Alpha Insurance-Individual Variables.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Individual Variable Data Exploration Notebook"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "import pandas as pd\n",
    "import missingno as msno\n",
    "import matplotlib.pyplot as plt\n",
    "import seaborn as sns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
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       "\n",
       "    .dataframe tbody tr th {\n",
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       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>State_Code</th>\n",
       "      <th>State</th>\n",
       "      <th>Claim_Amount</th>\n",
       "      <th>Education</th>\n",
       "      <th>Claim_Date</th>\n",
       "      <th>Employment_Status</th>\n",
       "      <th>Gender</th>\n",
       "      <th>Income</th>\n",
       "      <th>Location</th>\n",
       "      <th>Marital_Status</th>\n",
       "      <th>...</th>\n",
       "      <th>Months_Since_Last_Claim</th>\n",
       "      <th>Months_Since_Policy_Inception</th>\n",
       "      <th>Claim_Cause</th>\n",
       "      <th>Claim_Report_Type</th>\n",
       "      <th>Vehicle_Class</th>\n",
       "      <th>Vehicle_Size</th>\n",
       "      <th>Vehicle_Model</th>\n",
       "      <th>Outstanding_Balance</th>\n",
       "      <th>Fraudulent_Claim</th>\n",
       "      <th>Claimant__Number</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>3170</th>\n",
       "      <td>IA</td>\n",
       "      <td>Iowa</td>\n",
       "      <td>443.680621</td>\n",
       "      <td>Bachelor</td>\n",
       "      <td>12/15/2018</td>\n",
       "      <td>Employed</td>\n",
       "      <td>F</td>\n",
       "      <td>77609</td>\n",
       "      <td>Rural</td>\n",
       "      <td>Married</td>\n",
       "      <td>...</td>\n",
       "      <td>26</td>\n",
       "      <td>75</td>\n",
       "      <td>Hail</td>\n",
       "      <td>Branch</td>\n",
       "      <td>Sports Car</td>\n",
       "      <td>Midsize</td>\n",
       "      <td>Honda</td>\n",
       "      <td>2608.0</td>\n",
       "      <td>N</td>\n",
       "      <td>4171</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4062</th>\n",
       "      <td>NE</td>\n",
       "      <td>Nebraska</td>\n",
       "      <td>2843.564035</td>\n",
       "      <td>College</td>\n",
       "      <td>01/15/2019</td>\n",
       "      <td>Unemployed</td>\n",
       "      <td>F</td>\n",
       "      <td>0</td>\n",
       "      <td>Suburban</td>\n",
       "      <td>Single</td>\n",
       "      <td>...</td>\n",
       "      <td>29</td>\n",
       "      <td>22</td>\n",
       "      <td>Hail</td>\n",
       "      <td>Branch</td>\n",
       "      <td>SUV</td>\n",
       "      <td>Midsize</td>\n",
       "      <td>Toyota</td>\n",
       "      <td>25772.0</td>\n",
       "      <td>N</td>\n",
       "      <td>5063</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1602</th>\n",
       "      <td>IA</td>\n",
       "      <td>Iowa</td>\n",
       "      <td>272.639297</td>\n",
       "      <td>High School or Below</td>\n",
       "      <td>12/01/2018</td>\n",
       "      <td>Employed</td>\n",
       "      <td>F</td>\n",
       "      <td>48039</td>\n",
       "      <td>Rural</td>\n",
       "      <td>Married</td>\n",
       "      <td>...</td>\n",
       "      <td>4</td>\n",
       "      <td>37</td>\n",
       "      <td>Other</td>\n",
       "      <td>Branch</td>\n",
       "      <td>Four-Door Car</td>\n",
       "      <td>Midsize</td>\n",
       "      <td>Chevrolet</td>\n",
       "      <td>3805.0</td>\n",
       "      <td>N</td>\n",
       "      <td>2603</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>3 rows × 22 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "     State_Code     State  Claim_Amount             Education  Claim_Date  \\\n",
       "3170         IA      Iowa    443.680621              Bachelor  12/15/2018   \n",
       "4062         NE  Nebraska   2843.564035               College  01/15/2019   \n",
       "1602         IA      Iowa    272.639297  High School or Below  12/01/2018   \n",
       "\n",
       "     Employment_Status Gender  Income  Location Marital_Status  \\\n",
       "3170          Employed      F   77609     Rural        Married   \n",
       "4062        Unemployed      F       0  Suburban         Single   \n",
       "1602          Employed      F   48039     Rural        Married   \n",
       "\n",
       "            ...         Months_Since_Last_Claim  \\\n",
       "3170        ...                              26   \n",
       "4062        ...                              29   \n",
       "1602        ...                               4   \n",
       "\n",
       "      Months_Since_Policy_Inception  Claim_Cause  Claim_Report_Type  \\\n",
       "3170                             75         Hail             Branch   \n",
       "4062                             22         Hail             Branch   \n",
       "1602                             37        Other             Branch   \n",
       "\n",
       "      Vehicle_Class Vehicle_Size Vehicle_Model Outstanding_Balance  \\\n",
       "3170     Sports Car      Midsize         Honda              2608.0   \n",
       "4062            SUV      Midsize        Toyota             25772.0   \n",
       "1602  Four-Door Car      Midsize     Chevrolet              3805.0   \n",
       "\n",
       "     Fraudulent_Claim  Claimant__Number  \n",
       "3170                N              4171  \n",
       "4062                N              5063  \n",
       "1602                N              2603  \n",
       "\n",
       "[3 rows x 22 columns]"
      ]
     },
     "execution_count": 2,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data_train = pd.read_csv('claim_data_v2_train.csv')\n",
    "data_train.sample(3)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [],
   "source": [
    "def visualize_cat(attr, df=data_train):\n",
    "    df_i = df[['Fraudulent_Claim', attr]].groupby([attr])['Fraudulent_Claim'].agg(['count','sum', 'mean'])\n",
    "    m = 'Pct Fraud'\n",
    "    df_i = df_i.rename({'count': 'frequency', 'sum': 'Survivers', 'mean': m}, axis='columns')\n",
    "    print(df_i)\n",
    "    fig, ax1 = plt.subplots()\n",
    "    ax2 = ax1.twinx()\n",
    "    df_i['frequency'].plot.bar(ax=ax1, alpha=.8)\n",
    "    df_i[m].plot.line(ax=ax2, color='k')\n",
    "    ax1.set_ylabel('frequency')\n",
    "    ax2.set_ylabel('Pct Fraud')\n",
    "    ax2.set_ylim(bottom=0, top=1, auto=False)\n",
    "    ax2.legend()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "count     5001\n",
       "unique       2\n",
       "top          N\n",
       "freq      4694\n",
       "Name: Fraudulent_Claim, dtype: object"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data_train[\"Fraudulent_Claim\"].describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [],
   "source": [
    "#Convert Fraud Claim data from Y/N to 1/0\n",
    "data_train.Fraudulent_Claim.replace(('Y', 'N'), (1, 0), inplace=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "\n",
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       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>State_Code</th>\n",
       "      <th>State</th>\n",
       "      <th>Claim_Amount</th>\n",
       "      <th>Education</th>\n",
       "      <th>Claim_Date</th>\n",
       "      <th>Employment_Status</th>\n",
       "      <th>Gender</th>\n",
       "      <th>Income</th>\n",
       "      <th>Location</th>\n",
       "      <th>Marital_Status</th>\n",
       "      <th>...</th>\n",
       "      <th>Months_Since_Last_Claim</th>\n",
       "      <th>Months_Since_Policy_Inception</th>\n",
       "      <th>Claim_Cause</th>\n",
       "      <th>Claim_Report_Type</th>\n",
       "      <th>Vehicle_Class</th>\n",
       "      <th>Vehicle_Size</th>\n",
       "      <th>Vehicle_Model</th>\n",
       "      <th>Outstanding_Balance</th>\n",
       "      <th>Fraudulent_Claim</th>\n",
       "      <th>Claimant__Number</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>KS</td>\n",
       "      <td>Kansas</td>\n",
       "      <td>575.560000</td>\n",
       "      <td>Bachelor</td>\n",
       "      <td>12/01/2018</td>\n",
       "      <td>Employed</td>\n",
       "      <td>F</td>\n",
       "      <td>56274</td>\n",
       "      <td>Suburban</td>\n",
       "      <td>Married</td>\n",
       "      <td>...</td>\n",
       "      <td>32</td>\n",
       "      <td>5</td>\n",
       "      <td>Collision</td>\n",
       "      <td>Agent</td>\n",
       "      <td>Two-Door Car</td>\n",
       "      <td>Midsize</td>\n",
       "      <td>Toyota</td>\n",
       "      <td>25781.0</td>\n",
       "      <td>0</td>\n",
       "      <td>1001</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>NE</td>\n",
       "      <td>Nebraska</td>\n",
       "      <td>697.953590</td>\n",
       "      <td>Bachelor</td>\n",
       "      <td>12/01/2018</td>\n",
       "      <td>Unemployed</td>\n",
       "      <td>F</td>\n",
       "      <td>0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Single</td>\n",
       "      <td>...</td>\n",
       "      <td>13</td>\n",
       "      <td>42</td>\n",
       "      <td>Scratch/Dent</td>\n",
       "      <td>Agent</td>\n",
       "      <td>Four-Door Car</td>\n",
       "      <td>Midsize</td>\n",
       "      <td>Chevrolet</td>\n",
       "      <td>37061.0</td>\n",
       "      <td>0</td>\n",
       "      <td>1002</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>OK</td>\n",
       "      <td>Oklahoma</td>\n",
       "      <td>1288.743165</td>\n",
       "      <td>Bachelor</td>\n",
       "      <td>12/01/2018</td>\n",
       "      <td>Employed</td>\n",
       "      <td>F</td>\n",
       "      <td>48767</td>\n",
       "      <td>Suburban</td>\n",
       "      <td>Married</td>\n",
       "      <td>...</td>\n",
       "      <td>18</td>\n",
       "      <td>38</td>\n",
       "      <td>Collision</td>\n",
       "      <td>Agent</td>\n",
       "      <td>Two-Door Car</td>\n",
       "      <td>Midsize</td>\n",
       "      <td>Chevrolet</td>\n",
       "      <td>17236.0</td>\n",
       "      <td>0</td>\n",
       "      <td>1003</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>MO</td>\n",
       "      <td>Missouri</td>\n",
       "      <td>764.586183</td>\n",
       "      <td>Bachelor</td>\n",
       "      <td>12/01/2018</td>\n",
       "      <td>Unemployed</td>\n",
       "      <td>M</td>\n",
       "      <td>0</td>\n",
       "      <td>Suburban</td>\n",
       "      <td>Married</td>\n",
       "      <td>...</td>\n",
       "      <td>18</td>\n",
       "      <td>65</td>\n",
       "      <td>Collision</td>\n",
       "      <td>Call Center</td>\n",
       "      <td>SUV</td>\n",
       "      <td>Midsize</td>\n",
       "      <td>Honda</td>\n",
       "      <td>30270.0</td>\n",
       "      <td>0</td>\n",
       "      <td>1004</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>KS</td>\n",
       "      <td>Kansas</td>\n",
       "      <td>281.369258</td>\n",
       "      <td>Bachelor</td>\n",
       "      <td>12/01/2018</td>\n",
       "      <td>Employed</td>\n",
       "      <td>M</td>\n",
       "      <td>43836</td>\n",
       "      <td>Rural</td>\n",
       "      <td>Single</td>\n",
       "      <td>...</td>\n",
       "      <td>52</td>\n",
       "      <td>44</td>\n",
       "      <td>Collision</td>\n",
       "      <td>Agent</td>\n",
       "      <td>Four-Door Car</td>\n",
       "      <td>Midsize</td>\n",
       "      <td>Ford</td>\n",
       "      <td>16263.0</td>\n",
       "      <td>0</td>\n",
       "      <td>1005</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 22 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "  State_Code     State  Claim_Amount Education  Claim_Date Employment_Status  \\\n",
       "0         KS    Kansas    575.560000  Bachelor  12/01/2018          Employed   \n",
       "1         NE  Nebraska    697.953590  Bachelor  12/01/2018        Unemployed   \n",
       "2         OK  Oklahoma   1288.743165  Bachelor  12/01/2018          Employed   \n",
       "3         MO  Missouri    764.586183  Bachelor  12/01/2018        Unemployed   \n",
       "4         KS    Kansas    281.369258  Bachelor  12/01/2018          Employed   \n",
       "\n",
       "  Gender  Income  Location Marital_Status        ...         \\\n",
       "0      F   56274  Suburban        Married        ...          \n",
       "1      F       0       NaN         Single        ...          \n",
       "2      F   48767  Suburban        Married        ...          \n",
       "3      M       0  Suburban        Married        ...          \n",
       "4      M   43836     Rural         Single        ...          \n",
       "\n",
       "   Months_Since_Last_Claim  Months_Since_Policy_Inception   Claim_Cause  \\\n",
       "0                       32                              5     Collision   \n",
       "1                       13                             42  Scratch/Dent   \n",
       "2                       18                             38     Collision   \n",
       "3                       18                             65     Collision   \n",
       "4                       52                             44     Collision   \n",
       "\n",
       "   Claim_Report_Type  Vehicle_Class Vehicle_Size Vehicle_Model  \\\n",
       "0              Agent   Two-Door Car      Midsize        Toyota   \n",
       "1              Agent  Four-Door Car      Midsize     Chevrolet   \n",
       "2              Agent   Two-Door Car      Midsize     Chevrolet   \n",
       "3        Call Center            SUV      Midsize         Honda   \n",
       "4              Agent  Four-Door Car      Midsize          Ford   \n",
       "\n",
       "  Outstanding_Balance Fraudulent_Claim  Claimant__Number  \n",
       "0             25781.0                0              1001  \n",
       "1             37061.0                0              1002  \n",
       "2             17236.0                0              1003  \n",
       "3             30270.0                0              1004  \n",
       "4             16263.0                0              1005  \n",
       "\n",
       "[5 rows x 22 columns]"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#Test to see if fraud claim data converted correctly \n",
    "data_train.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Index(['State_Code', 'State', 'Claim_Amount', 'Education', 'Claim_Date',\n",
       "       'Employment_Status', 'Gender', 'Income', 'Location', 'Marital_Status',\n",
       "       'Monthly_Premium', 'Annual_Premium', 'Months_Since_Last_Claim',\n",
       "       'Months_Since_Policy_Inception', 'Claim_Cause', 'Claim_Report_Type',\n",
       "       'Vehicle_Class', 'Vehicle_Size', 'Vehicle_Model', 'Outstanding_Balance',\n",
       "       'Fraudulent_Claim', 'Claimant__Number'],\n",
       "      dtype='object')"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data_train.columns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "\n",
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>State_Code</th>\n",
       "      <th>State</th>\n",
       "      <th>Claim_Amount</th>\n",
       "      <th>Education</th>\n",
       "      <th>Claim_Date</th>\n",
       "      <th>Employment_Status</th>\n",
       "      <th>Gender</th>\n",
       "      <th>Income</th>\n",
       "      <th>Location</th>\n",
       "      <th>Marital_Status</th>\n",
       "      <th>...</th>\n",
       "      <th>Months_Since_Last_Claim</th>\n",
       "      <th>Months_Since_Policy_Inception</th>\n",
       "      <th>Claim_Cause</th>\n",
       "      <th>Claim_Report_Type</th>\n",
       "      <th>Vehicle_Class</th>\n",
       "      <th>Vehicle_Size</th>\n",
       "      <th>Vehicle_Model</th>\n",
       "      <th>Outstanding_Balance</th>\n",
       "      <th>Fraudulent_Claim</th>\n",
       "      <th>Claimant__Number</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>5001</td>\n",
       "      <td>5001</td>\n",
       "      <td>5001.000000</td>\n",
       "      <td>4990</td>\n",
       "      <td>5001</td>\n",
       "      <td>5001</td>\n",
       "      <td>5001</td>\n",
       "      <td>5.001000e+03</td>\n",
       "      <td>4998</td>\n",
       "      <td>5001</td>\n",
       "      <td>...</td>\n",
       "      <td>5001.000000</td>\n",
       "      <td>5001.000000</td>\n",
       "      <td>5001</td>\n",
       "      <td>5001</td>\n",
       "      <td>5001</td>\n",
       "      <td>5001</td>\n",
       "      <td>5001</td>\n",
       "      <td>4992.000000</td>\n",
       "      <td>5001</td>\n",
       "      <td>5001.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>unique</th>\n",
       "      <td>5</td>\n",
       "      <td>5</td>\n",
       "      <td>NaN</td>\n",
       "      <td>5</td>\n",
       "      <td>3</td>\n",
       "      <td>5</td>\n",
       "      <td>2</td>\n",
       "      <td>NaN</td>\n",
       "      <td>3</td>\n",
       "      <td>3</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>5</td>\n",
       "      <td>4</td>\n",
       "      <td>6</td>\n",
       "      <td>3</td>\n",
       "      <td>4</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>top</th>\n",
       "      <td>IA</td>\n",
       "      <td>Iowa</td>\n",
       "      <td>NaN</td>\n",
       "      <td>College</td>\n",
       "      <td>12/15/2018</td>\n",
       "      <td>Employed</td>\n",
       "      <td>M</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Suburban</td>\n",
       "      <td>Married</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Collision</td>\n",
       "      <td>Agent</td>\n",
       "      <td>Four-Door Car</td>\n",
       "      <td>Midsize</td>\n",
       "      <td>Toyota</td>\n",
       "      <td>NaN</td>\n",
       "      <td>N</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>freq</th>\n",
       "      <td>1546</td>\n",
       "      <td>1546</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1492</td>\n",
       "      <td>2000</td>\n",
       "      <td>3126</td>\n",
       "      <td>2531</td>\n",
       "      <td>NaN</td>\n",
       "      <td>3156</td>\n",
       "      <td>2930</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2077</td>\n",
       "      <td>1889</td>\n",
       "      <td>2535</td>\n",
       "      <td>3523</td>\n",
       "      <td>1278</td>\n",
       "      <td>NaN</td>\n",
       "      <td>4694</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>787.763253</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>4.131045e+04</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>...</td>\n",
       "      <td>15.004199</td>\n",
       "      <td>48.234953</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>23728.152644</td>\n",
       "      <td>NaN</td>\n",
       "      <td>3501.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>655.963269</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2.276904e+05</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>...</td>\n",
       "      <td>11.139649</td>\n",
       "      <td>28.096651</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>13827.401917</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1443.808678</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>189.868369</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>...</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>4.000000</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1001.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>402.296353</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>...</td>\n",
       "      <td>5.000000</td>\n",
       "      <td>24.000000</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>11528.750000</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2251.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>577.390039</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>3.464000e+04</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>...</td>\n",
       "      <td>13.000000</td>\n",
       "      <td>48.000000</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>23993.500000</td>\n",
       "      <td>NaN</td>\n",
       "      <td>3501.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>886.114954</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>6.300900e+04</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>...</td>\n",
       "      <td>23.000000</td>\n",
       "      <td>72.000000</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>35574.000000</td>\n",
       "      <td>NaN</td>\n",
       "      <td>4751.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>7422.851604</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1.596780e+07</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>...</td>\n",
       "      <td>60.000000</td>\n",
       "      <td>99.000000</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>47996.000000</td>\n",
       "      <td>NaN</td>\n",
       "      <td>6001.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>11 rows × 22 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "       State_Code State  Claim_Amount Education  Claim_Date Employment_Status  \\\n",
       "count        5001  5001   5001.000000      4990        5001              5001   \n",
       "unique          5     5           NaN         5           3                 5   \n",
       "top            IA  Iowa           NaN   College  12/15/2018          Employed   \n",
       "freq         1546  1546           NaN      1492        2000              3126   \n",
       "mean          NaN   NaN    787.763253       NaN         NaN               NaN   \n",
       "std           NaN   NaN    655.963269       NaN         NaN               NaN   \n",
       "min           NaN   NaN    189.868369       NaN         NaN               NaN   \n",
       "25%           NaN   NaN    402.296353       NaN         NaN               NaN   \n",
       "50%           NaN   NaN    577.390039       NaN         NaN               NaN   \n",
       "75%           NaN   NaN    886.114954       NaN         NaN               NaN   \n",
       "max           NaN   NaN   7422.851604       NaN         NaN               NaN   \n",
       "\n",
       "       Gender        Income  Location Marital_Status        ...         \\\n",
       "count    5001  5.001000e+03      4998           5001        ...          \n",
       "unique      2           NaN         3              3        ...          \n",
       "top         M           NaN  Suburban        Married        ...          \n",
       "freq     2531           NaN      3156           2930        ...          \n",
       "mean      NaN  4.131045e+04       NaN            NaN        ...          \n",
       "std       NaN  2.276904e+05       NaN            NaN        ...          \n",
       "min       NaN  0.000000e+00       NaN            NaN        ...          \n",
       "25%       NaN  0.000000e+00       NaN            NaN        ...          \n",
       "50%       NaN  3.464000e+04       NaN            NaN        ...          \n",
       "75%       NaN  6.300900e+04       NaN            NaN        ...          \n",
       "max       NaN  1.596780e+07       NaN            NaN        ...          \n",
       "\n",
       "        Months_Since_Last_Claim  Months_Since_Policy_Inception  Claim_Cause  \\\n",
       "count               5001.000000                    5001.000000         5001   \n",
       "unique                      NaN                            NaN            5   \n",
       "top                         NaN                            NaN    Collision   \n",
       "freq                        NaN                            NaN         2077   \n",
       "mean                  15.004199                      48.234953          NaN   \n",
       "std                   11.139649                      28.096651          NaN   \n",
       "min                    0.000000                       0.000000          NaN   \n",
       "25%                    5.000000                      24.000000          NaN   \n",
       "50%                   13.000000                      48.000000          NaN   \n",
       "75%                   23.000000                      72.000000          NaN   \n",
       "max                   60.000000                      99.000000          NaN   \n",
       "\n",
       "        Claim_Report_Type  Vehicle_Class Vehicle_Size Vehicle_Model  \\\n",
       "count                5001           5001         5001          5001   \n",
       "unique                  4              6            3             4   \n",
       "top                 Agent  Four-Door Car      Midsize        Toyota   \n",
       "freq                 1889           2535         3523          1278   \n",
       "mean                  NaN            NaN          NaN           NaN   \n",
       "std                   NaN            NaN          NaN           NaN   \n",
       "min                   NaN            NaN          NaN           NaN   \n",
       "25%                   NaN            NaN          NaN           NaN   \n",
       "50%                   NaN            NaN          NaN           NaN   \n",
       "75%                   NaN            NaN          NaN           NaN   \n",
       "max                   NaN            NaN          NaN           NaN   \n",
       "\n",
       "       Outstanding_Balance Fraudulent_Claim  Claimant__Number  \n",
       "count          4992.000000             5001       5001.000000  \n",
       "unique                 NaN                2               NaN  \n",
       "top                    NaN                N               NaN  \n",
       "freq                   NaN             4694               NaN  \n",
       "mean          23728.152644              NaN       3501.000000  \n",
       "std           13827.401917              NaN       1443.808678  \n",
       "min               4.000000              NaN       1001.000000  \n",
       "25%           11528.750000              NaN       2251.000000  \n",
       "50%           23993.500000              NaN       3501.000000  \n",
       "75%           35574.000000              NaN       4751.000000  \n",
       "max           47996.000000              NaN       6001.000000  \n",
       "\n",
       "[11 rows x 22 columns]"
      ]
     },
     "execution_count": 36,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data_train.describe(include='all')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x1d68cd96dd8>"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<Figure size 1800x720 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# missing data\n",
    "msno.matrix(data_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 864x648 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "corrmat= data_train.corr()\n",
    "f, ax = plt.subplots(figsize=(12, 9))\n",
    "sns.heatmap(corrmat, vmax=.8, square=True);"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Describe Each Variable - Find Outliers"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Claim Amount"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x1db82770>"
      ]
     },
     "execution_count": 40,
     "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": [
    "data_train['Claim_Amount'].hist()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Log tranformation"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x1d68d10e828>"
      ]
     },
     "execution_count": 16,
     "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": [
    "data_train['Claim_Amount'].apply(np.log).hist()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Income"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Income has a major outlier of $10M"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "count    5.001000e+03\n",
       "mean     4.131045e+04\n",
       "std      2.276904e+05\n",
       "min      0.000000e+00\n",
       "25%      0.000000e+00\n",
       "50%      3.464000e+04\n",
       "75%      6.300900e+04\n",
       "max      1.596780e+07\n",
       "Name: Income, dtype: float64"
      ]
     },
     "execution_count": 23,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data_train['Income'].describe()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Top 10 Earners"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "553     15967801\n",
       "484       933288\n",
       "574       250000\n",
       "1750       99981\n",
       "2306       99961\n",
       "532        99960\n",
       "932        99934\n",
       "4811       99875\n",
       "1159       99845\n",
       "1729       99845\n",
       "Name: Income, dtype: int64"
      ]
     },
     "execution_count": 37,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data_train['Income'].sort_values(ascending=False).head(10)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x1d68d20b588>"
      ]
     },
     "execution_count": 21,
     "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": [
    "data_train['Income'].plot.box()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Drop values above the boxplot (greater than Q3+1.5*IQR)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Cutoff value: 126018.0\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x1d690cac860>"
      ]
     },
     "execution_count": 41,
     "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": [
    "cutoff = data_train['Income'].quantile(.75) + (data_train['Income'].quantile(.75) - data_train['Income'].quantile(.25))\n",
    "print(\"Cutoff value:\", cutoff)\n",
    "\n",
    "data_train['Income'][data_train['Income']<cutoff].plot.box()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Graph histogram without 0 income and without outliers"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x1d690d63f60>"
      ]
     },
     "execution_count": 43,
     "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": [
    "data_train['Income'][(data_train['Income']<cutoff) & (data_train['Income']>0)].hist()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Premium"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Validate that Annual Premium is 12 times Monthly Premium"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>State_Code</th>\n",
       "      <th>State</th>\n",
       "      <th>Claim_Amount</th>\n",
       "      <th>Education</th>\n",
       "      <th>Claim_Date</th>\n",
       "      <th>Employment_Status</th>\n",
       "      <th>Gender</th>\n",
       "      <th>Income</th>\n",
       "      <th>Location</th>\n",
       "      <th>Marital_Status</th>\n",
       "      <th>...</th>\n",
       "      <th>Months_Since_Last_Claim</th>\n",
       "      <th>Months_Since_Policy_Inception</th>\n",
       "      <th>Claim_Cause</th>\n",
       "      <th>Claim_Report_Type</th>\n",
       "      <th>Vehicle_Class</th>\n",
       "      <th>Vehicle_Size</th>\n",
       "      <th>Vehicle_Model</th>\n",
       "      <th>Outstanding_Balance</th>\n",
       "      <th>Fraudulent_Claim</th>\n",
       "      <th>Claimant__Number</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>0 rows × 22 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "Empty DataFrame\n",
       "Columns: [State_Code, State, Claim_Amount, Education, Claim_Date, Employment_Status, Gender, Income, Location, Marital_Status, Monthly_Premium, Annual_Premium, Months_Since_Last_Claim, Months_Since_Policy_Inception, Claim_Cause, Claim_Report_Type, Vehicle_Class, Vehicle_Size, Vehicle_Model, Outstanding_Balance, Fraudulent_Claim, Claimant__Number]\n",
       "Index: []\n",
       "\n",
       "[0 rows x 22 columns]"
      ]
     },
     "execution_count": 44,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data_train[data_train['Monthly_Premium'] * 12 != data_train['Annual_Premium']]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "count    5001.000000\n",
       "mean       94.530694\n",
       "std        26.120856\n",
       "min        50.000000\n",
       "25%        72.000000\n",
       "50%        95.000000\n",
       "75%       116.000000\n",
       "max       140.000000\n",
       "Name: Monthly_Premium, dtype: float64"
      ]
     },
     "execution_count": 46,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data_train['Monthly_Premium'].describe()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Premium is approximately uniformly distributed between 50 and 140"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x1e0fcc30>"
      ]
     },
     "execution_count": 44,
     "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": [
    "data_train['Monthly_Premium'].hist()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "count    5001.000000\n",
       "mean       15.004199\n",
       "std        11.139649\n",
       "min         0.000000\n",
       "25%         5.000000\n",
       "50%        13.000000\n",
       "75%        23.000000\n",
       "max        60.000000\n",
       "Name: Months_Since_Last_Claim, dtype: float64"
      ]
     },
     "execution_count": 47,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data_train['Months_Since_Last_Claim'].describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 50,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x1d691da8978>"
      ]
     },
     "execution_count": 50,
     "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": [
    "data_train['Months_Since_Last_Claim'].plot.box()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 54,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x1d691f6d8d0>"
      ]
     },
     "execution_count": 54,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAYAAAAD8CAYAAAB+UHOxAAAABHNCSVQICAgIfAhkiAAAAAlwSFlzAAALEgAACxIB0t1+/AAAADl0RVh0U29mdHdhcmUAbWF0cGxvdGxpYiB2ZXJzaW9uIDIuMi4zLCBodHRwOi8vbWF0cGxvdGxpYi5vcmcvIxREBQAAEzpJREFUeJzt3X+s3XV9x/Hne1QQy6T80BvSNivGRmVWEW8Qx2IudJMCxvKHZBCixbE0S9DhaCJlJiPbYlKzISpxLI0gmDQURF0bIGJTODH7A5QKUqAiV+zgQqU6oO6CP3b1vT/Op9vJ5dJ7e87p95zbz/ORnJzv93M+5/t9HTi9r/v9nh83MhNJUn3+YNABJEmDYQFIUqUsAEmqlAUgSZWyACSpUhaAJFXKApCkSlkAklQpC0CSKrVg0AEO5MQTT8xly5Z1ff+XX36ZhQsX9i9QQ8zdLHM3y9yH3o4dO36RmW+abd5QF8CyZct48MEHu75/q9VibGysf4EaYu5mmbtZ5j70IuI/5zLPU0CSVCkLQJIqZQFIUqUsAEmqlAUgSZWyACSpUhaAJFXKApCkSlkAklSpof4kcK92PruPS9ff1ci+dm84v5H9SFK/eAQgSZWyACSpUhaAJFXKApCkSlkAklQpC0CSKmUBSFKlZi2AiLgpIvZGxKMdY/8cET+KiEci4lsRsajjtqsjYjwinoiIczrGV5Wx8YhY3/+HIkk6GHM5ArgZWDVtbBvwzsx8F/Bj4GqAiDgFuAj443Kff42IIyLiCODLwLnAKcDFZa4kaUBmLYDM/C7wwrSx72TmVFm9H1hSllcDmzPzN5n5U2AcOL1cxjPzqcz8LbC5zJUkDUg/vgriL4HbyvJi2oWw30QZA3hm2vj7ZtpYRKwF1gKMjIzQarW6DjZyNKxbMTX7xD7oJed0k5OTfd1eU8zdLHM3a77mPpCeCiAiPgNMAZv2D80wLZn5SCNn2mZmbgQ2AoyOjubY2FjX+a7ftIVrdzbzdUe7Lxnr27ZarRa9PO5BMXezzN2s+Zr7QLr+6RgRa4APASszc/8P8wlgace0JcBzZfm1xiVJA9DV20AjYhVwFfDhzHyl46atwEURcVREnAwsB74HfB9YHhEnR8SRtF8o3tpbdElSL2Y9AoiIW4Ex4MSImACuof2un6OAbREBcH9m/nVmPhYRtwOP0z41dHlm/q5s5xPAPcARwE2Z+dgheDySpDmatQAy8+IZhm88wPzPAp+dYfxu4O6DSidJOmT8JLAkVcoCkKRKWQCSVCkLQJIqZQFIUqUsAEmqlAUgSZWyACSpUhaAJFXKApCkSlkAklQpC0CSKmUBSFKlLABJqpQFIEmVsgAkqVIWgCRVygKQpEpZAJJUKQtAkiplAUhSpSwASaqUBSBJlZq1ACLipojYGxGPdowdHxHbIuLJcn1cGY+I+FJEjEfEIxFxWsd91pT5T0bEmkPzcCRJczWXI4CbgVXTxtYD2zNzObC9rAOcCywvl7XADdAuDOAa4H3A6cA1+0tDkjQYsxZAZn4XeGHa8GrglrJ8C3BBx/jXsu1+YFFEnAScA2zLzBcy80VgG68uFUlSg7p9DWAkM/cAlOs3l/HFwDMd8ybK2GuNS5IGZEGftxczjOUBxl+9gYi1tE8fMTIyQqvV6jrMyNGwbsVU1/c/GL3knG5ycrKv22uKuZtl7mbN19wH0m0BPB8RJ2XmnnKKZ28ZnwCWdsxbAjxXxsemjbdm2nBmbgQ2AoyOjubY2NhM0+bk+k1buHZnvztuZrsvGevbtlqtFr087kExd7PM3az5mvtAuj0FtBXY/06eNcCWjvGPlXcDnQHsK6eI7gE+GBHHlRd/P1jGJEkDMuuvxxFxK+3f3k+MiAna7+bZANweEZcBTwMXlul3A+cB48ArwMcBMvOFiPgn4Ptl3j9m5vQXliVJDZq1ADLz4te4aeUMcxO4/DW2cxNw00Glm0eWrb+rb9tat2KKS2fZ3u4N5/dtf5Lq5CeBJalSFoAkVcoCkKRKWQCSVCkLQJIqZQFIUqUsAEmqlAUgSZWyACSpUhaAJFXKApCkSlkAklQpC0CSKmUBSFKlLABJqpQFIEmVsgAkqVIWgCRVygKQpEpZAJJUKQtAkiplAUhSpSwASaqUBSBJleqpACLibyPisYh4NCJujYjXR8TJEfFARDwZEbdFxJFl7lFlfbzcvqwfD0CS1J0F3d4xIhYDfwOckpm/iojbgYuA84DrMnNzRPwbcBlwQ7l+MTPfGhEXAZ8D/qLnR1CpZevvanR/uzec3+j+JB16vZ4CWgAcHRELgDcAe4CzgTvK7bcAF5Tl1WWdcvvKiIge9y9J6lLXBZCZzwL/AjxN+wf/PmAH8FJmTpVpE8DisrwYeKbcd6rMP6Hb/UuSehOZ2d0dI44DvkH7NM5LwNfL+jWZ+dYyZylwd2auiIjHgHMyc6Lc9hPg9Mz8r2nbXQusBRgZGXnv5s2bu8oHsPeFfTz/q67vPjAjRzN0uVcsPnbWOZOTkxxzzDENpOkvczfL3IfeWWedtSMzR2eb1/VrAMCfAT/NzJ8DRMQ3gT8BFkXEgvJb/hLguTJ/AlgKTJRTRscCL0zfaGZuBDYCjI6O5tjYWNcBr9+0hWt39vIQB2Pdiqmhy737krFZ57RaLXr5/zUo5m6WuYdHL68BPA2cERFvKOfyVwKPA/cBHylz1gBbyvLWsk65/d7s9vBDktSzXl4DeID2i7k/AHaWbW0ErgKujIhx2uf4byx3uRE4oYxfCazvIbckqUc9nWfIzGuAa6YNPwWcPsPcXwMX9rI/SVL/+ElgSaqUBSBJlbIAJKlSFoAkVcoCkKRKWQCSVCkLQJIqZQFIUqUsAEmqlAUgSZUarq+c1NCay18gW7diikv78JfK/OtjUjM8ApCkSlkAklQpC0CSKmUBSFKlLABJqpQFIEmVsgAkqVIWgCRVygKQpEpZAJJUKb8KQkNnLl870U83r1rY6P6kYeERgCRVqqcCiIhFEXFHRPwoInZFxPsj4viI2BYRT5br48rciIgvRcR4RDwSEaf15yFIkrrR6xHAF4FvZ+bbgXcDu4D1wPbMXA5sL+sA5wLLy2UtcEOP+5Yk9aDrAoiINwIfAG4EyMzfZuZLwGrgljLtFuCCsrwa+Fq23Q8sioiTuk4uSepJL0cAbwF+Dnw1Ih6KiK9ExEJgJDP3AJTrN5f5i4FnOu4/UcYkSQPQy7uAFgCnAZ/MzAci4ov8/+memcQMY/mqSRFraZ8iYmRkhFar1XXAkaPbf6RkvjF3syYnJ3t6ng2KuZs1X3MfSC8FMAFMZOYDZf0O2gXwfESclJl7yimevR3zl3bcfwnw3PSNZuZGYCPA6Ohojo2NdR3w+k1buHbn/Hun67oVU+Zu0M2rFtLL82xQWq2WuRs0X3MfSNengDLzZ8AzEfG2MrQSeBzYCqwpY2uALWV5K/Cx8m6gM4B9+08VSZKa1+uva58ENkXEkcBTwMdpl8rtEXEZ8DRwYZl7N3AeMA68UuZKkgakpwLIzIeB0RluWjnD3AQu72V/kqT+8ZPAklQpC0CSKmUBSFKlLABJqpQFIEmVsgAkqVIWgCRVygKQpEpZAJJUqfn3zV1Sn+18dh+XNvh3iHdvOL+xfUkH4hGAJFXKApCkSlkAklQpC0CSKmUBSFKlLABJqpQFIEmVsgAkqVIWgCRVygKQpEpZAJJUKQtAkiplAUhSpSwASapUzwUQEUdExEMRcWdZPzkiHoiIJyPitog4sowfVdbHy+3Let23JKl7/TgCuALY1bH+OeC6zFwOvAhcVsYvA17MzLcC15V5kqQB6akAImIJcD7wlbIewNnAHWXKLcAFZXl1WafcvrLMlyQNQK9HAF8APg38vqyfALyUmVNlfQJYXJYXA88AlNv3lfmSpAHo+k9CRsSHgL2ZuSMixvYPzzA153Bb53bXAmsBRkZGaLVa3UZk5GhYt2Jq9olDxtzNajp3L8/pTpOTk33bVpPMPTx6+ZvAZwIfjojzgNcDb6R9RLAoIhaU3/KXAM+V+RPAUmAiIhYAxwIvTN9oZm4ENgKMjo7m2NhY1wGv37SFa3fOvz97vG7FlLkb1HTu3ZeM9WU7rVaLXv59DIq5h0fXp4Ay8+rMXJKZy4CLgHsz8xLgPuAjZdoaYEtZ3lrWKbffm5mvOgKQJDXjUHwO4CrgyogYp32O/8YyfiNwQhm/Elh/CPYtSZqjvhz3ZmYLaJXlp4DTZ5jza+DCfuxPktQ7PwksSZWyACSpUhaAJFXKApCkSlkAklQpC0CSKmUBSFKlLABJqpQFIEmVsgAkqVIWgCRVygKQpEpZAJJUKQtAkiplAUhSpSwASaqUBSBJlbIAJKlSFoAkVcoCkKRKWQCSVCkLQJIqZQFIUqUsAEmqVNcFEBFLI+K+iNgVEY9FxBVl/PiI2BYRT5br48p4RMSXImI8Ih6JiNP69SAkSQevlyOAKWBdZr4DOAO4PCJOAdYD2zNzObC9rAOcCywvl7XADT3sW5LUo64LIDP3ZOYPyvJ/A7uAxcBq4JYy7RbggrK8Gvhatt0PLIqIk7pOLknqSWRm7xuJWAZ8F3gn8HRmLuq47cXMPC4i7gQ2ZOZ/lPHtwFWZ+eC0ba2lfYTAyMjIezdv3tx1rr0v7OP5X3V994EZORpzN6jp3CsWH9uX7UxOTnLMMcf0ZVtNMvehd9ZZZ+3IzNHZ5i3odUcRcQzwDeBTmfnLiHjNqTOMvap9MnMjsBFgdHQ0x8bGus52/aYtXLuz54fYuHUrpszdoKZz775krC/babVa9PLvY1DMPTx6ehdQRLyO9g//TZn5zTL8/P5TO+V6bxmfAJZ23H0J8Fwv+5ckda+XdwEFcCOwKzM/33HTVmBNWV4DbOkY/1h5N9AZwL7M3NPt/iVJvenluPdM4KPAzoh4uIz9HbABuD0iLgOeBi4st90NnAeMA68AH+9h35KkHnVdAOXF3Nc64b9yhvkJXN7t/iRJ/eUngSWpUhaAJFXKApCkSlkAklQpC0CSKmUBSFKlLABJqpQFIEmVsgAkqVIWgCRVygKQpEpZAJJUKQtAkiplAUhSpSwASaqUBSBJlbIAJKlSFoAkVcoCkKRKWQCSVCkLQJIqZQFIUqUWDDqApMPHsvV3zTpn3YopLp3DvLnYveH8vmynVo0XQESsAr4IHAF8JTM3NJ1BqsVcfiCrXo2eAoqII4AvA+cCpwAXR8QpTWaQJLU1fQRwOjCemU8BRMRmYDXweMM5pIHp12/l/TyVMl81eYRz86qFje2rKU0XwGLgmY71CeB9DWeQpIO289l9jRZuE69vRGYe8p38384iLgTOycy/KusfBU7PzE92zFkLrC2rbwOe6GGXJwK/6OH+g2LuZpm7WeY+9P4oM98026SmjwAmgKUd60uA5zonZOZGYGM/dhYRD2bmaD+21SRzN8vczTL38Gj6cwDfB5ZHxMkRcSRwEbC14QySJBo+AsjMqYj4BHAP7beB3pSZjzWZQZLU1vjnADLzbuDuhnbXl1NJA2DuZpm7WeYeEo2+CCxJGh5+F5AkVeqwLICIWBURT0TEeESsH3SeA4mImyJib0Q82jF2fERsi4gny/Vxg8w4XUQsjYj7ImJXRDwWEVeU8WHP/fqI+F5E/LDk/ocyfnJEPFBy31beoDB0IuKIiHgoIu4s6/Ml9+6I2BkRD0fEg2VsqJ8rABGxKCLuiIgflef6++dD7oNx2BXAPPy6iZuBVdPG1gPbM3M5sL2sD5MpYF1mvgM4A7i8/Dce9ty/Ac7OzHcDpwKrIuIM4HPAdSX3i8BlA8x4IFcAuzrW50tugLMy89SOt1EO+3MF2t9Z9u3MfDvwbtr/7edD7rnLzMPqArwfuKdj/Wrg6kHnmiXzMuDRjvUngJPK8knAE4POOEv+LcCfz6fcwBuAH9D+JPovgAUzPX+G5UL7MzPbgbOBO4GYD7lLtt3AidPGhvq5ArwR+CnlddL5kvtgL4fdEQAzf93E4gFl6dZIZu4BKNdvHnCe1xQRy4D3AA8wD3KX0ygPA3uBbcBPgJcyc6pMGdbnyxeATwO/L+snMD9yAyTwnYjYUT7pD8P/XHkL8HPgq+W021ciYiHDn/ugHI4FEDOM+VanQyAijgG+AXwqM3856DxzkZm/y8xTaf9GfTrwjpmmNZvqwCLiQ8DezNzROTzD1KHK3eHMzDyN9mnZyyPiA4MONAcLgNOAGzLzPcDLzPfTPTM4HAtg1q+bmAeej4iTAMr13gHneZWIeB3tH/6bMvObZXjoc++XmS8BLdqvYSyKiP2fiRnG58uZwIcjYjewmfZpoC8w/LkByMznyvVe4Fu0i3fYnysTwERmPlDW76BdCMOe+6AcjgVwOHzdxFZgTVleQ/sc+9CIiABuBHZl5uc7bhr23G+KiEVl+Wjgz2i/sHcf8JEybehyZ+bVmbkkM5fRfj7fm5mXMOS5ASJiYUT84f5l4IPAowz5cyUzfwY8ExFvK0MraX9t/VDnPmiDfhHiUFyA84Af0z6/+5lB55kl663AHuB/aP/WcRnt87vbgSfL9fGDzjkt85/SPt3wCPBwuZw3D3K/C3io5H4U+Psy/hbge8A48HXgqEFnPcBjGAPunC+5S8Yflstj+/89DvtzpWQ8FXiwPF/+HThuPuQ+mIufBJakSh2Op4AkSXNgAUhSpSwASaqUBSBJlbIAJKlSFoAkVcoCkKRKWQCSVKn/BSSqwm3P3canAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "data_train['Months_Since_Last_Claim'].hist(bins=range(0, 72, 6))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x1e194850>"
      ]
     },
     "execution_count": 46,
     "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": [
    "data_train['Months_Since_Policy_Inception'].hist()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x1bceca28588>"
      ]
     },
     "execution_count": 4,
     "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": [
    "data_train['Outstanding_Balance'].hist()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "metadata": {},
   "outputs": [],
   "source": [
    "def get_categorical_dist(attr):\n",
    "    return data_train.groupby(attr).size().sort_values(ascending=False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "State_Code\n",
       "IA    1546\n",
       "MO    1446\n",
       "NE    1022\n",
       "OK     522\n",
       "KS     465\n",
       "dtype: int64"
      ]
     },
     "execution_count": 43,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "get_categorical_dist('State_Code')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Education\n",
       "College                 1492\n",
       "Bachelor                1491\n",
       "High School or Below    1405\n",
       "Master                   395\n",
       "Doctor                   207\n",
       "dtype: int64"
      ]
     },
     "execution_count": 44,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "get_categorical_dist('Education')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Employment_Status\n",
       "Employed         3126\n",
       "Unemployed       1270\n",
       "Medical Leave     234\n",
       "Disabled          213\n",
       "Retired           158\n",
       "dtype: int64"
      ]
     },
     "execution_count": 45,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "get_categorical_dist('Employment_Status')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Gender\n",
       "M    2531\n",
       "F    2470\n",
       "dtype: int64"
      ]
     },
     "execution_count": 46,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "get_categorical_dist('Gender')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 53,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Marital_Status\n",
       "Married     2930\n",
       "Single      1321\n",
       "Divorced     750\n",
       "dtype: int64"
      ]
     },
     "execution_count": 53,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "get_categorical_dist('Marital_Status')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Location\n",
       "Suburban    3156\n",
       "Rural        981\n",
       "Urban        861\n",
       "dtype: int64"
      ]
     },
     "execution_count": 47,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "get_categorical_dist('Location')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Claim_Cause\n",
       "Collision       2077\n",
       "Hail            1578\n",
       "Scratch/Dent     765\n",
       "Other            580\n",
       "Fire               1\n",
       "dtype: int64"
      ]
     },
     "execution_count": 48,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "get_categorical_dist('Claim_Cause')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Claim_Report_Type\n",
       "Agent          1889\n",
       "Branch         1411\n",
       "Call Center     958\n",
       "Web             743\n",
       "dtype: int64"
      ]
     },
     "execution_count": 49,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "get_categorical_dist('Claim_Report_Type')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 50,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Vehicle_Class\n",
       "Four-Door Car    2535\n",
       "Two-Door Car     1029\n",
       "SUV               988\n",
       "Sports Car        256\n",
       "Luxury SUV        115\n",
       "Luxury Car         78\n",
       "dtype: int64"
      ]
     },
     "execution_count": 50,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "get_categorical_dist('Vehicle_Class')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 51,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Vehicle_Model\n",
       "Toyota       1278\n",
       "Chevrolet    1267\n",
       "Ford         1265\n",
       "Honda        1191\n",
       "dtype: int64"
      ]
     },
     "execution_count": 51,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "get_categorical_dist('Vehicle_Model')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 52,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Fraudulent_Claim\n",
       "N    4694\n",
       "Y     307\n",
       "dtype: int64"
      ]
     },
     "execution_count": 52,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "get_categorical_dist('Fraudulent_Claim')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 54,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Claim_Date\n",
       "12/15/2018    2000\n",
       "12/01/2018    2000\n",
       "01/15/2019    1001\n",
       "dtype: int64"
      ]
     },
     "execution_count": 54,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "get_categorical_dist('Claim_Date')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "State_Code                        0\n",
       "State                             0\n",
       "Claim_Amount                      0\n",
       "Education                        11\n",
       "Claim_Date                        0\n",
       "Employment_Status                 0\n",
       "Gender                            0\n",
       "Income                            0\n",
       "Location                          3\n",
       "Marital_Status                    0\n",
       "Monthly_Premium                   0\n",
       "Annual_Premium                    0\n",
       "Months_Since_Last_Claim           0\n",
       "Months_Since_Policy_Inception     0\n",
       "Claim_Cause                       0\n",
       "Claim_Report_Type                 0\n",
       "Vehicle_Class                     0\n",
       "Vehicle_Size                      0\n",
       "Vehicle_Model                     0\n",
       "Outstanding_Balance               9\n",
       "Fraudulent_Claim                  0\n",
       "Claimant__Number                  0\n",
       "dtype: int64"
      ]
     },
     "execution_count": 48,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#missing data\n",
    "data_train.isna().sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "c:\\users\\student\\appdata\\local\\programs\\python\\python37-32\\lib\\site-packages\\scipy\\stats\\stats.py:1713: FutureWarning: Using a non-tuple sequence for multidimensional indexing is deprecated; use `arr[tuple(seq)]` instead of `arr[seq]`. In the future this will be interpreted as an array index, `arr[np.array(seq)]`, which will result either in an error or a different result.\n",
      "  return np.add.reduce(sorted[indexer] * weights, axis=axis) / sumval\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 360x360 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.catplot(x=\"Fraudulent_Claim\", y=\"Claim_Amount\", kind=\"violin\",data=data_train);"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 50,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "c:\\users\\student\\appdata\\local\\programs\\python\\python37-32\\lib\\site-packages\\scipy\\stats\\stats.py:1713: FutureWarning: Using a non-tuple sequence for multidimensional indexing is deprecated; use `arr[tuple(seq)]` instead of `arr[seq]`. In the future this will be interpreted as an array index, `arr[np.array(seq)]`, which will result either in an error or a different result.\n",
      "  return np.add.reduce(sorted[indexer] * weights, axis=axis) / sumval\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 360x360 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.catplot(x=\"Fraudulent_Claim\", y=\"Income\", kind=\"violin\",data=data_train);"
   ]
  }
 ],
 "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": "3.7.0"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}