eda.ipynb

{
  "cells": [
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "3zctPqLsPadv"
      },
      "source": [
        "## Data importing"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "z6h57VSoc3IG"
      },
      "outputs": [],
      "source": [
        "import pandas as pd\n",
        "import numpy as np\n",
        "from matplotlib import pyplot as plt\n",
        "from scipy import stats"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 424
        },
        "id": "fD-9ZgZCc3EM",
        "outputId": "f4f1b9d7-fb4a-442e-d5da-920764bd8741"
      },
      "outputs": [
        {
          "data": {
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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>VEHICLE_ID</th>\n",
              "      <th>VEHICLE_VALUE</th>\n",
              "      <th>VEHICLE_AGE</th>\n",
              "      <th>VEHICLE_ANNUALMILEAGE</th>\n",
              "      <th>VEHICLE_OWNEDMONTHS</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>vehicle104401</td>\n",
              "      <td>2355.228301</td>\n",
              "      <td>14.0</td>\n",
              "      <td>4985.366334</td>\n",
              "      <td>16.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>vehicle80628</td>\n",
              "      <td>17457.900654</td>\n",
              "      <td>2.0</td>\n",
              "      <td>7974.232760</td>\n",
              "      <td>11.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>vehicle211641R</td>\n",
              "      <td>9995.954859</td>\n",
              "      <td>15.0</td>\n",
              "      <td>9999.689025</td>\n",
              "      <td>53.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>vehicle46020</td>\n",
              "      <td>2104.581247</td>\n",
              "      <td>11.0</td>\n",
              "      <td>7012.133021</td>\n",
              "      <td>50.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>vehicle38531</td>\n",
              "      <td>3494.863930</td>\n",
              "      <td>10.0</td>\n",
              "      <td>8975.348352</td>\n",
              "      <td>20.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>...</th>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>90462</th>\n",
              "      <td>vehicle64265</td>\n",
              "      <td>1635.652886</td>\n",
              "      <td>12.0</td>\n",
              "      <td>5981.158836</td>\n",
              "      <td>59.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>90463</th>\n",
              "      <td>vehicle187921</td>\n",
              "      <td>599.244688</td>\n",
              "      <td>17.0</td>\n",
              "      <td>4986.564149</td>\n",
              "      <td>59.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>90464</th>\n",
              "      <td>vehicle214546</td>\n",
              "      <td>8993.225724</td>\n",
              "      <td>14.0</td>\n",
              "      <td>3002.508429</td>\n",
              "      <td>31.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>90465</th>\n",
              "      <td>vehicle2490</td>\n",
              "      <td>7011.831253</td>\n",
              "      <td>7.0</td>\n",
              "      <td>4998.673655</td>\n",
              "      <td>10.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>90466</th>\n",
              "      <td>vehicle147051R</td>\n",
              "      <td>14876.729577</td>\n",
              "      <td>1.0</td>\n",
              "      <td>5993.031157</td>\n",
              "      <td>12.0</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "<p>90467 rows × 5 columns</p>\n",
              "</div>"
            ],
            "text/plain": [
              "           VEHICLE_ID  VEHICLE_VALUE  VEHICLE_AGE  VEHICLE_ANNUALMILEAGE  \\\n",
              "0       vehicle104401    2355.228301         14.0            4985.366334   \n",
              "1        vehicle80628   17457.900654          2.0            7974.232760   \n",
              "2      vehicle211641R    9995.954859         15.0            9999.689025   \n",
              "3        vehicle46020    2104.581247         11.0            7012.133021   \n",
              "4        vehicle38531    3494.863930         10.0            8975.348352   \n",
              "...               ...            ...          ...                    ...   \n",
              "90462    vehicle64265    1635.652886         12.0            5981.158836   \n",
              "90463   vehicle187921     599.244688         17.0            4986.564149   \n",
              "90464   vehicle214546    8993.225724         14.0            3002.508429   \n",
              "90465     vehicle2490    7011.831253          7.0            4998.673655   \n",
              "90466  vehicle147051R   14876.729577          1.0            5993.031157   \n",
              "\n",
              "       VEHICLE_OWNEDMONTHS  \n",
              "0                     16.0  \n",
              "1                     11.0  \n",
              "2                     53.0  \n",
              "3                     50.0  \n",
              "4                     20.0  \n",
              "...                    ...  \n",
              "90462                 59.0  \n",
              "90463                 59.0  \n",
              "90464                 31.0  \n",
              "90465                 10.0  \n",
              "90466                 12.0  \n",
              "\n",
              "[90467 rows x 5 columns]"
            ]
          },
          "execution_count": 3,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "df_vehicles = pd.read_csv('vehicles.csv')\n",
        "df_vehicles"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 424
        },
        "id": "d6haX_Dhc3B8",
        "outputId": "ecb58976-ea54-4e7b-e68b-5b364ec0216d"
      },
      "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>POLICY_NUMBER</th>\n",
              "      <th>DATE_OF_ACCIDENT</th>\n",
              "      <th>CLAIM_ID</th>\n",
              "      <th>INCURRED</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>6.495393e+09</td>\n",
              "      <td>2019-09-13</td>\n",
              "      <td>claim150414</td>\n",
              "      <td>1019.306791</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>6.495821e+09</td>\n",
              "      <td>2020-10-07</td>\n",
              "      <td>claim33407</td>\n",
              "      <td>1.237647</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>6.495789e+09</td>\n",
              "      <td>2021-05-27 00:00:00</td>\n",
              "      <td>claim173327</td>\n",
              "      <td>13746.019943</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>6.495891e+09</td>\n",
              "      <td>2021-07-05</td>\n",
              "      <td>claim57413</td>\n",
              "      <td>2.599416</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>6.495447e+09</td>\n",
              "      <td>2019-05-25</td>\n",
              "      <td>claim258319</td>\n",
              "      <td>13858.684281</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>...</th>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>10483</th>\n",
              "      <td>6.495350e+09</td>\n",
              "      <td>2019-12-21</td>\n",
              "      <td>claim260687</td>\n",
              "      <td>125.047398</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>10484</th>\n",
              "      <td>6.495914e+09</td>\n",
              "      <td>2021-04-22</td>\n",
              "      <td>claim42091</td>\n",
              "      <td>99.302350</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>10485</th>\n",
              "      <td>6.495467e+09</td>\n",
              "      <td>2020-02-20</td>\n",
              "      <td>claim22384</td>\n",
              "      <td>85.308506</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>10486</th>\n",
              "      <td>6.495835e+09</td>\n",
              "      <td>2020-07-30</td>\n",
              "      <td>claim187487</td>\n",
              "      <td>1.256220</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>10487</th>\n",
              "      <td>6.495876e+09</td>\n",
              "      <td>2021-09-08</td>\n",
              "      <td>claim18733</td>\n",
              "      <td>8209.504522</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "<p>10488 rows × 4 columns</p>\n",
              "</div>"
            ],
            "text/plain": [
              "       POLICY_NUMBER     DATE_OF_ACCIDENT     CLAIM_ID      INCURRED\n",
              "0       6.495393e+09           2019-09-13  claim150414   1019.306791\n",
              "1       6.495821e+09           2020-10-07   claim33407      1.237647\n",
              "2       6.495789e+09  2021-05-27 00:00:00  claim173327  13746.019943\n",
              "3       6.495891e+09           2021-07-05   claim57413      2.599416\n",
              "4       6.495447e+09           2019-05-25  claim258319  13858.684281\n",
              "...              ...                  ...          ...           ...\n",
              "10483   6.495350e+09           2019-12-21  claim260687    125.047398\n",
              "10484   6.495914e+09           2021-04-22   claim42091     99.302350\n",
              "10485   6.495467e+09           2020-02-20   claim22384     85.308506\n",
              "10486   6.495835e+09           2020-07-30  claim187487      1.256220\n",
              "10487   6.495876e+09           2021-09-08   claim18733   8209.504522\n",
              "\n",
              "[10488 rows x 4 columns]"
            ]
          },
          "execution_count": 4,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "df_claims = pd.read_csv('claims.csv')\n",
        "df_claims"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 424
        },
        "id": "6Lvv7lfpd4Di",
        "outputId": "57d6d1bc-93ac-41cc-9216-28d2095d2fac"
      },
      "outputs": [
        {
          "data": {
            "text/html": [
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              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>CUSTOMER_ID</th>\n",
              "      <th>MARITALSTATUS</th>\n",
              "      <th>DATE_OF_BIRTH</th>\n",
              "      <th>NCD</th>\n",
              "      <th>EMPLOYMENTTYPE</th>\n",
              "      <th>VEHICLE_CLASSOFUSE</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>customer216583</td>\n",
              "      <td>Married</td>\n",
              "      <td>1980-11-11</td>\n",
              "      <td>3.0</td>\n",
              "      <td>Household Duties</td>\n",
              "      <td>Social only</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>customer103977R</td>\n",
              "      <td>Single</td>\n",
              "      <td>1964-10-22</td>\n",
              "      <td>9.0</td>\n",
              "      <td>Employed</td>\n",
              "      <td>Social inc. Comm</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>customer252093R</td>\n",
              "      <td>Widowed</td>\n",
              "      <td>1956-02-25</td>\n",
              "      <td>9.0</td>\n",
              "      <td>Retired</td>\n",
              "      <td>Social only</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>customer147628R</td>\n",
              "      <td>Single</td>\n",
              "      <td>1984-05-31</td>\n",
              "      <td>3.0</td>\n",
              "      <td>Employed</td>\n",
              "      <td>Social inc. Comm</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>customer38293</td>\n",
              "      <td>Single</td>\n",
              "      <td>1994-07-06</td>\n",
              "      <td>4.0</td>\n",
              "      <td>Employed</td>\n",
              "      <td>Social only</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>...</th>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>90464</th>\n",
              "      <td>customer18157</td>\n",
              "      <td>Married</td>\n",
              "      <td>1956-12-12</td>\n",
              "      <td>9.0</td>\n",
              "      <td>Employed</td>\n",
              "      <td>Social inc. Comm</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>90465</th>\n",
              "      <td>customer39863R</td>\n",
              "      <td>Single</td>\n",
              "      <td>1991-04-17</td>\n",
              "      <td>7.0</td>\n",
              "      <td>Employed</td>\n",
              "      <td>Business Use (PH)</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>90466</th>\n",
              "      <td>customer76592</td>\n",
              "      <td>Married</td>\n",
              "      <td>1978-05-16</td>\n",
              "      <td>3.0</td>\n",
              "      <td>Employed</td>\n",
              "      <td>Social only</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>90467</th>\n",
              "      <td>customer31548</td>\n",
              "      <td>Married</td>\n",
              "      <td>1943-10-22</td>\n",
              "      <td>9.0</td>\n",
              "      <td>Retired</td>\n",
              "      <td>Social only</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>90468</th>\n",
              "      <td>customer45964</td>\n",
              "      <td>Single</td>\n",
              "      <td>1970-11-26</td>\n",
              "      <td>9.0</td>\n",
              "      <td>Employed</td>\n",
              "      <td>Social inc. Comm</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "<p>90469 rows × 6 columns</p>\n",
              "</div>"
            ],
            "text/plain": [
              "           CUSTOMER_ID MARITALSTATUS DATE_OF_BIRTH  NCD    EMPLOYMENTTYPE  \\\n",
              "0       customer216583       Married    1980-11-11  3.0  Household Duties   \n",
              "1      customer103977R        Single    1964-10-22  9.0          Employed   \n",
              "2      customer252093R       Widowed    1956-02-25  9.0           Retired   \n",
              "3      customer147628R        Single    1984-05-31  3.0          Employed   \n",
              "4        customer38293        Single    1994-07-06  4.0          Employed   \n",
              "...                ...           ...           ...  ...               ...   \n",
              "90464    customer18157       Married    1956-12-12  9.0          Employed   \n",
              "90465   customer39863R        Single    1991-04-17  7.0          Employed   \n",
              "90466    customer76592       Married    1978-05-16  3.0          Employed   \n",
              "90467    customer31548       Married    1943-10-22  9.0           Retired   \n",
              "90468    customer45964        Single    1970-11-26  9.0          Employed   \n",
              "\n",
              "      VEHICLE_CLASSOFUSE  \n",
              "0            Social only  \n",
              "1       Social inc. Comm  \n",
              "2            Social only  \n",
              "3       Social inc. Comm  \n",
              "4            Social only  \n",
              "...                  ...  \n",
              "90464   Social inc. Comm  \n",
              "90465  Business Use (PH)  \n",
              "90466        Social only  \n",
              "90467        Social only  \n",
              "90468   Social inc. Comm  \n",
              "\n",
              "[90469 rows x 6 columns]"
            ]
          },
          "execution_count": 5,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "df_customers = pd.read_csv('customers.csv')\n",
        "df_customers"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 424
        },
        "id": "adAXAEj1c29n",
        "outputId": "90d9d695-3153-4c3c-b960-cd3c8137d531"
      },
      "outputs": [
        {
          "data": {
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              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>POLICY_NUMBER</th>\n",
              "      <th>START_DATE</th>\n",
              "      <th>END_DATE</th>\n",
              "      <th>POLICY_STATUS</th>\n",
              "      <th>PREMIUM</th>\n",
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              "      <th>VEHICLE_ID</th>\n",
              "      <th>CUSTOMER_ID</th>\n",
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              "  <tbody>\n",
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              "      <th>0</th>\n",
              "      <td>6.495726e+09</td>\n",
              "      <td>2021-02-01 00:00:00</td>\n",
              "      <td>2022-01-31 00:00:00</td>\n",
              "      <td>Renewal</td>\n",
              "      <td>370.401518</td>\n",
              "      <td>2.0</td>\n",
              "      <td>NaN</td>\n",
              "      <td>vehicle70018R</td>\n",
              "      <td>customer17135R</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>6.495676e+09</td>\n",
              "      <td>2020-10-17</td>\n",
              "      <td>2021-10-16</td>\n",
              "      <td>New Business</td>\n",
              "      <td>142.969466</td>\n",
              "      <td>2.0</td>\n",
              "      <td>NaN</td>\n",
              "      <td>vehicle37044</td>\n",
              "      <td>customer115167</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>6.495813e+09</td>\n",
              "      <td>2020-06-11</td>\n",
              "      <td>2021-06-10</td>\n",
              "      <td>New Business</td>\n",
              "      <td>244.005751</td>\n",
              "      <td>1.0</td>\n",
              "      <td>NaN</td>\n",
              "      <td>vehicle197044</td>\n",
              "      <td>customer171987</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>6.495567e+09</td>\n",
              "      <td>2020-06-15 00:00:00</td>\n",
              "      <td>2021-06-14 00:00:00</td>\n",
              "      <td>Renewal</td>\n",
              "      <td>412.400710</td>\n",
              "      <td>2.0</td>\n",
              "      <td>NaN</td>\n",
              "      <td>vehicle39531R</td>\n",
              "      <td>customer79839R</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>6.495893e+09</td>\n",
              "      <td>2020-10-25</td>\n",
              "      <td>2021-10-24</td>\n",
              "      <td>New Business</td>\n",
              "      <td>185.538033</td>\n",
              "      <td>2.0</td>\n",
              "      <td>Monthly</td>\n",
              "      <td>vehicle109016</td>\n",
              "      <td>customer28508</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>...</th>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>90470</th>\n",
              "      <td>6.495345e+09</td>\n",
              "      <td>2018-10-20</td>\n",
              "      <td>2019-10-19</td>\n",
              "      <td>New Business</td>\n",
              "      <td>171.775478</td>\n",
              "      <td>2.0</td>\n",
              "      <td>NaN</td>\n",
              "      <td>vehicle96992</td>\n",
              "      <td>customer242805</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>90471</th>\n",
              "      <td>6.495719e+09</td>\n",
              "      <td>2019-12-27</td>\n",
              "      <td>2020-12-26</td>\n",
              "      <td>New Business</td>\n",
              "      <td>275.025662</td>\n",
              "      <td>1.0</td>\n",
              "      <td>NaN</td>\n",
              "      <td>vehicle68360</td>\n",
              "      <td>customer270050</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>90472</th>\n",
              "      <td>6.495538e+09</td>\n",
              "      <td>2020-05-29 00:00:00</td>\n",
              "      <td>2021-05-28 00:00:00</td>\n",
              "      <td>Renewal</td>\n",
              "      <td>132.270723</td>\n",
              "      <td>2.0</td>\n",
              "      <td>Monthly</td>\n",
              "      <td>vehicle229315R</td>\n",
              "      <td>customer21360R</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>90473</th>\n",
              "      <td>6.495427e+09</td>\n",
              "      <td>2019-02-22</td>\n",
              "      <td>2020-02-21</td>\n",
              "      <td>New Business</td>\n",
              "      <td>232.072537</td>\n",
              "      <td>1.0</td>\n",
              "      <td>NaN</td>\n",
              "      <td>vehicle186506</td>\n",
              "      <td>customer53930</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>90474</th>\n",
              "      <td>6.495920e+09</td>\n",
              "      <td>2021-01-03</td>\n",
              "      <td>2022-01-02</td>\n",
              "      <td>New Business</td>\n",
              "      <td>120.339028</td>\n",
              "      <td>2.0</td>\n",
              "      <td>NaN</td>\n",
              "      <td>vehicle189187</td>\n",
              "      <td>customer107848</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "<p>90475 rows × 9 columns</p>\n",
              "</div>"
            ],
            "text/plain": [
              "       POLICY_NUMBER           START_DATE             END_DATE POLICY_STATUS  \\\n",
              "0       6.495726e+09  2021-02-01 00:00:00  2022-01-31 00:00:00       Renewal   \n",
              "1       6.495676e+09           2020-10-17           2021-10-16  New Business   \n",
              "2       6.495813e+09           2020-06-11           2021-06-10  New Business   \n",
              "3       6.495567e+09  2020-06-15 00:00:00  2021-06-14 00:00:00       Renewal   \n",
              "4       6.495893e+09           2020-10-25           2021-10-24  New Business   \n",
              "...              ...                  ...                  ...           ...   \n",
              "90470   6.495345e+09           2018-10-20           2019-10-19  New Business   \n",
              "90471   6.495719e+09           2019-12-27           2020-12-26  New Business   \n",
              "90472   6.495538e+09  2020-05-29 00:00:00  2021-05-28 00:00:00       Renewal   \n",
              "90473   6.495427e+09           2019-02-22           2020-02-21  New Business   \n",
              "90474   6.495920e+09           2021-01-03           2022-01-02  New Business   \n",
              "\n",
              "          PREMIUM  NUMBER_OF_DRIVERS PAYMENTMETHOD      VEHICLE_ID  \\\n",
              "0      370.401518                2.0           NaN   vehicle70018R   \n",
              "1      142.969466                2.0           NaN    vehicle37044   \n",
              "2      244.005751                1.0           NaN   vehicle197044   \n",
              "3      412.400710                2.0           NaN   vehicle39531R   \n",
              "4      185.538033                2.0       Monthly   vehicle109016   \n",
              "...           ...                ...           ...             ...   \n",
              "90470  171.775478                2.0           NaN    vehicle96992   \n",
              "90471  275.025662                1.0           NaN    vehicle68360   \n",
              "90472  132.270723                2.0       Monthly  vehicle229315R   \n",
              "90473  232.072537                1.0           NaN   vehicle186506   \n",
              "90474  120.339028                2.0           NaN   vehicle189187   \n",
              "\n",
              "          CUSTOMER_ID  \n",
              "0      customer17135R  \n",
              "1      customer115167  \n",
              "2      customer171987  \n",
              "3      customer79839R  \n",
              "4       customer28508  \n",
              "...               ...  \n",
              "90470  customer242805  \n",
              "90471  customer270050  \n",
              "90472  customer21360R  \n",
              "90473   customer53930  \n",
              "90474  customer107848  \n",
              "\n",
              "[90475 rows x 9 columns]"
            ]
          },
          "execution_count": 6,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "df_policies = pd.read_csv('policies.csv')\n",
        "df_policies"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 424
        },
        "id": "-4Bj5aIYc3A_",
        "outputId": "f50d2bba-4067-47ab-d144-0836d39b43e0"
      },
      "outputs": [
        {
          "data": {
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              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>POLICY_NUMBER</th>\n",
              "      <th>START_DATE</th>\n",
              "      <th>SEGMENT_1_FLAG</th>\n",
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              "      <th>4</th>\n",
              "      <td>6.495970e+09</td>\n",
              "      <td>2021-03-08</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
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              "      <th>75004</th>\n",
              "      <td>6.495521e+09</td>\n",
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              "    <tr>\n",
              "      <th>75005</th>\n",
              "      <td>6.495808e+09</td>\n",
              "      <td>2020-06-27</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
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              "      <td>6.495834e+09</td>\n",
              "      <td>2020-07-05</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
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              "      <td>6.495698e+09</td>\n",
              "      <td>2019-11-04</td>\n",
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              "      <td>2020-11-21</td>\n",
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              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
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              "<p>75009 rows × 8 columns</p>\n",
              "</div>"
            ],
            "text/plain": [
              "       POLICY_NUMBER  START_DATE  SEGMENT_1_FLAG  SEGMENT_2_FLAG  \\\n",
              "0       6.495779e+09  2020-04-24             0.0             0.0   \n",
              "1       6.495991e+09  2021-04-06             0.0             0.0   \n",
              "2       6.495992e+09  2021-04-28             1.0             0.0   \n",
              "3       6.495720e+09  2020-01-13             0.0             0.0   \n",
              "4       6.495970e+09  2021-03-08             0.0             0.0   \n",
              "...              ...         ...             ...             ...   \n",
              "75004   6.495521e+09  2019-05-22             0.0             0.0   \n",
              "75005   6.495808e+09  2020-06-27             0.0             0.0   \n",
              "75006   6.495834e+09  2020-07-05             0.0             0.0   \n",
              "75007   6.495698e+09  2019-11-04             0.0             0.0   \n",
              "75008   6.495696e+09  2020-11-21             0.0             0.0   \n",
              "\n",
              "       SEGMENT_3_FLAG  SEGMENT_4_FLAG  SEGMENT_5_FLAG  SEGMENT_6_FLAG  \n",
              "0                 0.0             0.0             0.0             0.0  \n",
              "1                 0.0             0.0             0.0             0.0  \n",
              "2                 0.0             0.0             0.0             0.0  \n",
              "3                 0.0             0.0             0.0             0.0  \n",
              "4                 0.0             0.0             1.0             1.0  \n",
              "...               ...             ...             ...             ...  \n",
              "75004             0.0             0.0             0.0             0.0  \n",
              "75005             0.0             0.0             0.0             0.0  \n",
              "75006             0.0             0.0             0.0             0.0  \n",
              "75007             0.0             0.0             0.0             0.0  \n",
              "75008             0.0             0.0             0.0             0.0  \n",
              "\n",
              "[75009 rows x 8 columns]"
            ]
          },
          "execution_count": 7,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "df_segments = pd.read_csv('segments.csv')\n",
        "df_segments"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "gL_tSfJRPVpa"
      },
      "source": [
        "## Data pre-processing\n",
        "Join the data frames using outer join"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "RUOJBlz6mcC6"
      },
      "outputs": [],
      "source": [
        "# Join the data frames\n",
        "df = df_policies.merge(df_customers, how='outer').merge(df_vehicles, how='outer').merge(df_claims, how='outer').merge(df_segments, how='outer')"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 652
        },
        "id": "UerowOnNolY1",
        "outputId": "160d3d16-9918-43eb-a4b5-2726f88546e7"
      },
      "outputs": [
        {
          "data": {
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              "      <th>...</th>\n",
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              "    <tr>\n",
              "      <th>2</th>\n",
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              "      <td>2020-10-17</td>\n",
              "      <td>2021-10-16</td>\n",
              "      <td>New Business</td>\n",
              "      <td>142.969466</td>\n",
              "      <td>2.0</td>\n",
              "      <td>NaN</td>\n",
              "      <td>vehicle37044</td>\n",
              "      <td>customer115167</td>\n",
              "      <td>Married</td>\n",
              "      <td>...</td>\n",
              "      <td>claim361411</td>\n",
              "      <td>89.241458</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>61.0</td>\n",
              "      <td>56-65</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>6.495813e+09</td>\n",
              "      <td>2020-06-11</td>\n",
              "      <td>2021-06-10</td>\n",
              "      <td>New Business</td>\n",
              "      <td>244.005751</td>\n",
              "      <td>1.0</td>\n",
              "      <td>NaN</td>\n",
              "      <td>vehicle197044</td>\n",
              "      <td>customer171987</td>\n",
              "      <td>Single</td>\n",
              "      <td>...</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>34.0</td>\n",
              "      <td>26-35</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>6.495567e+09</td>\n",
              "      <td>2020-06-15 00:00:00</td>\n",
              "      <td>2021-06-14 00:00:00</td>\n",
              "      <td>Renewal</td>\n",
              "      <td>412.400710</td>\n",
              "      <td>2.0</td>\n",
              "      <td>NaN</td>\n",
              "      <td>vehicle39531R</td>\n",
              "      <td>customer79839R</td>\n",
              "      <td>CommonLaw</td>\n",
              "      <td>...</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>31.0</td>\n",
              "      <td>26-35</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>...</th>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>90537</th>\n",
              "      <td>6.495733e+09</td>\n",
              "      <td>2020-02-06</td>\n",
              "      <td>2021-02-05</td>\n",
              "      <td>New Business</td>\n",
              "      <td>126.539718</td>\n",
              "      <td>2.0</td>\n",
              "      <td>NaN</td>\n",
              "      <td>vehicle62048</td>\n",
              "      <td>customer273560</td>\n",
              "      <td>Married</td>\n",
              "      <td>...</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>41.0</td>\n",
              "      <td>36-45</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>90538</th>\n",
              "      <td>6.495345e+09</td>\n",
              "      <td>2018-10-20</td>\n",
              "      <td>2019-10-19</td>\n",
              "      <td>New Business</td>\n",
              "      <td>171.775478</td>\n",
              "      <td>2.0</td>\n",
              "      <td>NaN</td>\n",
              "      <td>vehicle96992</td>\n",
              "      <td>customer242805</td>\n",
              "      <td>CommonLaw</td>\n",
              "      <td>...</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>50.0</td>\n",
              "      <td>46-55</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>90539</th>\n",
              "      <td>6.495719e+09</td>\n",
              "      <td>2019-12-27</td>\n",
              "      <td>2020-12-26</td>\n",
              "      <td>New Business</td>\n",
              "      <td>275.025662</td>\n",
              "      <td>1.0</td>\n",
              "      <td>NaN</td>\n",
              "      <td>vehicle68360</td>\n",
              "      <td>customer270050</td>\n",
              "      <td>Single</td>\n",
              "      <td>...</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>31.0</td>\n",
              "      <td>26-35</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>90540</th>\n",
              "      <td>6.495427e+09</td>\n",
              "      <td>2019-02-22</td>\n",
              "      <td>2020-02-21</td>\n",
              "      <td>New Business</td>\n",
              "      <td>232.072537</td>\n",
              "      <td>1.0</td>\n",
              "      <td>NaN</td>\n",
              "      <td>vehicle186506</td>\n",
              "      <td>customer53930</td>\n",
              "      <td>Married</td>\n",
              "      <td>...</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>0.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>50.0</td>\n",
              "      <td>46-55</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>90541</th>\n",
              "      <td>6.495920e+09</td>\n",
              "      <td>2021-01-03</td>\n",
              "      <td>2022-01-02</td>\n",
              "      <td>New Business</td>\n",
              "      <td>120.339028</td>\n",
              "      <td>2.0</td>\n",
              "      <td>NaN</td>\n",
              "      <td>vehicle189187</td>\n",
              "      <td>customer107848</td>\n",
              "      <td>Married</td>\n",
              "      <td>...</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>57.0</td>\n",
              "      <td>56-65</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "<p>90542 rows × 29 columns</p>\n",
              "</div>"
            ],
            "text/plain": [
              "       POLICY_NUMBER           START_DATE             END_DATE POLICY_STATUS  \\\n",
              "0       6.495726e+09  2021-02-01 00:00:00  2022-01-31 00:00:00       Renewal   \n",
              "1       6.495726e+09           2020-02-01           2021-01-31  New Business   \n",
              "2       6.495676e+09           2020-10-17           2021-10-16  New Business   \n",
              "3       6.495813e+09           2020-06-11           2021-06-10  New Business   \n",
              "4       6.495567e+09  2020-06-15 00:00:00  2021-06-14 00:00:00       Renewal   \n",
              "...              ...                  ...                  ...           ...   \n",
              "90537   6.495733e+09           2020-02-06           2021-02-05  New Business   \n",
              "90538   6.495345e+09           2018-10-20           2019-10-19  New Business   \n",
              "90539   6.495719e+09           2019-12-27           2020-12-26  New Business   \n",
              "90540   6.495427e+09           2019-02-22           2020-02-21  New Business   \n",
              "90541   6.495920e+09           2021-01-03           2022-01-02  New Business   \n",
              "\n",
              "          PREMIUM  NUMBER_OF_DRIVERS PAYMENTMETHOD     VEHICLE_ID  \\\n",
              "0      370.401518                2.0           NaN  vehicle70018R   \n",
              "1      369.137312                2.0           NaN   vehicle70018   \n",
              "2      142.969466                2.0           NaN   vehicle37044   \n",
              "3      244.005751                1.0           NaN  vehicle197044   \n",
              "4      412.400710                2.0           NaN  vehicle39531R   \n",
              "...           ...                ...           ...            ...   \n",
              "90537  126.539718                2.0           NaN   vehicle62048   \n",
              "90538  171.775478                2.0           NaN   vehicle96992   \n",
              "90539  275.025662                1.0           NaN   vehicle68360   \n",
              "90540  232.072537                1.0           NaN  vehicle186506   \n",
              "90541  120.339028                2.0           NaN  vehicle189187   \n",
              "\n",
              "          CUSTOMER_ID MARITALSTATUS  ...     CLAIM_ID   INCURRED  \\\n",
              "0      customer17135R        Single  ...  claim458112  79.462299   \n",
              "1       customer17135        Single  ...  claim458112  79.462299   \n",
              "2      customer115167       Married  ...  claim361411  89.241458   \n",
              "3      customer171987        Single  ...          NaN        NaN   \n",
              "4      customer79839R     CommonLaw  ...          NaN        NaN   \n",
              "...               ...           ...  ...          ...        ...   \n",
              "90537  customer273560       Married  ...          NaN        NaN   \n",
              "90538  customer242805     CommonLaw  ...          NaN        NaN   \n",
              "90539  customer270050        Single  ...          NaN        NaN   \n",
              "90540   customer53930       Married  ...          NaN        NaN   \n",
              "90541  customer107848       Married  ...          NaN        NaN   \n",
              "\n",
              "      SEGMENT_1_FLAG SEGMENT_2_FLAG  SEGMENT_3_FLAG  SEGMENT_4_FLAG  \\\n",
              "0                NaN            NaN             NaN             NaN   \n",
              "1                0.0            0.0             0.0             0.0   \n",
              "2                0.0            0.0             0.0             0.0   \n",
              "3                0.0            0.0             0.0             0.0   \n",
              "4                NaN            NaN             NaN             NaN   \n",
              "...              ...            ...             ...             ...   \n",
              "90537            0.0            0.0             0.0             0.0   \n",
              "90538            0.0            0.0             0.0             0.0   \n",
              "90539            0.0            0.0             0.0             0.0   \n",
              "90540            0.0            1.0             0.0             0.0   \n",
              "90541            0.0            0.0             0.0             0.0   \n",
              "\n",
              "       SEGMENT_5_FLAG  SEGMENT_6_FLAG   Age Age_group  \n",
              "0                 NaN             NaN  28.0     26-35  \n",
              "1                 0.0             0.0  28.0     26-35  \n",
              "2                 0.0             0.0  61.0     56-65  \n",
              "3                 0.0             0.0  34.0     26-35  \n",
              "4                 NaN             NaN  31.0     26-35  \n",
              "...               ...             ...   ...       ...  \n",
              "90537             0.0             0.0  41.0     36-45  \n",
              "90538             0.0             0.0  50.0     46-55  \n",
              "90539             0.0             0.0  31.0     26-35  \n",
              "90540             0.0             0.0  50.0     46-55  \n",
              "90541             0.0             0.0  57.0     56-65  \n",
              "\n",
              "[90542 rows x 29 columns]"
            ]
          },
          "execution_count": 9,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "from datetime import datetime, date\n",
        "\n",
        "# Convert to datetime format\n",
        "df['DATE_OF_BIRTH'] = pd.to_datetime(df['DATE_OF_BIRTH'])\n",
        "\n",
        "# Get today's date\n",
        "today = date.today()\n",
        "\n",
        "# Compute age and add it as a column\n",
        "df['Age'] = df['DATE_OF_BIRTH'].apply(lambda x: today.year - x.year - ((today.month, today.day) < (x.month, x.day)))\n",
        "\n",
        "# Categorise into age groups\n",
        "df['Age_group'] = pd.cut(df['Age'], bins=[16,25,35,45,55,65,90], labels=[\"17-25\",\"26-35\",\"36-45\",\"46-55\",\"56-65\",\"65+\"])\n",
        "df"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "H-oPUmrdPQN3"
      },
      "source": [
        "## Exploratory Data Analysis"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 374
        },
        "id": "1qYMTgRHFmfc",
        "outputId": "3d912a2d-340b-45c0-a9dc-d43daece029b"
      },
      "outputs": [
        {
          "data": {
            "text/plain": [
              "<matplotlib.axes._subplots.AxesSubplot at 0x7f047bf44710>"
            ]
          },
          "execution_count": 10,
          "metadata": {},
          "output_type": "execute_result"
        },
        {
          "data": {
            "image/png": 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",
            "text/plain": [
              "<Figure size 864x432 with 1 Axes>"
            ]
          },
          "metadata": {},
          "output_type": "display_data"
        }
      ],
      "source": [
        "# Display percentage of age groups\n",
        "df['Age_group'].value_counts().plot.pie(autopct='%1.1f%%', shadow=True, figsize=(12, 6))"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 530
        },
        "id": "FGDolI9s541z",
        "outputId": "ea5f0905-dd72-40fd-e108-7e328b8203f0"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "Married        42679\n",
            "Single         25060\n",
            "CommonLaw      12789\n",
            "Divorced        6942\n",
            "Widowed         2228\n",
            "Parthership      775\n",
            "None               1\n",
            "Name: MARITALSTATUS, dtype: int64\n"
          ]
        },
        {
          "data": {
            "text/plain": [
              "<matplotlib.axes._subplots.AxesSubplot at 0x7f047be27e90>"
            ]
          },
          "execution_count": 11,
          "metadata": {},
          "output_type": "execute_result"
        },
        {
          "data": {
            "image/png": 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",
            "text/plain": [
              "<Figure size 864x432 with 1 Axes>"
            ]
          },
          "metadata": {
            "needs_background": "light"
          },
          "output_type": "display_data"
        }
      ],
      "source": [
        "# Display marital status distribution\n",
        "print(df['MARITALSTATUS'].value_counts())\n",
        "df['MARITALSTATUS'].value_counts().plot.bar(rot=0, figsize=(12, 6))"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 300
        },
        "id": "foJK7cF_54v-",
        "outputId": "955563fd-28aa-4f1c-b2cd-294cc76a1963"
      },
      "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>PREMIUM</th>\n",
              "      <th>NUMBER_OF_DRIVERS</th>\n",
              "      <th>NCD</th>\n",
              "      <th>VEHICLE_VALUE</th>\n",
              "      <th>VEHICLE_AGE</th>\n",
              "      <th>VEHICLE_ANNUALMILEAGE</th>\n",
              "      <th>VEHICLE_OWNEDMONTHS</th>\n",
              "      <th>INCURRED</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>count</th>\n",
              "      <td>90446.000000</td>\n",
              "      <td>90439.000000</td>\n",
              "      <td>9.051000e+04</td>\n",
              "      <td>9.043400e+04</td>\n",
              "      <td>9.047000e+04</td>\n",
              "      <td>9.052800e+04</td>\n",
              "      <td>9.045000e+04</td>\n",
              "      <td>13585.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>mean</th>\n",
              "      <td>262.984099</td>\n",
              "      <td>1.600946</td>\n",
              "      <td>4.490944e+02</td>\n",
              "      <td>5.453424e+03</td>\n",
              "      <td>3.413855e+02</td>\n",
              "      <td>7.278506e+03</td>\n",
              "      <td>3.814423e+02</td>\n",
              "      <td>2132.334092</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>std</th>\n",
              "      <td>142.626035</td>\n",
              "      <td>0.606876</td>\n",
              "      <td>6.647742e+04</td>\n",
              "      <td>4.726958e+04</td>\n",
              "      <td>5.758417e+04</td>\n",
              "      <td>4.709407e+04</td>\n",
              "      <td>5.759033e+04</td>\n",
              "      <td>6949.723506</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>min</th>\n",
              "      <td>28.802259</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>0.000000e+00</td>\n",
              "      <td>-9.990000e+02</td>\n",
              "      <td>-9.990000e+02</td>\n",
              "      <td>-9.990000e+02</td>\n",
              "      <td>-9.999000e+03</td>\n",
              "      <td>-211.627233</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>25%</th>\n",
              "      <td>163.052862</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>5.000000e+00</td>\n",
              "      <td>1.947612e+03</td>\n",
              "      <td>7.000000e+00</td>\n",
              "      <td>4.985964e+03</td>\n",
              "      <td>2.400000e+01</td>\n",
              "      <td>10.026851</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>50%</th>\n",
              "      <td>219.863549</td>\n",
              "      <td>2.000000</td>\n",
              "      <td>9.000000e+00</td>\n",
              "      <td>3.885063e+03</td>\n",
              "      <td>1.000000e+01</td>\n",
              "      <td>6.973660e+03</td>\n",
              "      <td>4.100000e+01</td>\n",
              "      <td>96.937265</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>75%</th>\n",
              "      <td>317.081900</td>\n",
              "      <td>2.000000</td>\n",
              "      <td>9.000000e+00</td>\n",
              "      <td>6.985640e+03</td>\n",
              "      <td>1.300000e+01</td>\n",
              "      <td>9.973952e+03</td>\n",
              "      <td>6.700000e+01</td>\n",
              "      <td>2079.495907</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>max</th>\n",
              "      <td>2287.951049</td>\n",
              "      <td>7.000000</td>\n",
              "      <td>1.000000e+07</td>\n",
              "      <td>1.000001e+07</td>\n",
              "      <td>1.000000e+07</td>\n",
              "      <td>1.000000e+07</td>\n",
              "      <td>1.000000e+07</td>\n",
              "      <td>275280.578552</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "            PREMIUM  NUMBER_OF_DRIVERS           NCD  VEHICLE_VALUE  \\\n",
              "count  90446.000000       90439.000000  9.051000e+04   9.043400e+04   \n",
              "mean     262.984099           1.600946  4.490944e+02   5.453424e+03   \n",
              "std      142.626035           0.606876  6.647742e+04   4.726958e+04   \n",
              "min       28.802259           1.000000  0.000000e+00  -9.990000e+02   \n",
              "25%      163.052862           1.000000  5.000000e+00   1.947612e+03   \n",
              "50%      219.863549           2.000000  9.000000e+00   3.885063e+03   \n",
              "75%      317.081900           2.000000  9.000000e+00   6.985640e+03   \n",
              "max     2287.951049           7.000000  1.000000e+07   1.000001e+07   \n",
              "\n",
              "        VEHICLE_AGE  VEHICLE_ANNUALMILEAGE  VEHICLE_OWNEDMONTHS       INCURRED  \n",
              "count  9.047000e+04           9.052800e+04         9.045000e+04   13585.000000  \n",
              "mean   3.413855e+02           7.278506e+03         3.814423e+02    2132.334092  \n",
              "std    5.758417e+04           4.709407e+04         5.759033e+04    6949.723506  \n",
              "min   -9.990000e+02          -9.990000e+02        -9.999000e+03    -211.627233  \n",
              "25%    7.000000e+00           4.985964e+03         2.400000e+01      10.026851  \n",
              "50%    1.000000e+01           6.973660e+03         4.100000e+01      96.937265  \n",
              "75%    1.300000e+01           9.973952e+03         6.700000e+01    2079.495907  \n",
              "max    1.000000e+07           1.000000e+07         1.000000e+07  275280.578552  "
            ]
          },
          "execution_count": 12,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "# Display summary statistics\n",
        "df.iloc[:,1:-8].describe()"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "2zMLJdwyzpPi"
      },
      "source": [
        "### Handling outliers"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "9qaBt_V7GLGX"
      },
      "outputs": [],
      "source": [
        "# Keep only those values that are within +3 to -3 standard deviations and are non-negative\n",
        "df = df[(np.abs(df.NCD-df.NCD.mean()) <= (3*df.NCD.std())) & (df.NCD >=0)]\n",
        "df = df[(np.abs(df.VEHICLE_VALUE-df.VEHICLE_VALUE.mean()) <= (3*df.VEHICLE_VALUE.std())) & (df.VEHICLE_VALUE >=0)]\n",
        "df = df[(np.abs(df.VEHICLE_AGE-df.VEHICLE_AGE.mean()) <= (3*df.VEHICLE_AGE.std())) & (df.VEHICLE_AGE >=0)]\n",
        "df = df[(np.abs(df.VEHICLE_ANNUALMILEAGE-df.VEHICLE_ANNUALMILEAGE.mean()) <= (3*df.VEHICLE_ANNUALMILEAGE.std())) & (df.VEHICLE_ANNUALMILEAGE >=0)]\n",
        "df = df[(np.abs(df.VEHICLE_OWNEDMONTHS-df.VEHICLE_OWNEDMONTHS.mean()) <= (3*df.VEHICLE_OWNEDMONTHS.std())) & (df.VEHICLE_OWNEDMONTHS >=0)]"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 300
        },
        "id": "VLp1JaNIJ6uW",
        "outputId": "c7e1e433-2e0d-412f-e5e9-7abb97f9387e"
      },
      "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>PREMIUM</th>\n",
              "      <th>NUMBER_OF_DRIVERS</th>\n",
              "      <th>NCD</th>\n",
              "      <th>VEHICLE_VALUE</th>\n",
              "      <th>VEHICLE_AGE</th>\n",
              "      <th>VEHICLE_ANNUALMILEAGE</th>\n",
              "      <th>VEHICLE_OWNEDMONTHS</th>\n",
              "      <th>INCURRED</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>count</th>\n",
              "      <td>90104.000000</td>\n",
              "      <td>90098.000000</td>\n",
              "      <td>90200.000000</td>\n",
              "      <td>90200.000000</td>\n",
              "      <td>90200.000000</td>\n",
              "      <td>90200.000000</td>\n",
              "      <td>90200.000000</td>\n",
              "      <td>13539.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>mean</th>\n",
              "      <td>262.944152</td>\n",
              "      <td>1.601046</td>\n",
              "      <td>7.155067</td>\n",
              "      <td>5233.136282</td>\n",
              "      <td>9.796275</td>\n",
              "      <td>7057.624008</td>\n",
              "      <td>50.043348</td>\n",
              "      <td>2130.809081</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>std</th>\n",
              "      <td>142.601210</td>\n",
              "      <td>0.606839</td>\n",
              "      <td>2.683235</td>\n",
              "      <td>5018.882563</td>\n",
              "      <td>4.368040</td>\n",
              "      <td>3426.182359</td>\n",
              "      <td>36.441472</td>\n",
              "      <td>6955.893205</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>min</th>\n",
              "      <td>28.802259</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>-211.627233</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>25%</th>\n",
              "      <td>163.050237</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>5.000000</td>\n",
              "      <td>1947.561151</td>\n",
              "      <td>7.000000</td>\n",
              "      <td>4985.951079</td>\n",
              "      <td>24.000000</td>\n",
              "      <td>10.015530</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>50%</th>\n",
              "      <td>219.813922</td>\n",
              "      <td>2.000000</td>\n",
              "      <td>9.000000</td>\n",
              "      <td>3885.749063</td>\n",
              "      <td>10.000000</td>\n",
              "      <td>6973.253877</td>\n",
              "      <td>41.000000</td>\n",
              "      <td>96.941428</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>75%</th>\n",
              "      <td>317.041031</td>\n",
              "      <td>2.000000</td>\n",
              "      <td>9.000000</td>\n",
              "      <td>6985.598131</td>\n",
              "      <td>13.000000</td>\n",
              "      <td>9973.989271</td>\n",
              "      <td>67.000000</td>\n",
              "      <td>2078.086851</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>max</th>\n",
              "      <td>2287.951049</td>\n",
              "      <td>7.000000</td>\n",
              "      <td>9.000000</td>\n",
              "      <td>75014.190770</td>\n",
              "      <td>34.000000</td>\n",
              "      <td>128014.035124</td>\n",
              "      <td>322.000000</td>\n",
              "      <td>275280.578552</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "            PREMIUM  NUMBER_OF_DRIVERS           NCD  VEHICLE_VALUE  \\\n",
              "count  90104.000000       90098.000000  90200.000000   90200.000000   \n",
              "mean     262.944152           1.601046      7.155067    5233.136282   \n",
              "std      142.601210           0.606839      2.683235    5018.882563   \n",
              "min       28.802259           1.000000      0.000000       0.000000   \n",
              "25%      163.050237           1.000000      5.000000    1947.561151   \n",
              "50%      219.813922           2.000000      9.000000    3885.749063   \n",
              "75%      317.041031           2.000000      9.000000    6985.598131   \n",
              "max     2287.951049           7.000000      9.000000   75014.190770   \n",
              "\n",
              "        VEHICLE_AGE  VEHICLE_ANNUALMILEAGE  VEHICLE_OWNEDMONTHS       INCURRED  \n",
              "count  90200.000000           90200.000000         90200.000000   13539.000000  \n",
              "mean       9.796275            7057.624008            50.043348    2130.809081  \n",
              "std        4.368040            3426.182359            36.441472    6955.893205  \n",
              "min        0.000000               0.000000             0.000000    -211.627233  \n",
              "25%        7.000000            4985.951079            24.000000      10.015530  \n",
              "50%       10.000000            6973.253877            41.000000      96.941428  \n",
              "75%       13.000000            9973.989271            67.000000    2078.086851  \n",
              "max       34.000000          128014.035124           322.000000  275280.578552  "
            ]
          },
          "execution_count": 14,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "# Display summary statistics after removing outliers\n",
        "df.iloc[:,1:-8].describe()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 20,
      "metadata": {
        "id": "qheygnACvb_s"
      },
      "outputs": [],
      "source": [
        "df.to_csv('combined_df.csv', index=False)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "njmELKn_wEJp"
      },
      "outputs": [],
      "source": [
        "from google.colab import files\n",
        "files.download('combined_df.csv')"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "UXeQOb2OJWx2"
      },
      "source": [
        "## Loss Ratio\n",
        "**Loss Ratio = Sum of Incurred / Sum of Premium**"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "X4-HEYpbn890"
      },
      "source": [
        "### Loss Ratio per age group"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 237
        },
        "id": "HuoLMuzAJgPC",
        "outputId": "d4b8ecb0-1386-4b64-8873-5935a8b1d603"
      },
      "outputs": [
        {
          "data": {
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              "\n",
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              "</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>INCURRED</th>\n",
              "      <th>PREMIUM</th>\n",
              "      <th>Loss Ratio %</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>17-25</td>\n",
              "      <td>2.085900e+06</td>\n",
              "      <td>2.729137e+06</td>\n",
              "      <td>76.43</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>26-35</td>\n",
              "      <td>5.761164e+06</td>\n",
              "      <td>5.779965e+06</td>\n",
              "      <td>99.67</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>36-45</td>\n",
              "      <td>6.778268e+06</td>\n",
              "      <td>5.283707e+06</td>\n",
              "      <td>128.29</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>46-55</td>\n",
              "      <td>6.295869e+06</td>\n",
              "      <td>4.390563e+06</td>\n",
              "      <td>143.40</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>56-65</td>\n",
              "      <td>4.700162e+06</td>\n",
              "      <td>3.222513e+06</td>\n",
              "      <td>145.85</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>5</th>\n",
              "      <td>65+</td>\n",
              "      <td>3.218401e+06</td>\n",
              "      <td>2.279854e+06</td>\n",
              "      <td>141.17</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>\n",
              "      <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-ae42a928-5975-4881-b833-46588d03b765')\"\n",
              "              title=\"Convert this dataframe to an interactive table.\"\n",
              "              style=\"display:none;\">\n",
              "        \n",
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              "  <style>\n",
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              "\n",
              "    .colab-df-convert {\n",
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              "\n",
              "    .colab-df-convert:hover {\n",
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              "      box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
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              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert {\n",
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              "\n",
              "    [theme=dark] .colab-df-convert:hover {\n",
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              "      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
              "      filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
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              "\n",
              "      <script>\n",
              "        const buttonEl =\n",
              "          document.querySelector('#df-ae42a928-5975-4881-b833-46588d03b765 button.colab-df-convert');\n",
              "        buttonEl.style.display =\n",
              "          google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "\n",
              "        async function convertToInteractive(key) {\n",
              "          const element = document.querySelector('#df-ae42a928-5975-4881-b833-46588d03b765');\n",
              "          const dataTable =\n",
              "            await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                     [key], {});\n",
              "          if (!dataTable) return;\n",
              "\n",
              "          const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "            '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
              "            + ' to learn more about interactive tables.';\n",
              "          element.innerHTML = '';\n",
              "          dataTable['output_type'] = 'display_data';\n",
              "          await google.colab.output.renderOutput(dataTable, element);\n",
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              "          docLink.innerHTML = docLinkHtml;\n",
              "          element.appendChild(docLink);\n",
              "        }\n",
              "      </script>\n",
              "    </div>\n",
              "  </div>\n",
              "  "
            ],
            "text/plain": [
              "  Age_group      INCURRED       PREMIUM  Loss Ratio %\n",
              "0     17-25  2.085900e+06  2.729137e+06         76.43\n",
              "1     26-35  5.761164e+06  5.779965e+06         99.67\n",
              "2     36-45  6.778268e+06  5.283707e+06        128.29\n",
              "3     46-55  6.295869e+06  4.390563e+06        143.40\n",
              "4     56-65  4.700162e+06  3.222513e+06        145.85\n",
              "5       65+  3.218401e+06  2.279854e+06        141.17"
            ]
          },
          "execution_count": 15,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "df_lr_age = df[['Age_group', 'INCURRED', 'PREMIUM']]\n",
        "df_lr_age = df_lr_age.groupby(['Age_group']).sum()\n",
        "df_lr_age['Loss Ratio %'] = ((df_lr_age['INCURRED']/df_lr_age['PREMIUM']) * 100).round(2)\n",
        "df_lr_age.reset_index()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 421
        },
        "id": "fUXEuP6gVaXb",
        "outputId": "5832db5e-56ba-4b9c-b6d9-c6985b1f9576"
      },
      "outputs": [
        {
          "data": {
            "text/plain": [
              "Text(0, 0.5, 'Percentage')"
            ]
          },
          "execution_count": 16,
          "metadata": {},
          "output_type": "execute_result"
        },
        {
          "data": {
            "image/png": 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",
            "text/plain": [
              "<Figure size 864x432 with 1 Axes>"
            ]
          },
          "metadata": {
            "needs_background": "light"
          },
          "output_type": "display_data"
        }
      ],
      "source": [
        "df_lr_age['Loss Ratio %'].sort_values(ascending=False).plot.bar(rot=0, figsize=(12, 6))\n",
        "plt.title(\"Loss Ratio %\")\n",
        "plt.xlabel(\"Age Group\")\n",
        "plt.ylabel(\"Percentage\")"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "42QMo7C1oBSo"
      },
      "source": [
        "### Loss Ratio per marital status"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 237
        },
        "id": "Vvd_i-sDOt1j",
        "outputId": "e7306ad4-43e9-47b6-b2e6-fbfc45cba248"
      },
      "outputs": [
        {
          "data": {
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              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>MARITALSTATUS</th>\n",
              "      <th>INCURRED</th>\n",
              "      <th>PREMIUM</th>\n",
              "      <th>Loss Ratio %</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
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              "      <th>0</th>\n",
              "      <td>CommonLaw</td>\n",
              "      <td>3.594357e+06</td>\n",
              "      <td>3.252116e+06</td>\n",
              "      <td>110.52</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>Divorced</td>\n",
              "      <td>2.218979e+06</td>\n",
              "      <td>1.484095e+06</td>\n",
              "      <td>149.52</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>Married</td>\n",
              "      <td>1.322524e+07</td>\n",
              "      <td>9.788246e+06</td>\n",
              "      <td>135.11</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>Parthership</td>\n",
              "      <td>2.655061e+05</td>\n",
              "      <td>2.384059e+05</td>\n",
              "      <td>111.37</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>Single</td>\n",
              "      <td>8.993423e+06</td>\n",
              "      <td>8.416511e+06</td>\n",
              "      <td>106.85</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>5</th>\n",
              "      <td>Widowed</td>\n",
              "      <td>5.237301e+05</td>\n",
              "      <td>4.944688e+05</td>\n",
              "      <td>105.92</td>\n",
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            "text/plain": [
              "  MARITALSTATUS      INCURRED       PREMIUM  Loss Ratio %\n",
              "0     CommonLaw  3.594357e+06  3.252116e+06        110.52\n",
              "1      Divorced  2.218979e+06  1.484095e+06        149.52\n",
              "2       Married  1.322524e+07  9.788246e+06        135.11\n",
              "3   Parthership  2.655061e+05  2.384059e+05        111.37\n",
              "4        Single  8.993423e+06  8.416511e+06        106.85\n",
              "5       Widowed  5.237301e+05  4.944688e+05        105.92"
            ]
          },
          "execution_count": 17,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "df_lr_ms = df[['MARITALSTATUS', 'INCURRED', 'PREMIUM']]\n",
        "df_lr_ms = df_lr_ms.groupby(['MARITALSTATUS']).sum()\n",
        "df_lr_ms['Loss Ratio %'] = ((df_lr_ms['INCURRED']/df_lr_ms['PREMIUM']) * 100).round(2)\n",
        "df_lr_ms = df_lr_ms.drop(index=('None'))\n",
        "df_lr_ms.reset_index()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 421
        },
        "id": "UsAMKRI5XKCU",
        "outputId": "1d2f5a10-aac4-46e7-dcb8-e2e41b2735e3"
      },
      "outputs": [
        {
          "data": {
            "text/plain": [
              "Text(0, 0.5, 'Percentage')"
            ]
          },
          "execution_count": 18,
          "metadata": {},
          "output_type": "execute_result"
        },
        {
          "data": {
            "image/png": 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",
            "text/plain": [
              "<Figure size 864x432 with 1 Axes>"
            ]
          },
          "metadata": {
            "needs_background": "light"
          },
          "output_type": "display_data"
        }
      ],
      "source": [
        "df_lr_ms['Loss Ratio %'].sort_values(ascending=False).plot.bar(rot=0, figsize=(12, 6))\n",
        "plt.title(\"Loss Ratio %\")\n",
        "plt.xlabel(\"Marital Status\")\n",
        "plt.ylabel(\"Percentage\")"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "Btwz9hOTJc3y"
      },
      "source": [
        "## Claim Frequency\n",
        "**Claim Frequency\t= Sum of claims / Sum of Policies**"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "99J8LnPcoFov"
      },
      "source": [
        "### Claim Frequency per age group"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 237
        },
        "id": "tDwg8nHo54sT",
        "outputId": "a62a7eab-430f-4c29-98a7-6686b739c1b0"
      },
      "outputs": [
        {
          "data": {
            "text/html": [
              "\n",
              "  <div id=\"df-0eec6e03-b1af-465b-b119-e4584a8dbd4b\">\n",
              "    <div class=\"colab-df-container\">\n",
              "      <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>CLAIM_ID</th>\n",
              "      <th>POLICY_NUMBER</th>\n",
              "      <th>Claim Frequency %</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>17-25</td>\n",
              "      <td>828</td>\n",
              "      <td>5618</td>\n",
              "      <td>14.74</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>26-35</td>\n",
              "      <td>2649</td>\n",
              "      <td>16042</td>\n",
              "      <td>16.51</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>36-45</td>\n",
              "      <td>3342</td>\n",
              "      <td>20264</td>\n",
              "      <td>16.49</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>46-55</td>\n",
              "      <td>3084</td>\n",
              "      <td>20676</td>\n",
              "      <td>14.92</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>56-65</td>\n",
              "      <td>2292</td>\n",
              "      <td>16920</td>\n",
              "      <td>13.55</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>5</th>\n",
              "      <td>65+</td>\n",
              "      <td>1361</td>\n",
              "      <td>10651</td>\n",
              "      <td>12.78</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>\n",
              "      <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-0eec6e03-b1af-465b-b119-e4584a8dbd4b')\"\n",
              "              title=\"Convert this dataframe to an interactive table.\"\n",
              "              style=\"display:none;\">\n",
              "        \n",
              "  <svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 24\"\n",
              "       width=\"24px\">\n",
              "    <path d=\"M0 0h24v24H0V0z\" fill=\"none\"/>\n",
              "    <path d=\"M18.56 5.44l.94 2.06.94-2.06 2.06-.94-2.06-.94-.94-2.06-.94 2.06-2.06.94zm-11 1L8.5 8.5l.94-2.06 2.06-.94-2.06-.94L8.5 2.5l-.94 2.06-2.06.94zm10 10l.94 2.06.94-2.06 2.06-.94-2.06-.94-.94-2.06-.94 2.06-2.06.94z\"/><path d=\"M17.41 7.96l-1.37-1.37c-.4-.4-.92-.59-1.43-.59-.52 0-1.04.2-1.43.59L10.3 9.45l-7.72 7.72c-.78.78-.78 2.05 0 2.83L4 21.41c.39.39.9.59 1.41.59.51 0 1.02-.2 1.41-.59l7.78-7.78 2.81-2.81c.8-.78.8-2.07 0-2.86zM5.41 20L4 18.59l7.72-7.72 1.47 1.35L5.41 20z\"/>\n",
              "  </svg>\n",
              "      </button>\n",
              "      \n",
              "  <style>\n",
              "    .colab-df-container {\n",
              "      display:flex;\n",
              "      flex-wrap:wrap;\n",
              "      gap: 12px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert {\n",
              "      background-color: #E8F0FE;\n",
              "      border: none;\n",
              "      border-radius: 50%;\n",
              "      cursor: pointer;\n",
              "      display: none;\n",
              "      fill: #1967D2;\n",
              "      height: 32px;\n",
              "      padding: 0 0 0 0;\n",
              "      width: 32px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert:hover {\n",
              "      background-color: #E2EBFA;\n",
              "      box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "      fill: #174EA6;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert {\n",
              "      background-color: #3B4455;\n",
              "      fill: #D2E3FC;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert:hover {\n",
              "      background-color: #434B5C;\n",
              "      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
              "      filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
              "      fill: #FFFFFF;\n",
              "    }\n",
              "  </style>\n",
              "\n",
              "      <script>\n",
              "        const buttonEl =\n",
              "          document.querySelector('#df-0eec6e03-b1af-465b-b119-e4584a8dbd4b button.colab-df-convert');\n",
              "        buttonEl.style.display =\n",
              "          google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "\n",
              "        async function convertToInteractive(key) {\n",
              "          const element = document.querySelector('#df-0eec6e03-b1af-465b-b119-e4584a8dbd4b');\n",
              "          const dataTable =\n",
              "            await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                     [key], {});\n",
              "          if (!dataTable) return;\n",
              "\n",
              "          const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "            '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
              "            + ' to learn more about interactive tables.';\n",
              "          element.innerHTML = '';\n",
              "          dataTable['output_type'] = 'display_data';\n",
              "          await google.colab.output.renderOutput(dataTable, element);\n",
              "          const docLink = document.createElement('div');\n",
              "          docLink.innerHTML = docLinkHtml;\n",
              "          element.appendChild(docLink);\n",
              "        }\n",
              "      </script>\n",
              "    </div>\n",
              "  </div>\n",
              "  "
            ],
            "text/plain": [
              "  Age_group  CLAIM_ID  POLICY_NUMBER  Claim Frequency %\n",
              "0     17-25       828           5618              14.74\n",
              "1     26-35      2649          16042              16.51\n",
              "2     36-45      3342          20264              16.49\n",
              "3     46-55      3084          20676              14.92\n",
              "4     56-65      2292          16920              13.55\n",
              "5       65+      1361          10651              12.78"
            ]
          },
          "execution_count": 19,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "df_cf_age = df[['Age_group', 'CLAIM_ID', 'POLICY_NUMBER']]\n",
        "df_cf_age = df_cf_age.groupby(['Age_group']).count()\n",
        "df_cf_age['Claim Frequency %'] = ((df_cf_age['CLAIM_ID']/df_cf_age['POLICY_NUMBER']) * 100).round(2)\n",
        "df_cf_age.reset_index()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 421
        },
        "id": "vHSKPYpycmfK",
        "outputId": "259701cd-f0fa-49b7-a403-8e780e2eba01"
      },
      "outputs": [
        {
          "data": {
            "text/plain": [
              "Text(0, 0.5, 'Percentage')"
            ]
          },
          "execution_count": 20,
          "metadata": {},
          "output_type": "execute_result"
        },
        {
          "data": {
            "image/png": 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            "text/plain": [
              "<Figure size 864x432 with 1 Axes>"
            ]
          },
          "metadata": {
            "needs_background": "light"
          },
          "output_type": "display_data"
        }
      ],
      "source": [
        "df_cf_age['Claim Frequency %'].sort_values(ascending=False).plot.bar(rot=0, figsize=(12, 6))\n",
        "plt.title(\"Claim Frequency %\")\n",
        "plt.xlabel(\"Age Group\")\n",
        "plt.ylabel(\"Percentage\")"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "dFib6HYioJFl"
      },
      "source": [
        "### Claim Frequency per marital status"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 237
        },
        "id": "D3ajiut4JpSd",
        "outputId": "c35cb30d-2b1a-4eee-b300-d5302f4d356e"
      },
      "outputs": [
        {
          "data": {
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              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>MARITALSTATUS</th>\n",
              "      <th>CLAIM_ID</th>\n",
              "      <th>POLICY_NUMBER</th>\n",
              "      <th>Claim Frequency %</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>CommonLaw</td>\n",
              "      <td>1897</td>\n",
              "      <td>12740</td>\n",
              "      <td>14.89</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>Divorced</td>\n",
              "      <td>1009</td>\n",
              "      <td>6915</td>\n",
              "      <td>14.59</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>Married</td>\n",
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              "      <td>15.60</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>Parthership</td>\n",
              "      <td>97</td>\n",
              "      <td>769</td>\n",
              "      <td>12.61</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>Single</td>\n",
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              "      <td>14.66</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>5</th>\n",
              "      <td>Widowed</td>\n",
              "      <td>258</td>\n",
              "      <td>2211</td>\n",
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              "                                                     [key], {});\n",
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              "\n",
              "          const docLinkHtml = 'Like what you see? Visit the ' +\n",
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            ],
            "text/plain": [
              "  MARITALSTATUS  CLAIM_ID  POLICY_NUMBER  Claim Frequency %\n",
              "0     CommonLaw      1897          12740              14.89\n",
              "1      Divorced      1009           6915              14.59\n",
              "2       Married      6632          42523              15.60\n",
              "3   Parthership        97            769              12.61\n",
              "4        Single      3662          24974              14.66\n",
              "5       Widowed       258           2211              11.67"
            ]
          },
          "execution_count": 21,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "df_cf_ms = df[['MARITALSTATUS', 'CLAIM_ID', 'POLICY_NUMBER']]\n",
        "df_cf_ms = df_cf_ms.groupby(['MARITALSTATUS']).count()\n",
        "df_cf_ms['Claim Frequency %'] = ((df_cf_ms['CLAIM_ID']/df_cf_ms['POLICY_NUMBER']) * 100).round(2)\n",
        "df_cf_ms = df_cf_ms.drop(index=('None'))\n",
        "df_cf_ms.reset_index()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 421
        },
        "id": "o1ugZO03UX5W",
        "outputId": "3620c24c-92c9-4730-c13c-4d0f87c9b9b2"
      },
      "outputs": [
        {
          "data": {
            "text/plain": [
              "Text(0, 0.5, 'Percentage')"
            ]
          },
          "execution_count": 22,
          "metadata": {},
          "output_type": "execute_result"
        },
        {
          "data": {
            "image/png": 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",
            "text/plain": [
              "<Figure size 864x432 with 1 Axes>"
            ]
          },
          "metadata": {
            "needs_background": "light"
          },
          "output_type": "display_data"
        }
      ],
      "source": [
        "df_cf_ms['Claim Frequency %'].sort_values(ascending=False).plot.bar(rot=0, figsize=(12, 6))\n",
        "plt.title(\"Claim Frequency %\")\n",
        "plt.xlabel(\"Marital Status\")\n",
        "plt.ylabel(\"Percentage\")"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "xCRPbhMtoNXb"
      },
      "source": [
        "## Segment analysis"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "SUBqWfEnjo94"
      },
      "outputs": [],
      "source": [
        "df_flags = df.drop(columns=['START_DATE', 'END_DATE', 'PAYMENTMETHOD', 'VEHICLE_ID', 'CUSTOMER_ID', 'DATE_OF_BIRTH', 'DATE_OF_ACCIDENT', 'Age'])"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "jxzNcGZZoeFp"
      },
      "source": [
        "### Segment 1"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 272
        },
        "id": "ivtBbcWFudcP",
        "outputId": "97213476-5290-4e11-9eeb-a8908e7c8a0b"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "Avg. Claim Freq. per age group for Segment 1: 13.81 % \n",
            "\n"
          ]
        },
        {
          "data": {
            "text/html": [
              "\n",
              "  <div id=\"df-62a97ec8-a0a2-46ca-932c-1c2578e65f50\">\n",
              "    <div class=\"colab-df-container\">\n",
              "      <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>CLAIM_ID</th>\n",
              "      <th>POLICY_NUMBER</th>\n",
              "      <th>Claim Frequency %</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>17-25</td>\n",
              "      <td>21</td>\n",
              "      <td>146</td>\n",
              "      <td>14.38</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>26-35</td>\n",
              "      <td>175</td>\n",
              "      <td>1143</td>\n",
              "      <td>15.31</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>36-45</td>\n",
              "      <td>300</td>\n",
              "      <td>1969</td>\n",
              "      <td>15.24</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>46-55</td>\n",
              "      <td>303</td>\n",
              "      <td>2392</td>\n",
              "      <td>12.67</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>56-65</td>\n",
              "      <td>254</td>\n",
              "      <td>2187</td>\n",
              "      <td>11.61</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>5</th>\n",
              "      <td>65+</td>\n",
              "      <td>218</td>\n",
              "      <td>1597</td>\n",
              "      <td>13.65</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>\n",
              "      <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-62a97ec8-a0a2-46ca-932c-1c2578e65f50')\"\n",
              "              title=\"Convert this dataframe to an interactive table.\"\n",
              "              style=\"display:none;\">\n",
              "        \n",
              "  <svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 24\"\n",
              "       width=\"24px\">\n",
              "    <path d=\"M0 0h24v24H0V0z\" fill=\"none\"/>\n",
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              "    }\n",
              "  </style>\n",
              "\n",
              "      <script>\n",
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              "          document.querySelector('#df-62a97ec8-a0a2-46ca-932c-1c2578e65f50 button.colab-df-convert');\n",
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              "\n",
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              "            await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                     [key], {});\n",
              "          if (!dataTable) return;\n",
              "\n",
              "          const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "            '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
              "            + ' to learn more about interactive tables.';\n",
              "          element.innerHTML = '';\n",
              "          dataTable['output_type'] = 'display_data';\n",
              "          await google.colab.output.renderOutput(dataTable, element);\n",
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              "          docLink.innerHTML = docLinkHtml;\n",
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              "        }\n",
              "      </script>\n",
              "    </div>\n",
              "  </div>\n",
              "  "
            ],
            "text/plain": [
              "  Age_group  CLAIM_ID  POLICY_NUMBER  Claim Frequency %\n",
              "0     17-25        21            146              14.38\n",
              "1     26-35       175           1143              15.31\n",
              "2     36-45       300           1969              15.24\n",
              "3     46-55       303           2392              12.67\n",
              "4     56-65       254           2187              11.61\n",
              "5       65+       218           1597              13.65"
            ]
          },
          "execution_count": 24,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "df_age1 = df[['Age_group', 'CLAIM_ID', 'POLICY_NUMBER', 'SEGMENT_1_FLAG']]\n",
        "df_age1 = df_age1[df_age1['SEGMENT_1_FLAG']==1].drop(columns='SEGMENT_1_FLAG')\n",
        "df_age1 = df_age1.groupby(['Age_group']).count()\n",
        "df_age1['Claim Frequency %'] = ((df_age1['CLAIM_ID']/df_age1['POLICY_NUMBER']) * 100).round(2)\n",
        "print(\"Avg. Claim Freq. per age group for Segment 1: {:0.2f} % \\n\".format(df_age1['Claim Frequency %'].mean()))\n",
        "df_age1.reset_index()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 272
        },
        "id": "mo0lv9vouf0w",
        "outputId": "a409c862-9624-43ee-f57e-f5fb34e4bc07"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "Avg. Claim Freq. per marital status for Segment 1: 13.02 % \n",
            "\n"
          ]
        },
        {
          "data": {
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              "\n",
              "  <div id=\"df-ac12d0f5-42e0-4da2-8983-98c5cdb1fabb\">\n",
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              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>MARITALSTATUS</th>\n",
              "      <th>CLAIM_ID</th>\n",
              "      <th>POLICY_NUMBER</th>\n",
              "      <th>Claim Frequency %</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>CommonLaw</td>\n",
              "      <td>157</td>\n",
              "      <td>1120</td>\n",
              "      <td>14.02</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>Divorced</td>\n",
              "      <td>110</td>\n",
              "      <td>801</td>\n",
              "      <td>13.73</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>Married</td>\n",
              "      <td>693</td>\n",
              "      <td>5108</td>\n",
              "      <td>13.57</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>Parthership</td>\n",
              "      <td>9</td>\n",
              "      <td>69</td>\n",
              "      <td>13.04</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>Single</td>\n",
              "      <td>261</td>\n",
              "      <td>1952</td>\n",
              "      <td>13.37</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>5</th>\n",
              "      <td>Widowed</td>\n",
              "      <td>39</td>\n",
              "      <td>375</td>\n",
              "      <td>10.40</td>\n",
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              "            await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                     [key], {});\n",
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              "\n",
              "          const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "            '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
              "            + ' to learn more about interactive tables.';\n",
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              "        }\n",
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              "    </div>\n",
              "  </div>\n",
              "  "
            ],
            "text/plain": [
              "  MARITALSTATUS  CLAIM_ID  POLICY_NUMBER  Claim Frequency %\n",
              "0     CommonLaw       157           1120              14.02\n",
              "1      Divorced       110            801              13.73\n",
              "2       Married       693           5108              13.57\n",
              "3   Parthership         9             69              13.04\n",
              "4        Single       261           1952              13.37\n",
              "5       Widowed        39            375              10.40"
            ]
          },
          "execution_count": 25,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "df_ms1 = df[['MARITALSTATUS', 'CLAIM_ID', 'POLICY_NUMBER', 'SEGMENT_1_FLAG']]\n",
        "df_ms1 = df_ms1[df_ms1['SEGMENT_1_FLAG']==1].drop(columns='SEGMENT_1_FLAG')\n",
        "df_ms1 = df_ms1.groupby(['MARITALSTATUS']).count()\n",
        "df_ms1['Claim Frequency %'] = ((df_ms1['CLAIM_ID']/df_ms1['POLICY_NUMBER']) * 100).round(2)\n",
        "print(\"Avg. Claim Freq. per marital status for Segment 1: {:0.2f} % \\n\".format(df_ms1['Claim Frequency %'].mean()))\n",
        "df_ms1.reset_index()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "yizohOblsPWS",
        "outputId": "e96938c6-6777-43a4-e2cd-da43d0a7ec39"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "\n",
            "---- POLICY_STATUS percentage ---\n",
            "Cancelled       10.43\n",
            "New Business    12.70\n",
            "Renewal           NaN\n",
            "Name: POLICY_STATUS, dtype: float64\n",
            "\n",
            "---- MARITALSTATUS percentage ---\n",
            "CommonLaw       8.79\n",
            "Divorced       11.58\n",
            "Married        12.01\n",
            "None             NaN\n",
            "Parthership     8.97\n",
            "Single          7.82\n",
            "Widowed        16.96\n",
            "Name: MARITALSTATUS, dtype: float64\n",
            "\n",
            "---- EMPLOYMENTTYPE percentage ---\n",
            "Employed                           9.98\n",
            "Household Duties                  10.11\n",
            "In Full Or Part Time Education     2.83\n",
            "Independent Means                 13.04\n",
            "Not employed due to disability     6.43\n",
            "Retired                           15.49\n",
            "Self Employed                      9.11\n",
            "Unemployed                         8.90\n",
            "Unknown                             NaN\n",
            "Voluntary Work                     3.57\n",
            "Name: EMPLOYMENTTYPE, dtype: float64\n",
            "\n",
            "---- VEHICLE_CLASSOFUSE percentage ---\n",
            "Business Use (PH)                             12.29\n",
            "Business use (PH + Spouse / Civil Partner)    14.18\n",
            "Business use (spouse / Civil Parnter)         11.17\n",
            "Business use by all drivers                    8.47\n",
            "None                                          25.00\n",
            "Social inc. Comm                               9.91\n",
            "Social only                                   10.73\n",
            "Unknown                                         NaN\n",
            "unk                                             NaN\n",
            "Name: VEHICLE_CLASSOFUSE, dtype: float64\n",
            "\n",
            "---- Age_group percentage ---\n",
            "17-25     2.60\n",
            "26-35     7.13\n",
            "36-45     9.72\n",
            "46-55    11.57\n",
            "56-65    12.93\n",
            "65+      14.99\n",
            "Name: Age_group, dtype: float64\n"
          ]
        }
      ],
      "source": [
        "# Categorical columns\n",
        "for c in df_flags.drop(columns=['CLAIM_ID']).select_dtypes(include='object').columns:\n",
        "    print (\"\\n---- %s percentage ---\" % c)\n",
        "    print (((df_flags[c][df_flags['SEGMENT_1_FLAG']==1].value_counts()/df_flags[c].value_counts()) * 100).round(2))\n",
        "\n",
        "print (\"\\n---- %s percentage ---\" % \"Age_group\")\n",
        "print (((df_flags['Age_group'][df_flags['SEGMENT_1_FLAG']==1].value_counts()/df_flags['Age_group'].value_counts()) * 100).round(2))"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 300
        },
        "id": "ZhgSBge7q4Fj",
        "outputId": "10f2a38d-f14a-4d44-fe7a-d6fc4ec9e910"
      },
      "outputs": [
        {
          "data": {
            "text/html": [
              "\n",
              "  <div id=\"df-b29ba772-ffcb-47ca-b3b2-398d0e35f00d\">\n",
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              "<table border=\"1\" class=\"dataframe\">\n",
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              "      <th></th>\n",
              "      <th>PREMIUM</th>\n",
              "      <th>NUMBER_OF_DRIVERS</th>\n",
              "      <th>NCD</th>\n",
              "      <th>VEHICLE_VALUE</th>\n",
              "      <th>VEHICLE_AGE</th>\n",
              "      <th>VEHICLE_ANNUALMILEAGE</th>\n",
              "      <th>VEHICLE_OWNEDMONTHS</th>\n",
              "      <th>INCURRED</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>count</th>\n",
              "      <td>9428.000000</td>\n",
              "      <td>9425.000000</td>\n",
              "      <td>9436.000000</td>\n",
              "      <td>9436.000000</td>\n",
              "      <td>9436.000000</td>\n",
              "      <td>9436.000000</td>\n",
              "      <td>9436.000000</td>\n",
              "      <td>1267.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>mean</th>\n",
              "      <td>218.446896</td>\n",
              "      <td>1.629072</td>\n",
              "      <td>8.225837</td>\n",
              "      <td>6178.397484</td>\n",
              "      <td>7.791543</td>\n",
              "      <td>7053.816948</td>\n",
              "      <td>71.212378</td>\n",
              "      <td>2063.580458</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>std</th>\n",
              "      <td>91.490736</td>\n",
              "      <td>0.598104</td>\n",
              "      <td>1.712442</td>\n",
              "      <td>4551.503571</td>\n",
              "      <td>2.262970</td>\n",
              "      <td>3326.684500</td>\n",
              "      <td>22.373518</td>\n",
              "      <td>4669.710849</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>min</th>\n",
              "      <td>102.246233</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>297.928258</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>160.654575</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>25%</th>\n",
              "      <td>152.549927</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>9.000000</td>\n",
              "      <td>3395.242558</td>\n",
              "      <td>6.000000</td>\n",
              "      <td>4988.149833</td>\n",
              "      <td>58.000000</td>\n",
              "      <td>9.625352</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>50%</th>\n",
              "      <td>192.877656</td>\n",
              "      <td>2.000000</td>\n",
              "      <td>9.000000</td>\n",
              "      <td>4995.337776</td>\n",
              "      <td>8.000000</td>\n",
              "      <td>6977.238440</td>\n",
              "      <td>70.000000</td>\n",
              "      <td>96.261822</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>75%</th>\n",
              "      <td>254.004801</td>\n",
              "      <td>2.000000</td>\n",
              "      <td>9.000000</td>\n",
              "      <td>7490.839224</td>\n",
              "      <td>9.000000</td>\n",
              "      <td>9958.657548</td>\n",
              "      <td>84.000000</td>\n",
              "      <td>2221.656654</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>max</th>\n",
              "      <td>1047.855278</td>\n",
              "      <td>6.000000</td>\n",
              "      <td>9.000000</td>\n",
              "      <td>60008.979309</td>\n",
              "      <td>16.000000</td>\n",
              "      <td>70305.999705</td>\n",
              "      <td>149.000000</td>\n",
              "      <td>40923.052095</td>\n",
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              "\n",
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              "\n",
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              "            await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                     [key], {});\n",
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              "\n",
              "          const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "            '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
              "            + ' to learn more about interactive tables.';\n",
              "          element.innerHTML = '';\n",
              "          dataTable['output_type'] = 'display_data';\n",
              "          await google.colab.output.renderOutput(dataTable, element);\n",
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              "        }\n",
              "      </script>\n",
              "    </div>\n",
              "  </div>\n",
              "  "
            ],
            "text/plain": [
              "           PREMIUM  NUMBER_OF_DRIVERS          NCD  VEHICLE_VALUE  \\\n",
              "count  9428.000000        9425.000000  9436.000000    9436.000000   \n",
              "mean    218.446896           1.629072     8.225837    6178.397484   \n",
              "std      91.490736           0.598104     1.712442    4551.503571   \n",
              "min     102.246233           1.000000     0.000000     297.928258   \n",
              "25%     152.549927           1.000000     9.000000    3395.242558   \n",
              "50%     192.877656           2.000000     9.000000    4995.337776   \n",
              "75%     254.004801           2.000000     9.000000    7490.839224   \n",
              "max    1047.855278           6.000000     9.000000   60008.979309   \n",
              "\n",
              "       VEHICLE_AGE  VEHICLE_ANNUALMILEAGE  VEHICLE_OWNEDMONTHS      INCURRED  \n",
              "count  9436.000000            9436.000000          9436.000000   1267.000000  \n",
              "mean      7.791543            7053.816948            71.212378   2063.580458  \n",
              "std       2.262970            3326.684500            22.373518   4669.710849  \n",
              "min       0.000000             160.654575             0.000000      0.000000  \n",
              "25%       6.000000            4988.149833            58.000000      9.625352  \n",
              "50%       8.000000            6977.238440            70.000000     96.261822  \n",
              "75%       9.000000            9958.657548            84.000000   2221.656654  \n",
              "max      16.000000           70305.999705           149.000000  40923.052095  "
            ]
          },
          "execution_count": 27,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "# Status for numerical columns\n",
        "df_flags[df_flags['SEGMENT_1_FLAG']==1].iloc[:,:-7].describe().drop(columns=['POLICY_NUMBER'])"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "7O2GnJb8orLb"
      },
      "source": [
        "### Segment 2"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 272
        },
        "id": "-4qLx_5x2nYX",
        "outputId": "4a9c5e4e-7d01-4038-ca63-75af254652c5"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "Avg. Claim Freq. per age group for Segment 2: 12.92 % \n",
            "\n"
          ]
        },
        {
          "data": {
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              "\n",
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              "      <th></th>\n",
              "      <th>Age_group</th>\n",
              "      <th>CLAIM_ID</th>\n",
              "      <th>POLICY_NUMBER</th>\n",
              "      <th>Claim Frequency %</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>17-25</td>\n",
              "      <td>4</td>\n",
              "      <td>47</td>\n",
              "      <td>8.51</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
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              "      <td>14.69</td>\n",
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              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>46-55</td>\n",
              "      <td>104</td>\n",
              "      <td>690</td>\n",
              "      <td>15.07</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>56-65</td>\n",
              "      <td>64</td>\n",
              "      <td>607</td>\n",
              "      <td>10.54</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>5</th>\n",
              "      <td>65+</td>\n",
              "      <td>54</td>\n",
              "      <td>386</td>\n",
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              "                                                     [key], {});\n",
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              "\n",
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              "            '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
              "            + ' to learn more about interactive tables.';\n",
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              "    </div>\n",
              "  </div>\n",
              "  "
            ],
            "text/plain": [
              "  Age_group  CLAIM_ID  POLICY_NUMBER  Claim Frequency %\n",
              "0     17-25         4             47               8.51\n",
              "1     26-35        57            387              14.73\n",
              "2     36-45        94            640              14.69\n",
              "3     46-55       104            690              15.07\n",
              "4     56-65        64            607              10.54\n",
              "5       65+        54            386              13.99"
            ]
          },
          "execution_count": 28,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "df_age2 = df[['Age_group', 'CLAIM_ID', 'POLICY_NUMBER', 'SEGMENT_2_FLAG']]\n",
        "df_age2 = df_age2[df_age2['SEGMENT_2_FLAG']==1].drop(columns='SEGMENT_2_FLAG')\n",
        "df_age2 = df_age2.groupby(['Age_group']).count()\n",
        "df_age2['Claim Frequency %'] = ((df_age2['CLAIM_ID']/df_age2['POLICY_NUMBER']) * 100).round(2)\n",
        "print(\"Avg. Claim Freq. per age group for Segment 2: {:0.2f} % \\n\".format(df_age2['Claim Frequency %'].mean()))\n",
        "df_age2.reset_index()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
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          "base_uri": "https://localhost:8080/",
          "height": 272
        },
        "id": "PGifDgUW2nV2",
        "outputId": "5298bdbb-5964-4dcd-e46d-7ead597ca704"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "Avg. Claim Freq. per marital status for Segment 2: 14.80 % \n",
            "\n"
          ]
        },
        {
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              "      <td>CommonLaw</td>\n",
              "      <td>35</td>\n",
              "      <td>245</td>\n",
              "      <td>14.29</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>Divorced</td>\n",
              "      <td>34</td>\n",
              "      <td>186</td>\n",
              "      <td>18.28</td>\n",
              "    </tr>\n",
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              "      </script>\n",
              "    </div>\n",
              "  </div>\n",
              "  "
            ],
            "text/plain": [
              "  MARITALSTATUS  CLAIM_ID  POLICY_NUMBER  Claim Frequency %\n",
              "0     CommonLaw        35            245              14.29\n",
              "1      Divorced        34            186              18.28\n",
              "2       Married       202           1485              13.60\n",
              "3   Parthership         2             17              11.76\n",
              "4        Single        92            759              12.12\n",
              "5       Widowed        12             64              18.75"
            ]
          },
          "execution_count": 29,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "df_ms2 = df[['MARITALSTATUS', 'CLAIM_ID', 'POLICY_NUMBER', 'SEGMENT_2_FLAG']]\n",
        "df_ms2 = df_ms2[df_ms2['SEGMENT_2_FLAG']==1].drop(columns='SEGMENT_2_FLAG')\n",
        "df_ms2 = df_ms2.groupby(['MARITALSTATUS']).count()\n",
        "df_ms2['Claim Frequency %'] = ((df_ms2['CLAIM_ID']/df_ms2['POLICY_NUMBER']) * 100).round(2)\n",
        "print(\"Avg. Claim Freq. per marital status for Segment 2: {:0.2f} % \\n\".format(df_ms2['Claim Frequency %'].mean()))\n",
        "df_ms2.reset_index()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "OwkFN7ojvy05",
        "outputId": "686ea888-8b40-464e-fdba-cfcf82fd152f"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "\n",
            "---- POLICY_STATUS percentage ---\n",
            "Cancelled       3.79\n",
            "New Business    3.68\n",
            "Renewal          NaN\n",
            "Name: POLICY_STATUS, dtype: float64\n",
            "\n",
            "---- MARITALSTATUS percentage ---\n",
            "CommonLaw      1.92\n",
            "Divorced       2.69\n",
            "Married        3.49\n",
            "None            NaN\n",
            "Parthership    2.21\n",
            "Single         3.04\n",
            "Widowed        2.89\n",
            "Name: MARITALSTATUS, dtype: float64\n",
            "\n",
            "---- EMPLOYMENTTYPE percentage ---\n",
            "Employed                          2.97\n",
            "Household Duties                  3.62\n",
            "In Full Or Part Time Education    0.57\n",
            "Independent Means                 2.90\n",
            "Not employed due to disability    1.25\n",
            "Retired                           3.29\n",
            "Self Employed                     3.97\n",
            "Unemployed                        2.18\n",
            "Unknown                            NaN\n",
            "Voluntary Work                     NaN\n",
            "Name: EMPLOYMENTTYPE, dtype: float64\n",
            "\n",
            "---- VEHICLE_CLASSOFUSE percentage ---\n",
            "Business Use (PH)                             1.21\n",
            "Business use (PH + Spouse / Civil Partner)    1.04\n",
            "Business use (spouse / Civil Parnter)         1.60\n",
            "Business use by all drivers                   1.61\n",
            "None                                           NaN\n",
            "Social inc. Comm                              1.99\n",
            "Social only                                   4.95\n",
            "Unknown                                        NaN\n",
            "unk                                            NaN\n",
            "Name: VEHICLE_CLASSOFUSE, dtype: float64\n",
            "\n",
            "---- Age_group percentage ---\n",
            "46-55    3.34\n",
            "36-45    3.16\n",
            "56-65    3.59\n",
            "26-35    2.41\n",
            "65+      3.62\n",
            "17-25    0.84\n",
            "Name: Age_group, dtype: float64\n"
          ]
        }
      ],
      "source": [
        "# Categorical columns\n",
        "for c in df_flags.drop(columns=['CLAIM_ID']).select_dtypes(include='object').columns:\n",
        "    print (\"\\n---- %s percentage ---\" % c)\n",
        "    print (((df_flags[c][df_flags['SEGMENT_2_FLAG']==1].value_counts()/df_flags[c].value_counts()) * 100).round(2))\n",
        "\n",
        "print (\"\\n---- %s percentage ---\" % \"Age_group\")\n",
        "print (((df_flags['Age_group'][df_flags['SEGMENT_2_FLAG']==1].value_counts()/df_flags['Age_group'].value_counts()) * 100).round(2))"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 300
        },
        "id": "fQ-AZhCb4QZE",
        "outputId": "cc213590-387b-4fca-fe09-dacf5160f517"
      },
      "outputs": [
        {
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              "\n",
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              "    <div class=\"colab-df-container\">\n",
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              "</style>\n",
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              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>PREMIUM</th>\n",
              "      <th>NUMBER_OF_DRIVERS</th>\n",
              "      <th>NCD</th>\n",
              "      <th>VEHICLE_VALUE</th>\n",
              "      <th>VEHICLE_AGE</th>\n",
              "      <th>VEHICLE_ANNUALMILEAGE</th>\n",
              "      <th>VEHICLE_OWNEDMONTHS</th>\n",
              "      <th>INCURRED</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>count</th>\n",
              "      <td>2753.000000</td>\n",
              "      <td>2747.000000</td>\n",
              "      <td>2757.000000</td>\n",
              "      <td>2757.000000</td>\n",
              "      <td>2757.000000</td>\n",
              "      <td>2757.000000</td>\n",
              "      <td>2757.000000</td>\n",
              "      <td>376.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>mean</th>\n",
              "      <td>286.748897</td>\n",
              "      <td>1.555879</td>\n",
              "      <td>7.788176</td>\n",
              "      <td>4030.173340</td>\n",
              "      <td>11.562931</td>\n",
              "      <td>4853.405822</td>\n",
              "      <td>85.947044</td>\n",
              "      <td>2216.183795</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>std</th>\n",
              "      <td>132.711411</td>\n",
              "      <td>0.626605</td>\n",
              "      <td>2.117236</td>\n",
              "      <td>4164.203331</td>\n",
              "      <td>4.346993</td>\n",
              "      <td>2680.952422</td>\n",
              "      <td>43.058826</td>\n",
              "      <td>4928.709085</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>min</th>\n",
              "      <td>113.522572</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>46.251744</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>204.518939</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>25%</th>\n",
              "      <td>189.728602</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>7.000000</td>\n",
              "      <td>1456.488278</td>\n",
              "      <td>8.000000</td>\n",
              "      <td>2998.703681</td>\n",
              "      <td>55.000000</td>\n",
              "      <td>4.790647</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>50%</th>\n",
              "      <td>258.039659</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>9.000000</td>\n",
              "      <td>2903.588323</td>\n",
              "      <td>11.000000</td>\n",
              "      <td>4982.802730</td>\n",
              "      <td>77.000000</td>\n",
              "      <td>98.907250</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>75%</th>\n",
              "      <td>345.940865</td>\n",
              "      <td>2.000000</td>\n",
              "      <td>9.000000</td>\n",
              "      <td>5013.311175</td>\n",
              "      <td>14.000000</td>\n",
              "      <td>6004.476852</td>\n",
              "      <td>110.000000</td>\n",
              "      <td>2282.114465</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>max</th>\n",
              "      <td>1307.090863</td>\n",
              "      <td>4.000000</td>\n",
              "      <td>9.000000</td>\n",
              "      <td>55101.984246</td>\n",
              "      <td>30.000000</td>\n",
              "      <td>51985.021385</td>\n",
              "      <td>308.000000</td>\n",
              "      <td>52863.502420</td>\n",
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              "                                                     [key], {});\n",
              "          if (!dataTable) return;\n",
              "\n",
              "          const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "            '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
              "            + ' to learn more about interactive tables.';\n",
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              "  </div>\n",
              "  "
            ],
            "text/plain": [
              "           PREMIUM  NUMBER_OF_DRIVERS          NCD  VEHICLE_VALUE  \\\n",
              "count  2753.000000        2747.000000  2757.000000    2757.000000   \n",
              "mean    286.748897           1.555879     7.788176    4030.173340   \n",
              "std     132.711411           0.626605     2.117236    4164.203331   \n",
              "min     113.522572           1.000000     0.000000      46.251744   \n",
              "25%     189.728602           1.000000     7.000000    1456.488278   \n",
              "50%     258.039659           1.000000     9.000000    2903.588323   \n",
              "75%     345.940865           2.000000     9.000000    5013.311175   \n",
              "max    1307.090863           4.000000     9.000000   55101.984246   \n",
              "\n",
              "       VEHICLE_AGE  VEHICLE_ANNUALMILEAGE  VEHICLE_OWNEDMONTHS      INCURRED  \n",
              "count  2757.000000            2757.000000          2757.000000    376.000000  \n",
              "mean     11.562931            4853.405822            85.947044   2216.183795  \n",
              "std       4.346993            2680.952422            43.058826   4928.709085  \n",
              "min       0.000000             204.518939             0.000000      0.000000  \n",
              "25%       8.000000            2998.703681            55.000000      4.790647  \n",
              "50%      11.000000            4982.802730            77.000000     98.907250  \n",
              "75%      14.000000            6004.476852           110.000000   2282.114465  \n",
              "max      30.000000           51985.021385           308.000000  52863.502420  "
            ]
          },
          "execution_count": 31,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "# Status for numerical columns\n",
        "df_flags[df_flags['SEGMENT_2_FLAG']==1].iloc[:,:-7].describe().drop(columns=['POLICY_NUMBER'])"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "98Wgt8QwoteU"
      },
      "source": [
        "### Segment 3"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 272
        },
        "id": "DE4njw1V2ojm",
        "outputId": "ebab1b3e-b333-42ec-b5d0-021624e2f71a"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "Avg. Claim Freq. per age group for Segment 3: 17.04 % \n",
            "\n"
          ]
        },
        {
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              "\n",
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              "      <script>\n",
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              "          document.querySelector('#df-9a8d0d94-49d3-4464-926b-509db1fd9144 button.colab-df-convert');\n",
              "        buttonEl.style.display =\n",
              "          google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "\n",
              "        async function convertToInteractive(key) {\n",
              "          const element = document.querySelector('#df-9a8d0d94-49d3-4464-926b-509db1fd9144');\n",
              "          const dataTable =\n",
              "            await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                     [key], {});\n",
              "          if (!dataTable) return;\n",
              "\n",
              "          const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "            '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
              "            + ' to learn more about interactive tables.';\n",
              "          element.innerHTML = '';\n",
              "          dataTable['output_type'] = 'display_data';\n",
              "          await google.colab.output.renderOutput(dataTable, element);\n",
              "          const docLink = document.createElement('div');\n",
              "          docLink.innerHTML = docLinkHtml;\n",
              "          element.appendChild(docLink);\n",
              "        }\n",
              "      </script>\n",
              "    </div>\n",
              "  </div>\n",
              "  "
            ],
            "text/plain": [
              "  Age_group  CLAIM_ID  POLICY_NUMBER  Claim Frequency %\n",
              "0     17-25         5             15              33.33\n",
              "1     26-35        41            246              16.67\n",
              "2     36-45        45            323              13.93\n",
              "3     46-55        30            311               9.65\n",
              "4     56-65        41            300              13.67\n",
              "5       65+        35            233              15.02"
            ]
          },
          "execution_count": 32,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "df_age3 = df[['Age_group', 'CLAIM_ID', 'POLICY_NUMBER', 'SEGMENT_3_FLAG']]\n",
        "df_age3 = df_age3[df_age3['SEGMENT_3_FLAG']==1].drop(columns='SEGMENT_3_FLAG')\n",
        "df_age3 = df_age3.groupby(['Age_group']).count()\n",
        "df_age3['Claim Frequency %'] = ((df_age3['CLAIM_ID']/df_age3['POLICY_NUMBER']) * 100).round(2)\n",
        "print(\"Avg. Claim Freq. per age group for Segment 3: {:0.2f} % \\n\".format(df_age3['Claim Frequency %'].mean()))\n",
        "df_age3.reset_index()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 272
        },
        "id": "PAdlvxc22ogw",
        "outputId": "7345a24b-8ee8-4148-b6c9-6b6ae369d4cc"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "Avg. Claim Freq. per marital status for Segment 3: 14.65 % \n",
            "\n"
          ]
        },
        {
          "data": {
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              "\n",
              "  <div id=\"df-368b1da2-8b21-4f9d-b29f-cd13ff1fd6d3\">\n",
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              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>MARITALSTATUS</th>\n",
              "      <th>CLAIM_ID</th>\n",
              "      <th>POLICY_NUMBER</th>\n",
              "      <th>Claim Frequency %</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>CommonLaw</td>\n",
              "      <td>26</td>\n",
              "      <td>195</td>\n",
              "      <td>13.33</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>Divorced</td>\n",
              "      <td>11</td>\n",
              "      <td>85</td>\n",
              "      <td>12.94</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>Married</td>\n",
              "      <td>125</td>\n",
              "      <td>894</td>\n",
              "      <td>13.98</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>Parthership</td>\n",
              "      <td>3</td>\n",
              "      <td>13</td>\n",
              "      <td>23.08</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>Single</td>\n",
              "      <td>28</td>\n",
              "      <td>204</td>\n",
              "      <td>13.73</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>5</th>\n",
              "      <td>Widowed</td>\n",
              "      <td>4</td>\n",
              "      <td>37</td>\n",
              "      <td>10.81</td>\n",
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              "      <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-368b1da2-8b21-4f9d-b29f-cd13ff1fd6d3')\"\n",
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              "            await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                     [key], {});\n",
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              "\n",
              "          const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "            '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
              "            + ' to learn more about interactive tables.';\n",
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              "        }\n",
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              "    </div>\n",
              "  </div>\n",
              "  "
            ],
            "text/plain": [
              "  MARITALSTATUS  CLAIM_ID  POLICY_NUMBER  Claim Frequency %\n",
              "0     CommonLaw        26            195              13.33\n",
              "1      Divorced        11             85              12.94\n",
              "2       Married       125            894              13.98\n",
              "3   Parthership         3             13              23.08\n",
              "4        Single        28            204              13.73\n",
              "5       Widowed         4             37              10.81"
            ]
          },
          "execution_count": 33,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "df_ms3 = df[['MARITALSTATUS', 'CLAIM_ID', 'POLICY_NUMBER', 'SEGMENT_3_FLAG']]\n",
        "df_ms3 = df_ms3[df_ms3['SEGMENT_3_FLAG']==1].drop(columns='SEGMENT_3_FLAG')\n",
        "df_ms3 = df_ms3.groupby(['MARITALSTATUS']).count()\n",
        "df_ms3['Claim Frequency %'] = ((df_ms3['CLAIM_ID']/df_ms3['POLICY_NUMBER']) * 100).round(2)\n",
        "print(\"Avg. Claim Freq. per marital status for Segment 3: {:0.2f} % \\n\".format(df_ms3['Claim Frequency %'].mean()))\n",
        "df_ms3.reset_index()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "LYEgVaQtv0Hi",
        "outputId": "f67fc4bc-b2ff-4841-a150-05d68f26e578"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "\n",
            "---- POLICY_STATUS percentage ---\n",
            "Cancelled       2.11\n",
            "New Business    1.90\n",
            "Renewal          NaN\n",
            "Name: POLICY_STATUS, dtype: float64\n",
            "\n",
            "---- MARITALSTATUS percentage ---\n",
            "CommonLaw      1.53\n",
            "Divorced       1.23\n",
            "Married        2.10\n",
            "None            NaN\n",
            "Parthership    1.69\n",
            "Single         0.82\n",
            "Widowed        1.67\n",
            "Name: MARITALSTATUS, dtype: float64\n",
            "\n",
            "---- EMPLOYMENTTYPE percentage ---\n",
            "Employed                          1.55\n",
            "Household Duties                  1.08\n",
            "In Full Or Part Time Education    0.35\n",
            "Independent Means                  NaN\n",
            "Not employed due to disability    1.07\n",
            "Retired                           2.49\n",
            "Self Employed                     1.20\n",
            "Unemployed                        0.17\n",
            "Unknown                            NaN\n",
            "Voluntary Work                     NaN\n",
            "Name: EMPLOYMENTTYPE, dtype: float64\n",
            "\n",
            "---- VEHICLE_CLASSOFUSE percentage ---\n",
            "Business Use (PH)                             1.59\n",
            "Business use (PH + Spouse / Civil Partner)    1.61\n",
            "Business use (spouse / Civil Parnter)         2.13\n",
            "Business use by all drivers                   1.61\n",
            "None                                           NaN\n",
            "Social inc. Comm                              1.55\n",
            "Social only                                   1.62\n",
            "Unknown                                        NaN\n",
            "unk                                            NaN\n",
            "Name: VEHICLE_CLASSOFUSE, dtype: float64\n",
            "\n",
            "---- Age_group percentage ---\n",
            "17-25    0.27\n",
            "26-35    1.53\n",
            "36-45    1.59\n",
            "46-55    1.50\n",
            "56-65    1.77\n",
            "65+      2.19\n",
            "Name: Age_group, dtype: float64\n"
          ]
        }
      ],
      "source": [
        "# Categorical columns\n",
        "for c in df_flags.drop(columns=['CLAIM_ID']).select_dtypes(include='object').columns:\n",
        "    print (\"\\n---- %s percentage ---\" % c)\n",
        "    print (((df_flags[c][df_flags['SEGMENT_3_FLAG']==1].value_counts()/df_flags[c].value_counts()) * 100).round(2))\n",
        "\n",
        "print (\"\\n---- %s percentage ---\" % \"Age_group\")\n",
        "print (((df_flags['Age_group'][df_flags['SEGMENT_3_FLAG']==1].value_counts()/df_flags['Age_group'].value_counts()) * 100).round(2))"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
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          "height": 300
        },
        "id": "Sms17obQv0Eq",
        "outputId": "180f20d9-6b4d-4698-bce7-ec10a7e92591"
      },
      "outputs": [
        {
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              "\n",
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              "      <th>PREMIUM</th>\n",
              "      <th>NUMBER_OF_DRIVERS</th>\n",
              "      <th>NCD</th>\n",
              "      <th>VEHICLE_VALUE</th>\n",
              "      <th>VEHICLE_AGE</th>\n",
              "      <th>VEHICLE_ANNUALMILEAGE</th>\n",
              "      <th>VEHICLE_OWNEDMONTHS</th>\n",
              "      <th>INCURRED</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>count</th>\n",
              "      <td>1426.000000</td>\n",
              "      <td>1425.000000</td>\n",
              "      <td>1428.000000</td>\n",
              "      <td>1428.000000</td>\n",
              "      <td>1428.000000</td>\n",
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              "      <td>197.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>mean</th>\n",
              "      <td>271.607366</td>\n",
              "      <td>1.679298</td>\n",
              "      <td>7.707983</td>\n",
              "      <td>14713.328547</td>\n",
              "      <td>3.817227</td>\n",
              "      <td>7333.973986</td>\n",
              "      <td>25.756303</td>\n",
              "      <td>2223.364477</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>std</th>\n",
              "      <td>142.653006</td>\n",
              "      <td>0.558665</td>\n",
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              "    </tr>\n",
              "    <tr>\n",
              "      <th>min</th>\n",
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              "      <td>1596.045762</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>491.701092</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
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              "      <th>25%</th>\n",
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              "      <td>7.000000</td>\n",
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              "      <td>2.000000</td>\n",
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              "      <td>23.000000</td>\n",
              "      <td>110.575476</td>\n",
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              "      <th>75%</th>\n",
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              "      <td>9.000000</td>\n",
              "      <td>18188.422111</td>\n",
              "      <td>5.000000</td>\n",
              "      <td>9975.600898</td>\n",
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              "      <th>max</th>\n",
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              "      <td>13.000000</td>\n",
              "      <td>44981.548622</td>\n",
              "      <td>117.000000</td>\n",
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              "      filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
              "      fill: #FFFFFF;\n",
              "    }\n",
              "  </style>\n",
              "\n",
              "      <script>\n",
              "        const buttonEl =\n",
              "          document.querySelector('#df-c54ff2a1-e28c-4ec3-b32e-f7b14b3828b4 button.colab-df-convert');\n",
              "        buttonEl.style.display =\n",
              "          google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "\n",
              "        async function convertToInteractive(key) {\n",
              "          const element = document.querySelector('#df-c54ff2a1-e28c-4ec3-b32e-f7b14b3828b4');\n",
              "          const dataTable =\n",
              "            await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                     [key], {});\n",
              "          if (!dataTable) return;\n",
              "\n",
              "          const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "            '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
              "            + ' to learn more about interactive tables.';\n",
              "          element.innerHTML = '';\n",
              "          dataTable['output_type'] = 'display_data';\n",
              "          await google.colab.output.renderOutput(dataTable, element);\n",
              "          const docLink = document.createElement('div');\n",
              "          docLink.innerHTML = docLinkHtml;\n",
              "          element.appendChild(docLink);\n",
              "        }\n",
              "      </script>\n",
              "    </div>\n",
              "  </div>\n",
              "  "
            ],
            "text/plain": [
              "           PREMIUM  NUMBER_OF_DRIVERS          NCD  VEHICLE_VALUE  \\\n",
              "count  1426.000000        1425.000000  1428.000000    1428.000000   \n",
              "mean    271.607366           1.679298     7.707983   14713.328547   \n",
              "std     142.653006           0.558665     2.394696    7737.585421   \n",
              "min     107.491760           1.000000     0.000000    1596.045762   \n",
              "25%     177.053578           1.000000     7.000000    9301.663121   \n",
              "50%     231.110634           2.000000     9.000000   12769.432313   \n",
              "75%     321.019513           2.000000     9.000000   18188.422111   \n",
              "max    1730.556247           4.000000     9.000000   69992.145563   \n",
              "\n",
              "       VEHICLE_AGE  VEHICLE_ANNUALMILEAGE  VEHICLE_OWNEDMONTHS      INCURRED  \n",
              "count  1428.000000            1428.000000          1428.000000    197.000000  \n",
              "mean      3.817227            7333.973986            25.756303   2223.364477  \n",
              "std       2.400843            2738.761336            16.460975   4875.549524  \n",
              "min       0.000000             491.701092             0.000000      0.000000  \n",
              "25%       2.000000            5018.292536            14.000000      7.417300  \n",
              "50%       4.000000            7975.112254            23.000000    110.575476  \n",
              "75%       5.000000            9975.600898            35.000000   2390.218175  \n",
              "max      13.000000           44981.548622           117.000000  41526.414518  "
            ]
          },
          "execution_count": 35,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "# Status for numerical columns\n",
        "df_flags[df_flags['SEGMENT_3_FLAG']==1].iloc[:,:-7].describe().drop(columns=['POLICY_NUMBER'])"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "IjZQaGzhovD6"
      },
      "source": [
        "### Segment 4"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 272
        },
        "id": "s5mv4wmo2pVk",
        "outputId": "9d8d3dee-57cd-48ca-d58d-7ece1dc96523"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "Avg. Claim Freq. per age group for Segment 4: 8.89 % \n",
            "\n"
          ]
        },
        {
          "data": {
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              "\n",
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              "\n",
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              "</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>CLAIM_ID</th>\n",
              "      <th>POLICY_NUMBER</th>\n",
              "      <th>Claim Frequency %</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>17-25</td>\n",
              "      <td>0</td>\n",
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              "      <td>NaN</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>26-35</td>\n",
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              "      <td>0</td>\n",
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              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>36-45</td>\n",
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              "      <td>0.00</td>\n",
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              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>46-55</td>\n",
              "      <td>1</td>\n",
              "      <td>25</td>\n",
              "      <td>4.00</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>56-65</td>\n",
              "      <td>80</td>\n",
              "      <td>463</td>\n",
              "      <td>17.28</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>5</th>\n",
              "      <td>65+</td>\n",
              "      <td>126</td>\n",
              "      <td>882</td>\n",
              "      <td>14.29</td>\n",
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              "            await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                     [key], {});\n",
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              "\n",
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              "            '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
              "            + ' to learn more about interactive tables.';\n",
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              "    </div>\n",
              "  </div>\n",
              "  "
            ],
            "text/plain": [
              "  Age_group  CLAIM_ID  POLICY_NUMBER  Claim Frequency %\n",
              "0     17-25         0              0                NaN\n",
              "1     26-35         0              0                NaN\n",
              "2     36-45         0              2               0.00\n",
              "3     46-55         1             25               4.00\n",
              "4     56-65        80            463              17.28\n",
              "5       65+       126            882              14.29"
            ]
          },
          "execution_count": 36,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "df_age4 = df[['Age_group', 'CLAIM_ID', 'POLICY_NUMBER', 'SEGMENT_4_FLAG']]\n",
        "df_age4 = df_age4[df_age4['SEGMENT_4_FLAG']==1].drop(columns='SEGMENT_4_FLAG')\n",
        "df_age4 = df_age4.groupby(['Age_group']).count()\n",
        "df_age4['Claim Frequency %'] = ((df_age4['CLAIM_ID']/df_age4['POLICY_NUMBER']) * 100).round(2)\n",
        "print(\"Avg. Claim Freq. per age group for Segment 4: {:0.2f} % \\n\".format(df_age4['Claim Frequency %'].mean()))\n",
        "df_age4.reset_index()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 272
        },
        "id": "38AtvARm2pS8",
        "outputId": "c1791690-52ae-4f1e-fbbc-bb43fc83dfcd"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "Avg. Claim Freq. per marital status for Segment 4: 10.31 % \n",
            "\n"
          ]
        },
        {
          "data": {
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              "\n",
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              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>MARITALSTATUS</th>\n",
              "      <th>CLAIM_ID</th>\n",
              "      <th>POLICY_NUMBER</th>\n",
              "      <th>Claim Frequency %</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>CommonLaw</td>\n",
              "      <td>11</td>\n",
              "      <td>83</td>\n",
              "      <td>13.25</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>Divorced</td>\n",
              "      <td>16</td>\n",
              "      <td>131</td>\n",
              "      <td>12.21</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>Married</td>\n",
              "      <td>162</td>\n",
              "      <td>958</td>\n",
              "      <td>16.91</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>Parthership</td>\n",
              "      <td>0</td>\n",
              "      <td>3</td>\n",
              "      <td>0.00</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>Single</td>\n",
              "      <td>6</td>\n",
              "      <td>112</td>\n",
              "      <td>5.36</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>5</th>\n",
              "      <td>Widowed</td>\n",
              "      <td>12</td>\n",
              "      <td>85</td>\n",
              "      <td>14.12</td>\n",
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              "  </tbody>\n",
              "</table>\n",
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              "\n",
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              "            await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                     [key], {});\n",
              "          if (!dataTable) return;\n",
              "\n",
              "          const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "            '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
              "            + ' to learn more about interactive tables.';\n",
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              "    </div>\n",
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              "  "
            ],
            "text/plain": [
              "  MARITALSTATUS  CLAIM_ID  POLICY_NUMBER  Claim Frequency %\n",
              "0     CommonLaw        11             83              13.25\n",
              "1      Divorced        16            131              12.21\n",
              "2       Married       162            958              16.91\n",
              "3   Parthership         0              3               0.00\n",
              "4        Single         6            112               5.36\n",
              "5       Widowed        12             85              14.12"
            ]
          },
          "execution_count": 37,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "df_ms4 = df[['MARITALSTATUS', 'CLAIM_ID', 'POLICY_NUMBER', 'SEGMENT_4_FLAG']]\n",
        "df_ms4 = df_ms4[df_ms4['SEGMENT_4_FLAG']==1].drop(columns='SEGMENT_4_FLAG')\n",
        "df_ms4 = df_ms4.groupby(['MARITALSTATUS']).count()\n",
        "df_ms4['Claim Frequency %'] = ((df_ms4['CLAIM_ID']/df_ms4['POLICY_NUMBER']) * 100).round(2)\n",
        "print(\"Avg. Claim Freq. per marital status for Segment 4: {:0.2f} % \\n\".format(df_ms4['Claim Frequency %'].mean()))\n",
        "df_ms4.reset_index()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "_o_Bduhjv0-4",
        "outputId": "c1c673dc-b0dd-4cd5-dce7-3286dd8cc591"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "\n",
            "---- POLICY_STATUS percentage ---\n",
            "Cancelled       1.13\n",
            "New Business    1.86\n",
            "Renewal          NaN\n",
            "Name: POLICY_STATUS, dtype: float64\n",
            "\n",
            "---- MARITALSTATUS percentage ---\n",
            "CommonLaw      0.65\n",
            "Divorced       1.89\n",
            "Married        2.25\n",
            "None            NaN\n",
            "Parthership    0.39\n",
            "Single         0.45\n",
            "Widowed        3.84\n",
            "Name: MARITALSTATUS, dtype: float64\n",
            "\n",
            "---- EMPLOYMENTTYPE percentage ---\n",
            "Employed                           0.02\n",
            "Household Duties                    NaN\n",
            "In Full Or Part Time Education      NaN\n",
            "Independent Means                   NaN\n",
            "Not employed due to disability     0.18\n",
            "Retired                           11.82\n",
            "Self Employed                      0.11\n",
            "Unemployed                         0.09\n",
            "Unknown                             NaN\n",
            "Voluntary Work                      NaN\n",
            "Name: EMPLOYMENTTYPE, dtype: float64\n",
            "\n",
            "---- VEHICLE_CLASSOFUSE percentage ---\n",
            "Business Use (PH)                             0.04\n",
            "Business use (PH + Spouse / Civil Partner)    0.19\n",
            "Business use (spouse / Civil Parnter)          NaN\n",
            "Business use by all drivers                    NaN\n",
            "None                                           NaN\n",
            "Social inc. Comm                              0.07\n",
            "Social only                                   3.83\n",
            "Unknown                                        NaN\n",
            "unk                                            NaN\n",
            "Name: VEHICLE_CLASSOFUSE, dtype: float64\n",
            "\n",
            "---- Age_group percentage ---\n",
            "17-25    0.00\n",
            "26-35    0.00\n",
            "36-45    0.01\n",
            "46-55    0.12\n",
            "56-65    2.74\n",
            "65+      8.28\n",
            "Name: Age_group, dtype: float64\n"
          ]
        }
      ],
      "source": [
        "# Categorical columns\n",
        "for c in df_flags.drop(columns=['CLAIM_ID']).select_dtypes(include='object').columns:\n",
        "    print (\"\\n---- %s percentage ---\" % c)\n",
        "    print (((df_flags[c][df_flags['SEGMENT_4_FLAG']==1].value_counts()/df_flags[c].value_counts()) * 100).round(2))\n",
        "\n",
        "print (\"\\n---- %s percentage ---\" % \"Age_group\")\n",
        "print (((df_flags['Age_group'][df_flags['SEGMENT_4_FLAG']==1].value_counts()/df_flags['Age_group'].value_counts()) * 100).round(2))"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 300
        },
        "id": "sTTWBJrxv08V",
        "outputId": "2725c500-10e2-4b38-fcf7-4549ec417a08"
      },
      "outputs": [
        {
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              "\n",
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              "      <th>PREMIUM</th>\n",
              "      <th>NUMBER_OF_DRIVERS</th>\n",
              "      <th>NCD</th>\n",
              "      <th>VEHICLE_VALUE</th>\n",
              "      <th>VEHICLE_AGE</th>\n",
              "      <th>VEHICLE_ANNUALMILEAGE</th>\n",
              "      <th>VEHICLE_OWNEDMONTHS</th>\n",
              "      <th>INCURRED</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
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              "      <th>count</th>\n",
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              "      <td>1372.000000</td>\n",
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              "      <th>mean</th>\n",
              "      <td>195.585764</td>\n",
              "      <td>1.616508</td>\n",
              "      <td>8.485423</td>\n",
              "      <td>8452.166791</td>\n",
              "      <td>7.365160</td>\n",
              "      <td>8612.394735</td>\n",
              "      <td>44.946064</td>\n",
              "      <td>1623.869997</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>std</th>\n",
              "      <td>61.385265</td>\n",
              "      <td>0.606776</td>\n",
              "      <td>1.642504</td>\n",
              "      <td>6510.616292</td>\n",
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              "      <td>2126.928429</td>\n",
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              "      <td>3414.106792</td>\n",
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              "    <tr>\n",
              "      <th>min</th>\n",
              "      <td>113.268042</td>\n",
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              "      <td>19.492191</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>1984.450397</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>25%</th>\n",
              "      <td>152.961164</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>9.000000</td>\n",
              "      <td>3623.653518</td>\n",
              "      <td>4.000000</td>\n",
              "      <td>7020.080808</td>\n",
              "      <td>21.000000</td>\n",
              "      <td>15.022563</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>50%</th>\n",
              "      <td>182.937661</td>\n",
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              "      <td>9.000000</td>\n",
              "      <td>7042.626197</td>\n",
              "      <td>7.000000</td>\n",
              "      <td>8015.921308</td>\n",
              "      <td>37.000000</td>\n",
              "      <td>96.203066</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>75%</th>\n",
              "      <td>218.630567</td>\n",
              "      <td>2.000000</td>\n",
              "      <td>9.000000</td>\n",
              "      <td>11440.149576</td>\n",
              "      <td>10.000000</td>\n",
              "      <td>9995.177429</td>\n",
              "      <td>62.000000</td>\n",
              "      <td>1874.238925</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>max</th>\n",
              "      <td>659.538233</td>\n",
              "      <td>4.000000</td>\n",
              "      <td>9.000000</td>\n",
              "      <td>57595.269254</td>\n",
              "      <td>27.000000</td>\n",
              "      <td>23027.007682</td>\n",
              "      <td>225.000000</td>\n",
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              "                                                     [key], {});\n",
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              "\n",
              "          const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "            '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
              "            + ' to learn more about interactive tables.';\n",
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              "        }\n",
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              "    </div>\n",
              "  </div>\n",
              "  "
            ],
            "text/plain": [
              "           PREMIUM  NUMBER_OF_DRIVERS          NCD  VEHICLE_VALUE  \\\n",
              "count  1370.000000        1369.000000  1372.000000    1372.000000   \n",
              "mean    195.585764           1.616508     8.485423    8452.166791   \n",
              "std      61.385265           0.606776     1.642504    6510.616292   \n",
              "min     113.268042           1.000000     0.000000      19.492191   \n",
              "25%     152.961164           1.000000     9.000000    3623.653518   \n",
              "50%     182.937661           2.000000     9.000000    7042.626197   \n",
              "75%     218.630567           2.000000     9.000000   11440.149576   \n",
              "max     659.538233           4.000000     9.000000   57595.269254   \n",
              "\n",
              "       VEHICLE_AGE  VEHICLE_ANNUALMILEAGE  VEHICLE_OWNEDMONTHS      INCURRED  \n",
              "count  1372.000000            1372.000000          1372.000000    205.000000  \n",
              "mean      7.365160            8612.394735            44.946064   1623.869997  \n",
              "std       4.365341            2126.928429            32.757180   3414.106792  \n",
              "min       0.000000            1984.450397             0.000000      0.000000  \n",
              "25%       4.000000            7020.080808            21.000000     15.022563  \n",
              "50%       7.000000            8015.921308            37.000000     96.203066  \n",
              "75%      10.000000            9995.177429            62.000000   1874.238925  \n",
              "max      27.000000           23027.007682           225.000000  25987.008698  "
            ]
          },
          "execution_count": 39,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "# Status for numerical columns\n",
        "df_flags[df_flags['SEGMENT_4_FLAG']==1].iloc[:,:-7].describe().drop(columns=['POLICY_NUMBER'])"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "CeUvtZmFov8W"
      },
      "source": [
        "### Segment 5"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 272
        },
        "id": "ySeLb2mC2qPq",
        "outputId": "8e382f81-5ed0-4454-9d94-19b3462ac0eb"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "Avg. Claim Freq. per age group for Segment 5: 11.41 % \n",
            "\n"
          ]
        },
        {
          "data": {
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              "\n",
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              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>Age_group</th>\n",
              "      <th>CLAIM_ID</th>\n",
              "      <th>POLICY_NUMBER</th>\n",
              "      <th>Claim Frequency %</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
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              "      <th>0</th>\n",
              "      <td>17-25</td>\n",
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              "      <td>10.87</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
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              "      <td>1219</td>\n",
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              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>36-45</td>\n",
              "      <td>82</td>\n",
              "      <td>561</td>\n",
              "      <td>14.62</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>46-55</td>\n",
              "      <td>56</td>\n",
              "      <td>461</td>\n",
              "      <td>12.15</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>56-65</td>\n",
              "      <td>44</td>\n",
              "      <td>370</td>\n",
              "      <td>11.89</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>5</th>\n",
              "      <td>65+</td>\n",
              "      <td>19</td>\n",
              "      <td>345</td>\n",
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              "\n",
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              "      fill: #FFFFFF;\n",
              "    }\n",
              "  </style>\n",
              "\n",
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              "        const buttonEl =\n",
              "          document.querySelector('#df-bed94fa1-6cd9-420c-afb9-78959362d63a button.colab-df-convert');\n",
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              "          google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "\n",
              "        async function convertToInteractive(key) {\n",
              "          const element = document.querySelector('#df-bed94fa1-6cd9-420c-afb9-78959362d63a');\n",
              "          const dataTable =\n",
              "            await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                     [key], {});\n",
              "          if (!dataTable) return;\n",
              "\n",
              "          const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "            '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
              "            + ' to learn more about interactive tables.';\n",
              "          element.innerHTML = '';\n",
              "          dataTable['output_type'] = 'display_data';\n",
              "          await google.colab.output.renderOutput(dataTable, element);\n",
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              "        }\n",
              "      </script>\n",
              "    </div>\n",
              "  </div>\n",
              "  "
            ],
            "text/plain": [
              "  Age_group  CLAIM_ID  POLICY_NUMBER  Claim Frequency %\n",
              "0     17-25        99            911              10.87\n",
              "1     26-35       164           1219              13.45\n",
              "2     36-45        82            561              14.62\n",
              "3     46-55        56            461              12.15\n",
              "4     56-65        44            370              11.89\n",
              "5       65+        19            345               5.51"
            ]
          },
          "execution_count": 40,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "df_age5 = df[['Age_group', 'CLAIM_ID', 'POLICY_NUMBER', 'SEGMENT_5_FLAG']]\n",
        "df_age5 = df_age5[df_age5['SEGMENT_5_FLAG']==1].drop(columns='SEGMENT_5_FLAG')\n",
        "df_age5 = df_age5.groupby(['Age_group']).count()\n",
        "df_age5['Claim Frequency %'] = ((df_age5['CLAIM_ID']/df_age5['POLICY_NUMBER']) * 100).round(2)\n",
        "print(\"Avg. Claim Freq. per age group for Segment 5: {:0.2f} % \\n\".format(df_age5['Claim Frequency %'].mean()))\n",
        "df_age5.reset_index()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 272
        },
        "id": "kltex8iQ2qNL",
        "outputId": "55d56df4-c19b-4a9e-e885-d6506ef819dc"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "Avg. Claim Freq. per marital status for Segment 5: 9.06 % \n",
            "\n"
          ]
        },
        {
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              "\n",
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              "      <th></th>\n",
              "      <th>MARITALSTATUS</th>\n",
              "      <th>CLAIM_ID</th>\n",
              "      <th>POLICY_NUMBER</th>\n",
              "      <th>Claim Frequency %</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>CommonLaw</td>\n",
              "      <td>11</td>\n",
              "      <td>105</td>\n",
              "      <td>10.48</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>Divorced</td>\n",
              "      <td>4</td>\n",
              "      <td>23</td>\n",
              "      <td>17.39</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>Married</td>\n",
              "      <td>1</td>\n",
              "      <td>19</td>\n",
              "      <td>5.26</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>Parthership</td>\n",
              "      <td>0</td>\n",
              "      <td>19</td>\n",
              "      <td>0.00</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>Single</td>\n",
              "      <td>418</td>\n",
              "      <td>3368</td>\n",
              "      <td>12.41</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>5</th>\n",
              "      <td>Widowed</td>\n",
              "      <td>29</td>\n",
              "      <td>330</td>\n",
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              "                                                     [key], {});\n",
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              "\n",
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              "  "
            ],
            "text/plain": [
              "  MARITALSTATUS  CLAIM_ID  POLICY_NUMBER  Claim Frequency %\n",
              "0     CommonLaw        11            105              10.48\n",
              "1      Divorced         4             23              17.39\n",
              "2       Married         1             19               5.26\n",
              "3   Parthership         0             19               0.00\n",
              "4        Single       418           3368              12.41\n",
              "5       Widowed        29            330               8.79"
            ]
          },
          "execution_count": 41,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "df_ms5 = df[['MARITALSTATUS', 'CLAIM_ID', 'POLICY_NUMBER', 'SEGMENT_5_FLAG']]\n",
        "df_ms5 = df_ms5[df_ms5['SEGMENT_5_FLAG']==1].drop(columns='SEGMENT_5_FLAG')\n",
        "df_ms5 = df_ms5.groupby(['MARITALSTATUS']).count()\n",
        "df_ms5['Claim Frequency %'] = ((df_ms5['CLAIM_ID']/df_ms5['POLICY_NUMBER']) * 100).round(2)\n",
        "print(\"Avg. Claim Freq. per marital status for Segment 5: {:0.2f} % \\n\".format(df_ms5['Claim Frequency %'].mean()))\n",
        "df_ms5.reset_index()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "K_xmNbmqv1rz",
        "outputId": "99c27e22-e17c-44bc-a5e2-ea1c31c85e02"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "\n",
            "---- POLICY_STATUS percentage ---\n",
            "Cancelled       5.55\n",
            "New Business    5.16\n",
            "Renewal          NaN\n",
            "Name: POLICY_STATUS, dtype: float64\n",
            "\n",
            "---- MARITALSTATUS percentage ---\n",
            "CommonLaw       0.82\n",
            "Divorced        0.33\n",
            "Married         0.04\n",
            "None             NaN\n",
            "Parthership     2.47\n",
            "Single         13.49\n",
            "Widowed        14.93\n",
            "Name: MARITALSTATUS, dtype: float64\n",
            "\n",
            "---- EMPLOYMENTTYPE percentage ---\n",
            "Employed                           4.43\n",
            "Household Duties                   0.41\n",
            "In Full Or Part Time Education    18.05\n",
            "Independent Means                   NaN\n",
            "Not employed due to disability     7.14\n",
            "Retired                            3.50\n",
            "Self Employed                      3.17\n",
            "Unemployed                         4.01\n",
            "Unknown                             NaN\n",
            "Voluntary Work                     3.57\n",
            "Name: EMPLOYMENTTYPE, dtype: float64\n",
            "\n",
            "---- VEHICLE_CLASSOFUSE percentage ---\n",
            "Business Use (PH)                              3.81\n",
            "Business use (PH + Spouse / Civil Partner)      NaN\n",
            "Business use (spouse / Civil Parnter)          0.53\n",
            "Business use by all drivers                    2.42\n",
            "None                                            NaN\n",
            "Social inc. Comm                               4.52\n",
            "Social only                                    4.24\n",
            "Unknown                                         NaN\n",
            "unk                                           16.67\n",
            "Name: VEHICLE_CLASSOFUSE, dtype: float64\n",
            "\n",
            "---- Age_group percentage ---\n",
            "17-25    16.22\n",
            "26-35     7.60\n",
            "36-45     2.77\n",
            "46-55     2.23\n",
            "56-65     2.19\n",
            "65+       3.24\n",
            "Name: Age_group, dtype: float64\n"
          ]
        }
      ],
      "source": [
        "# Categorical columns\n",
        "for c in df_flags.drop(columns=['CLAIM_ID']).select_dtypes(include='object').columns:\n",
        "    print (\"\\n---- %s percentage ---\" % c)\n",
        "    print (((df_flags[c][df_flags['SEGMENT_5_FLAG']==1].value_counts()/df_flags[c].value_counts()) * 100).round(2))\n",
        "\n",
        "print (\"\\n---- %s percentage ---\" % \"Age_group\")\n",
        "print (((df_flags['Age_group'][df_flags['SEGMENT_5_FLAG']==1].value_counts()/df_flags['Age_group'].value_counts()) * 100).round(2))"
      ]
    },
    {
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        },
        "id": "Og6ukA30v1pQ",
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      },
      "outputs": [
        {
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              "      <th>PREMIUM</th>\n",
              "      <th>NUMBER_OF_DRIVERS</th>\n",
              "      <th>NCD</th>\n",
              "      <th>VEHICLE_VALUE</th>\n",
              "      <th>VEHICLE_AGE</th>\n",
              "      <th>VEHICLE_ANNUALMILEAGE</th>\n",
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              "      <th>INCURRED</th>\n",
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              "  </thead>\n",
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              "      <th>count</th>\n",
              "      <td>3864.000000</td>\n",
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              "      <td>41.560238</td>\n",
              "      <td>2178.764492</td>\n",
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              "      <th>std</th>\n",
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              "      <th>min</th>\n",
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              "      <td>0.000000</td>\n",
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              "      <td>32.000000</td>\n",
              "      <td>103.929896</td>\n",
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              "      <th>75%</th>\n",
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              "      <td>9.000000</td>\n",
              "      <td>6242.500545</td>\n",
              "      <td>12.000000</td>\n",
              "      <td>8378.595484</td>\n",
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              "      <td>51004.084585</td>\n",
              "      <td>308.000000</td>\n",
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              "                                                     [key], {});\n",
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            "text/plain": [
              "           PREMIUM  NUMBER_OF_DRIVERS          NCD  VEHICLE_VALUE  \\\n",
              "count  3864.000000        3867.000000  3868.000000    3868.000000   \n",
              "mean    329.783476           1.411947     5.908480    4703.714796   \n",
              "std     176.773368           0.669096     3.011125    4143.348277   \n",
              "min      99.713746           1.000000     0.000000     143.749573   \n",
              "25%     196.569490           1.000000     3.000000    1786.169846   \n",
              "50%     296.224347           1.000000     6.000000    3553.753197   \n",
              "75%     414.360748           2.000000     9.000000    6242.500545   \n",
              "max    2177.693062           4.000000     9.000000   42012.498793   \n",
              "\n",
              "       VEHICLE_AGE  VEHICLE_ANNUALMILEAGE  VEHICLE_OWNEDMONTHS      INCURRED  \n",
              "count  3868.000000            3868.000000          3868.000000    464.000000  \n",
              "mean      9.392968            6623.182993            41.560238   2178.764492  \n",
              "std       4.401633            3208.557125            34.547550   4220.172359  \n",
              "min       0.000000             206.957438             0.000000      0.000000  \n",
              "25%       6.000000            4966.376855            19.000000     10.507006  \n",
              "50%       9.000000            6008.224685            32.000000    103.929896  \n",
              "75%      12.000000            8378.595484            53.000000   2433.882323  \n",
              "max      34.000000           51004.084585           308.000000  27182.551008  "
            ]
          },
          "execution_count": 43,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "# Status for numerical columns\n",
        "df_flags[df_flags['SEGMENT_5_FLAG']==1].iloc[:,:-7].describe().drop(columns=['POLICY_NUMBER'])"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "nkGaImoQoxYU"
      },
      "source": [
        "### Segment 6"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 272
        },
        "id": "MjmqBf2v2rE7",
        "outputId": "ce361086-e3e2-4b6f-d6de-48da8b7bfdeb"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "Avg. Claim Freq. per age group for Segment 6: 11.72 % \n",
            "\n"
          ]
        },
        {
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              "      <th></th>\n",
              "      <th>Age_group</th>\n",
              "      <th>CLAIM_ID</th>\n",
              "      <th>POLICY_NUMBER</th>\n",
              "      <th>Claim Frequency %</th>\n",
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              "      <th>0</th>\n",
              "      <td>17-25</td>\n",
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              "      <td>10.84</td>\n",
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              "    <tr>\n",
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              "      <td>36-45</td>\n",
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              "      <td>46-55</td>\n",
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              "      <td>513</td>\n",
              "      <td>11.11</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>56-65</td>\n",
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              "      <td>218</td>\n",
              "      <td>11.93</td>\n",
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              "            await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                     [key], {});\n",
              "          if (!dataTable) return;\n",
              "\n",
              "          const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "            '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
              "            + ' to learn more about interactive tables.';\n",
              "          element.innerHTML = '';\n",
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              "    </div>\n",
              "  </div>\n",
              "  "
            ],
            "text/plain": [
              "  Age_group  CLAIM_ID  POLICY_NUMBER  Claim Frequency %\n",
              "0     17-25       212           1956              10.84\n",
              "1     26-35       368           2764              13.31\n",
              "2     36-45       129           1065              12.11\n",
              "3     46-55        57            513              11.11\n",
              "4     56-65        26            218              11.93\n",
              "5       65+        11            100              11.00"
            ]
          },
          "execution_count": 44,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "df_age6 = df[['Age_group', 'CLAIM_ID', 'POLICY_NUMBER', 'SEGMENT_6_FLAG']]\n",
        "df_age6 = df_age6[df_age6['SEGMENT_6_FLAG']==1].drop(columns='SEGMENT_6_FLAG')\n",
        "df_age6 = df_age6.groupby(['Age_group']).count()\n",
        "df_age6['Claim Frequency %'] = ((df_age6['CLAIM_ID']/df_age6['POLICY_NUMBER']) * 100).round(2)\n",
        "print(\"Avg. Claim Freq. per age group for Segment 6: {:0.2f} % \\n\".format(df_age6['Claim Frequency %'].mean()))\n",
        "df_age6.reset_index()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 272
        },
        "id": "r48agYsx2rCY",
        "outputId": "c8628a91-a299-433e-ce7e-229e89770a84"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "Avg. Claim Freq. per marital status for Segment 6: 10.51 % \n",
            "\n"
          ]
        },
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              "    <tr>\n",
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              "      <td>13.39</td>\n",
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              "      <td>Parthership</td>\n",
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              "      <td>106</td>\n",
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              "      <td>11.92</td>\n",
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              "                                                     [key], {});\n",
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              "\n",
              "          const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "            '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
              "            + ' to learn more about interactive tables.';\n",
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              "  "
            ],
            "text/plain": [
              "  MARITALSTATUS  CLAIM_ID  POLICY_NUMBER  Claim Frequency %\n",
              "0     CommonLaw       106            872              12.16\n",
              "1      Divorced        20            167              11.98\n",
              "2       Married       177           1322              13.39\n",
              "3   Parthership         8            106               7.55\n",
              "4        Single       490           4112              11.92\n",
              "5       Widowed         2             33               6.06"
            ]
          },
          "execution_count": 45,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "df_ms6 = df[['MARITALSTATUS', 'CLAIM_ID', 'POLICY_NUMBER', 'SEGMENT_6_FLAG']]\n",
        "df_ms6 = df_ms6[df_ms6['SEGMENT_6_FLAG']==1].drop(columns='SEGMENT_6_FLAG')\n",
        "df_ms6 = df_ms6.groupby(['MARITALSTATUS']).count()\n",
        "df_ms6['Claim Frequency %'] = ((df_ms6['CLAIM_ID']/df_ms6['POLICY_NUMBER']) * 100).round(2)\n",
        "print(\"Avg. Claim Freq. per marital status for Segment 6: {:0.2f} % \\n\".format(df_ms6['Claim Frequency %'].mean()))\n",
        "df_ms6.reset_index()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "OTyCD6emv2UH",
        "outputId": "6dd907a9-8430-4ceb-df2e-5410e5587520"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "\n",
            "---- POLICY_STATUS percentage ---\n",
            "Cancelled       12.27\n",
            "New Business     8.73\n",
            "Renewal           NaN\n",
            "Name: POLICY_STATUS, dtype: float64\n",
            "\n",
            "---- MARITALSTATUS percentage ---\n",
            "CommonLaw       6.84\n",
            "Divorced        2.42\n",
            "Married         3.11\n",
            "None             NaN\n",
            "Parthership    13.78\n",
            "Single         16.47\n",
            "Widowed         1.49\n",
            "Name: MARITALSTATUS, dtype: float64\n",
            "\n",
            "---- EMPLOYMENTTYPE percentage ---\n",
            "Employed                           8.04\n",
            "Household Duties                   6.93\n",
            "In Full Or Part Time Education    32.13\n",
            "Independent Means                  2.90\n",
            "Not employed due to disability     3.57\n",
            "Retired                            0.83\n",
            "Self Employed                      6.08\n",
            "Unemployed                        13.26\n",
            "Unknown                             NaN\n",
            "Voluntary Work                     3.57\n",
            "Name: EMPLOYMENTTYPE, dtype: float64\n",
            "\n",
            "---- VEHICLE_CLASSOFUSE percentage ---\n",
            "Business Use (PH)                              5.62\n",
            "Business use (PH + Spouse / Civil Partner)     1.61\n",
            "Business use (spouse / Civil Parnter)          1.60\n",
            "Business use by all drivers                   11.69\n",
            "None                                          25.00\n",
            "Social inc. Comm                               7.94\n",
            "Social only                                    7.04\n",
            "Unknown                                         NaN\n",
            "unk                                           33.33\n",
            "Name: VEHICLE_CLASSOFUSE, dtype: float64\n",
            "\n",
            "---- Age_group percentage ---\n",
            "17-25    34.82\n",
            "26-35    17.23\n",
            "36-45     5.26\n",
            "46-55     2.48\n",
            "56-65     1.29\n",
            "65+       0.94\n",
            "Name: Age_group, dtype: float64\n"
          ]
        }
      ],
      "source": [
        "# Categorical columns\n",
        "for c in df_flags.drop(columns=['CLAIM_ID']).select_dtypes(include='object').columns:\n",
        "    print (\"\\n---- %s percentage ---\" % c)\n",
        "    print (((df_flags[c][df_flags['SEGMENT_6_FLAG']==1].value_counts()/df_flags[c].value_counts()) * 100).round(2))\n",
        "\n",
        "print (\"\\n---- %s percentage ---\" % \"Age_group\")\n",
        "print (((df_flags['Age_group'][df_flags['SEGMENT_6_FLAG']==1].value_counts()/df_flags['Age_group'].value_counts()) * 100).round(2))"
      ]
    },
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        "id": "Dasb9-_vv2RZ",
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      },
      "outputs": [
        {
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              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>PREMIUM</th>\n",
              "      <th>NUMBER_OF_DRIVERS</th>\n",
              "      <th>NCD</th>\n",
              "      <th>VEHICLE_VALUE</th>\n",
              "      <th>VEHICLE_AGE</th>\n",
              "      <th>VEHICLE_ANNUALMILEAGE</th>\n",
              "      <th>VEHICLE_OWNEDMONTHS</th>\n",
              "      <th>INCURRED</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>count</th>\n",
              "      <td>6607.000000</td>\n",
              "      <td>6612.000000</td>\n",
              "      <td>6617.000000</td>\n",
              "      <td>6617.000000</td>\n",
              "      <td>6617.000000</td>\n",
              "      <td>6617.000000</td>\n",
              "      <td>6617.000000</td>\n",
              "      <td>803.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>mean</th>\n",
              "      <td>453.241373</td>\n",
              "      <td>1.675287</td>\n",
              "      <td>4.276863</td>\n",
              "      <td>5089.316383</td>\n",
              "      <td>8.991839</td>\n",
              "      <td>7032.874759</td>\n",
              "      <td>28.239535</td>\n",
              "      <td>2470.169412</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>std</th>\n",
              "      <td>151.610977</td>\n",
              "      <td>0.736276</td>\n",
              "      <td>2.659961</td>\n",
              "      <td>3933.905194</td>\n",
              "      <td>4.188564</td>\n",
              "      <td>3506.841318</td>\n",
              "      <td>20.895879</td>\n",
              "      <td>4875.628942</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>min</th>\n",
              "      <td>124.544915</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>102.979850</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>195.205683</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>-103.257999</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>25%</th>\n",
              "      <td>354.943144</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>2.000000</td>\n",
              "      <td>2000.172576</td>\n",
              "      <td>6.000000</td>\n",
              "      <td>4992.965402</td>\n",
              "      <td>15.000000</td>\n",
              "      <td>4.494106</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>50%</th>\n",
              "      <td>425.186170</td>\n",
              "      <td>2.000000</td>\n",
              "      <td>4.000000</td>\n",
              "      <td>4014.271770</td>\n",
              "      <td>9.000000</td>\n",
              "      <td>6977.957322</td>\n",
              "      <td>25.000000</td>\n",
              "      <td>98.226884</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>75%</th>\n",
              "      <td>516.440168</td>\n",
              "      <td>2.000000</td>\n",
              "      <td>6.000000</td>\n",
              "      <td>7017.398676</td>\n",
              "      <td>12.000000</td>\n",
              "      <td>9030.984042</td>\n",
              "      <td>37.000000</td>\n",
              "      <td>2891.603559</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>max</th>\n",
              "      <td>1846.677362</td>\n",
              "      <td>7.000000</td>\n",
              "      <td>9.000000</td>\n",
              "      <td>27491.305098</td>\n",
              "      <td>30.000000</td>\n",
              "      <td>128014.035124</td>\n",
              "      <td>226.000000</td>\n",
              "      <td>35230.709357</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
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              "  "
            ],
            "text/plain": [
              "           PREMIUM  NUMBER_OF_DRIVERS          NCD  VEHICLE_VALUE  \\\n",
              "count  6607.000000        6612.000000  6617.000000    6617.000000   \n",
              "mean    453.241373           1.675287     4.276863    5089.316383   \n",
              "std     151.610977           0.736276     2.659961    3933.905194   \n",
              "min     124.544915           1.000000     0.000000     102.979850   \n",
              "25%     354.943144           1.000000     2.000000    2000.172576   \n",
              "50%     425.186170           2.000000     4.000000    4014.271770   \n",
              "75%     516.440168           2.000000     6.000000    7017.398676   \n",
              "max    1846.677362           7.000000     9.000000   27491.305098   \n",
              "\n",
              "       VEHICLE_AGE  VEHICLE_ANNUALMILEAGE  VEHICLE_OWNEDMONTHS      INCURRED  \n",
              "count  6617.000000            6617.000000          6617.000000    803.000000  \n",
              "mean      8.991839            7032.874759            28.239535   2470.169412  \n",
              "std       4.188564            3506.841318            20.895879   4875.628942  \n",
              "min       0.000000             195.205683             0.000000   -103.257999  \n",
              "25%       6.000000            4992.965402            15.000000      4.494106  \n",
              "50%       9.000000            6977.957322            25.000000     98.226884  \n",
              "75%      12.000000            9030.984042            37.000000   2891.603559  \n",
              "max      30.000000          128014.035124           226.000000  35230.709357  "
            ]
          },
          "execution_count": 47,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "# Status for numerical columns\n",
        "df_flags[df_flags['SEGMENT_6_FLAG']==1].iloc[:,:-7].describe().drop(columns=['POLICY_NUMBER'])"
      ]
    }
  ],
  "metadata": {
    "colab": {
      "collapsed_sections": [],
      "name": "task_1.ipynb",
      "provenance": []
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    "kernelspec": {
      "display_name": "Python 3",
      "name": "python3"
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    "language_info": {
      "name": "python"
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  "nbformat": 4,
  "nbformat_minor": 0
}