Insurance Claims - Data Analysis [12-10-2021].ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "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": [],
   "source": [
    "from datetime import datetime, timedelta"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "cust = pd.read_csv('cust_demographics.csv')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "claims = pd.read_csv('claims.csv')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>CUST_ID</th>\n",
       "      <th>gender</th>\n",
       "      <th>DateOfBirth</th>\n",
       "      <th>State</th>\n",
       "      <th>Contact</th>\n",
       "      <th>Segment</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>21868593</td>\n",
       "      <td>Female</td>\n",
       "      <td>12-Jan-79</td>\n",
       "      <td>VT</td>\n",
       "      <td>789-916-8172</td>\n",
       "      <td>Platinum</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>75740424</td>\n",
       "      <td>Female</td>\n",
       "      <td>13-Jan-70</td>\n",
       "      <td>ME</td>\n",
       "      <td>265-543-1264</td>\n",
       "      <td>Silver</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "    CUST_ID  gender DateOfBirth State       Contact   Segment\n",
       "0  21868593  Female   12-Jan-79    VT  789-916-8172  Platinum\n",
       "1  75740424  Female   13-Jan-70    ME  265-543-1264    Silver"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "cust.head(2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>claim_id</th>\n",
       "      <th>customer_id</th>\n",
       "      <th>incident_cause</th>\n",
       "      <th>claim_date</th>\n",
       "      <th>claim_area</th>\n",
       "      <th>police_report</th>\n",
       "      <th>claim_type</th>\n",
       "      <th>claim_amount</th>\n",
       "      <th>total_policy_claims</th>\n",
       "      <th>fraudulent</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>54004764</td>\n",
       "      <td>21868593</td>\n",
       "      <td>Driver error</td>\n",
       "      <td>11/27/2017</td>\n",
       "      <td>Auto</td>\n",
       "      <td>No</td>\n",
       "      <td>Material only</td>\n",
       "      <td>$2980</td>\n",
       "      <td>1.0</td>\n",
       "      <td>No</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>33985796</td>\n",
       "      <td>75740424</td>\n",
       "      <td>Crime</td>\n",
       "      <td>10/03/2018</td>\n",
       "      <td>Home</td>\n",
       "      <td>Unknown</td>\n",
       "      <td>Material only</td>\n",
       "      <td>$2980</td>\n",
       "      <td>3.0</td>\n",
       "      <td>No</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   claim_id  customer_id incident_cause  claim_date claim_area police_report  \\\n",
       "0  54004764     21868593   Driver error  11/27/2017       Auto            No   \n",
       "1  33985796     75740424          Crime  10/03/2018       Home       Unknown   \n",
       "\n",
       "      claim_type claim_amount  total_policy_claims fraudulent  \n",
       "0  Material only        $2980                  1.0         No  \n",
       "1  Material only        $2980                  3.0         No  "
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "claims.head(2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [],
   "source": [
    "df = claims.merge(cust,how='left',left_on='customer_id',right_on='CUST_ID')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>claim_id</th>\n",
       "      <th>customer_id</th>\n",
       "      <th>incident_cause</th>\n",
       "      <th>claim_date</th>\n",
       "      <th>claim_area</th>\n",
       "      <th>police_report</th>\n",
       "      <th>claim_type</th>\n",
       "      <th>claim_amount</th>\n",
       "      <th>total_policy_claims</th>\n",
       "      <th>fraudulent</th>\n",
       "      <th>CUST_ID</th>\n",
       "      <th>gender</th>\n",
       "      <th>DateOfBirth</th>\n",
       "      <th>State</th>\n",
       "      <th>Contact</th>\n",
       "      <th>Segment</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>54004764</td>\n",
       "      <td>21868593</td>\n",
       "      <td>Driver error</td>\n",
       "      <td>11/27/2017</td>\n",
       "      <td>Auto</td>\n",
       "      <td>No</td>\n",
       "      <td>Material only</td>\n",
       "      <td>$2980</td>\n",
       "      <td>1.0</td>\n",
       "      <td>No</td>\n",
       "      <td>21868593.0</td>\n",
       "      <td>Female</td>\n",
       "      <td>12-Jan-79</td>\n",
       "      <td>VT</td>\n",
       "      <td>789-916-8172</td>\n",
       "      <td>Platinum</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>33985796</td>\n",
       "      <td>75740424</td>\n",
       "      <td>Crime</td>\n",
       "      <td>10/03/2018</td>\n",
       "      <td>Home</td>\n",
       "      <td>Unknown</td>\n",
       "      <td>Material only</td>\n",
       "      <td>$2980</td>\n",
       "      <td>3.0</td>\n",
       "      <td>No</td>\n",
       "      <td>75740424.0</td>\n",
       "      <td>Female</td>\n",
       "      <td>13-Jan-70</td>\n",
       "      <td>ME</td>\n",
       "      <td>265-543-1264</td>\n",
       "      <td>Silver</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>53522022</td>\n",
       "      <td>30308357</td>\n",
       "      <td>Other driver error</td>\n",
       "      <td>02/02/2018</td>\n",
       "      <td>Auto</td>\n",
       "      <td>No</td>\n",
       "      <td>Material only</td>\n",
       "      <td>$3369.5</td>\n",
       "      <td>1.0</td>\n",
       "      <td>Yes</td>\n",
       "      <td>30308357.0</td>\n",
       "      <td>Female</td>\n",
       "      <td>11-Mar-84</td>\n",
       "      <td>TN</td>\n",
       "      <td>798-631-4758</td>\n",
       "      <td>Silver</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>13015401</td>\n",
       "      <td>47830476</td>\n",
       "      <td>Natural causes</td>\n",
       "      <td>06/17/2018</td>\n",
       "      <td>Auto</td>\n",
       "      <td>No</td>\n",
       "      <td>Material only</td>\n",
       "      <td>$1680</td>\n",
       "      <td>1.0</td>\n",
       "      <td>No</td>\n",
       "      <td>47830476.0</td>\n",
       "      <td>Female</td>\n",
       "      <td>01-May-86</td>\n",
       "      <td>MA</td>\n",
       "      <td>413-187-7945</td>\n",
       "      <td>Silver</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>22890252</td>\n",
       "      <td>19269962</td>\n",
       "      <td>Crime</td>\n",
       "      <td>01/13/2018</td>\n",
       "      <td>Auto</td>\n",
       "      <td>No</td>\n",
       "      <td>Material only</td>\n",
       "      <td>$2680</td>\n",
       "      <td>1.0</td>\n",
       "      <td>No</td>\n",
       "      <td>19269962.0</td>\n",
       "      <td>Male</td>\n",
       "      <td>13-May-77</td>\n",
       "      <td>NV</td>\n",
       "      <td>956-871-8691</td>\n",
       "      <td>Gold</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   claim_id  customer_id      incident_cause  claim_date claim_area  \\\n",
       "0  54004764     21868593        Driver error  11/27/2017       Auto   \n",
       "1  33985796     75740424               Crime  10/03/2018       Home   \n",
       "2  53522022     30308357  Other driver error  02/02/2018       Auto   \n",
       "3  13015401     47830476      Natural causes  06/17/2018       Auto   \n",
       "4  22890252     19269962               Crime  01/13/2018       Auto   \n",
       "\n",
       "  police_report     claim_type claim_amount  total_policy_claims fraudulent  \\\n",
       "0            No  Material only        $2980                  1.0         No   \n",
       "1       Unknown  Material only        $2980                  3.0         No   \n",
       "2            No  Material only      $3369.5                  1.0        Yes   \n",
       "3            No  Material only        $1680                  1.0         No   \n",
       "4            No  Material only        $2680                  1.0         No   \n",
       "\n",
       "      CUST_ID  gender DateOfBirth State       Contact   Segment  \n",
       "0  21868593.0  Female   12-Jan-79    VT  789-916-8172  Platinum  \n",
       "1  75740424.0  Female   13-Jan-70    ME  265-543-1264    Silver  \n",
       "2  30308357.0  Female   11-Mar-84    TN  798-631-4758    Silver  \n",
       "3  47830476.0  Female   01-May-86    MA  413-187-7945    Silver  \n",
       "4  19269962.0    Male   13-May-77    NV  956-871-8691      Gold  "
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "claim_id                 int64\n",
       "customer_id              int64\n",
       "incident_cause          object\n",
       "claim_date              object\n",
       "claim_area              object\n",
       "police_report           object\n",
       "claim_type              object\n",
       "claim_amount            object\n",
       "total_policy_claims    float64\n",
       "fraudulent              object\n",
       "CUST_ID                float64\n",
       "gender                  object\n",
       "DateOfBirth             object\n",
       "State                   object\n",
       "Contact                 object\n",
       "Segment                 object\n",
       "dtype: object"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.dtypes"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Claim amount needs to be in float64. Total policy claims can be int."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0      $2980\n",
       "1      $2980\n",
       "2    $3369.5\n",
       "3      $1680\n",
       "4      $2680\n",
       "Name: claim_amount, dtype: object"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df['claim_amount'].head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 3. Convert column claim amount to numeric "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [],
   "source": [
    "df['claim_amount'] = df['claim_amount'].str[1:].astype('float64') "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "claim_id                 int64\n",
       "customer_id              int64\n",
       "incident_cause          object\n",
       "claim_date              object\n",
       "claim_area              object\n",
       "police_report           object\n",
       "claim_type              object\n",
       "claim_amount           float64\n",
       "total_policy_claims    float64\n",
       "fraudulent              object\n",
       "CUST_ID                float64\n",
       "gender                  object\n",
       "DateOfBirth             object\n",
       "State                   object\n",
       "Contact                 object\n",
       "Segment                 object\n",
       "dtype: object"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.dtypes"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>claim_id</th>\n",
       "      <th>customer_id</th>\n",
       "      <th>incident_cause</th>\n",
       "      <th>claim_date</th>\n",
       "      <th>claim_area</th>\n",
       "      <th>police_report</th>\n",
       "      <th>claim_type</th>\n",
       "      <th>claim_amount</th>\n",
       "      <th>total_policy_claims</th>\n",
       "      <th>fraudulent</th>\n",
       "      <th>CUST_ID</th>\n",
       "      <th>gender</th>\n",
       "      <th>DateOfBirth</th>\n",
       "      <th>State</th>\n",
       "      <th>Contact</th>\n",
       "      <th>Segment</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>54004764</td>\n",
       "      <td>21868593</td>\n",
       "      <td>Driver error</td>\n",
       "      <td>11/27/2017</td>\n",
       "      <td>Auto</td>\n",
       "      <td>No</td>\n",
       "      <td>Material only</td>\n",
       "      <td>2980.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>No</td>\n",
       "      <td>21868593.0</td>\n",
       "      <td>Female</td>\n",
       "      <td>12-Jan-79</td>\n",
       "      <td>VT</td>\n",
       "      <td>789-916-8172</td>\n",
       "      <td>Platinum</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>33985796</td>\n",
       "      <td>75740424</td>\n",
       "      <td>Crime</td>\n",
       "      <td>10/03/2018</td>\n",
       "      <td>Home</td>\n",
       "      <td>Unknown</td>\n",
       "      <td>Material only</td>\n",
       "      <td>2980.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>No</td>\n",
       "      <td>75740424.0</td>\n",
       "      <td>Female</td>\n",
       "      <td>13-Jan-70</td>\n",
       "      <td>ME</td>\n",
       "      <td>265-543-1264</td>\n",
       "      <td>Silver</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   claim_id  customer_id incident_cause  claim_date claim_area police_report  \\\n",
       "0  54004764     21868593   Driver error  11/27/2017       Auto            No   \n",
       "1  33985796     75740424          Crime  10/03/2018       Home       Unknown   \n",
       "\n",
       "      claim_type  claim_amount  total_policy_claims fraudulent     CUST_ID  \\\n",
       "0  Material only        2980.0                  1.0         No  21868593.0   \n",
       "1  Material only        2980.0                  3.0         No  75740424.0   \n",
       "\n",
       "   gender DateOfBirth State       Contact   Segment  \n",
       "0  Female   12-Jan-79    VT  789-916-8172  Platinum  \n",
       "1  Female   13-Jan-70    ME  265-543-1264    Silver  "
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.head(2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Material only          663\n",
       "Material and injury    241\n",
       "Injury only            196\n",
       "Name: claim_type, dtype: int64"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.claim_type.value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "255    Material and injury\n",
       "Name: claim_type, dtype: object"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.claim_type.sample()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "No         630\n",
       "Unknown    300\n",
       "Yes        170\n",
       "Name: police_report, dtype: int64"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.police_report.value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [],
   "source": [
    "df['isReported?'] = np.where((df['claim_type']=='Injury only') & (df['police_report']=='No'),0,1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
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       "\n",
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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>police_report</th>\n",
       "      <th>claim_type</th>\n",
       "      <th>isReported?</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>190</th>\n",
       "      <td>No</td>\n",
       "      <td>Material only</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>334</th>\n",
       "      <td>Unknown</td>\n",
       "      <td>Material only</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>259</th>\n",
       "      <td>No</td>\n",
       "      <td>Material only</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>No</td>\n",
       "      <td>Material only</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1057</th>\n",
       "      <td>No</td>\n",
       "      <td>Material and injury</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "     police_report           claim_type  isReported?\n",
       "190             No        Material only            1\n",
       "334        Unknown        Material only            1\n",
       "259             No        Material only            1\n",
       "7               No        Material only            1\n",
       "1057            No  Material and injury            1"
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df[['police_report','claim_type','isReported?']].sample(5)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 4. In the above table isReported? column displays zeros for those with claim_type injury only and no reported to police"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "    }\n",
       "\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>claim_id</th>\n",
       "      <th>customer_id</th>\n",
       "      <th>incident_cause</th>\n",
       "      <th>claim_date</th>\n",
       "      <th>claim_area</th>\n",
       "      <th>police_report</th>\n",
       "      <th>claim_type</th>\n",
       "      <th>claim_amount</th>\n",
       "      <th>total_policy_claims</th>\n",
       "      <th>fraudulent</th>\n",
       "      <th>CUST_ID</th>\n",
       "      <th>gender</th>\n",
       "      <th>DateOfBirth</th>\n",
       "      <th>State</th>\n",
       "      <th>Contact</th>\n",
       "      <th>Segment</th>\n",
       "      <th>isReported?</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>54004764</td>\n",
       "      <td>21868593</td>\n",
       "      <td>Driver error</td>\n",
       "      <td>11/27/2017</td>\n",
       "      <td>Auto</td>\n",
       "      <td>No</td>\n",
       "      <td>Material only</td>\n",
       "      <td>2980.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>No</td>\n",
       "      <td>21868593.0</td>\n",
       "      <td>Female</td>\n",
       "      <td>12-Jan-79</td>\n",
       "      <td>VT</td>\n",
       "      <td>789-916-8172</td>\n",
       "      <td>Platinum</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>33985796</td>\n",
       "      <td>75740424</td>\n",
       "      <td>Crime</td>\n",
       "      <td>10/03/2018</td>\n",
       "      <td>Home</td>\n",
       "      <td>Unknown</td>\n",
       "      <td>Material only</td>\n",
       "      <td>2980.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>No</td>\n",
       "      <td>75740424.0</td>\n",
       "      <td>Female</td>\n",
       "      <td>13-Jan-70</td>\n",
       "      <td>ME</td>\n",
       "      <td>265-543-1264</td>\n",
       "      <td>Silver</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>53522022</td>\n",
       "      <td>30308357</td>\n",
       "      <td>Other driver error</td>\n",
       "      <td>02/02/2018</td>\n",
       "      <td>Auto</td>\n",
       "      <td>No</td>\n",
       "      <td>Material only</td>\n",
       "      <td>3369.5</td>\n",
       "      <td>1.0</td>\n",
       "      <td>Yes</td>\n",
       "      <td>30308357.0</td>\n",
       "      <td>Female</td>\n",
       "      <td>11-Mar-84</td>\n",
       "      <td>TN</td>\n",
       "      <td>798-631-4758</td>\n",
       "      <td>Silver</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>13015401</td>\n",
       "      <td>47830476</td>\n",
       "      <td>Natural causes</td>\n",
       "      <td>06/17/2018</td>\n",
       "      <td>Auto</td>\n",
       "      <td>No</td>\n",
       "      <td>Material only</td>\n",
       "      <td>1680.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>No</td>\n",
       "      <td>47830476.0</td>\n",
       "      <td>Female</td>\n",
       "      <td>01-May-86</td>\n",
       "      <td>MA</td>\n",
       "      <td>413-187-7945</td>\n",
       "      <td>Silver</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>22890252</td>\n",
       "      <td>19269962</td>\n",
       "      <td>Crime</td>\n",
       "      <td>01/13/2018</td>\n",
       "      <td>Auto</td>\n",
       "      <td>No</td>\n",
       "      <td>Material only</td>\n",
       "      <td>2680.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>No</td>\n",
       "      <td>19269962.0</td>\n",
       "      <td>Male</td>\n",
       "      <td>13-May-77</td>\n",
       "      <td>NV</td>\n",
       "      <td>956-871-8691</td>\n",
       "      <td>Gold</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   claim_id  customer_id      incident_cause  claim_date claim_area  \\\n",
       "0  54004764     21868593        Driver error  11/27/2017       Auto   \n",
       "1  33985796     75740424               Crime  10/03/2018       Home   \n",
       "2  53522022     30308357  Other driver error  02/02/2018       Auto   \n",
       "3  13015401     47830476      Natural causes  06/17/2018       Auto   \n",
       "4  22890252     19269962               Crime  01/13/2018       Auto   \n",
       "\n",
       "  police_report     claim_type  claim_amount  total_policy_claims fraudulent  \\\n",
       "0            No  Material only        2980.0                  1.0         No   \n",
       "1       Unknown  Material only        2980.0                  3.0         No   \n",
       "2            No  Material only        3369.5                  1.0        Yes   \n",
       "3            No  Material only        1680.0                  1.0         No   \n",
       "4            No  Material only        2680.0                  1.0         No   \n",
       "\n",
       "      CUST_ID  gender DateOfBirth State       Contact   Segment  isReported?  \n",
       "0  21868593.0  Female   12-Jan-79    VT  789-916-8172  Platinum            1  \n",
       "1  75740424.0  Female   13-Jan-70    ME  265-543-1264    Silver            1  \n",
       "2  30308357.0  Female   11-Mar-84    TN  798-631-4758    Silver            1  \n",
       "3  47830476.0  Female   01-May-86    MA  413-187-7945    Silver            1  \n",
       "4  19269962.0    Male   13-May-77    NV  956-871-8691      Gold            1  "
      ]
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(1100, 17)"
      ]
     },
     "execution_count": 20,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.shape"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 5. Drop duplicates based on customer_id column"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [],
   "source": [
    "df.drop_duplicates(subset = ['customer_id'], inplace = True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(1093, 17)"
      ]
     },
     "execution_count": 22,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.shape"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 6. Check for missing values and impute with mean for continuous and mode for categorical value"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>claim_id</th>\n",
       "      <th>customer_id</th>\n",
       "      <th>incident_cause</th>\n",
       "      <th>claim_date</th>\n",
       "      <th>claim_area</th>\n",
       "      <th>police_report</th>\n",
       "      <th>claim_type</th>\n",
       "      <th>claim_amount</th>\n",
       "      <th>total_policy_claims</th>\n",
       "      <th>fraudulent</th>\n",
       "      <th>CUST_ID</th>\n",
       "      <th>gender</th>\n",
       "      <th>DateOfBirth</th>\n",
       "      <th>State</th>\n",
       "      <th>Contact</th>\n",
       "      <th>Segment</th>\n",
       "      <th>isReported?</th>\n",
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       "  <tbody>\n",
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       "      <th>0</th>\n",
       "      <td>54004764</td>\n",
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       "      <td>Driver error</td>\n",
       "      <td>11/27/2017</td>\n",
       "      <td>Auto</td>\n",
       "      <td>No</td>\n",
       "      <td>Material only</td>\n",
       "      <td>2980.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>No</td>\n",
       "      <td>21868593.0</td>\n",
       "      <td>Female</td>\n",
       "      <td>12-Jan-79</td>\n",
       "      <td>VT</td>\n",
       "      <td>789-916-8172</td>\n",
       "      <td>Platinum</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>33985796</td>\n",
       "      <td>75740424</td>\n",
       "      <td>Crime</td>\n",
       "      <td>10/03/2018</td>\n",
       "      <td>Home</td>\n",
       "      <td>Unknown</td>\n",
       "      <td>Material only</td>\n",
       "      <td>2980.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>No</td>\n",
       "      <td>75740424.0</td>\n",
       "      <td>Female</td>\n",
       "      <td>13-Jan-70</td>\n",
       "      <td>ME</td>\n",
       "      <td>265-543-1264</td>\n",
       "      <td>Silver</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>53522022</td>\n",
       "      <td>30308357</td>\n",
       "      <td>Other driver error</td>\n",
       "      <td>02/02/2018</td>\n",
       "      <td>Auto</td>\n",
       "      <td>No</td>\n",
       "      <td>Material only</td>\n",
       "      <td>3369.5</td>\n",
       "      <td>1.0</td>\n",
       "      <td>Yes</td>\n",
       "      <td>30308357.0</td>\n",
       "      <td>Female</td>\n",
       "      <td>11-Mar-84</td>\n",
       "      <td>TN</td>\n",
       "      <td>798-631-4758</td>\n",
       "      <td>Silver</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   claim_id  customer_id      incident_cause  claim_date claim_area  \\\n",
       "0  54004764     21868593        Driver error  11/27/2017       Auto   \n",
       "1  33985796     75740424               Crime  10/03/2018       Home   \n",
       "2  53522022     30308357  Other driver error  02/02/2018       Auto   \n",
       "\n",
       "  police_report     claim_type  claim_amount  total_policy_claims fraudulent  \\\n",
       "0            No  Material only        2980.0                  1.0         No   \n",
       "1       Unknown  Material only        2980.0                  3.0         No   \n",
       "2            No  Material only        3369.5                  1.0        Yes   \n",
       "\n",
       "      CUST_ID  gender DateOfBirth State       Contact   Segment  isReported?  \n",
       "0  21868593.0  Female   12-Jan-79    VT  789-916-8172  Platinum            1  \n",
       "1  75740424.0  Female   13-Jan-70    ME  265-543-1264    Silver            1  \n",
       "2  30308357.0  Female   11-Mar-84    TN  798-631-4758    Silver            1  "
      ]
     },
     "execution_count": 23,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.head(3)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "Int64Index: 1093 entries, 0 to 1099\n",
      "Data columns (total 17 columns):\n",
      "claim_id               1093 non-null int64\n",
      "customer_id            1093 non-null int64\n",
      "incident_cause         1093 non-null object\n",
      "claim_date             1093 non-null object\n",
      "claim_area             1093 non-null object\n",
      "police_report          1093 non-null object\n",
      "claim_type             1093 non-null object\n",
      "claim_amount           1028 non-null float64\n",
      "total_policy_claims    1083 non-null float64\n",
      "fraudulent             1093 non-null object\n",
      "CUST_ID                1078 non-null float64\n",
      "gender                 1078 non-null object\n",
      "DateOfBirth            1078 non-null object\n",
      "State                  1078 non-null object\n",
      "Contact                1078 non-null object\n",
      "Segment                1078 non-null object\n",
      "isReported?            1093 non-null int32\n",
      "dtypes: float64(3), int32(1), int64(2), object(11)\n",
      "memory usage: 149.4+ KB\n"
     ]
    }
   ],
   "source": [
    "df.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [],
   "source": [
    "#continuous: claim amount, total_policy_claims\n",
    "#categorical: gender, State, Segment"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [],
   "source": [
    "#continuous variables\n",
    "df['claim_amount'] = df['claim_amount'].fillna(df['claim_amount'].mean(axis=0))\n",
    "df['total_policy_claims'] = df['total_policy_claims'].fillna(df['total_policy_claims'].mean(axis=0))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [],
   "source": [
    "df['gender'] = df['gender'].fillna(df['gender'].mode())\n",
    "df['State'] = df['State'].fillna(df['State'].mode())\n",
    "df['Segment'] = df['Segment'].fillna(df['Segment'].mode())"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 7. Age of Customers (in years). And Age group column"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>claim_id</th>\n",
       "      <th>customer_id</th>\n",
       "      <th>incident_cause</th>\n",
       "      <th>claim_date</th>\n",
       "      <th>claim_area</th>\n",
       "      <th>police_report</th>\n",
       "      <th>claim_type</th>\n",
       "      <th>claim_amount</th>\n",
       "      <th>total_policy_claims</th>\n",
       "      <th>fraudulent</th>\n",
       "      <th>CUST_ID</th>\n",
       "      <th>gender</th>\n",
       "      <th>DateOfBirth</th>\n",
       "      <th>State</th>\n",
       "      <th>Contact</th>\n",
       "      <th>Segment</th>\n",
       "      <th>isReported?</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>54004764</td>\n",
       "      <td>21868593</td>\n",
       "      <td>Driver error</td>\n",
       "      <td>11/27/2017</td>\n",
       "      <td>Auto</td>\n",
       "      <td>No</td>\n",
       "      <td>Material only</td>\n",
       "      <td>2980.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>No</td>\n",
       "      <td>21868593.0</td>\n",
       "      <td>Female</td>\n",
       "      <td>12-Jan-79</td>\n",
       "      <td>VT</td>\n",
       "      <td>789-916-8172</td>\n",
       "      <td>Platinum</td>\n",
       "      <td>1</td>\n",
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       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>33985796</td>\n",
       "      <td>75740424</td>\n",
       "      <td>Crime</td>\n",
       "      <td>10/03/2018</td>\n",
       "      <td>Home</td>\n",
       "      <td>Unknown</td>\n",
       "      <td>Material only</td>\n",
       "      <td>2980.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>No</td>\n",
       "      <td>75740424.0</td>\n",
       "      <td>Female</td>\n",
       "      <td>13-Jan-70</td>\n",
       "      <td>ME</td>\n",
       "      <td>265-543-1264</td>\n",
       "      <td>Silver</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>53522022</td>\n",
       "      <td>30308357</td>\n",
       "      <td>Other driver error</td>\n",
       "      <td>02/02/2018</td>\n",
       "      <td>Auto</td>\n",
       "      <td>No</td>\n",
       "      <td>Material only</td>\n",
       "      <td>3369.5</td>\n",
       "      <td>1.0</td>\n",
       "      <td>Yes</td>\n",
       "      <td>30308357.0</td>\n",
       "      <td>Female</td>\n",
       "      <td>11-Mar-84</td>\n",
       "      <td>TN</td>\n",
       "      <td>798-631-4758</td>\n",
       "      <td>Silver</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>13015401</td>\n",
       "      <td>47830476</td>\n",
       "      <td>Natural causes</td>\n",
       "      <td>06/17/2018</td>\n",
       "      <td>Auto</td>\n",
       "      <td>No</td>\n",
       "      <td>Material only</td>\n",
       "      <td>1680.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>No</td>\n",
       "      <td>47830476.0</td>\n",
       "      <td>Female</td>\n",
       "      <td>01-May-86</td>\n",
       "      <td>MA</td>\n",
       "      <td>413-187-7945</td>\n",
       "      <td>Silver</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>22890252</td>\n",
       "      <td>19269962</td>\n",
       "      <td>Crime</td>\n",
       "      <td>01/13/2018</td>\n",
       "      <td>Auto</td>\n",
       "      <td>No</td>\n",
       "      <td>Material only</td>\n",
       "      <td>2680.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>No</td>\n",
       "      <td>19269962.0</td>\n",
       "      <td>Male</td>\n",
       "      <td>13-May-77</td>\n",
       "      <td>NV</td>\n",
       "      <td>956-871-8691</td>\n",
       "      <td>Gold</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   claim_id  customer_id      incident_cause  claim_date claim_area  \\\n",
       "0  54004764     21868593        Driver error  11/27/2017       Auto   \n",
       "1  33985796     75740424               Crime  10/03/2018       Home   \n",
       "2  53522022     30308357  Other driver error  02/02/2018       Auto   \n",
       "3  13015401     47830476      Natural causes  06/17/2018       Auto   \n",
       "4  22890252     19269962               Crime  01/13/2018       Auto   \n",
       "\n",
       "  police_report     claim_type  claim_amount  total_policy_claims fraudulent  \\\n",
       "0            No  Material only        2980.0                  1.0         No   \n",
       "1       Unknown  Material only        2980.0                  3.0         No   \n",
       "2            No  Material only        3369.5                  1.0        Yes   \n",
       "3            No  Material only        1680.0                  1.0         No   \n",
       "4            No  Material only        2680.0                  1.0         No   \n",
       "\n",
       "      CUST_ID  gender DateOfBirth State       Contact   Segment  isReported?  \n",
       "0  21868593.0  Female   12-Jan-79    VT  789-916-8172  Platinum            1  \n",
       "1  75740424.0  Female   13-Jan-70    ME  265-543-1264    Silver            1  \n",
       "2  30308357.0  Female   11-Mar-84    TN  798-631-4758    Silver            1  \n",
       "3  47830476.0  Female   01-May-86    MA  413-187-7945    Silver            1  \n",
       "4  19269962.0    Male   13-May-77    NV  956-871-8691      Gold            1  "
      ]
     },
     "execution_count": 28,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.head(5)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
   "outputs": [],
   "source": [
    "df['DateOfBirth'] = pd.to_datetime(df['DateOfBirth'],format=\"%d-%b-%y\")\n",
    "df.loc[df['DateOfBirth'].dt.year >= 2020, 'DateOfBirth'] -= pd.DateOffset(years=100)  #issue with pandas"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>claim_id</th>\n",
       "      <th>customer_id</th>\n",
       "      <th>incident_cause</th>\n",
       "      <th>claim_date</th>\n",
       "      <th>claim_area</th>\n",
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       "      <th>CUST_ID</th>\n",
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       "      <th>DateOfBirth</th>\n",
       "      <th>State</th>\n",
       "      <th>Contact</th>\n",
       "      <th>Segment</th>\n",
       "      <th>isReported?</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>54004764</td>\n",
       "      <td>21868593</td>\n",
       "      <td>Driver error</td>\n",
       "      <td>11/27/2017</td>\n",
       "      <td>Auto</td>\n",
       "      <td>No</td>\n",
       "      <td>Material only</td>\n",
       "      <td>2980.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>No</td>\n",
       "      <td>21868593.0</td>\n",
       "      <td>Female</td>\n",
       "      <td>1979-01-12</td>\n",
       "      <td>VT</td>\n",
       "      <td>789-916-8172</td>\n",
       "      <td>Platinum</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>33985796</td>\n",
       "      <td>75740424</td>\n",
       "      <td>Crime</td>\n",
       "      <td>10/03/2018</td>\n",
       "      <td>Home</td>\n",
       "      <td>Unknown</td>\n",
       "      <td>Material only</td>\n",
       "      <td>2980.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>No</td>\n",
       "      <td>75740424.0</td>\n",
       "      <td>Female</td>\n",
       "      <td>1970-01-13</td>\n",
       "      <td>ME</td>\n",
       "      <td>265-543-1264</td>\n",
       "      <td>Silver</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>53522022</td>\n",
       "      <td>30308357</td>\n",
       "      <td>Other driver error</td>\n",
       "      <td>02/02/2018</td>\n",
       "      <td>Auto</td>\n",
       "      <td>No</td>\n",
       "      <td>Material only</td>\n",
       "      <td>3369.5</td>\n",
       "      <td>1.0</td>\n",
       "      <td>Yes</td>\n",
       "      <td>30308357.0</td>\n",
       "      <td>Female</td>\n",
       "      <td>1984-03-11</td>\n",
       "      <td>TN</td>\n",
       "      <td>798-631-4758</td>\n",
       "      <td>Silver</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>13015401</td>\n",
       "      <td>47830476</td>\n",
       "      <td>Natural causes</td>\n",
       "      <td>06/17/2018</td>\n",
       "      <td>Auto</td>\n",
       "      <td>No</td>\n",
       "      <td>Material only</td>\n",
       "      <td>1680.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>No</td>\n",
       "      <td>47830476.0</td>\n",
       "      <td>Female</td>\n",
       "      <td>1986-05-01</td>\n",
       "      <td>MA</td>\n",
       "      <td>413-187-7945</td>\n",
       "      <td>Silver</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>22890252</td>\n",
       "      <td>19269962</td>\n",
       "      <td>Crime</td>\n",
       "      <td>01/13/2018</td>\n",
       "      <td>Auto</td>\n",
       "      <td>No</td>\n",
       "      <td>Material only</td>\n",
       "      <td>2680.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>No</td>\n",
       "      <td>19269962.0</td>\n",
       "      <td>Male</td>\n",
       "      <td>1977-05-13</td>\n",
       "      <td>NV</td>\n",
       "      <td>956-871-8691</td>\n",
       "      <td>Gold</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>24050443</td>\n",
       "      <td>21831191</td>\n",
       "      <td>Other driver error</td>\n",
       "      <td>10/04/2017</td>\n",
       "      <td>Auto</td>\n",
       "      <td>No</td>\n",
       "      <td>Injury only</td>\n",
       "      <td>38306.5</td>\n",
       "      <td>3.0</td>\n",
       "      <td>Yes</td>\n",
       "      <td>21831191.0</td>\n",
       "      <td>Male</td>\n",
       "      <td>1977-05-17</td>\n",
       "      <td>NH</td>\n",
       "      <td>419-712-8513</td>\n",
       "      <td>Gold</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>12878692</td>\n",
       "      <td>18401412</td>\n",
       "      <td>Driver error</td>\n",
       "      <td>01/13/2018</td>\n",
       "      <td>Auto</td>\n",
       "      <td>No</td>\n",
       "      <td>Material only</td>\n",
       "      <td>1730.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>No</td>\n",
       "      <td>18401412.0</td>\n",
       "      <td>Male</td>\n",
       "      <td>1999-03-02</td>\n",
       "      <td>AR</td>\n",
       "      <td>752-398-2914</td>\n",
       "      <td>Gold</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>27026412</td>\n",
       "      <td>73486606</td>\n",
       "      <td>Natural causes</td>\n",
       "      <td>04/10/2018</td>\n",
       "      <td>Auto</td>\n",
       "      <td>No</td>\n",
       "      <td>Material only</td>\n",
       "      <td>1160.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>No</td>\n",
       "      <td>73486606.0</td>\n",
       "      <td>Male</td>\n",
       "      <td>1999-10-25</td>\n",
       "      <td>AK</td>\n",
       "      <td>256-968-9172</td>\n",
       "      <td>Silver</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>43908336</td>\n",
       "      <td>32813689</td>\n",
       "      <td>Crime</td>\n",
       "      <td>02/15/2018</td>\n",
       "      <td>Auto</td>\n",
       "      <td>Unknown</td>\n",
       "      <td>Material only</td>\n",
       "      <td>2620.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>No</td>\n",
       "      <td>32813689.0</td>\n",
       "      <td>Male</td>\n",
       "      <td>1993-10-12</td>\n",
       "      <td>ID</td>\n",
       "      <td>142-324-7481</td>\n",
       "      <td>Silver</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>63246959</td>\n",
       "      <td>33507197</td>\n",
       "      <td>Crime</td>\n",
       "      <td>07/22/2017</td>\n",
       "      <td>Auto</td>\n",
       "      <td>No</td>\n",
       "      <td>Material only</td>\n",
       "      <td>2748.5</td>\n",
       "      <td>2.0</td>\n",
       "      <td>Yes</td>\n",
       "      <td>33507197.0</td>\n",
       "      <td>Female</td>\n",
       "      <td>1966-09-09</td>\n",
       "      <td>RI</td>\n",
       "      <td>165-519-4583</td>\n",
       "      <td>Gold</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   claim_id  customer_id      incident_cause  claim_date claim_area  \\\n",
       "0  54004764     21868593        Driver error  11/27/2017       Auto   \n",
       "1  33985796     75740424               Crime  10/03/2018       Home   \n",
       "2  53522022     30308357  Other driver error  02/02/2018       Auto   \n",
       "3  13015401     47830476      Natural causes  06/17/2018       Auto   \n",
       "4  22890252     19269962               Crime  01/13/2018       Auto   \n",
       "5  24050443     21831191  Other driver error  10/04/2017       Auto   \n",
       "6  12878692     18401412        Driver error  01/13/2018       Auto   \n",
       "7  27026412     73486606      Natural causes  04/10/2018       Auto   \n",
       "8  43908336     32813689               Crime  02/15/2018       Auto   \n",
       "9  63246959     33507197               Crime  07/22/2017       Auto   \n",
       "\n",
       "  police_report     claim_type  claim_amount  total_policy_claims fraudulent  \\\n",
       "0            No  Material only        2980.0                  1.0         No   \n",
       "1       Unknown  Material only        2980.0                  3.0         No   \n",
       "2            No  Material only        3369.5                  1.0        Yes   \n",
       "3            No  Material only        1680.0                  1.0         No   \n",
       "4            No  Material only        2680.0                  1.0         No   \n",
       "5            No    Injury only       38306.5                  3.0        Yes   \n",
       "6            No  Material only        1730.0                  4.0         No   \n",
       "7            No  Material only        1160.0                  3.0         No   \n",
       "8       Unknown  Material only        2620.0                  1.0         No   \n",
       "9            No  Material only        2748.5                  2.0        Yes   \n",
       "\n",
       "      CUST_ID  gender DateOfBirth State       Contact   Segment  isReported?  \n",
       "0  21868593.0  Female  1979-01-12    VT  789-916-8172  Platinum            1  \n",
       "1  75740424.0  Female  1970-01-13    ME  265-543-1264    Silver            1  \n",
       "2  30308357.0  Female  1984-03-11    TN  798-631-4758    Silver            1  \n",
       "3  47830476.0  Female  1986-05-01    MA  413-187-7945    Silver            1  \n",
       "4  19269962.0    Male  1977-05-13    NV  956-871-8691      Gold            1  \n",
       "5  21831191.0    Male  1977-05-17    NH  419-712-8513      Gold            0  \n",
       "6  18401412.0    Male  1999-03-02    AR  752-398-2914      Gold            1  \n",
       "7  73486606.0    Male  1999-10-25    AK  256-968-9172    Silver            1  \n",
       "8  32813689.0    Male  1993-10-12    ID  142-324-7481    Silver            1  \n",
       "9  33507197.0  Female  1966-09-09    RI  165-519-4583      Gold            1  "
      ]
     },
     "execution_count": 30,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.head(10)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {},
   "outputs": [],
   "source": [
    "today = pd.to_datetime('2021-10-11')\n",
    "df['Age'] = (today - df['DateOfBirth'])/timedelta(days=365)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "      <th></th>\n",
       "      <th>claim_id</th>\n",
       "      <th>customer_id</th>\n",
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       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>54004764</td>\n",
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       "      <td>Driver error</td>\n",
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       "      <td>No</td>\n",
       "      <td>21868593.0</td>\n",
       "      <td>Female</td>\n",
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       "      <td>VT</td>\n",
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       "      <td>Platinum</td>\n",
       "      <td>1</td>\n",
       "      <td>42.775342</td>\n",
       "    </tr>\n",
       "    <tr>\n",
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       "      <td>33985796</td>\n",
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       "      <td>10/03/2018</td>\n",
       "      <td>Home</td>\n",
       "      <td>Unknown</td>\n",
       "      <td>Material only</td>\n",
       "      <td>2980.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>No</td>\n",
       "      <td>75740424.0</td>\n",
       "      <td>Female</td>\n",
       "      <td>1970-01-13</td>\n",
       "      <td>ME</td>\n",
       "      <td>265-543-1264</td>\n",
       "      <td>Silver</td>\n",
       "      <td>1</td>\n",
       "      <td>51.778082</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>53522022</td>\n",
       "      <td>30308357</td>\n",
       "      <td>Other driver error</td>\n",
       "      <td>02/02/2018</td>\n",
       "      <td>Auto</td>\n",
       "      <td>No</td>\n",
       "      <td>Material only</td>\n",
       "      <td>3369.5</td>\n",
       "      <td>1.0</td>\n",
       "      <td>Yes</td>\n",
       "      <td>30308357.0</td>\n",
       "      <td>Female</td>\n",
       "      <td>1984-03-11</td>\n",
       "      <td>TN</td>\n",
       "      <td>798-631-4758</td>\n",
       "      <td>Silver</td>\n",
       "      <td>1</td>\n",
       "      <td>37.610959</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>13015401</td>\n",
       "      <td>47830476</td>\n",
       "      <td>Natural causes</td>\n",
       "      <td>06/17/2018</td>\n",
       "      <td>Auto</td>\n",
       "      <td>No</td>\n",
       "      <td>Material only</td>\n",
       "      <td>1680.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>No</td>\n",
       "      <td>47830476.0</td>\n",
       "      <td>Female</td>\n",
       "      <td>1986-05-01</td>\n",
       "      <td>MA</td>\n",
       "      <td>413-187-7945</td>\n",
       "      <td>Silver</td>\n",
       "      <td>1</td>\n",
       "      <td>35.471233</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>22890252</td>\n",
       "      <td>19269962</td>\n",
       "      <td>Crime</td>\n",
       "      <td>01/13/2018</td>\n",
       "      <td>Auto</td>\n",
       "      <td>No</td>\n",
       "      <td>Material only</td>\n",
       "      <td>2680.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>No</td>\n",
       "      <td>19269962.0</td>\n",
       "      <td>Male</td>\n",
       "      <td>1977-05-13</td>\n",
       "      <td>NV</td>\n",
       "      <td>956-871-8691</td>\n",
       "      <td>Gold</td>\n",
       "      <td>1</td>\n",
       "      <td>44.443836</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   claim_id  customer_id      incident_cause  claim_date claim_area  \\\n",
       "0  54004764     21868593        Driver error  11/27/2017       Auto   \n",
       "1  33985796     75740424               Crime  10/03/2018       Home   \n",
       "2  53522022     30308357  Other driver error  02/02/2018       Auto   \n",
       "3  13015401     47830476      Natural causes  06/17/2018       Auto   \n",
       "4  22890252     19269962               Crime  01/13/2018       Auto   \n",
       "\n",
       "  police_report     claim_type  claim_amount  total_policy_claims fraudulent  \\\n",
       "0            No  Material only        2980.0                  1.0         No   \n",
       "1       Unknown  Material only        2980.0                  3.0         No   \n",
       "2            No  Material only        3369.5                  1.0        Yes   \n",
       "3            No  Material only        1680.0                  1.0         No   \n",
       "4            No  Material only        2680.0                  1.0         No   \n",
       "\n",
       "      CUST_ID  gender DateOfBirth State       Contact   Segment  isReported?  \\\n",
       "0  21868593.0  Female  1979-01-12    VT  789-916-8172  Platinum            1   \n",
       "1  75740424.0  Female  1970-01-13    ME  265-543-1264    Silver            1   \n",
       "2  30308357.0  Female  1984-03-11    TN  798-631-4758    Silver            1   \n",
       "3  47830476.0  Female  1986-05-01    MA  413-187-7945    Silver            1   \n",
       "4  19269962.0    Male  1977-05-13    NV  956-871-8691      Gold            1   \n",
       "\n",
       "         Age  \n",
       "0  42.775342  \n",
       "1  51.778082  \n",
       "2  37.610959  \n",
       "3  35.471233  \n",
       "4  44.443836  "
      ]
     },
     "execution_count": 32,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 7. Age group column"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {},
   "outputs": [],
   "source": [
    "df['Age Group'] = 'Children'\n",
    "df.loc[(df['Age']>=18) & (df['Age']<30), 'Age Group'] = 'Youth'\n",
    "df.loc[(df['Age']>=30) & (df['Age']<60), 'Age Group'] = 'Adult'\n",
    "df.loc[df['Age']>=60, 'Age Group'] = 'Senior'"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
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       "\n",
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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>claim_id</th>\n",
       "      <th>customer_id</th>\n",
       "      <th>incident_cause</th>\n",
       "      <th>claim_date</th>\n",
       "      <th>claim_area</th>\n",
       "      <th>police_report</th>\n",
       "      <th>claim_type</th>\n",
       "      <th>claim_amount</th>\n",
       "      <th>total_policy_claims</th>\n",
       "      <th>fraudulent</th>\n",
       "      <th>CUST_ID</th>\n",
       "      <th>gender</th>\n",
       "      <th>DateOfBirth</th>\n",
       "      <th>State</th>\n",
       "      <th>Contact</th>\n",
       "      <th>Segment</th>\n",
       "      <th>isReported?</th>\n",
       "      <th>Age</th>\n",
       "      <th>Age Group</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>54004764</td>\n",
       "      <td>21868593</td>\n",
       "      <td>Driver error</td>\n",
       "      <td>11/27/2017</td>\n",
       "      <td>Auto</td>\n",
       "      <td>No</td>\n",
       "      <td>Material only</td>\n",
       "      <td>2980.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>No</td>\n",
       "      <td>21868593.0</td>\n",
       "      <td>Female</td>\n",
       "      <td>1979-01-12</td>\n",
       "      <td>VT</td>\n",
       "      <td>789-916-8172</td>\n",
       "      <td>Platinum</td>\n",
       "      <td>1</td>\n",
       "      <td>42.775342</td>\n",
       "      <td>Adult</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>33985796</td>\n",
       "      <td>75740424</td>\n",
       "      <td>Crime</td>\n",
       "      <td>10/03/2018</td>\n",
       "      <td>Home</td>\n",
       "      <td>Unknown</td>\n",
       "      <td>Material only</td>\n",
       "      <td>2980.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>No</td>\n",
       "      <td>75740424.0</td>\n",
       "      <td>Female</td>\n",
       "      <td>1970-01-13</td>\n",
       "      <td>ME</td>\n",
       "      <td>265-543-1264</td>\n",
       "      <td>Silver</td>\n",
       "      <td>1</td>\n",
       "      <td>51.778082</td>\n",
       "      <td>Adult</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>53522022</td>\n",
       "      <td>30308357</td>\n",
       "      <td>Other driver error</td>\n",
       "      <td>02/02/2018</td>\n",
       "      <td>Auto</td>\n",
       "      <td>No</td>\n",
       "      <td>Material only</td>\n",
       "      <td>3369.5</td>\n",
       "      <td>1.0</td>\n",
       "      <td>Yes</td>\n",
       "      <td>30308357.0</td>\n",
       "      <td>Female</td>\n",
       "      <td>1984-03-11</td>\n",
       "      <td>TN</td>\n",
       "      <td>798-631-4758</td>\n",
       "      <td>Silver</td>\n",
       "      <td>1</td>\n",
       "      <td>37.610959</td>\n",
       "      <td>Adult</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   claim_id  customer_id      incident_cause  claim_date claim_area  \\\n",
       "0  54004764     21868593        Driver error  11/27/2017       Auto   \n",
       "1  33985796     75740424               Crime  10/03/2018       Home   \n",
       "2  53522022     30308357  Other driver error  02/02/2018       Auto   \n",
       "\n",
       "  police_report     claim_type  claim_amount  total_policy_claims fraudulent  \\\n",
       "0            No  Material only        2980.0                  1.0         No   \n",
       "1       Unknown  Material only        2980.0                  3.0         No   \n",
       "2            No  Material only        3369.5                  1.0        Yes   \n",
       "\n",
       "      CUST_ID  gender DateOfBirth State       Contact   Segment  isReported?  \\\n",
       "0  21868593.0  Female  1979-01-12    VT  789-916-8172  Platinum            1   \n",
       "1  75740424.0  Female  1970-01-13    ME  265-543-1264    Silver            1   \n",
       "2  30308357.0  Female  1984-03-11    TN  798-631-4758    Silver            1   \n",
       "\n",
       "         Age Age Group  \n",
       "0  42.775342     Adult  \n",
       "1  51.778082     Adult  \n",
       "2  37.610959     Adult  "
      ]
     },
     "execution_count": 34,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.head(3)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 8. Avg amount claimed by customers from various segments"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
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       "\n",
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       "        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>claim_amount</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Segment</th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>Gold</th>\n",
       "      <td>12754.951475</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Platinum</th>\n",
       "      <td>12368.233815</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Silver</th>\n",
       "      <td>12267.349306</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "          claim_amount\n",
       "Segment               \n",
       "Gold      12754.951475\n",
       "Platinum  12368.233815\n",
       "Silver    12267.349306"
      ]
     },
     "execution_count": 35,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.groupby('Segment').mean().drop(['claim_id','customer_id','total_policy_claims','CUST_ID','isReported?','Age'], axis=1)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 9. Total claim amount based on incident cause before 20 days prior to Oct 1, 2018"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {},
   "outputs": [],
   "source": [
    "date = pd.to_datetime('2018-10-1') - timedelta(days=20)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {},
   "outputs": [],
   "source": [
    "df['claim_date'] = pd.to_datetime(df['claim_date'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>claim_id</th>\n",
       "      <th>customer_id</th>\n",
       "      <th>incident_cause</th>\n",
       "      <th>claim_date</th>\n",
       "      <th>claim_area</th>\n",
       "      <th>police_report</th>\n",
       "      <th>claim_type</th>\n",
       "      <th>claim_amount</th>\n",
       "      <th>total_policy_claims</th>\n",
       "      <th>fraudulent</th>\n",
       "      <th>CUST_ID</th>\n",
       "      <th>gender</th>\n",
       "      <th>DateOfBirth</th>\n",
       "      <th>State</th>\n",
       "      <th>Contact</th>\n",
       "      <th>Segment</th>\n",
       "      <th>isReported?</th>\n",
       "      <th>Age</th>\n",
       "      <th>Age Group</th>\n",
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       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>54004764</td>\n",
       "      <td>21868593</td>\n",
       "      <td>Driver error</td>\n",
       "      <td>2017-11-27</td>\n",
       "      <td>Auto</td>\n",
       "      <td>No</td>\n",
       "      <td>Material only</td>\n",
       "      <td>2980.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>No</td>\n",
       "      <td>21868593.0</td>\n",
       "      <td>Female</td>\n",
       "      <td>1979-01-12</td>\n",
       "      <td>VT</td>\n",
       "      <td>789-916-8172</td>\n",
       "      <td>Platinum</td>\n",
       "      <td>1</td>\n",
       "      <td>42.775342</td>\n",
       "      <td>Adult</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>33985796</td>\n",
       "      <td>75740424</td>\n",
       "      <td>Crime</td>\n",
       "      <td>2018-10-03</td>\n",
       "      <td>Home</td>\n",
       "      <td>Unknown</td>\n",
       "      <td>Material only</td>\n",
       "      <td>2980.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>No</td>\n",
       "      <td>75740424.0</td>\n",
       "      <td>Female</td>\n",
       "      <td>1970-01-13</td>\n",
       "      <td>ME</td>\n",
       "      <td>265-543-1264</td>\n",
       "      <td>Silver</td>\n",
       "      <td>1</td>\n",
       "      <td>51.778082</td>\n",
       "      <td>Adult</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   claim_id  customer_id incident_cause claim_date claim_area police_report  \\\n",
       "0  54004764     21868593   Driver error 2017-11-27       Auto            No   \n",
       "1  33985796     75740424          Crime 2018-10-03       Home       Unknown   \n",
       "\n",
       "      claim_type  claim_amount  total_policy_claims fraudulent     CUST_ID  \\\n",
       "0  Material only        2980.0                  1.0         No  21868593.0   \n",
       "1  Material only        2980.0                  3.0         No  75740424.0   \n",
       "\n",
       "   gender DateOfBirth State       Contact   Segment  isReported?        Age  \\\n",
       "0  Female  1979-01-12    VT  789-916-8172  Platinum            1  42.775342   \n",
       "1  Female  1970-01-13    ME  265-543-1264    Silver            1  51.778082   \n",
       "\n",
       "  Age Group  \n",
       "0     Adult  \n",
       "1     Adult  "
      ]
     },
     "execution_count": 38,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.head(2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "metadata": {},
   "outputs": [],
   "source": [
    "df_incident_cause = df[df['claim_date']<date]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "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",
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       "    }\n",
       "\n",
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       "        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>claim_amount</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>incident_cause</th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>Crime</th>\n",
       "      <td>7.294856e+05</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Driver error</th>\n",
       "      <td>3.292987e+06</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Natural causes</th>\n",
       "      <td>1.315024e+06</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Other causes</th>\n",
       "      <td>3.779919e+06</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Other driver error</th>\n",
       "      <td>3.384079e+06</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                    claim_amount\n",
       "incident_cause                  \n",
       "Crime               7.294856e+05\n",
       "Driver error        3.292987e+06\n",
       "Natural causes      1.315024e+06\n",
       "Other causes        3.779919e+06\n",
       "Other driver error  3.384079e+06"
      ]
     },
     "execution_count": 40,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_incident_cause.groupby('incident_cause').sum()\\\n",
    ".drop(['claim_id','customer_id','total_policy_claims','CUST_ID','isReported?','Age'], axis=1)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 10. How many adults from TX, DE, AK claimed insurance for driver related issues and causes"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>claim_id</th>\n",
       "      <th>customer_id</th>\n",
       "      <th>incident_cause</th>\n",
       "      <th>claim_date</th>\n",
       "      <th>claim_area</th>\n",
       "      <th>police_report</th>\n",
       "      <th>claim_type</th>\n",
       "      <th>claim_amount</th>\n",
       "      <th>total_policy_claims</th>\n",
       "      <th>fraudulent</th>\n",
       "      <th>CUST_ID</th>\n",
       "      <th>gender</th>\n",
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       "      <th>Age</th>\n",
       "      <th>Age Group</th>\n",
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       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>54004764</td>\n",
       "      <td>21868593</td>\n",
       "      <td>Driver error</td>\n",
       "      <td>2017-11-27</td>\n",
       "      <td>Auto</td>\n",
       "      <td>No</td>\n",
       "      <td>Material only</td>\n",
       "      <td>2980.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>No</td>\n",
       "      <td>21868593.0</td>\n",
       "      <td>Female</td>\n",
       "      <td>1979-01-12</td>\n",
       "      <td>VT</td>\n",
       "      <td>789-916-8172</td>\n",
       "      <td>Platinum</td>\n",
       "      <td>1</td>\n",
       "      <td>42.775342</td>\n",
       "      <td>Adult</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>33985796</td>\n",
       "      <td>75740424</td>\n",
       "      <td>Crime</td>\n",
       "      <td>2018-10-03</td>\n",
       "      <td>Home</td>\n",
       "      <td>Unknown</td>\n",
       "      <td>Material only</td>\n",
       "      <td>2980.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>No</td>\n",
       "      <td>75740424.0</td>\n",
       "      <td>Female</td>\n",
       "      <td>1970-01-13</td>\n",
       "      <td>ME</td>\n",
       "      <td>265-543-1264</td>\n",
       "      <td>Silver</td>\n",
       "      <td>1</td>\n",
       "      <td>51.778082</td>\n",
       "      <td>Adult</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>53522022</td>\n",
       "      <td>30308357</td>\n",
       "      <td>Other driver error</td>\n",
       "      <td>2018-02-02</td>\n",
       "      <td>Auto</td>\n",
       "      <td>No</td>\n",
       "      <td>Material only</td>\n",
       "      <td>3369.5</td>\n",
       "      <td>1.0</td>\n",
       "      <td>Yes</td>\n",
       "      <td>30308357.0</td>\n",
       "      <td>Female</td>\n",
       "      <td>1984-03-11</td>\n",
       "      <td>TN</td>\n",
       "      <td>798-631-4758</td>\n",
       "      <td>Silver</td>\n",
       "      <td>1</td>\n",
       "      <td>37.610959</td>\n",
       "      <td>Adult</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   claim_id  customer_id      incident_cause claim_date claim_area  \\\n",
       "0  54004764     21868593        Driver error 2017-11-27       Auto   \n",
       "1  33985796     75740424               Crime 2018-10-03       Home   \n",
       "2  53522022     30308357  Other driver error 2018-02-02       Auto   \n",
       "\n",
       "  police_report     claim_type  claim_amount  total_policy_claims fraudulent  \\\n",
       "0            No  Material only        2980.0                  1.0         No   \n",
       "1       Unknown  Material only        2980.0                  3.0         No   \n",
       "2            No  Material only        3369.5                  1.0        Yes   \n",
       "\n",
       "      CUST_ID  gender DateOfBirth State       Contact   Segment  isReported?  \\\n",
       "0  21868593.0  Female  1979-01-12    VT  789-916-8172  Platinum            1   \n",
       "1  75740424.0  Female  1970-01-13    ME  265-543-1264    Silver            1   \n",
       "2  30308357.0  Female  1984-03-11    TN  798-631-4758    Silver            1   \n",
       "\n",
       "         Age Age Group  \n",
       "0  42.775342     Adult  \n",
       "1  51.778082     Adult  \n",
       "2  37.610959     Adult  "
      ]
     },
     "execution_count": 41,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.head(3)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "metadata": {},
   "outputs": [],
   "source": [
    "df_driver = df[((df['State']=='TX') | (df['State']=='DE') | (df['State']=='AK')) & (df['Age Group']=='Adult')]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "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>claim_amount</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>incident_cause</th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>Driver error</th>\n",
       "      <td>229562.18142</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Natural causes</th>\n",
       "      <td>50968.00000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Other causes</th>\n",
       "      <td>246852.22714</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Other driver error</th>\n",
       "      <td>137931.72714</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                    claim_amount\n",
       "incident_cause                  \n",
       "Driver error        229562.18142\n",
       "Natural causes       50968.00000\n",
       "Other causes        246852.22714\n",
       "Other driver error  137931.72714"
      ]
     },
     "execution_count": 43,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_driver.groupby('incident_cause').sum()\\\n",
    ".drop(['claim_id','customer_id','total_policy_claims','CUST_ID','isReported?','Age'], axis=1)\\\n",
    ".drop('Crime',axis=0)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 11. Pie chart between aggregated value of claim amount based on gender and based on segment"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "metadata": {},
   "outputs": [],
   "source": [
    "df_gender = df.groupby('gender').sum().drop(['claim_id','customer_id','total_policy_claims','CUST_ID','isReported?','Age'], axis=1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "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>claim_amount</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>gender</th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>Female</th>\n",
       "      <td>6.377078e+06</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Male</th>\n",
       "      <td>7.064444e+06</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "        claim_amount\n",
       "gender              \n",
       "Female  6.377078e+06\n",
       "Male    7.064444e+06"
      ]
     },
     "execution_count": 45,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_gender"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:ylabel='claim_amount'>"
      ]
     },
     "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": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "#df_gender.plot.pie(y='claim_amount', figsize = (8,8))\n",
    "df_gender.claim_amount.plot(kind = 'pie', autopct='%1.1f%%')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "metadata": {},
   "outputs": [],
   "source": [
    "df_segment = df.groupby('Segment').sum().drop(['claim_id','customer_id','total_policy_claims','CUST_ID','isReported?','Age'], axis=1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "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>claim_amount</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Segment</th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>Gold</th>\n",
       "      <td>4.732087e+06</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Platinum</th>\n",
       "      <td>4.464932e+06</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Silver</th>\n",
       "      <td>4.244503e+06</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "          claim_amount\n",
       "Segment               \n",
       "Gold      4.732087e+06\n",
       "Platinum  4.464932e+06\n",
       "Silver    4.244503e+06"
      ]
     },
     "execution_count": 48,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_segment"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:ylabel='claim_amount'>"
      ]
     },
     "execution_count": 49,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "df_segment.claim_amount.plot(kind = 'pie', autopct='%1.1f%%')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 12. Which gender claimed the most for driver related issues? use bar plot"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 50,
   "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>claim_id</th>\n",
       "      <th>customer_id</th>\n",
       "      <th>incident_cause</th>\n",
       "      <th>claim_date</th>\n",
       "      <th>claim_area</th>\n",
       "      <th>police_report</th>\n",
       "      <th>claim_type</th>\n",
       "      <th>claim_amount</th>\n",
       "      <th>total_policy_claims</th>\n",
       "      <th>fraudulent</th>\n",
       "      <th>CUST_ID</th>\n",
       "      <th>gender</th>\n",
       "      <th>DateOfBirth</th>\n",
       "      <th>State</th>\n",
       "      <th>Contact</th>\n",
       "      <th>Segment</th>\n",
       "      <th>isReported?</th>\n",
       "      <th>Age</th>\n",
       "      <th>Age Group</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>54004764</td>\n",
       "      <td>21868593</td>\n",
       "      <td>Driver error</td>\n",
       "      <td>2017-11-27</td>\n",
       "      <td>Auto</td>\n",
       "      <td>No</td>\n",
       "      <td>Material only</td>\n",
       "      <td>2980.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>No</td>\n",
       "      <td>21868593.0</td>\n",
       "      <td>Female</td>\n",
       "      <td>1979-01-12</td>\n",
       "      <td>VT</td>\n",
       "      <td>789-916-8172</td>\n",
       "      <td>Platinum</td>\n",
       "      <td>1</td>\n",
       "      <td>42.775342</td>\n",
       "      <td>Adult</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>33985796</td>\n",
       "      <td>75740424</td>\n",
       "      <td>Crime</td>\n",
       "      <td>2018-10-03</td>\n",
       "      <td>Home</td>\n",
       "      <td>Unknown</td>\n",
       "      <td>Material only</td>\n",
       "      <td>2980.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>No</td>\n",
       "      <td>75740424.0</td>\n",
       "      <td>Female</td>\n",
       "      <td>1970-01-13</td>\n",
       "      <td>ME</td>\n",
       "      <td>265-543-1264</td>\n",
       "      <td>Silver</td>\n",
       "      <td>1</td>\n",
       "      <td>51.778082</td>\n",
       "      <td>Adult</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>53522022</td>\n",
       "      <td>30308357</td>\n",
       "      <td>Other driver error</td>\n",
       "      <td>2018-02-02</td>\n",
       "      <td>Auto</td>\n",
       "      <td>No</td>\n",
       "      <td>Material only</td>\n",
       "      <td>3369.5</td>\n",
       "      <td>1.0</td>\n",
       "      <td>Yes</td>\n",
       "      <td>30308357.0</td>\n",
       "      <td>Female</td>\n",
       "      <td>1984-03-11</td>\n",
       "      <td>TN</td>\n",
       "      <td>798-631-4758</td>\n",
       "      <td>Silver</td>\n",
       "      <td>1</td>\n",
       "      <td>37.610959</td>\n",
       "      <td>Adult</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>13015401</td>\n",
       "      <td>47830476</td>\n",
       "      <td>Natural causes</td>\n",
       "      <td>2018-06-17</td>\n",
       "      <td>Auto</td>\n",
       "      <td>No</td>\n",
       "      <td>Material only</td>\n",
       "      <td>1680.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>No</td>\n",
       "      <td>47830476.0</td>\n",
       "      <td>Female</td>\n",
       "      <td>1986-05-01</td>\n",
       "      <td>MA</td>\n",
       "      <td>413-187-7945</td>\n",
       "      <td>Silver</td>\n",
       "      <td>1</td>\n",
       "      <td>35.471233</td>\n",
       "      <td>Adult</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>22890252</td>\n",
       "      <td>19269962</td>\n",
       "      <td>Crime</td>\n",
       "      <td>2018-01-13</td>\n",
       "      <td>Auto</td>\n",
       "      <td>No</td>\n",
       "      <td>Material only</td>\n",
       "      <td>2680.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>No</td>\n",
       "      <td>19269962.0</td>\n",
       "      <td>Male</td>\n",
       "      <td>1977-05-13</td>\n",
       "      <td>NV</td>\n",
       "      <td>956-871-8691</td>\n",
       "      <td>Gold</td>\n",
       "      <td>1</td>\n",
       "      <td>44.443836</td>\n",
       "      <td>Adult</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   claim_id  customer_id      incident_cause claim_date claim_area  \\\n",
       "0  54004764     21868593        Driver error 2017-11-27       Auto   \n",
       "1  33985796     75740424               Crime 2018-10-03       Home   \n",
       "2  53522022     30308357  Other driver error 2018-02-02       Auto   \n",
       "3  13015401     47830476      Natural causes 2018-06-17       Auto   \n",
       "4  22890252     19269962               Crime 2018-01-13       Auto   \n",
       "\n",
       "  police_report     claim_type  claim_amount  total_policy_claims fraudulent  \\\n",
       "0            No  Material only        2980.0                  1.0         No   \n",
       "1       Unknown  Material only        2980.0                  3.0         No   \n",
       "2            No  Material only        3369.5                  1.0        Yes   \n",
       "3            No  Material only        1680.0                  1.0         No   \n",
       "4            No  Material only        2680.0                  1.0         No   \n",
       "\n",
       "      CUST_ID  gender DateOfBirth State       Contact   Segment  isReported?  \\\n",
       "0  21868593.0  Female  1979-01-12    VT  789-916-8172  Platinum            1   \n",
       "1  75740424.0  Female  1970-01-13    ME  265-543-1264    Silver            1   \n",
       "2  30308357.0  Female  1984-03-11    TN  798-631-4758    Silver            1   \n",
       "3  47830476.0  Female  1986-05-01    MA  413-187-7945    Silver            1   \n",
       "4  19269962.0    Male  1977-05-13    NV  956-871-8691      Gold            1   \n",
       "\n",
       "         Age Age Group  \n",
       "0  42.775342     Adult  \n",
       "1  51.778082     Adult  \n",
       "2  37.610959     Adult  \n",
       "3  35.471233     Adult  \n",
       "4  44.443836     Adult  "
      ]
     },
     "execution_count": 50,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 51,
   "metadata": {},
   "outputs": [],
   "source": [
    "df_driver_related = df[(df['incident_cause']=='Driver error') | (df['incident_cause']=='Other driver error')]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 52,
   "metadata": {},
   "outputs": [],
   "source": [
    "df_driver_related1 = df_driver_related.groupby(['gender','incident_cause']).sum()\\\n",
    ".drop(['claim_id','customer_id','total_policy_claims','CUST_ID','isReported?','Age'],axis=1)\\\n",
    ".reset_index().groupby('gender').sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 53,
   "metadata": {},
   "outputs": [],
   "source": [
    "df_driver_related2 = df_driver_related1.reset_index()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 54,
   "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>gender</th>\n",
       "      <th>claim_amount</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Female</td>\n",
       "      <td>3.137396e+06</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Male</td>\n",
       "      <td>3.938469e+06</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   gender  claim_amount\n",
       "0  Female  3.137396e+06\n",
       "1    Male  3.938469e+06"
      ]
     },
     "execution_count": 54,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_driver_related2"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 55,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:xlabel='gender'>"
      ]
     },
     "execution_count": 55,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "df_driver_related2.plot.bar(x='gender', y = 'claim_amount')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Males have claimed the most for driver related issues"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 13. Which age group has the maximum fraudulant policy claims?"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 56,
   "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>claim_id</th>\n",
       "      <th>customer_id</th>\n",
       "      <th>incident_cause</th>\n",
       "      <th>claim_date</th>\n",
       "      <th>claim_area</th>\n",
       "      <th>police_report</th>\n",
       "      <th>claim_type</th>\n",
       "      <th>claim_amount</th>\n",
       "      <th>total_policy_claims</th>\n",
       "      <th>fraudulent</th>\n",
       "      <th>CUST_ID</th>\n",
       "      <th>gender</th>\n",
       "      <th>DateOfBirth</th>\n",
       "      <th>State</th>\n",
       "      <th>Contact</th>\n",
       "      <th>Segment</th>\n",
       "      <th>isReported?</th>\n",
       "      <th>Age</th>\n",
       "      <th>Age Group</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>54004764</td>\n",
       "      <td>21868593</td>\n",
       "      <td>Driver error</td>\n",
       "      <td>2017-11-27</td>\n",
       "      <td>Auto</td>\n",
       "      <td>No</td>\n",
       "      <td>Material only</td>\n",
       "      <td>2980.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>No</td>\n",
       "      <td>21868593.0</td>\n",
       "      <td>Female</td>\n",
       "      <td>1979-01-12</td>\n",
       "      <td>VT</td>\n",
       "      <td>789-916-8172</td>\n",
       "      <td>Platinum</td>\n",
       "      <td>1</td>\n",
       "      <td>42.775342</td>\n",
       "      <td>Adult</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>33985796</td>\n",
       "      <td>75740424</td>\n",
       "      <td>Crime</td>\n",
       "      <td>2018-10-03</td>\n",
       "      <td>Home</td>\n",
       "      <td>Unknown</td>\n",
       "      <td>Material only</td>\n",
       "      <td>2980.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>No</td>\n",
       "      <td>75740424.0</td>\n",
       "      <td>Female</td>\n",
       "      <td>1970-01-13</td>\n",
       "      <td>ME</td>\n",
       "      <td>265-543-1264</td>\n",
       "      <td>Silver</td>\n",
       "      <td>1</td>\n",
       "      <td>51.778082</td>\n",
       "      <td>Adult</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   claim_id  customer_id incident_cause claim_date claim_area police_report  \\\n",
       "0  54004764     21868593   Driver error 2017-11-27       Auto            No   \n",
       "1  33985796     75740424          Crime 2018-10-03       Home       Unknown   \n",
       "\n",
       "      claim_type  claim_amount  total_policy_claims fraudulent     CUST_ID  \\\n",
       "0  Material only        2980.0                  1.0         No  21868593.0   \n",
       "1  Material only        2980.0                  3.0         No  75740424.0   \n",
       "\n",
       "   gender DateOfBirth State       Contact   Segment  isReported?        Age  \\\n",
       "0  Female  1979-01-12    VT  789-916-8172  Platinum            1  42.775342   \n",
       "1  Female  1970-01-13    ME  265-543-1264    Silver            1  51.778082   \n",
       "\n",
       "  Age Group  \n",
       "0     Adult  \n",
       "1     Adult  "
      ]
     },
     "execution_count": 56,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.head(2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 57,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "No     839\n",
       "Yes    254\n",
       "Name: fraudulent, dtype: int64"
      ]
     },
     "execution_count": 57,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df['fraudulent'].value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 58,
   "metadata": {},
   "outputs": [],
   "source": [
    "df_fraud = df[['fraudulent','Age Group']]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 59,
   "metadata": {},
   "outputs": [],
   "source": [
    "df_fraud = df_fraud[df_fraud['fraudulent']=='Yes']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 60,
   "metadata": {},
   "outputs": [],
   "source": [
    "df_fraud = df_fraud.groupby(['Age Group']).count().reset_index()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 61,
   "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>Age Group</th>\n",
       "      <th>fraudulent</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Adult</td>\n",
       "      <td>187</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Children</td>\n",
       "      <td>7</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Senior</td>\n",
       "      <td>12</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Youth</td>\n",
       "      <td>48</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "  Age Group  fraudulent\n",
       "0     Adult         187\n",
       "1  Children           7\n",
       "2    Senior          12\n",
       "3     Youth          48"
      ]
     },
     "execution_count": 61,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_fraud"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 62,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:xlabel='Age Group'>"
      ]
     },
     "execution_count": 62,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "df_fraud.plot.bar(x='Age Group', y = 'fraudulent')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Adult age group has the maximum fraudulant policy claims"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 14. Monthly trend of total amount claimed by customers"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 63,
   "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>claim_id</th>\n",
       "      <th>customer_id</th>\n",
       "      <th>incident_cause</th>\n",
       "      <th>claim_date</th>\n",
       "      <th>claim_area</th>\n",
       "      <th>police_report</th>\n",
       "      <th>claim_type</th>\n",
       "      <th>claim_amount</th>\n",
       "      <th>total_policy_claims</th>\n",
       "      <th>fraudulent</th>\n",
       "      <th>CUST_ID</th>\n",
       "      <th>gender</th>\n",
       "      <th>DateOfBirth</th>\n",
       "      <th>State</th>\n",
       "      <th>Contact</th>\n",
       "      <th>Segment</th>\n",
       "      <th>isReported?</th>\n",
       "      <th>Age</th>\n",
       "      <th>Age Group</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>54004764</td>\n",
       "      <td>21868593</td>\n",
       "      <td>Driver error</td>\n",
       "      <td>2017-11-27</td>\n",
       "      <td>Auto</td>\n",
       "      <td>No</td>\n",
       "      <td>Material only</td>\n",
       "      <td>2980.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>No</td>\n",
       "      <td>21868593.0</td>\n",
       "      <td>Female</td>\n",
       "      <td>1979-01-12</td>\n",
       "      <td>VT</td>\n",
       "      <td>789-916-8172</td>\n",
       "      <td>Platinum</td>\n",
       "      <td>1</td>\n",
       "      <td>42.775342</td>\n",
       "      <td>Adult</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>33985796</td>\n",
       "      <td>75740424</td>\n",
       "      <td>Crime</td>\n",
       "      <td>2018-10-03</td>\n",
       "      <td>Home</td>\n",
       "      <td>Unknown</td>\n",
       "      <td>Material only</td>\n",
       "      <td>2980.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>No</td>\n",
       "      <td>75740424.0</td>\n",
       "      <td>Female</td>\n",
       "      <td>1970-01-13</td>\n",
       "      <td>ME</td>\n",
       "      <td>265-543-1264</td>\n",
       "      <td>Silver</td>\n",
       "      <td>1</td>\n",
       "      <td>51.778082</td>\n",
       "      <td>Adult</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>53522022</td>\n",
       "      <td>30308357</td>\n",
       "      <td>Other driver error</td>\n",
       "      <td>2018-02-02</td>\n",
       "      <td>Auto</td>\n",
       "      <td>No</td>\n",
       "      <td>Material only</td>\n",
       "      <td>3369.5</td>\n",
       "      <td>1.0</td>\n",
       "      <td>Yes</td>\n",
       "      <td>30308357.0</td>\n",
       "      <td>Female</td>\n",
       "      <td>1984-03-11</td>\n",
       "      <td>TN</td>\n",
       "      <td>798-631-4758</td>\n",
       "      <td>Silver</td>\n",
       "      <td>1</td>\n",
       "      <td>37.610959</td>\n",
       "      <td>Adult</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>13015401</td>\n",
       "      <td>47830476</td>\n",
       "      <td>Natural causes</td>\n",
       "      <td>2018-06-17</td>\n",
       "      <td>Auto</td>\n",
       "      <td>No</td>\n",
       "      <td>Material only</td>\n",
       "      <td>1680.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>No</td>\n",
       "      <td>47830476.0</td>\n",
       "      <td>Female</td>\n",
       "      <td>1986-05-01</td>\n",
       "      <td>MA</td>\n",
       "      <td>413-187-7945</td>\n",
       "      <td>Silver</td>\n",
       "      <td>1</td>\n",
       "      <td>35.471233</td>\n",
       "      <td>Adult</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>22890252</td>\n",
       "      <td>19269962</td>\n",
       "      <td>Crime</td>\n",
       "      <td>2018-01-13</td>\n",
       "      <td>Auto</td>\n",
       "      <td>No</td>\n",
       "      <td>Material only</td>\n",
       "      <td>2680.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>No</td>\n",
       "      <td>19269962.0</td>\n",
       "      <td>Male</td>\n",
       "      <td>1977-05-13</td>\n",
       "      <td>NV</td>\n",
       "      <td>956-871-8691</td>\n",
       "      <td>Gold</td>\n",
       "      <td>1</td>\n",
       "      <td>44.443836</td>\n",
       "      <td>Adult</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   claim_id  customer_id      incident_cause claim_date claim_area  \\\n",
       "0  54004764     21868593        Driver error 2017-11-27       Auto   \n",
       "1  33985796     75740424               Crime 2018-10-03       Home   \n",
       "2  53522022     30308357  Other driver error 2018-02-02       Auto   \n",
       "3  13015401     47830476      Natural causes 2018-06-17       Auto   \n",
       "4  22890252     19269962               Crime 2018-01-13       Auto   \n",
       "\n",
       "  police_report     claim_type  claim_amount  total_policy_claims fraudulent  \\\n",
       "0            No  Material only        2980.0                  1.0         No   \n",
       "1       Unknown  Material only        2980.0                  3.0         No   \n",
       "2            No  Material only        3369.5                  1.0        Yes   \n",
       "3            No  Material only        1680.0                  1.0         No   \n",
       "4            No  Material only        2680.0                  1.0         No   \n",
       "\n",
       "      CUST_ID  gender DateOfBirth State       Contact   Segment  isReported?  \\\n",
       "0  21868593.0  Female  1979-01-12    VT  789-916-8172  Platinum            1   \n",
       "1  75740424.0  Female  1970-01-13    ME  265-543-1264    Silver            1   \n",
       "2  30308357.0  Female  1984-03-11    TN  798-631-4758    Silver            1   \n",
       "3  47830476.0  Female  1986-05-01    MA  413-187-7945    Silver            1   \n",
       "4  19269962.0    Male  1977-05-13    NV  956-871-8691      Gold            1   \n",
       "\n",
       "         Age Age Group  \n",
       "0  42.775342     Adult  \n",
       "1  51.778082     Adult  \n",
       "2  37.610959     Adult  \n",
       "3  35.471233     Adult  \n",
       "4  44.443836     Adult  "
      ]
     },
     "execution_count": 63,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 64,
   "metadata": {},
   "outputs": [],
   "source": [
    "df['claim_month'] = pd.DatetimeIndex(df['claim_date']).month"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 65,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>claim_id</th>\n",
       "      <th>customer_id</th>\n",
       "      <th>incident_cause</th>\n",
       "      <th>claim_date</th>\n",
       "      <th>claim_area</th>\n",
       "      <th>police_report</th>\n",
       "      <th>claim_type</th>\n",
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       "      <th>total_policy_claims</th>\n",
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       "      <th>CUST_ID</th>\n",
       "      <th>gender</th>\n",
       "      <th>DateOfBirth</th>\n",
       "      <th>State</th>\n",
       "      <th>Contact</th>\n",
       "      <th>Segment</th>\n",
       "      <th>isReported?</th>\n",
       "      <th>Age</th>\n",
       "      <th>Age Group</th>\n",
       "      <th>claim_month</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>54004764</td>\n",
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       "      <td>Driver error</td>\n",
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       "      <td>Auto</td>\n",
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       "      <td>Material only</td>\n",
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       "      <td>1.0</td>\n",
       "      <td>No</td>\n",
       "      <td>21868593.0</td>\n",
       "      <td>Female</td>\n",
       "      <td>1979-01-12</td>\n",
       "      <td>VT</td>\n",
       "      <td>789-916-8172</td>\n",
       "      <td>Platinum</td>\n",
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       "      <td>Adult</td>\n",
       "      <td>11</td>\n",
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       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>33985796</td>\n",
       "      <td>75740424</td>\n",
       "      <td>Crime</td>\n",
       "      <td>2018-10-03</td>\n",
       "      <td>Home</td>\n",
       "      <td>Unknown</td>\n",
       "      <td>Material only</td>\n",
       "      <td>2980.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>No</td>\n",
       "      <td>75740424.0</td>\n",
       "      <td>Female</td>\n",
       "      <td>1970-01-13</td>\n",
       "      <td>ME</td>\n",
       "      <td>265-543-1264</td>\n",
       "      <td>Silver</td>\n",
       "      <td>1</td>\n",
       "      <td>51.778082</td>\n",
       "      <td>Adult</td>\n",
       "      <td>10</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>53522022</td>\n",
       "      <td>30308357</td>\n",
       "      <td>Other driver error</td>\n",
       "      <td>2018-02-02</td>\n",
       "      <td>Auto</td>\n",
       "      <td>No</td>\n",
       "      <td>Material only</td>\n",
       "      <td>3369.5</td>\n",
       "      <td>1.0</td>\n",
       "      <td>Yes</td>\n",
       "      <td>30308357.0</td>\n",
       "      <td>Female</td>\n",
       "      <td>1984-03-11</td>\n",
       "      <td>TN</td>\n",
       "      <td>798-631-4758</td>\n",
       "      <td>Silver</td>\n",
       "      <td>1</td>\n",
       "      <td>37.610959</td>\n",
       "      <td>Adult</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>13015401</td>\n",
       "      <td>47830476</td>\n",
       "      <td>Natural causes</td>\n",
       "      <td>2018-06-17</td>\n",
       "      <td>Auto</td>\n",
       "      <td>No</td>\n",
       "      <td>Material only</td>\n",
       "      <td>1680.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>No</td>\n",
       "      <td>47830476.0</td>\n",
       "      <td>Female</td>\n",
       "      <td>1986-05-01</td>\n",
       "      <td>MA</td>\n",
       "      <td>413-187-7945</td>\n",
       "      <td>Silver</td>\n",
       "      <td>1</td>\n",
       "      <td>35.471233</td>\n",
       "      <td>Adult</td>\n",
       "      <td>6</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>22890252</td>\n",
       "      <td>19269962</td>\n",
       "      <td>Crime</td>\n",
       "      <td>2018-01-13</td>\n",
       "      <td>Auto</td>\n",
       "      <td>No</td>\n",
       "      <td>Material only</td>\n",
       "      <td>2680.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>No</td>\n",
       "      <td>19269962.0</td>\n",
       "      <td>Male</td>\n",
       "      <td>1977-05-13</td>\n",
       "      <td>NV</td>\n",
       "      <td>956-871-8691</td>\n",
       "      <td>Gold</td>\n",
       "      <td>1</td>\n",
       "      <td>44.443836</td>\n",
       "      <td>Adult</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   claim_id  customer_id      incident_cause claim_date claim_area  \\\n",
       "0  54004764     21868593        Driver error 2017-11-27       Auto   \n",
       "1  33985796     75740424               Crime 2018-10-03       Home   \n",
       "2  53522022     30308357  Other driver error 2018-02-02       Auto   \n",
       "3  13015401     47830476      Natural causes 2018-06-17       Auto   \n",
       "4  22890252     19269962               Crime 2018-01-13       Auto   \n",
       "\n",
       "  police_report     claim_type  claim_amount  total_policy_claims fraudulent  \\\n",
       "0            No  Material only        2980.0                  1.0         No   \n",
       "1       Unknown  Material only        2980.0                  3.0         No   \n",
       "2            No  Material only        3369.5                  1.0        Yes   \n",
       "3            No  Material only        1680.0                  1.0         No   \n",
       "4            No  Material only        2680.0                  1.0         No   \n",
       "\n",
       "      CUST_ID  gender DateOfBirth State       Contact   Segment  isReported?  \\\n",
       "0  21868593.0  Female  1979-01-12    VT  789-916-8172  Platinum            1   \n",
       "1  75740424.0  Female  1970-01-13    ME  265-543-1264    Silver            1   \n",
       "2  30308357.0  Female  1984-03-11    TN  798-631-4758    Silver            1   \n",
       "3  47830476.0  Female  1986-05-01    MA  413-187-7945    Silver            1   \n",
       "4  19269962.0    Male  1977-05-13    NV  956-871-8691      Gold            1   \n",
       "\n",
       "         Age Age Group  claim_month  \n",
       "0  42.775342     Adult           11  \n",
       "1  51.778082     Adult           10  \n",
       "2  37.610959     Adult            2  \n",
       "3  35.471233     Adult            6  \n",
       "4  44.443836     Adult            1  "
      ]
     },
     "execution_count": 65,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 66,
   "metadata": {},
   "outputs": [],
   "source": [
    "df_claim = df[['claim_month','claim_amount']]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 67,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>claim_month</th>\n",
       "      <th>claim_amount</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>11</td>\n",
       "      <td>2980.0</td>\n",
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       "      <td>10</td>\n",
       "      <td>2980.0</td>\n",
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       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2</td>\n",
       "      <td>3369.5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>6</td>\n",
       "      <td>1680.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>1</td>\n",
       "      <td>2680.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   claim_month  claim_amount\n",
       "0           11        2980.0\n",
       "1           10        2980.0\n",
       "2            2        3369.5\n",
       "3            6        1680.0\n",
       "4            1        2680.0"
      ]
     },
     "execution_count": 67,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_claim.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 68,
   "metadata": {},
   "outputs": [],
   "source": [
    "monthly_claims = df_claim.groupby('claim_month').sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 69,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>claim_amount</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>claim_month</th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1.331832e+06</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>1.156579e+06</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>1.348315e+06</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>1.276020e+06</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>1.115035e+06</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>1.317728e+06</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>1.382493e+06</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>9.134147e+05</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>9.803903e+05</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>1.428671e+06</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>6.259235e+05</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>7.256857e+05</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "             claim_amount\n",
       "claim_month              \n",
       "1            1.331832e+06\n",
       "2            1.156579e+06\n",
       "3            1.348315e+06\n",
       "4            1.276020e+06\n",
       "5            1.115035e+06\n",
       "6            1.317728e+06\n",
       "7            1.382493e+06\n",
       "8            9.134147e+05\n",
       "9            9.803903e+05\n",
       "10           1.428671e+06\n",
       "11           6.259235e+05\n",
       "12           7.256857e+05"
      ]
     },
     "execution_count": 69,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "monthly_claims"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 70,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:xlabel='claim_month'>"
      ]
     },
     "execution_count": 70,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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xJMT2b0NXXk4qTa0u1u2o6df7Bouu1m/pTLbDRpvLsGNvg7/DCno9JnQRmS8ie0RkXRfH54rIGhFZJSL5InK078Ps9L7cfc4EAG55fV3QraH+wDubaGhp47azcg+5uyIxNoo75x7Gy1ccSXxMJD/71wp+85+vqdzf5ONog0tRRR3PLi3hgulZfeop74v2biUtu3RuaWEl6Und18/bOdO008Vb3ozQFwCzujn+ITDJGDMZuBT4Z9/D8s7QlHh+d9oYPt1czhurdvbXbftsdWk1L64s5dKjc7qcUNEbh2fbWXTN0Vxz0igWrd3FKQ9/yutf7wi6/+R85f+9s5GYqAiuO8W6zYUdnod9+mD0+7pa/7wrHXvRVfd6TOjGmE+BLr8rjTH7zbeZIwHo1yzykyOdTM1K4c431wfFyNTlMtzx5nocCbFcfeJIn103NiqS608ZzVtXH0OW3cZvXljFpQtWsKM6vH5MzS+u4u11u/nlsSMYmGTtrkp5OXbyS/YG9YN7fyiurOebfU3M6GK6/8HSE2OxxUTqCN0LPqmhi8g5IrIRWIR7lN7VeZd7yjL5vtqVKDJCuO+8iexvauXuRQU+uaY/vfb1Dr7eXs1Np48lKS7a59cfMziJV66cyW2zc1lWWMWpD33Cv78sxhUGScUYwz2LCxiYFMsvjrV+o5S8HDu1ja0h0V7rS72pn4O7vKqdLt7xSUI3xrxmjBkLnA38qZvznjLGTDPGTEtPT/fFrQH3rMqrjh/Ja1/v4OMAfjBY29jCfe9sZPKwFM6d4r/1siMjhEuPdk9Impqdyu0L1/ODeV+yJcQnJC1eu5uvt1dz46ljsMVYv6pFex1dyy7ftcxTPx+e1nP9vF223aYjdC/4tMvFU54ZLiJpvryuN646YQQjByZyy2vrAnbK9eNLtlJe28Sdc8b3S/vcMLuNZy7N46EfTqKwoo4zH/ucRz/YQnNr6LV/NbW2cf87Gxk7OInzDg+M/U4zU20MGRDHimKdK9Cut/XzdtlpNsqqGrR81YM+J3QRGSmevxkRmQrEAv2+gHFsVCT3nzeBnTUNPPjepv6+fY+2le9n/hdF/HBaJpOGpfTbfUWEc6dm8sH1xzHrsME8/MFmZv/1s5Cblv7s0hK2V9XzhzPGEdlPvebeyMtxbxwdrg+oD1ZUUcc3+5q6ne7fGacjgeY2F7tqwuuZUG9507b4PLAUGCMiZSJymYhcISJXeE45D1gnIquAJ4ALjEXfvYdn2/nJjGwWfFnM1wGUsIwx3PXmBuKiIvntaWMtiSEtMZbHLprC0xdPo7axlfP+9iV3vrk+YH+a6Y3q+mb+umQrx45O59jRvivl+cL0HDsV+5so1vov4F4uF/D6gWg77XTxjjddLhcZYzKMMdHGmExjzNPGmHnGmHme4/cbY8YbYyYbY440xnzu/7C79tvTxjA4OY6bXlkbMKWFJRv38Mnmcq49eRTpSdZuWHHSuEG8d92x/GRGNv/6opjTHvmUnUHeCfP4kq3UNrbwhzOs+c+yO3laR/+OZYWVDEyKJacX9XPQVRe9FbQzRbuSFBfN3WcfxqZvavn7J9usDoem1jbuemsDIwcmcvFMp9XhAO4/o7vmHsaLvzySqrpmbnxpddB2wWyvrOffS4s5//BhjB2cbHU43zNyYCL2hBidYMSh188BBifHERMVoSP0HoRcQgf3KPSsSUP465KtbN2z39JYnv68iJLKem4/K5foyMD6487LsfPH2bl8ua2S+V8UWR3OIbn/3Y1ERURw/amjrQ6lUyLCtOxU3fACd/18T22T1+2KHUVECFl2GyU6Qu9WYGUYH7r9rFxssZHc/Ooay0afu2saeXzJVk7NHcQxowKrttvuwunDOHncQP7fu5vYtDu42hpXluxl0ZpdXH7scAYlWzuJqDt5OXa2V9Wzu6bR6lAsdaj183ZOh01H6D0I2YSelhjLrWfmsqJ4L88t325JDPe9XUCry3DrmbmW3N8bIsKfz51IUmwUv3lhVdCsDmiM4d7FBaQnxXL5scOtDqdb7RtHLw/zUfrSwkoGJfe+ft4u2+FeF107hroWsgkd4LypQzl6ZBr3v72x39ud8oureH3VTn557HCyHIG9F2h6Uiz3nzeRgl37eOj9zVaH45V31u1mZclebjhldL+vpNhbuRnJJMREhvWD0b7Uz9s5HTYaW1zsqQ38JT6sEtIJXUS495wJtLpc/LEfV2RscxluX7iejAFxXHn8iH65Z1+dnDuIi/KG8dSnhQemZgeq5lYX972zkdGDEjl/2jCrw+lRVGQEU8O8jl5YUUf5IdbP22W3d7pUaB29KyGd0AGyHDZuOGUMHxTsYfHa3f1yzxdWlLJ+5z7+cMa4gJiC7q1bz8wly27jhhdXs6+xxepwuvS/y0ooqazn5gCbRNSdPKedTd/UUl3fbHUolujt+i2daW9d1Dp610I+oQP87CgnEzMHcPvCdX7/B1VT38ID724kL8fO7IkZfr2XryXERvHQDyezq6aBOxautzqcTtU0tPDYki0cPTKN4wNsElF3pufYMQbyw3QZgGWFVQxKjsXZh/LjkJQ4oiKEkiodoXclLBJ6VGQE9507kb31Ldy72L8rMj78wWZqGlq446zxh1wrtNLh2an8+oSRvPrfHSxeu8vqcL7nyY+2UtPQws1njA2qP9/Jw1KIjpSwLLv4on4O7n/HmanxOuu2G2GR0AFyhyTzy2OH82J+GV9srfDLPTbu3sezy0r48RHZ5A4JvEku3rr6pFFMzBzAH15byzf7AqfVrrSqnn99Ucx5UzMZP8SanYgOVVx0JJMyU8Ky02Vbubt+3tv1WzrjXkZXR+hdCZuEDnDNSaPISUvg5lfX0tDs2/Y8Ywx3LtxAUlwU158SmJNcvBUdGcHDF0ymsaWN3768JmDaxB54dxMREXBDgE4i6sn0HDtry2qobw7+9XN6wxf183ZOh42SivqA+Z4MNGGV0OOiI/nzuRPYXlXPIx/4tj3v7XW7WVpYyQ2njiE1Ican17bCiPREbjljHJ9uLueZpSVWh8Oq0moWrt7JL44ZTsaAeKvDOSR5TjutLsOq7dVWh9KvlhVWMjg57sACW32R7UigtqmVqrrwfLjck7BK6OAeJVyUN4x/fFbosx3ZG5rbuGdRAeMykvlRXpZPrhkI/mdGNseNTufexQWWLqFgjOHeRQWkJcbwy+OCow20M4c7UxEJrwlG7vp5FTOG233yzMOZ5v5PQevonQu7hA5w0+njSEuM5Xcvr6Glre8rMs77ZBs7qhu446zcoGmj84aI8MAPJmKLieS6F1ZZtnrlexu+YXlxFdedMprEAJ9E1J3kuGjGDU5meRhNMNpWXkfF/r71n3eUZXe3Lm7XTpdOhWVCHxDvXm1ww659PP153xalKq2qZ94n2zhr0hCO8NE3bSAZmBzHn8+dwNodNTz24ZZ+v39Lm4v73t7IyIGJXBAEk4h6kpdj5+vt1T4ZSASDpT6snwMMs8cjAsUVOkLvjDcbXMwXkT0isq6L4z8WkTUislZEvhSRSb4P0/dmHTaYWeMH8/D7m/s08+zexQVEiHDz6YG3FrevzDosgx8cnsmTH29lZUn/ji7/76vtFFXU8YczxhIVYKtVHoq8HDsNLW0+K/cFumWFlWQM8E39HNw7kw0ZEK+dLl3w5l/IAmBWN8eLgOOMMRNwbxD9lA/i6hd3zh1PTFQEN7+69pCemn+xtYK31+3mVyeMYEhKcD6o89btZ+UyJCWe615Yzf5+2uVoX2MLj3ywmZkjHJwwZmC/3NPf2jeODoeyizGGr3zQf34wZ5pNa+hd8GbHok+BLr/7jDFfGmPap78tAwJjh14vDEqO4w9njGNpYSUv5pf26mtb2lzc+eZ6htnj+fkxgb3any8kxUXz0A8nU7q3nj+9uaFf7vnkR9uobmjhD2eMC6pJRN1J9+zWEw4TjLaV76dif/MhL5fbFe1F75qvf4a9DHjbx9f0qwumDeOIHDv3LCpgTy8m0fzvshI2f7OfP56ZS1x0pB8jDBx5OXauOG4EL+SX8t56/66LU7a3nvlfFHHO5KEcNjS4JhH1JM9pZ0Xx3qDdJcpbSw+sf+7bZ0tOh4299S3UNATuekNW8VlCF5ETcCf033dzzuUiki8i+eXl5b66dZ9ERAh/PncCja0u7njTu/VLKvc38dD7mzlmVBqn5A7yc4SB5bqTR5ObkczNr66l3I/LmP7lvc0IcMNpY/x2D6tMz7FT09DC5j3BtaFIb7XXz7Psvl0+un3Vxe1advkenyR0EZkI/BOYa4zpcu1VY8xTxphpxphp6emBs7DS8PRErj1pFIvX7uZdL0aeD763iYbmNm4/KzdkSgHeiomK4JELJ1Pb1MrvX/HPLNK1ZTW89vUOLjs6h6Eh+GwiHDaO9lf9HDjwgFU3jP6+Pid0EckCXgV+YowJjt0ROnH5scMZl5HMbW+s63bp2LVlNfxnRSmXzHQycmBSP0YYOEYPSuKmWWNZsnEPzy/v3bOHnhhjuGfxBhwJMUGzlnxvDbPHMzg5juUhvPLi1j3u+rkv1m85WPuIX+vo3+dN2+LzwFJgjIiUichlInKFiFzhOeU2wAE8KSKrRCTfj/H6TXRkBPefN4Hy2ibuf3tjp+cYY7h94TocCbFcc/Kofo4wsFwy08nRI9P401sbKPLhhgMfFuxhWWEVvzl5FElx0T67biAREabn2FleVBmya5L4cv2Wg9liohiUHKudLp3wpsvlImNMhjEm2hiTaYx52hgzzxgzz3P858aYVGPMZM+vaf4P2z8mZqZw2dE5PPfV9k7byl5ftYP/bq/m97PGkByiycZbERHCA+dPJDpSuO6FVbT6YKJMS5uLe98uYHh6AheG0BIKnclzpvLNviZKq/p3a8T+sqywiiED4hhm90/JTDtdOhf8MzV87LpTRjPMHs9Nr66hseXbFRn3N7Xy58UbmTQshfOmBk1npl9lDIjnnnMmsKq0mic+2tbn6/1nRSmF5XXcfPo4okNgElF38nLcI9dQXNfFV+ufd8fp0F70zoT2v5pDYIuJ4t5zJlBYXsfjS7YeeP2vS7awp7aJO+eMJyKE1mvpq7MmDeHsyUN4bMkWVpVWH/J1ahtbeOT9zRyRY+fkcaExiag7owYmMiA+muVFgb1/66HYumc/lXXNfim3tMt2JFBe2xR2SxH3RBN6J44Zlc55UzOZ98k2Cnbto7B8P/M/L+L8wzOZPCzF6vACzp1zD2NQUizXvbDqkP+BzftkG5V1zdxyZuhMIupORIQw3ZnKihB8MOrr9Vs6097povuLfpcm9C7ceuY4BsRHc9Mra7jrrQ3ERUXyu1mhu15LXwyIj+bBH06iuLKOexb1fou/ndUN/POzIs6ePISJmSm+DzBA5eXYKaqoY09t4OwK5QvLCisZmhLvt/o5dNwwWuvoHWlC70JqQgx3zBnP6rIaPt5UzrUnjyI9KdbqsALWzBFp/NzzQPmjjXt69bUPvrcJA9wYgpOIujP9QD966IzS29c/P8JH6593Jcuh66J3RhN6N2ZPzODMiRkcNjSZnx7ptDqcgHfjaWMYOziJ3768hsr93s0iXbfDPYno0qNyyEz17YzCQHfY0AHER0eG1LouW/bsp8rP9XNwry3vSIjREfpBNKF3Q0R4/KIpvH7VUcRE6R9VT2KjInn4gsnsa2jxagVLYwz3Li4gJT6aq04IzUlE3YmOjGBqdkpIrbzY3n/ujwlFB8t22HRd9INoluqBiITEOtz9ZVxGMjeeNpr3NnzDSyvLuj33403lfLmtkt+cPDps+/qnO+0U7N4XMgtNtdfPM1P9v2SD05HA9ipN6B1pplI+9/OjhzNjuJ07F67vcgGl1jYX9y4uICctgR8dEdqTiLqT57RjDPy3JPjr6C5X+/6h/us/7yjLYWNnTcN35ouEO03oyuciIoQHz59EhAjXv7iKtk6WiX0xv4wte/bz+1ljQ34SUXemZKUSFSEhMcHo2/q5b9c/74rTkYAx7qWWlVv4/ktSfpWZauOus8eTX7KXeZ98dxbp/qZWHnp/M9OdqZw2PryWHz5YfEwkEzIHhEQd3Z/rt3TmwKqLWkc/QBO68puzJw/lzIkZPPz+5u/sofnUJ9uo2N8UUjsR9UWe086asuqgLx1823/eP91K7b3ouozutzShK78REe45+zAciTH85oVVNLa0sbumkac+K+SsSUOYkpVqdYgBYbrTTkub6dPSCVZzuQxfFVX12+gcIMUWTXJclM4W7UATuvKrFFsMD54/ia179nPf2xv5y3ubcLngd2E2iag70512RIJ74+j+rp+De8DgTEugRDtdDoiyOgAV+o4Zlc4lM50s+LIYEfjFMcP77cfyYDDAFs2YQUlBPcFo6bYKoP/q5+2y7DbWdijnhTsdoat+cdPpYw+sMPir40daHU7Ame60s7Jkr0/WlbfCssIqMlP7r37ezulIoGxvAy1B+ufma97sWDRfRPaIyLoujo8VkaUi0iQiN/o+RBUK4qIjefmKmbx19dEMsIXnJKLu5OXYqW9uY/3OfVaH0mvu+nllv4/Owd3p0uYy7NgbmhuF9JY3I/QFwKxujlcB1wAP+iIgFboG2KLDbr0Wb+XleBbqCsKyy+Y9teytb7EkoTvTtNOlI2+2oPsUd9Lu6vgeY8wKIDTmLitlgUHJcWTZbUH5YHTZNnf/+RE5/fdAtJ2ui/5d/VpDF5HLRSRfRPLLy8v789ZKBby8HDsriqtwdTKzNpBZVT8HSE+MxRYTqQndo18TujHmKWPMNGPMtPT09P68tVIBL89pZ299C9vK91sditdcLsOyosp+WV2xMyJClt2my+h6aJeLUgFiuqdkEUzrumz6ppZqi+rn7ZyOBK2he2hCVypAOB020pNig6qO3r5+yxH9OKHoYNlpNkqrGjpdBC7c9DixSESeB44H0kSkDLgdiAYwxswTkcFAPpAMuETkN0CuMSb4+q+UspCIkOe0syLIEvowe7yl3UtORwLNbS521TSEfRdVjwndGHNRD8d3A5k+i0ipMDbdmcqitbso21sf8Mmpff2WU8ZZu2Jmx06XQP8z8zctuSgVQPJy3LXoYCi7BEL9HL5ddVE7XTShKxVQxgxOIikuKigmGC319J/PGGFtQh+cHEdMVIR2uqAJXamAEhkhTMtODYoR+rLCSrLsNoam+H//0O5ERLhbF7XTRRO6UgEnL8fBtvI6KvY3WR1Kl75d/9y67paOnA6bllzQhK5UwMnLcW/8kR/AZZeNu2upabC+ft4u29OLbkx4ty5qQlcqwEwYmkJsVATLi/ZaHUqXvu0/D4yE7nTYaGxxsac2cH+q6Q+a0JUKMDFREUzJSmF5caXVoXQpUOrn7bK10wXQhK5UQMpz2tmwcx+1jYG3iGl7/dyq9Vs6096LHu4PRjWhKxWApufYcRn47/Zqq0P5noLd+9z18xGB8UAUYGhKPFEREvati5rQlQpAU7NSiYwQlhcFXtllWaH7Ye0ROYEzQo+KjCAzNZ5iLbkopQJNQmwUhw1JZkUAPhhdVlhJtsPGkACpn7fLdiToCN3qAJRSnZvutLOqrJrGljarQznA5TIsL6piRgCNztu196KHc+uiJnSlAlRejp3mVhdrymqsDuWADbsCr37eLtuRQG1jK3vrA+9Bcn/RhK5UgJruDLyNo9v7zwNlQlFH2umiCV2pgJWaEMOogYkBta7LssIqnA4bGQMCq34OHXvRNaErpQJQXo6dlSV7A2I3njaXYXlRZUCOzgGG2eMRgeKK8O106TGhi8h8EdkjIuu6OC4i8piIbBWRNSIy1fdhKhWe8nLs7G9qpWCX9RuAFezax77G1oBN6LFRkQwZEK8j9B4sAGZ1c/x0YJTn1+XA3/oellIKvq2jB0LZJRD2D+2JM81GSZWO0LtkjPkU6O67aS7wjHFbBqSISIavAlQqnA1JiSczNT5gEnqg1s/buXvRNaH3xVCgtMPnZZ7XvkdELheRfBHJLy8v98GtlQp9eU47K4qrLO2vbmtfv8Xi3Yl6km23UVXXTE1DeLYu9utDUWPMU8aYacaYaenp6f15a6WC1vQcO5V1zRRWWFcbLti1j9oArp+3a+902R6mo3RfJPQdwLAOn2d6XlNK+UBejqcf3cKyy4H6eQDOEO3ImRbevei+SOgLgZ96ul1mADXGmF0+uK5SChielkBaYoyldfRlhZXkpCUweECcZTF4I8vuTujh2ukS1dMJIvI8cDyQJiJlwO1ANIAxZh6wGDgD2ArUAz/zV7BKhSMRYVq2neUWzBhtcxlezC/l860VnDOl00djAcUWE8Wg5NiwfTDaY0I3xlzUw3ED/MpnESmlvicvx84763ezs7qh31Y5XFVazW1vrGNNWQ15Tju/PnFUv9y3r8K506XHhK6Ust6BOnpxFXMn+3ekXLm/iQfe3cQL+aWkJ8by6IWTmTNpCCLi1/v6Srbdxiebw7OLThO6UkFgXEYyibFRLC/yX0Jvcxn+76sSHnxvM3VNrfz86ByuOWkUSXHRfrmfvzjTEnhpZRn1za3YYsIrxYXXu1UqSEVGCIdnp/pt5cWVJVX88fX1bNi1j5kjHNw5ZzyjBiX55V7+1r7qYkllPeMyki2Opn/p4lxKBYm8HDubv9nP3rpmn12zvLaJG15czXl/W8re+mae+NFUnvv5EUGbzAGcYbzqoo7QlQoSHddHP3X84D5dq7XNxTNLS3j4/c00trZx1fEj+NUJI0mIDf6UkNVhhB5ugv9vT6kwMTFzADFREX1O6F8VVnLbG+vZ9E0tx45O546zchmenujDSK2VHBeNIyEmLDeM1oSuVJCIi45kcmbKIU8w+mZfI/cuLuCNVTsZmhLP339yOKfmDgqa7pXeyHLYtOSilAps03NSmfdJIXVNrV6XR1raXPzriyIe/WALLS7DNSeN4srjRhAfE+nnaK3jdCQExAqV/U0fiioVRPJyHLS5DF9vr/bq/C+2VnD6o59x7+KNHDnCwfvXHcv1p4wO6WQO7k6XnTUNNLa0WR1Kv9IRulJBZGpWChECy4sqOXpUWpfn7axu4J5FBSxau4ssu435l0zjxLGD+jFSazkdCRgDZXvrGTkweDt2eksTulJBJCkumtwhyV2u69LU2sY/Pyvi8SVbMRhuOGU0vzh2OHHRoT0iP1jHXnRN6EqpgJXndPDcVyU0t7qIifq2avrxpj3c+eYGiirqOG38IP44O5fMVJuFkVqnvRc93DpdtIauVJDJy0mlqdXF2h3VAJRW1XP5M/lc8q8VCPDvS/P4+0+mhW0yB0ixRZMUFxV2nS46QlcqyEzzTDD6bEsFn2+p5MmPtxIZIfx+1lguPdpJbFR4lVc6IyI4HQlhN0LXhK5UkElLjGVEegKPfLAFgDMnZnDrmeMCevNmK2Q7bKzdUWN1GP3Kq5KLiMwSkU0islVEburkeLaIfCgia0TkYxHJ9H2oSql2507NZFLmAP7v50fwxI+majLvhNORQNneBlraXFaH0m96TOgiEgk8AZwO5AIXiUjuQac9CDxjjJkI3AX82deBKqW+9asTRvLGr49m5siuWxfDXbbDRpvLsLO6wepQ+o03I/Q8YKsxptAY0wz8B5h70Dm5wBLPxx91clwppfqVMy38Ol28SehDgdIOn5d5XutoNXCu5+NzgCQRCeztwZVSIS07DDeM9lXb4o3AcSLyNXAcsAP43pxbEblcRPJFJL+8PDy3iFJK9Y/0pFjioyMprtARekc7gGEdPs/0vHaAMWanMeZcY8wU4BbPa9UHX8gY85QxZpoxZlp6evqhR62UUj0QEbLDbNVFbxL6CmCUiOSISAxwIbCw4wkikiYi7de6GZjv2zCVUqr33L3omtAPMMa0Ar8G3gUKgBeNMetF5C4RmeM57Xhgk4hsBgYB9/gpXqWU8lp2mo3SqgbaXMbqUPqFVxOLjDGLgcUHvXZbh49fBl72bWhKKdU3TkcCzW0udu9rZGhK6Pfq61ouSqmQdaDTpSI8yi6a0JVSISs7zHrRNaErpUJWRnIcMVERYdPpogldKRWyIiKELLstbDpdNKErpUKa02GjREsuSikV/LIdCZRU1mNM6LcuakJXSoU0p8NGQ0sb5bVNVofid5rQlVIhLSuM9hfVhK6UCmlOh7sXPRwejGpCV0qFtKEp8URFSFi0LmpCV0qFtKjICDJT48Oi00UTulIq5LV3uoQ6TehKqZDndLgnF4V666ImdKVUyMtyJFDb2Mre+harQ/ErTehKqZAXLp0umtCVUiEv29OLHuqdLl4ldBGZJSKbRGSriNzUyfEsEflIRL4WkTUicobvQ1VKqUMzzB6PCCH/YLTHhC4ikcATwOlALnCRiOQedNqtuLemm4J7z9EnfR2oUkodqtioSIYMCP3WRW9G6HnAVmNMoTGmGfgPMPegcwyQ7Pl4ALDTdyEqpVTfOdNCfxldbxL6UKC0w+dlntc6ugP4HxEpw7336NWdXUhELheRfBHJLy8vP4RwlVLq0GTZQ78X3VcPRS8CFhhjMoEzgGdF5HvXNsY8ZYyZZoyZlp6e7qNbK6VUz5wOG1V1zexrDN3WRW8S+g5gWIfPMz2vdXQZ8CKAMWYpEAek+SJApZTyhfZOl+0hPEr3JqGvAEaJSI6IxOB+6LnwoHO2AycBiMg43AldaypKqYDhTAv9XvQeE7oxphX4NfAuUIC7m2W9iNwlInM8p90A/EJEVgPPA5eYUJ9jq5QKKll2d0IP5Tp6lDcnGWMW437Y2fG12zp8vAE4yrehKaWU79hiohiUHEtxRRiP0JVSKlRkh3iniyZ0pVTYyHaEdi+6JnSlVNhwpiWwp7aJ+uZWq0PxC03oSqmwke1ZdXF7VWiWXTShK6XChtPTi15coQldKaWCWpajvXUxNOvomtCVUmEjOS4ae0IMxSHa6aIJXSkVVrIdNh2hK6VUKHA6QrcXXRO6UiqsZDts7KxpoKm1zepQfE4TulIqrDgdCRgDpVUNVofic5rQlVJhJdviTpcPC76hYn+TX66tCV0pFVba10W3otNlwRdF/PyZfB5+f7Nfru/VaotKKRUqUm3RJMVF9esI3eUy3P/ORv7+aSGn5g7i1jNz/XIfTehKqbAiIv3a6dLc6uK3L6/mjVU7+cmMbO6YM57ICPHLvTShK6XCTrbDxrodNX6/z77GFq54diVfbqvkd7PGcOVxIxDxTzIHL2voIjJLRDaJyFYRuamT4w+LyCrPr80iUu3zSJVSykecjgTK9jbQ0uby2z121zTyw3lLWV5UxUM/nMRVx4/0azIHL0boIhIJPAGcApQBK0RkoWeXIgCMMdd1OP9qYIofYlVKKZ/IdthodRl2VjcceEjqS5u/qeWS+cupaWjhXz+bzjGj0n1+j854M0LPA7YaYwqNMc3Af4C53Zx/Ee59RZVSKiD5s9Plq8JKfvC3L2lxGV684sh+S+bgXUIfCpR2+LzM89r3iEg2kAMs6eL45SKSLyL55eXlvY1VKaV8wumnXvRFa3bxk6eXk54Uy6tXzmT8kAE+vX5PfN2HfiHwsjGm0zm1xpinjDHTjDHT0tP7738tpZTqKD0plvjoSJ92ujz9eRG/fv6/TMwcwCtXzmSY3eaza3vLmy6XHcCwDp9nel7rzIXAr/oalFJK+ZOI+GzVRZfL8Oe3C/jHZ0WcNn4Qj144hbjoSB9E2XveJPQVwCgRycGdyC8EfnTwSSIyFkgFlvo0QqWU8gOnI4Gt5fv7dI2m1jZufGkNb67eycVHZnPbWf7rMfdGjyUXY0wr8GvgXaAAeNEYs15E7hKROR1OvRD4jzHG+CdUpZTynew0G9sr62lzHVrKqmlo4eL5y3lz9U5uOn2sXycMecuriUXGmMXA4oNeu+2gz+/wXVhKKeVf2fYEmttc7N7XyNCU+F597a6aBi6Zv4LCiv08csFkzp7SaZ9Iv9OZokqpsHSg06WirlcJfdPuWi7513JqG1tZ8LM8jhqZ5q8Qe01XW1RKhaXsNHcvekmV950uS7dV8oN5X9LmMrz4yyMDKpmDjtCVUmEqIzmOmKgIir3sdHlz9U5ueHE1WQ4b/740r9dlmv6gCV0pFZYiIoQsu42Sip5H6P/8rJC7FxWQ57Tz1E8PJ8UW0w8R9p4mdKVU2HI6bN2O0F0uw92LCpj/RRFnTBjMQz+cbFmPuTc0oSulwlaWPYEvtlZijPneSoiNLW3c8NJqFq3Zxc+OcvLHM3OJsLgtsSea0JVSYcuZZqOhpY3y2iYGJscdeL2mvoVfPJvP8qIqbjljHD8/JsfvS9/6giZ0pVTYal91saSq/kBC31ndwMXzl1NcWcejF05m7uTA6DH3hrYtKqXCVnsvenGFu45esGsf5zz5BbtrGvn3pXlBlcxBR+hKqTA2NCWeqAihpLKeL7dW8MtnV5IQG8VLVx7J2MHJVofXa5rQlVJhKyoygszUeN5cs5O/f7qNnLQEFvwsjyEB2GPuDS25KKXCWpYjgZLKeqZmpfLSFTODNpmDjtCVUmHuJzOyGTc4ietPHU1sVOD2mHtDE7pSKqydkjuIU3IHWR2GT2jJRSmlQoRXCV1EZonIJhHZKiI3dXHOD0Vkg4isF5H/822YSimletJjyUVEIoEngFOAMmCFiCw0xmzocM4o4GbgKGPMXhEZ6K+AlVJKdc6bEXoesNUYU2iMaQb+A8w96JxfAE8YY/YCGGP2+DZMpZRSPfEmoQ8FSjt8XuZ5raPRwGgR+UJElonIrM4uJCKXi0i+iOSXl5cfWsRKKaU65auHolHAKOB44CLgHyKScvBJxpinjDHTjDHT0tPTfXRrpZRS4F1C3wEM6/B5pue1jsqAhcaYFmNMEbAZd4JXSinVT7xJ6CuAUSKSIyIxwIXAwoPOeR336BwRScNdgin0XZhKKaV60mOXizGmVUR+DbwLRALzjTHrReQuIN8Ys9Bz7FQR2QC0Ab81xlR2d92VK1dWiEhJ399Cv0gDKqwOwk9C+b1BaL8/fW/Bqy/vL7urA2KMOcRrhg8RyTfGTLM6Dn8I5fcGof3+9L0FL3+9P50pqpRSIUITulJKhQhN6N55yuoA/CiU3xuE9vvT9xa8/PL+tIaulFIhQkfoSikVIjShK6VUiNCE3g0RGSYiH3VYFvhaq2PyNRGJFJGvReQtq2PxJRFJEZGXRWSjiBSIyJFWx+RLInKd53tynYg8LyJxVsd0qERkvojsEZF1HV6zi8j7IrLF83uqlTH2RRfv7wHP9+YaEXmts6VSDoUm9O61AjcYY3KBGcCvRCTX4ph87VqgwOog/OBR4B1jzFhgEiH0HkVkKHANMM0YcxjuCX8XWhtVnywADl7Q7ybgQ2PMKOBDz+fBagHff3/vA4cZYybiXirlZl/cSBN6N4wxu4wx//V8XIs7KRy80mTQEpFM4Ezgn1bH4ksiMgA4FngawBjTbIyptjQo34sC4kUkCrABOy2O55AZYz4Fqg56eS7wb8/H/wbO7s+YfKmz92eMec8Y0+r5dBnuNbL6TBO6l0TECUwBvrI4FF96BPgd4LI4Dl/LAcqBf3nKSf8UkQSrg/IVY8wO4EFgO7ALqDHGvGdtVD43yBizy/PxbiA0Nv3s3KXA2764kCZ0L4hIIvAK8BtjzD6r4/EFEZkN7DHGrLQ6Fj+IAqYCfzPGTAHqCO4f2b/DU0+ei/s/riFAgoj8j7VR+Y9x91aHZH+1iNyCu7T7nC+upwm9ByISjTuZP2eMedXqeHzoKGCOiBTj3oXqRBH5X2tD8pkyoMwY0/7T1Mu4E3yoOBkoMsaUG2NagFeBmRbH5GvfiEgGgOf3kNsFTUQuAWYDPzY+mhCkCb0bIiK467AFxpiHrI7Hl4wxNxtjMo0xTtwP1JYYY0JilGeM2Q2UisgYz0snARu6+ZJgsx2YISI2z/foSYTQQ1+PhcDFno8vBt6wMBaf8+zq9jtgjjGm3lfX1YTevaOAn+Aeva7y/DrD6qCUV64GnhORNcBk4F5rw/Edz08eLwP/Bdbi/ncctFPlReR5YCkwRkTKROQy4D7gFBHZgvsnkvusjLEvunh/jwNJwPuevDLPJ/fSqf9KKRUadISulFIhQhO6UkqFCE3oSikVIjShK6VUiNCErpRSIUITulJKhQhN6CpoiMgdInJjD+dcISI/7a+YesOzpO9VHT4/PtSWLVbW0oSuQooxZp4x5hmr4+hCCnBVTycpdag0oauAJSI/9WwAsFpEnj3o2C9EZIXn2CsiYvO8fmAULyIfi8jDIpLv2eRiuoi86tk04e5u7uv0bD6wQEQ2i8hzInKyiHzh+do8z3l2EXndE+MyEZnYIYb5nvsXisg1nkvfB4zwzAx8wPNaYoeNOJ7zTOVX6pBoQlcBSUTGA7cCJxpjJuHeiKOjV40x0z3HCoDLurhUszFmGjAP93ogvwIOAy4REUc3IYwE/gKM9fz6EXA0cCPwB885dwJfezYp+APQ8SeDscBpQB5wu2eRt5uAbcaYycaY33rOmwL8BsgFhuNebkKpQ6IJXQWqE4GXjDEVAMaYgzdAOExEPhORtcCPgfFdXGeh5/e1wHrPpiVNQCEwrJv7Fxlj1hpjXMB63LvnGM91nJ5zjgae9cS3BHCISLLn2CJjTJMn/j10vZ73cmNMmec+qzpcW6le04SugtUC4NfGmAm4R8pd7anZ5Pnd1eHj9s+jurn+wed2vE53X9fZ17d18zXenqdUjzShq0C1BDi/vSwiIvaDjicBuzyljB/3d3Aen7XfW0SOByp62AClFnfcSvmFjgZUQDLGrBeRe4BPRKQN+Boo7nDKH3FvB1ju+d2KRHkHMN+zRG89367f3SljTKXnweo63FuOLfJ/iCqc6PK5SikVIrTkopRSIUJLLipseerzH3Zy6CRjTGV/x6NUX2nJRSmlQoSWXJRSKkRoQldKqRChCV0ppUKEJnSllAoR/x+suoc0zo5oaAAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "monthly_claims.plot(y = 'claim_amount', use_index = True)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 15. Average claim amount for gender and age categories"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 71,
   "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>claim_id</th>\n",
       "      <th>customer_id</th>\n",
       "      <th>incident_cause</th>\n",
       "      <th>claim_date</th>\n",
       "      <th>claim_area</th>\n",
       "      <th>police_report</th>\n",
       "      <th>claim_type</th>\n",
       "      <th>claim_amount</th>\n",
       "      <th>total_policy_claims</th>\n",
       "      <th>fraudulent</th>\n",
       "      <th>CUST_ID</th>\n",
       "      <th>gender</th>\n",
       "      <th>DateOfBirth</th>\n",
       "      <th>State</th>\n",
       "      <th>Contact</th>\n",
       "      <th>Segment</th>\n",
       "      <th>isReported?</th>\n",
       "      <th>Age</th>\n",
       "      <th>Age Group</th>\n",
       "      <th>claim_month</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>54004764</td>\n",
       "      <td>21868593</td>\n",
       "      <td>Driver error</td>\n",
       "      <td>2017-11-27</td>\n",
       "      <td>Auto</td>\n",
       "      <td>No</td>\n",
       "      <td>Material only</td>\n",
       "      <td>2980.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>No</td>\n",
       "      <td>21868593.0</td>\n",
       "      <td>Female</td>\n",
       "      <td>1979-01-12</td>\n",
       "      <td>VT</td>\n",
       "      <td>789-916-8172</td>\n",
       "      <td>Platinum</td>\n",
       "      <td>1</td>\n",
       "      <td>42.775342</td>\n",
       "      <td>Adult</td>\n",
       "      <td>11</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>33985796</td>\n",
       "      <td>75740424</td>\n",
       "      <td>Crime</td>\n",
       "      <td>2018-10-03</td>\n",
       "      <td>Home</td>\n",
       "      <td>Unknown</td>\n",
       "      <td>Material only</td>\n",
       "      <td>2980.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>No</td>\n",
       "      <td>75740424.0</td>\n",
       "      <td>Female</td>\n",
       "      <td>1970-01-13</td>\n",
       "      <td>ME</td>\n",
       "      <td>265-543-1264</td>\n",
       "      <td>Silver</td>\n",
       "      <td>1</td>\n",
       "      <td>51.778082</td>\n",
       "      <td>Adult</td>\n",
       "      <td>10</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>53522022</td>\n",
       "      <td>30308357</td>\n",
       "      <td>Other driver error</td>\n",
       "      <td>2018-02-02</td>\n",
       "      <td>Auto</td>\n",
       "      <td>No</td>\n",
       "      <td>Material only</td>\n",
       "      <td>3369.5</td>\n",
       "      <td>1.0</td>\n",
       "      <td>Yes</td>\n",
       "      <td>30308357.0</td>\n",
       "      <td>Female</td>\n",
       "      <td>1984-03-11</td>\n",
       "      <td>TN</td>\n",
       "      <td>798-631-4758</td>\n",
       "      <td>Silver</td>\n",
       "      <td>1</td>\n",
       "      <td>37.610959</td>\n",
       "      <td>Adult</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>13015401</td>\n",
       "      <td>47830476</td>\n",
       "      <td>Natural causes</td>\n",
       "      <td>2018-06-17</td>\n",
       "      <td>Auto</td>\n",
       "      <td>No</td>\n",
       "      <td>Material only</td>\n",
       "      <td>1680.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>No</td>\n",
       "      <td>47830476.0</td>\n",
       "      <td>Female</td>\n",
       "      <td>1986-05-01</td>\n",
       "      <td>MA</td>\n",
       "      <td>413-187-7945</td>\n",
       "      <td>Silver</td>\n",
       "      <td>1</td>\n",
       "      <td>35.471233</td>\n",
       "      <td>Adult</td>\n",
       "      <td>6</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>22890252</td>\n",
       "      <td>19269962</td>\n",
       "      <td>Crime</td>\n",
       "      <td>2018-01-13</td>\n",
       "      <td>Auto</td>\n",
       "      <td>No</td>\n",
       "      <td>Material only</td>\n",
       "      <td>2680.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>No</td>\n",
       "      <td>19269962.0</td>\n",
       "      <td>Male</td>\n",
       "      <td>1977-05-13</td>\n",
       "      <td>NV</td>\n",
       "      <td>956-871-8691</td>\n",
       "      <td>Gold</td>\n",
       "      <td>1</td>\n",
       "      <td>44.443836</td>\n",
       "      <td>Adult</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   claim_id  customer_id      incident_cause claim_date claim_area  \\\n",
       "0  54004764     21868593        Driver error 2017-11-27       Auto   \n",
       "1  33985796     75740424               Crime 2018-10-03       Home   \n",
       "2  53522022     30308357  Other driver error 2018-02-02       Auto   \n",
       "3  13015401     47830476      Natural causes 2018-06-17       Auto   \n",
       "4  22890252     19269962               Crime 2018-01-13       Auto   \n",
       "\n",
       "  police_report     claim_type  claim_amount  total_policy_claims fraudulent  \\\n",
       "0            No  Material only        2980.0                  1.0         No   \n",
       "1       Unknown  Material only        2980.0                  3.0         No   \n",
       "2            No  Material only        3369.5                  1.0        Yes   \n",
       "3            No  Material only        1680.0                  1.0         No   \n",
       "4            No  Material only        2680.0                  1.0         No   \n",
       "\n",
       "      CUST_ID  gender DateOfBirth State       Contact   Segment  isReported?  \\\n",
       "0  21868593.0  Female  1979-01-12    VT  789-916-8172  Platinum            1   \n",
       "1  75740424.0  Female  1970-01-13    ME  265-543-1264    Silver            1   \n",
       "2  30308357.0  Female  1984-03-11    TN  798-631-4758    Silver            1   \n",
       "3  47830476.0  Female  1986-05-01    MA  413-187-7945    Silver            1   \n",
       "4  19269962.0    Male  1977-05-13    NV  956-871-8691      Gold            1   \n",
       "\n",
       "         Age Age Group  claim_month  \n",
       "0  42.775342     Adult           11  \n",
       "1  51.778082     Adult           10  \n",
       "2  37.610959     Adult            2  \n",
       "3  35.471233     Adult            6  \n",
       "4  44.443836     Adult            1  "
      ]
     },
     "execution_count": 71,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 82,
   "metadata": {},
   "outputs": [],
   "source": [
    "gender_claims = df.groupby(['fraudulent','gender']).mean().drop(['claim_id','customer_id','total_policy_claims','CUST_ID','isReported?','Age','claim_month'], axis=1).reset_index()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 83,
   "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>fraudulent</th>\n",
       "      <th>gender</th>\n",
       "      <th>claim_amount</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>No</td>\n",
       "      <td>Female</td>\n",
       "      <td>12424.388568</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>No</td>\n",
       "      <td>Male</td>\n",
       "      <td>12191.830435</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Yes</td>\n",
       "      <td>Female</td>\n",
       "      <td>11070.621202</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Yes</td>\n",
       "      <td>Male</td>\n",
       "      <td>15057.496611</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "  fraudulent  gender  claim_amount\n",
       "0         No  Female  12424.388568\n",
       "1         No    Male  12191.830435\n",
       "2        Yes  Female  11070.621202\n",
       "3        Yes    Male  15057.496611"
      ]
     },
     "execution_count": 83,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "gender_claims"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 93,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<seaborn.axisgrid.FacetGrid at 0x173b47e0860>"
      ]
     },
     "execution_count": 93,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 420.25x360 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.catplot(x='gender', y='claim_amount', hue='fraudulent',kind='bar', data = gender_claims)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 86,
   "metadata": {},
   "outputs": [],
   "source": [
    "age_claims = df.groupby(['fraudulent','Age Group']).mean().drop(['claim_id','customer_id','total_policy_claims','CUST_ID','isReported?','Age','claim_month'], axis=1).reset_index()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 87,
   "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>fraudulent</th>\n",
       "      <th>Age Group</th>\n",
       "      <th>claim_amount</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>No</td>\n",
       "      <td>Adult</td>\n",
       "      <td>12080.974083</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>No</td>\n",
       "      <td>Children</td>\n",
       "      <td>10866.250000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>No</td>\n",
       "      <td>Senior</td>\n",
       "      <td>12884.961020</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>No</td>\n",
       "      <td>Youth</td>\n",
       "      <td>12912.920627</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Yes</td>\n",
       "      <td>Adult</td>\n",
       "      <td>14208.997318</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>Yes</td>\n",
       "      <td>Children</td>\n",
       "      <td>10519.214286</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>Yes</td>\n",
       "      <td>Senior</td>\n",
       "      <td>10349.041667</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>Yes</td>\n",
       "      <td>Youth</td>\n",
       "      <td>9074.674226</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "  fraudulent Age Group  claim_amount\n",
       "0         No     Adult  12080.974083\n",
       "1         No  Children  10866.250000\n",
       "2         No    Senior  12884.961020\n",
       "3         No     Youth  12912.920627\n",
       "4        Yes     Adult  14208.997318\n",
       "5        Yes  Children  10519.214286\n",
       "6        Yes    Senior  10349.041667\n",
       "7        Yes     Youth   9074.674226"
      ]
     },
     "execution_count": 87,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "age_claims"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 95,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<seaborn.axisgrid.FacetGrid at 0x173b495f4e0>"
      ]
     },
     "execution_count": 95,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 420.25x360 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.catplot(x='Age Group', y='claim_amount', hue='fraudulent',kind='bar', data = age_claims)"
   ]
  },
  {
   "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": "3.6.5"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}