMgxD1dXV44+pqalRR0eHVREAAIALNDU2qCKddjrGOI9G9/1w4lt+ePfdszp+/Jh2rF2kstDsv6HpZJWF/NqxdpGOHz+md98963Qc21h+uPm5c+f05S9/Wd/4xjdUUFCg+vr68a+ZpjnlNZdVVUxVwjnV1SVORwBmhJ9h2G1kZETt7W1a75L9PhkVaVPNlJ9ZzzRNPfnkT1QWCugTa/LnhqaT9Yk1i3TkfJuefPIneuihR/NiL5Sl5efNN9/U1772NT300EPatWuXXn/9dXV1dY1/vaurSzU1NVO6Zk9PVOn0zP4F5ckf09XVFXE6giR+hjF9M/0Z9ngM3oTClHR0tCmVTrnmpLeMSknnImGFw2GVlpY6HQcWOXHi9zp37l197oZV8nsLnI7jOn5vge68dol+9ua7OnHi99q4cbPTkSxn2bK3trY23X///Xr88ce1a9cuSdLGjRt18eJFNTQ0KJVK6eDBg9q2bZtVEQAAgMMyS8uqXDbzkzl5jn0/s5dpmvr5z3+mOcUhfXT5PKfjuNZHl8/TnOKQfv7UT2WaLvsP1QKWzfz8wz/8g5LJpL773e+Of+5zn/ucvvvd7+qBBx5QMpnU9u3btXPnTqsiAAAAhzU1NcojqczpIB+QmYlqamrUtdde52gWWOPUqRNqbGzQF29enZc3NJ0sb4FHd167RE+8/o5OnTqh667b6HQkS1lWfh5++GE9/PDDl/3agQMHrBoWAAC4SFNTgypkqEDueke5UFLI8DDzM4s99+wBlYUCumnpXKejuN5NS2t04A/1eu7ZA7O+/FCDAQCAZUZPenNX8cmoSKfV2FDvdAxYoKHhok6fOaXbVy+Qj1mfCfkKPLp99QKdPnNK9fUXnY5jKX4aAACAJWKxmPr6+1xzc9MPqjSl1tYWpV10DDey49e/flG+ggJ9bMUCp6PkjFtXLJDfW6DDh190OoqlKD8AAMASra3Nkt47XMBtKiQNjwyru7vT6SjIouHhYb3x+lFtWlSlQr/ld3WZNUJ+rzYurNIbrx/V8PCw03EsQ/kBAACWaG5ukiRXz/xIUlNTk7NBkFVvv/2WYvG4brmGvT5Tdcs1cxWLx/X22285HcUylB8AAGCJ9vZWeQ1Dbr0zVPnYrx0dbY7mQHb97ndHVRoMaM1ct9Zu91ozt0KloYB+97tXnY5iGcoPAACwRHt7m8okufWe8X5JhYZH7e2Un9nCNE2dOXNS6+aVq8Dj1p889yrwGFo3t1xnzpyatff8ofwAAABLtLW2qNSlJ71llKbTam9rdToGsqS1tVnRaFSra8onfjAua3VNuaLR6PievdmG8gMAALIunU6rp6dHJe7uPio1pc6OdqdjIEvOnj0jSVpV47bb6uaO1WN/d2fOnHY4iTUoPwAAIOui0YhS6ZSKnA4ygUJJ4UiY465niZaWJhX6fZpTFHQ6Ss6qKgqqMOBj5gcAAGCy+vr6JElFLp/5KZKUNk2FwwNOR0EW9PT0qKIwIMNgv890GYahilBAPT09TkexBOUHAABkXX//aPkpdDjHRArHylkmL3Jbb2+3Kgr9TsfIeRWFfvX2djsdwxKUHwAAkHWDg4OSpIDDOSaSyZfJi9wWCYdVGqD8zFRp0D9rZ0MpPwAAIOuSyYQkyetwjolk8iUSCUdzIDu83gKlZukRzXZKpU35vG7/r3d6KD8AACDrMmXC53COiWTyZcoacpvf79dwKuV0jJw3nErJ75+dM2iUHwAAkHWpsRegbn+hkck3MjLiaA5kh88X0NAIJ/fN1NBIWj6f2xetTo/b/00CAAA5yOstkCS5/WVoJp/X6/Y5KkxG1Zw56o4lnY6R87pjSVXNmeN0DEtQfgAAQNZ5x/YLuH0BUiafd5bub8g3ixYtUWckruGUO2v34PCIgsGg7r77bgWDQQ0Ou2/GcTiVVmckrkWLljgdxRKUHwAAkHWZmZTcKT8FjuZAdixatERp01R7OOZ0lMsaHB7Rjh07dN9996m2ttaV5ac9HFPaNGdt+eFtDgAAkHUlJSWSpISkEmejXFXmmIOSklJHcyA7li69RpJU1x3W4gr3/eSFfF49//zzMk1TL7zwguYE3fdSvK47IklasmSpw0mswcwPAADIurKycklS3OEcE4kbo79m8iK31dTMVfWcOTrV5s6b1oZ8XiUSCR04cECJREIhn/vKz+n2XlXPmaO5c+c5HcUSlB8AAJB14+XHcDjIBDLljPIzOxiGoQ3Xb9a7nf2u3ffjZsOptN7p6NeG6zfLMFz+H+80UX4AAEDWlZWVq8BToIjTQSYQkVRaXCKfj9PeZosNGzYpOZLSuc5+p6PknPNd/UqOpLRhwyano1iG8gMAALLO6/Vq7ty56nP5m8f9HkMLF8/Ojd35av36DQoGgzrW2OV0lJzzRkOXgsGg1q/f4HQUy1B+AACAJRYuWqJ+j3tfapiS+gxpwYJFTkdBFvn9ft1440f0VnM3S9+mYDiV1lvN3brxxo/I7/c7Hccy7v0XCQAA5LRFixYrbKY17HSQK4hKGjZNLVxI+Zltbrnlo0oMj+gPrT1OR8kZf2jtUWJ4RLfc8lGno1iK8gMAACyxbNkKSVKnwzmupGNsSd6KFSudDYKsW7duvcrLyvVaXbvTUXLGa3XtKisr07p1652OYinKDwAAsMTKlatl6L2S4TYdkgJ+/6y9mWM+Kygo0K3bbtOptl71xBITf0Oe64kldKqtV9u23a6Cgtl9w1/KDwAAsERhYaEWLFikdreWH4+hFStXz/oXe/lq+/bbJcPQ0bo2p6O43tG6NskwRv/OZjnKDwAgr33ve9/TN7/5TUnSmTNntGfPHu3YsUPf+ta3NDIy4nC63Ld23Xp1eAy57W9yUFKPTK1Zs87pKLDInDnV2rBho16p6+Dgg6sYSaX1Sl2Hrrvues2ZU+10HMtRfgAAeeu1117TM888M/7xX//1X+uRRx7R888/L9M0tW/fPgfTzQ4bNmzUiGnKbTsvmsdmo66/fvbezwRSbe2dCg8m9WajW3eeOe9YY6fCg0nt2HGX01FsQfkBAOSl/v5+7d27V1/5ylckSS0tLUokEtq0afTF8J49e3To0CEnI84K69atl7fAqyaXLX1rklRSXKylS5c5HQUWWr/+ei1csFC/eqdFpmk6Hcd1TNPUr95p0cIFC7V+/fVOx7EF5QcAkJceeeQRPfjggyotLZUkdXZ2qrr6vSUf1dXV6ujocCrerBEIBLR23bVqKjDklpeeaUktBYY2XL9ZHhffhwgzZxiGduz8pFr6ozrb0e90HNc529Gvlv6oduz8pAzDZe9QWMTrdAAAAOz25JNPav78+dq6dauefvppSVI6nb7kyd80zWm9GKiqKs5aztli+/Zb9XcnT6hPUqXTYSS1SUqYpm67bZuqq0ucjgOL7d69U794Zp8OnW7UunkVTsdxlUNnGlVRUa7du3fO6hubvh/lBwCQd5577jl1dXXp7rvv1sDAgOLxuAzDUFdX1/hjuru7VVNTM+Vr9/RElU67ZY7DHVatuk6GpHpDqnTBX029Ifm8Pi1ZslpdXRGn48AGO3bu1k9/+mOd7xrQyuoyp+O4woWuAb3b0a/Pf/5eDQwkJSWdjpQVHo9x1TehmOsFAOSdH/3oRzp48KD279+vr33ta7r99tv12GOPKRAI6M0335Qk7d+/X9u2bXM46exQXl6hFStWqd7j/LIaU1JDgaHrN25SIBBwOg5s8vGP366S4hL966kGp6O4xnOnG1VSXKKPf3z2H2/9fpQfAADGPP7443rssce0c+dOxeNx3XvvvU5HmjVuuvkW9chU2OEcnZJipqkbbviIw0lgp0AgqB07P6nT7X262OP0T6Hz6nvCOt3Wqx07P6lAIOh0HFtRfgAAeW3Pnj367ne/K0lau3atnnrqKR06dEj//b//97xZA2+HG264WZJ00eHJn4uGVFBQoM2btzgbBLb7xCdqVVxUpGdPMvvz7MkGFRcV6ROfqHU6iu0oPwAAwHJz5lRr2dJlji59MyXVFxi67rrrFQoVOpYDzgiFQtp556d0qq1X9Xk8+1PfE9bJtl7tvPNTCoVCTsexHeUHAADY4sabb1GnTEUdGr9HUsQ0deONLHnLV+OzP3m89+fZU/k76yNRfgAAgE02b75Rkhy74WmjIRmSNm5kyVu+CoVCqt2xSydbe9Xc51QNd05zX1QnW3tVu2NXXs76SJQfAABgk/nzF2hO1Rw1OlV+PIaWL185fmNb5KdPfKJWwWBQz59pdDqK7Z4/06hgMJi3sz4S5QcAANjEMAxt3nKjWjyGRmweOy6pS6Y2bb7B5pHhNkVFxbr99lq92dStzkjc6Ti26YwM6s2mbt1+e62KivL3ZsyUHwAAYJvrrtuolGmqw+ZxW8dmmzZs2GjzyHCj2tq75PF4dPhcq9NRbHP4XIs8Ho9qa+9yOoqjKD8AAMA2q1atkWEYard56VubpGAgoCVLrrF3YLhSeXm5brrpFv22vkPJkZTTcSyXHEnpt/UduummW1ReXu50HEdRfgAAgG0KCwu1ZPEStdlcfjoKDK1avVYeDy99MOoTn6jV4NCI3mjodDqK5d5o6NTg0Ehe7/XJ4F8AAABgqzVrr1Wnx1DapvESkvpMU2vWXGvTiMgFK1eu1uJFi/XKhXano1julQvtWrRwsVauXO10FMdRfgAAgK2uuWa5UqapfpvG6xkfd5lNIyIXGIahrX+0TQ29YXVHB52OY5nu6KAaesP6o49uk2E4d5Nht6D8AAAAW2X23fTa9Dqsx7h0XCDjpptGb3h7vKnL4STWyfzZMn/WfEf5AQAAtpo/f4G8BV512zRej6TysjLu74MPqa6u0bJrlut4k10/jfY73tSta65ZpurqGqejuALlBwAA2KqgoEBz587TgE0zPwMeQwsWLrZnMOSczVtuVENvRNHksNNRsi6aHFZDb0RbttzkdBTXoPwAAADbzZ03XxGPPe0nYkg1NfNsGQu5Z9269ZKkdzvt2oVmn8yfKfNnBOUHAAA4oKamRmFTMi0eJykpYZqqqZlr8UjIVcuWrVDA79c7HbOv/LzT0a+A369ly1Y4HcU1KD8AAMB2c+bUKCVTVp+xFR37tbq62uKRkKu8Xq9WrV6rC91hp6Nk3YXusFauWiOv1+t0FNeg/AAAANuVlZVJkuXlJ3P90tIyi0dCLlu+fKVaB2IaGkk5HSVrhkZSah2IacWKVU5HcRXKDwAAsF2mjCQsHidhXDoecDnLlq2QaZpq6otO/OAc0dQXlWmaLHn7AMoPAACwXUnJ6LHTgxafeZCZ+cmMB1zOsmXLJUn1vRGHk2RPw9ifhZv7XoryAwAAbBcKhSRJIxaPkzm8ODMecDnl5RUqKy1Vs8UzP35vwVU/zqamvqjKSktVUVFp2Ri5iPIDAABsl9mAbXX5SUkyDEMFBda9yMTssGTpMjX1xywdY8OCqqt+nE1N/TEtWcqszwdRfgAAgO18Pr8kKW3xOClJPk66wiQsXXqN2sIxDaes+6nctnK+qouDKgn49PkbV2nbyvmWjDOcSqstHNPSpddYcv1cRvkBAAC283hGX4JYXX7S7xsLuJqlS5cpnTbV0m/d0jfDMFQeCmheaaG2rVwgw7Bm01vrQEzptKklS5j5+SD+NQAAALZLpUaPFJ7sC5EhScFgUHfffbeCwaCGJvl9BZJSFr6Tj9lj+fKVkqT6ntw/9OBiz+g9i1asWOlwEveh/AAAANuNjIweRTCV8rNjxw7dd999qq2tnXT58UgaSVm9swizQWVllcpKS2fFiW/1PRGVlZaqstK6PUW5ikWwAADAdiMjo4VksscQ+CU9//zzMk1TL7zwgoom+X0FkkzTVCqV4tADXJVhGFq+YrXqzp92OsqMXeyJaPnKay1bVpfLmPkBAAC2i8VGT9UKmJN7vF9SIpHQgQMHlEgk5J/kOIEPjAdczdq116orEldvzOrb71qnN5ZQZySutWvXOR3FlSg/AADAduFwvyTJ6rvvBMfHG7B4JMwG1157nSTpbEe/w0mm752x7Ndeu8HhJO5E+QEAALYbGBgtI8EJHjdTobGZpUzZAq5m0aLFKi0p0dmOPqejTNvZjj6VlpRo0aLFTkdxJcoPAACwXW9vjyRNeu/OdGWu39PTY/FImA0MC0/V+wAAIABJREFUw9B1GzbpdHufUulJrsl0kVTa1Kn2fq2/biP7fa6A8gMAAGzX0tKsIsMz6b0701Wi0UMPWlqaLR4Js8WWLTcplhzW+a7cmy0839WvWHJIN9xws9NRXIvyAwAAbNfS3KTytPX33/FIKpeh1lbKDyZnw4br5fP59HZL7s0Wvt3SI5/Ppw0brnc6imtRfgAAgK1SqZTa2lpUYdOqovK0qaaGensGQ84LBIJav/56/b65R2kzd5a+pU1Tv2/u0fr11ysQsHo3Xe6i/AAAAFvV1Z3X0PCw5tr0unKupL6BfnV2ttszIHLezTdvVV88oYvdYaejTNrF7rD64gndfPNWp6O4GuUHAADY6uTJEzIkLbBpvIXme+MCk7F58w3y+Xw61tjpdJRJO9bYKZ/Pp82bb3A6iqtRfgAAgK1O/uFtVcuw/JjrjDJJJYZB+cGkhUIhbdy4RcebepTOgVPf0mlTx5t6tHHjZoVCVt89K7dRfgAAgG26ujpVV3dei218QWlIWpwy9YcTbysWi9o2LnLbRz7yRwonknq30/2nvr3b2a9wIqmbb/4jp6O4nuXlJxqN6pOf/KSam0dPWTl69Kh2796t2tpa7d271+rhAQCAixw58pIkabXNb6avMaXhkWEdPfqyvQMjZ23cuFmBQEBv5MDStzcaOxUIBLRp0xano7iepeXn7bff1uc//3nV19dLkhKJhB566CH98Ic/1HPPPaeTJ0/qyJEjVkYAAAAuMTIyot8cflGLTanY5rHnSKqRoV+/+EuZOXSCF5zj9/u1ZctN+n1zj0ZS1h/LPl0jqbR+39yjLVtukt9v9Z2zcp+l5Wffvn36m7/5G9XU1EiSTpw4oaVLl2rx4sXyer3avXu3Dh06ZGUEAADgEq+//luFoxGtc+h15Nq0qdb2Vp069QdnAiDn3HLLRxUfGtap9l6no1zR6fZexYeG9ZH/v717j4+qPPA//jmTyY2EW0ISkkBR7kkgATGQECBclLuXVlSgYrstLtoilq7LWktLC1pp65YV3bXbX9VVi6K1UNEuigIiIUEuQoBA5JZwCSEEAoFAbjPn+f0RmupWq0AyZ5L5vv/KJGSe72sy4eR7nuc8Z4iWvH0VzVp+Hn/8cW688cbGx6dOnSImJqbxcWxsLGVlZc0ZQURE5As99dRTTJw4kUmTJvHCCy8AWp7dXOrq6vjTH1+hExZdHcrQwzRsfPDa8j9g++AGq9LypaT0JzIigm1H/Hfp29Yjp4iMiKBfP93Y9Ktw+3Iw27axLKvxsTHmM4+/iuhoX0+Ui/xNTExbpyOIXBO9h/9my5YtbN68mVWrVuHxeJg4cSKZmZk8+uijvPzyy8THxzNr1iw2bNhAdna203FbvPfeW82ZsxVM8jZsQOAEN5DuNaw7fpRNmz5k+PCRDiWRlsLtdpM+OJOcD9dTU+8lLDjI6UifUVPvJb+kgmEjRuF2+/TP+hbLp69S586dKS8vb3xcXl7euCTuqzpzpuqatxzUwV+uVnn5BacjAHoPy9W71vewy2W1mpNQgwcP5qWXXsLtdlNWVobX6+X8+fONy7OBxuXZKj/XprLyHG+tWkE347t7+3yR7gb2YPHGH18hPT2DsDBfbbgtLVVGRhbr179PfslphlwX53Scz9hVcpp6r5eMjCyno7QYPt3qOi0tjaKiIo4cOYLX6+Xtt99mxIgRvowgIiLSKDg4mKVLlzJp0iQyMzO1PLsZ2LbNc7//LfV1dQz2g5VmFpDhNVSeP88f/vCC03GkBejVqw+dojvxUbH//V+wubiMTtGd6NWrj9NRWgyfzvyEhoayePFiHnzwQWpra8nOzmb8+PG+jCAiIvIZc+bM4b777uP++++nuLhYy7Ob2MqVK9m1eydDbejgdJjL4oCBNuTkbCAjI51Ro0Y5HUn83M1jb2b5q69ScbGGqAj/mC2suFhD4cmzTJ02jbi49k7HaTF8Un7WrVvX+HFmZiarVq3yxbAiIiJf6NChQ9TV1ZGUlER4eDhjx47lnXfeISjob2v6nVqe3VoUFR3iheefp5uBZD97SW4wUAo8vXQpMTFdiIvr7HQk8WMDBw7h1Vdf5aPiMiakdHM6DgBbjpzC0JDNX5bl+4MvW57t02VvIiIi/uL48ePMnz+furo66urqWLt2LVOnTtXy7CZy9mwFzzz9G8KNYYTt3CYHX8QFjPIC9fUsXfokFy9WOR1J/FhsbGf69Ekit6gM2w/uE2Ubw6bDJ+nduw+xsSruV0LlR0REAlJ2djYjR47k9ttv54477mDgwIFMmjSpcXn2xIkT6d69u5ZnX4Wqqiqe/PXjnD9bwU0eg38sEvp7kcBoj6G05DhLfvNLamtrnY4kfmzkyDGcrqrmk7KzTkfhk7KznK6qZuTIm52O0uJoTzwREQlYDz74IA8++OBnPqfl2demtraGJb9ZzMkTJxjnNcR8+bc4qgsNM0BrDx3gP59ZwpyHHtaWwfK5brxxCMv+8AI5h0pJ6hzlaJacQ6VERkRw442DHc3REmnmR0RERJpEbW0tTy/9dw4fPsgoryHR6UBfUXdgmA27du/k//3uP/F4PE5HEj8UHBzMsOEj2VlyhsrqOsdyVFbXsbPkDFnDRhISEuJYjpZK5UdERESuWWXlORY/8XP2FOxmuA3XOx3oCiUZGGzDR1vyWPKbxVy6dMnpSOKHsrPHYNuGvKKTjmXIKzqJbRtGjhzjWIaWTOVHRERErsmJEyUsWjifY0eKuNkLfZy/HvyqpBkYYcO+vXt4/LGfcubMaacjiZ+Jj0+gb99kcg6fdGTjA9sYcg6fpG/fZOLjnb5lcMuk8iMiIiJXrbBwL4sWzudixRkmeQzXOR3oGvUxMN4L5aUlLPz5oxQXFzkdSfzMqFE3caaqmsKTvt/4oPDkWc5UVTNq1E0+H7u1UPkRERGRK+bxeHjzzT/xq189RlhtDbd6DFd2RyT/lQjc4jF4L1zg8cd+ynvvrca2badjiZ+44YZ0wsPC2H6s3Odjbz9WTnhYGDfckO7zsVsLlR8RERG5IiUlx1i0cD4rV/6R6z02t3gM7ZwO1cSigNs8hs719Sxb9iK/+uUiystPOR1L/EBwcDBpAwaRX1KB14c3NPbahvySM6QNGERwcLDPxm1tVH5ERETkK7Ftm//937f46U8foezYEW7ywmgDoU4HayZtgHE2DLfh8P5C5v/4YT744H2MH9zkUpw1aNBgLtbWcbD8nM/GPFh+jou19QwapO2tr4U2shcREZEvdfjwIV5Z9j8cPHSA60zD1tDhTofyAQvoayDRY/jQ1PE///N7tm79iOnT7yUxsavT8cQhqalpWJbF/lPn6BPX0Sdj7j91DsuySE1N88l4rZXKj4iIiHyhY8eOsGLF6+zYsZ0wy2KUDT1MQykIJG2BiV7Ya8G2vXuY/+N/ZUhGFrffPoXOneOdjic+FhoaRnRUNOVVNT4bs7yqhuioaEJDw3w2Zmuk8iMiIiJ/p7T0BCtXvs6WLZsJsSwG2dDPGAL5looWkGKgh8ewy4JtH+Wy5aNcsoaN5Pbb7yA6upPTEcWHYuPiKC875rPxyqtqiI3TbOO1UvkRERGRRuXlp3jzzT+xadOHuIEBNqQa02qv67kaYcBgA/08hp0W5G78gNzcDxk58iYmT76Njh2jnI4oPtCpUyw7iw76bLyKS7V0jY7x2XitlcqPiIhIgLNtmz178lm37j3y83fgAvp5DWkmMK7ruVptgKEGUr2GHbaX9Wvf5YP17zHoxiGMHn0zffokYVmBtkAwcNTUVBPmDvLZeGHuIGprfbfMrrVS+REREQlQ589XsnHjB6xf9x6nz5wm3LJI8xqSDUQ4Ha4FiQSGG0jzQoFtk791M1u25BHfOZ7RY8YydOgIIiL0irY2p0+XE9XGdwtBo9qEcPq07+8t1Nqo/IiIiAQQYwz79xeybt17bNu6Ga9tEw+MtuE6Y/DdeezWpx2QaSDdYzhswb6TJ1m27EVef+0VMjKzGD36Zq6/vofTMaWJnDldTkp0G5+NF9UmjAKVn2um8iMiIhIAKivPsXnzJjZ8sJYTpScIsSz6eg1JBnyzUW/gcAO9DfT2Gk4D++x68nI2sHHjB1zX7XpGZI9i8OChREZGOh1VrtLJk6VUnj9PQg/fXYOT0CGC3KKTnDxZqh0Gr4HKj4iISCtVV1fHzp3bycnZwJ7d+djGEIPFCBt6GKM/AnygEw1L4oZ4DAcsKDxSzEsvPc+yZS8ycMAgsoaNoH//Abjd+mm0JB99lIsF3NDVd+Xnhq4x/GnHIT76KJfbbrvDZ+O2NvpNExERaUWMMRw8uJ+cnA1s+SiX6poaIiyL/l5DLwMdMU5HDEghNGyTnew1nAEO2F4KPt7Ktu1biIyIIHPocLKyRtCt2/XaJMHPGWPYnJdDz5j2dGzju30QO7YJpWdMezbn5XDrrd/Q++QqqfyIiIi0AuXlp9i06UM25Wyg/HQ5bsviOq+ht4F4DC6nAwrQcK+gTkCny7NBx4ADVRdZ9967vPfeOyTEJzBs+EgyM4dpy2w/VVR0mNKTpUy7sZfPx07vFssr2w5QVHSI7t17+nz81kDlR0REpIWybZvdu/NZu/Zddu/aiQESgGwbrjeGYKcDyj/kAroB3WyopWGThAOlpbz++iu88cdXuWFQOmPGjKNv32Sd5fcj7777F8KC3aR3i/X52OndYlmRX8S77/4vDzwwx+fjtwYqPyIiIi1MVVUVGzeuZ93771J+5jRtLIsBNvQx0NbpcHJVQoEkA0lewzmg0DLs3r6Vbdu2kBCfwJibxjN06HDCw3XnJSdVVJxh29bNjOwVT3iw7/+MDgt2k9W9M+u3buauu6YTHd3J5xlaOpUfERGRFqK4+DDvv/8uH23eRL3HQ2e0RXVr1AHIMHCjx3DIgr2lpbz88vP88fVlZA3LZvTosSQmdnE6ZkBau3YNtjGM6uXc6z+qdyLr9pewdu0a7rprumM5WiqVHxERET9XXFzESy/+nsNFhwi2LHpcvhFptNPBpFm5aZjN6+01lAMF1bV8sPY91q5dQ1JSCvfe+13i4xOcjhkwbNsmJ+cD+sVHER0Z5liO6IgwUhOi2LRpA1OmTMXl0hV9V0KvloiIiJ+ybZu//OVNFi38MWXFh8m0YbrHMFzFJ6BYQCwwysA0ryHdhqLCvSz46b+xfv37GKMd/HyhsHAvlZWVDLnO99f6/F/p3eKorKyksLDA6SgtjmZ+RERE/NCZM6f53X8/wyf7C7newDAbnDvXLP4iHBhgoJfHsMHU8+KLvyc/fwff+c4s2rVr53S8Vi0vL4fQYDf9E5w/9dA/IYqwYDd5eZtITu7vdJwWRTM/IiIifmbLljzm//hhDh/4hGwbxqj4/N3Z2kA/exsBTPBChg27d37M/Ef/hd27852O1art3rWT/vEdCXE7f4VdiDuIfvFR7N610+koLY7Kj4iIiB85d+4szz67lLY1tXzD03CfHm1yDF8z//hxILKA/gZu9xqCqqpYuvRJ6urqnI7VKnk8HirPVxLbto3TURrFtQ2n8nwlHo/H6SgtisqPiIiIH6msrMQYQ6rXoEVMf5NkoJ2BMANZdsNjaRAFJNuG+vp6Ll265HScVqmy8hzGGDq2CXU6SqMObUIxxlBZec7pKC2Kyo+IiIgfqampBiDE4Rz+xqJhqVdHIFmzYX/nr++Xv75/pGmdPXsWgPZh/vOb2T68IcvZsxUOJ2lZVH5ERET8SE1NDQBavCRX4q/vF5Wf5tGxY0cAzlyscTjJ35ypasjSsWOUw0laFpUfERERP3L99d3p2KED64Ms9lqg1V3yjxhgpwW5Lugc25n4+ESnI7VKUVHRdGjfgaIz552O0qjozHk6tO9AVJTzu8+1JCo/IiIifqRdu/YsXPQrkvr1Z5ML1ltQ73Qo8Us1wLsu2OqCwYMzWfDzJwgN9Z9rUloTy7Lo2as3h89ccDpKo8NnLtCzV28sS4tAr4TKj4iIiJ9p27YdP/zhI3zjG3dzOMjiz26L006HEr9SCqx0W5xwBzFjxne4/4E5hIeHOx2rVUtO7s/pqmr2ljp/jc3e0gpOV1XrHj9XQeVHRETED7lcLm699evMmzcfOyKSlUGwKgj2W6CNbQNTHbDPgpVBFm8HQWjHKObPX8iYMWN19t8Hhg8fSUynGFbkF2Hbzi1ItW3DivwiYjp1YvjwkY7laKlUfkRERPxYUlIKv1j8G+6++5uYmFg2uOAVt0WuBc6ffxZfKAc2Wg0/9xwXhCYmcs89/8Rjjz/J9df3cDpewAgODubOu75JybkqNhefdCzH5uKTlJyr4s67vklwcLBjOVqqQL9BsoiIiN+LjGzLhAm3MH78ZAoL9/LBB++zbdsWCrxe4oC+NnQ3Oqi3JnXAIQsKXRanMYQEB5ORkcXIkWPo3r2nZnockp4+hB49evHnXcX0jetIVESYT8evuFjDn3cV0717D9LTM3w6dmuh/ydFRERaCMuySEpKISkphfPnz7Np04d8sP49NpwqY7Nl8TWvoSvQxYAue295qoHjFhwDjgZZ1BtDl8REZoweS2bmMNq0aeN0xIBnWRbf/e79LFr4Y57NKeDhMQMIdQf5ZOxaj5dncwrw4OK7331ABfgqqfyIiIi0QO3atWPChMmMHz+JwsK9fPjhenbt/JgD1ZewgM5AFxu+ZhpuDKo/k/yPAU4Dxyw45rI4dXlj83aRbcm8IZ3s7FGa5fFDCQmJPPC9h1iy5Fe8uLmQmVnJuJr5Z2Qbw4ubCyk5d5G5c+eRmNilWcdrzVR+REREWrBPzwbZts2hQwfIz99B/s6P2Xr8KFuBSMuii9fwNQMJgK4ScE4dcJyGwnM8yOKSMVg03N8pa8ANpKYOoFu363G5dFm2P0tNHcjdd9/D8uUvs2pXEbendW/W8d7aXcyO46eZOnUGqakDm3Ws1k7lR0REpJVwuVz06tWHXr36MGXKVM6erWDXrp3k7/yYPXt2UVhfR5BlEW8buhpINNABzQo1J0PDxhQlFhy1oMwCGwgPC6d/6gDS0gbSv38a7dq1dzipXKlx4yZSWlrCuxvWEeoOYkJKt2YZZ3XBEd7Ze5Ts7NGMGzexWcYIJCo/IiIirVTHjlFkZ48mO3s09fX17N9fyK5dO8jfsZ28U2UAtLEs4r2GBCDeQDtUhq6FAc4BJywotaDUZVFjGpazJSZ0YcLAQaSmDqBnz94EBfnmWhFpHpZl8a1vzaSuro5VeTm4g1zc3Ldrk47xXuExVu0uJjNzGN/61kwtgWwCKj8iIhKwnnnmGVavXg1AdnY28+bNIzc3lyeeeILa2lomTJjA3LlzHU7ZNIKDg0lJ6U9KSn+mTbuXU6fK2LevgH37CthbsJtDF84DDUvk/lqGEgxEOhvb7xngPA1F5wRQenkpG0BUx44MSu5PcnI/+vZNJjq6k5NRpRm4XC5mznwAj8fDiq2bCXJZjO7dNNfjrNt/nBU7D5OensHMmQ9oKWQTUfkREZGAlJubS05ODitXrsSyLGbOnMnbb7/Nk08+ycsvv0x8fDyzZs1iw4YNZGdnOx23ycXGxhEbG0d29miMMZSWnqCwsIC9ewso3LeHAxcvAtDu/5Qh7TcGF/hs2am6XHbat21Hakr/xmuwYmJidaY+AAQFBTFr1mxs28sft28l2OVieM+Ea3rOjQdP8MePD3HDDenMmjVbs4RNSOVHREQCUkxMDI888gghISEA9OjRg+LiYrp160bXrg1LV2655RbeeeedVll+Ps2yLBISEklISGT06LHYtk1JybHLM0N7Kdy3h09qagDoaFkkeA2JAbR5Qi0NRee4BSeCLM5fLjuREREkJfcjKSmFvn1TiI9PUNkJUG63mwceeIinl/47r27bQXCQi4zrO1/Vc20uOsmr2w6QljqQ733vIdxu/bnelPRqiohIQOrVq1fjx8XFxaxevZp77rmHmJiYxs/HxsZSVlbmRDxHuVwuunbtRteu3Rg7diK2bXP0aDH79hVQULCbTwr3UeCpxwXEAQl2w72FOgGtYWGODZTRsElBicuiHIMBQkNCSEruR0pKf/r2TSExsYuWIkkjt9vN92fP5T/+41e8tKUAd5CLG78We0XPsf3oKV7asp+k5H58f/ZcFZ9moFdUREQC2oEDB5g1axbz5s0jKCiI4uLixq8ZY674TH50dOu8SiYuLo309DQA6urq2LdvHx9//DEfb9/O9qIitgOhlkWC3TArlHh584Sm1Ns08RNeZoBKGsrOcQtOuizqLv/se/fqxZhBgxg4cCB9+vTRH6PypRYt+jk//clPeCFvH22C3STHR33m60O7f/6M0L6TFbywuZDkpCQWLvo5YWFhvogbcPQbLCIiAWv79u3MmTOHRx99lEmTJrFlyxbKy8sbv15eXk5s7JWduT1zpgrbbqa/0v1IQkJ3EhK6M3nyFM6fP8/evXsoKNjFnl07Kao8B0D7y0vkvmagC9c+K9SU5ccDHKPhfjsln7pup1N0J7JSB5CSkkpSUgoRERGN33P2bHXTBZBWbfaDD/P4Yz/lubxCHhk7kJjI8Mavfd5yuPKqan6fW0h8fCKzH3yYCxfquXCh3peRWw2Xy/qHJ6FUfkREJCCVlpby/e9/nyVLlpCZmQlAWloaRUVFHDlyhC5duvD2229zxx13OJzU/7Vr146MjKFkZAxt3DyhoGAXe/bsYt/eAvbV1xFmWXT3GnoaiMWZ7bQNUAocsKA4qGF2JzwsjOSUVFJS+tOvX39iY6/uOg2RTwsPb8Ochx7mZwt+xH/n7OVfbxpAqPvzNy2o9Xj575y9EORmzkMPEx6ubUWak8qPiIgEpOeee47a2loWL17c+LmpU6eyePFiHnzwQWpra8nOzmb8+PEOpmx5Pr15ws03T8Dj8bBnTz65uTns+Hgrez0e2lkWPb2GHpdvstrczgAHLTgUZHHRGEJDQkhPzyAzcxhJSSnaSUuaRWxsZ+5/4CGWLPklf9iyn+9k9v27ZbTGGP6wZT8nKi8yd+6/qXz7gMqPiIgEpPnz5zN//vzP/dqqVat8nKb1crvdDBgwiAEDBlFdfYlt27aQl5vDjn17+BiIwaKnbejexNtoVwGHLDjosqjA4HK56N8/jaFDhzNgwCBCQ0ObcDSRz5eaOoCvf/1OVqx4nSHXxdIvIfozXy8orWDb0VN8/et3kpo6wKGUgUXlR0RERHwiPLwNw4ePZPjwkZw9W8Hmzbnk5X5I3rGjbAZ62TDYQPiXPtMXqwI2u6Do8gn2Ht17MClrBIMHZ9C2bVNvwSDy5SZOvJWcjR+wMr+I5M5RuFwNb07bNqzMLyI2JpZJk25zOGXgUPkRERERn+vYMYoJEyYzYcJkSkqOsWHDOt5//12KjeFGryHJXNkGCV5gtwU7giysIDe3TJjM8OHZWkYkjnO73Uy5czr/9V//webikwztHg/A5uKTnKi8yPe+d592EfQhvdIiIiLiqMTErkyf/i1GjryJl19+ntx9BXyCRZbXEPcVvv84kOu2qDSGGwbeyPTp99KpU8yXfp+Ir6SnD6FH9568vedo425vfyk4SvfuPUhPH+JwusCiO3OJiIiIX0hISGTevPl873sP4W3fnlVBsMGCui/49zXA+y5YHQShnWL44Q//jTlz/kXFR/yOZVmMuWkcZy/VcKTiAkcrLlBxsYabbhp/xfcSk2ujmR8RERHxG5ZlMXhwJqmpA1m16k+sXv029bZhjP3Z7bFt4P0gKA8K4hu3TWH8+EmEhIQ4FVvkS6WlDcTlcpF//DQALpeLtLSBDqcKPCo/IiIi4nfCwsK4665vEhnZjtdfX0aBBf0+dZPT7VbDPXvu+84ssrJGOJZT5KuKiIikb58k8kuKwYK+fZKIiPjim3FK89CyNxEREfFbEyZMZsCAQXzkgrLLnzsK7HTBiBGjVHykRUlOSeXk+YucrLxIckp/p+MEJJUfERER8VuWZXHffQ8QFd2Jt4LgebfFu0HQtcvXuOeef3I6nsgV6dw5/lMfJziYJHBp2ZuIiIj4tYiISB7+1x+Tk/MBtm0TFORm1KibdI2PtDifLT/x/+BfSnNR+RERERG/17lzPFOmTHM6hsg1iY2N+9yPxXdUfkREREREfCAkJITFi3+DMWjm0iEqPyIiIiIiPqJrfZylDQ9ERERERCQgqPyIiIiIiEhAUPkREREREZGAoPIjIiIiIiIBQeVHREREREQCgsqPiIiIiIgEBJUfEREREREJCCo/IiIiIiISEFR+REREREQkIKj8iIiIiIhIQFD5ERERERGRgOBI+XnrrbeYOHEiY8eOZdmyZU5EEBERERGRAOP29YBlZWUsWbKEFStWEBISwtSpUxkyZAg9e/b0dRQREREREQkgPi8/ubm5ZGRk0KFDBwDGjRvHO++8w+zZs7/S97tcVpPkaBMX2yTPI4Glqd5/TSEoop3TEaQFutb3sD/9DvgrvUYiIs75sv+DfV5+Tp06RUxMTOPj2NhYdu3a9ZW/v2PHiCbJMfbFF5rkeSSwREdHOh2hUeI37nc6grRA/vQebq2a6jglIiJNz+fX/Ni2jWX9rZEZYz7zWEREREREpDn4vPx07tyZ8vLyxsfl5eXExmoJmoiIiIiINC+fl5+hQ4eSl5dHRUUF1dXVrFmzhhEjRvg6hoiIiIiIBBifX/MTFxfH3Llzuffee6mvr2fKlCmkpqb6OoaIiIiIiAQYyxhjnA4hIiIiIiLS3By5yamIiIiIiIivqfyIiIiIiEhAUPkREREREZGAoPIjIiIiIiIBQeVHAHjrrbeYOHEiY8eOZdmyZU7HEblqVVVVTJ48mePHjzsdRUSakI5T0lroOOUslR+hrKyMJUuW8Morr/DnP/+Z1157jYMHDzodS+SK5efnM23aNIqLi52OIiJNSMcpaS10nHKeyo+Qm5vOevGTAAAF3UlEQVRLRkYGHTp0oE2bNowbN4533nnH6VgiV+z1119nwYIFxMbGOh1FRJqQjlPSWug45Tyf3+RU/M+pU6eIiYlpfBwbG8uuXbscTCRydR5//HGnI4hIM9BxSloLHaecp5kfwbZtLMtqfGyM+cxjERERJ+k4JSJNReVH6Ny5M+Xl5Y2Py8vLNR0rIiJ+Q8cpEWkqKj/C0KFDycvLo6KigurqatasWcOIESOcjiUiIgLoOCUiTUfX/AhxcXHMnTuXe++9l/r6eqZMmUJqaqrTsURERAAdp0Sk6VjGGON0CBERERERkeamZW8iIiIiIhIQVH5ERERERCQgqPyIiIiIiEhAUPkREREREZGAoPIjIiIiIiIBQVtdi1zWp08fevfujcvlwrIsqquriYyM5Gc/+xn9+/d3Op6IiAQ4HadErp22uha5rE+fPuTl5REVFdX4ueeee441a9bw2muvOZhMRERExymRpqCZH5Ev4PF4KC0tpX379o2fe/bZZ1mzZg22bZOYmMiCBQuIi4tjzZo1PPvss1iWRVBQEPPmzSM9PZ0ZM2aQnJzM9u3bOXv2LLfddhtz5swB4P333+eZZ57Btm0iIiL40Y9+RGpqKk8//TQlJSWUl5dTUlJCXFwcv/71r4mNjeWVV15h+fLlBAcHExoaysKFC+nZsydlZWUsXLiQ0tJS6uvrmTRpEvfff79TL52IiPiAjlMiV8GIiDHGmN69e5vJkyebyZMnm6ysLDN69GizaNEic/r0aWOMMStXrjQ/+MEPTH19vTHGmOXLl5uZM2caY4wZM2aM2bFjhzHGmI0bN5qnn37aGGPMPffcY+677z5TV1dnKisrzbhx48y6devMwYMHzdChQ83Ro0eNMcbk5uaarKwsc+HCBbN06VIzZswYc+HCBWOMMbNmzTJPPfWU8Xg8JiUlxZSVlTXmWb58uTHGmBkzZpi1a9caY4ypqakxM2bMMH/5y1988bKJiIiP6Dglcu008yPyKS+++CJRUVEUFBTwz//8zwwZMoTo6GgA1q9fz+7du7njjjsAsG2b6upqACZNmsTs2bPJzs4mKyuL++67r/E57777boKDgwkODmb8+PHk5OTQvXt3MjIy6Nq1KwCZmZlERUWxZ88eAAYPHkxkZCQAycnJVFZWEhQUxPjx45k6dSojR45k2LBhZGdnc+nSJbZu3UplZSVPPfUUAJcuXaKwsJCJEyf65oUTERGf0HFK5Nqo/Ih8jpSUFH70ox/xyCOPkJSURJcuXbBtm5kzZzJ9+nQA6urqqKysBGDu3LnccccdbNq0iRUrVvD888/zxhtvAOB2/+3XzBiDy+XCtm0sy/rMmMYYPB4PAGFhYY2ftywLc/nSvCeffJL9+/eTm5vL7373O958800ef/xxjDEsX76c8PBwACoqKggNDW2mV0dERJym45TI1dFW1yJfYPLkyaSmpvLEE08AMGzYMN544w2qqqoAeOqpp5g3bx4ej4fRo0dTXV3NtGnTWLBgAZ988gl1dXUArFq1Ctu2qaysZPXq1YwePZrMzExycnI4duwYAHl5eZSWlpKWlvaFeSoqKsjOzqZDhw58+9vf5gc/+AG7d+8mMjKSAQMG8MILLwBw/vx5pk2bxtq1a5vz5REREYfpOCVy5TTzI/IP/OQnP+HWW29l48aN3HnnnZSVlXHXXXdhWRbx8fEsXrwYt9vNo48+ysMPP4zb7cayLH7xi18QEhICQE1NDVOmTOHixYtMnz6dzMxMABYsWMDs2bPxer2EhYXx29/+lrZt235hlqioKB544AG+/e1vExYWRlBQEI899hjQcKZt0aJF3HLLLdTV1TF58mRuvfXW5n+BRETEUTpOiVwZbXUt0oxmzJjBN7/5TcaPH+90FBERkb+j45QEGi17ExERERGRgKCZHxERERERCQia+RERERERkYCg8iMiIiIiIgFB5UdERERERAKCyo+IiIiIiAQElR8REREREQkIKj8iIiIiIhIQ/j8vBSKnKcjesAAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<Figure size 1008x504 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "numerical_Response(\"Age\",train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 51,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "P-Value is 0.0\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1008x504 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "numerical_Response(\"Annual_Premium\",train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 52,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "P-Value is 0.51699\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1008x504 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "numerical_Response(\"Vintage\",train)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Takeaways-**\n",
    "- Average age of getting a Insurance is significantly higher than that of not getting\n",
    "- People with slightly higher premiums are more likely to get a car insurance \n",
    "- Vintage doesn't affect Insurance at all"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.7.6"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 4
}