EDA_The_Insomniacs.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "id": "3889f470",
   "metadata": {
    "id": "3889f470"
   },
   "source": [
    "### Kelompok: The Insomniacs"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "2ff986b3-d25b-4c37-b645-f5bd74f38cb0",
   "metadata": {
    "id": "2ff986b3-d25b-4c37-b645-f5bd74f38cb0"
   },
   "source": [
    "### Kelas: Data Science Batch 19A"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "5969b478-fddd-48bb-85c8-2af06db8bcc1",
   "metadata": {
    "id": "5969b478-fddd-48bb-85c8-2af06db8bcc1"
   },
   "source": [
    "### Nama Anggota:\n",
    "    1. Vannessa Yoanita Oktorio Naibaho\n",
    "    2. Shindy Atila Putri\n",
    "    3. Rizki Amanda Putri\n",
    "    4. Ebit Nur Alam\n",
    "    5. Qonita Sungsang\n",
    "    6. Ulfathea Mulyadita\n",
    "    7. Muhammad Hafiz Abdul Aziz"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "f57d91c0-02cd-4a32-9691-8c1a1291095f",
   "metadata": {
    "id": "f57d91c0-02cd-4a32-9691-8c1a1291095f"
   },
   "source": [
    "# Health Insurance Cross Sell Prediction"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "0106556e",
   "metadata": {
    "id": "0106556e"
   },
   "source": [
    "Industri kendaraan bermotor di Indonesia sempat mengalami penurunan saat terjadinya pandemi pada tahun 2020. Untuk penjualan mobil, pada bulan Mei dalam sebulan hanya terjual 3,551 unit. Jumlah tersebut jauh dari rata-rata penjualan pra pandemi di kisaran 80.000-90.000 unit per bulan. Berdasarkan data Gabungan Industri Kendaraan Bermotor Indonesia (Gaikindo), penjualan mobil ritel atau penjualan dari dealer ke konsumen pada tahun 2021 melesat 49,3% menjadi 864.348 unit, atau rata-rata per bulannya terjual sekitar 70.000 unit. Sedangkan untuk penjualan motor terjual 5.057.516 unit pada tahun 2021. Jumlah tersebut meningkat kurang lebih 14% dari tahun 2020 yang hanya terjual 3.660.616 unit.\n",
    "\n",
    "Kenaikan penjualan kendaraan bermotor tersebut dapat dipengaruhi oleh beberapa faktor, salah satunya yaitu insentif PPnBM yang disediakan oleh Pemerintah. Selain itu, berdasarkan data yang dihimpun olehh Detikoto, Rata-rata atau sebesar 80% masyarakat Indonesia membeli mobil secara kredit dan dalam proses kredit itu terdapat fasilitas asuransi. Berdasarkan data tersebut, Trusthealth (suatu perusahaan yang bergerak di bidang jasa asuransi kesehatan) berinisiatif untuk mengembangkan lini bisnis nya ke sektor asuransi kendaraan bermotor. Merujuk kepada hasil survey yang dilakukan oleh Bank Mandiri, terdapat kemungkinan kenaikan penjualan bermotor senilai 8,2% di tahun 2022. Kabar ini menjadi suatu angin segar bagi perusahaan, dengan masuk ke sektor ini perusahaan berpotensi mendapatkan tambahan revenue baru senilai $31.250 per tahunnya. \n",
    " \n",
    "Trust Health adalah sebuah perusahaan yang bergerak di layanan asuransi kesehatan. Seiring dengan naiknya penjualan kendaraan bermotor, Trust Health berencana untuk mengembangkan lini bisnis nya untuk masuk juga ke sektor asuransi kendaraan bermotor. Cara termudah mendapatkan customer untuk lini bisnis baru tersebut adalah dengan mengajak existing customer saat ini untuk turut menggunakan jasa asuransi kendaraan bermotor. Hal ini bertujuan untuk mempermudah tim sales dalam mengakuisisi customer baru, sehingga perlu diprediksi customer existing mana saja yang akan tertarik untuk mendaftar asuransi kendaraan."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "0abe8ab8",
   "metadata": {
    "id": "0abe8ab8"
   },
   "source": [
    "### Description about the data:"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "6427a3b9",
   "metadata": {
    "id": "6427a3b9"
   },
   "source": [
    "1.\tDriving_License\t: 1: Customer already has DL, 0: Customer does not have Driving License.\n",
    "2.\tPreviously_Insured : 1: Customer already has Vehicle Insurance, 0: Customer doesn’t have Vehicle Insurance.\n",
    "3.\tVehicle_Age\t: Age of the Vehicle.\n",
    "4.\tVehicle_Damage : 1: Customer got his/her vehicle damaged in the past. 0: Customer didn’t get his/her vehicle damaged in the past.\n",
    "5.\tResponse : 1: Customer is interested, 0: Customer isn’t interested."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "2894a311",
   "metadata": {
    "id": "2894a311"
   },
   "source": [
    "### Additional description:"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "db6399c4",
   "metadata": {
    "id": "db6399c4"
   },
   "source": [
    "1.\tId : Unique ID for the customer.\n",
    "2.\tGender : Gender of the customer; male and female.\n",
    "3.\tAge\t: Age of the customer.\n",
    "4.\tRegion_Code\t: Unique code for the region of the customer.\n",
    "5.\tAnnual_Premium : The amount customer needs to pay as premium in the year.\n",
    "6.\tPolicySalesChannel : Anonymized Code for the channel of outreaching to the customer ie. Different Agents, Over Mail, Over Phone, In Person, etc.\n",
    "7.  Vintage : Total berapa lama user bergabung menjadi customer perusahaan dalam satuan hari."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "164786b6",
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "164786b6",
    "outputId": "559330a7-5362-45e2-ddee-85b769ae2ec2"
   },
   "outputs": [],
   "source": [
    "## Pandas dan Numpy\n",
    "import pandas as pd\n",
    "import numpy as np\n",
    "\n",
    "# Visualization Library\n",
    "import matplotlib.pyplot as plt\n",
    "import seaborn as sns\n",
    "import scipy.stats as st\n",
    "from scipy.stats import chi2_contingency\n",
    "import statsmodels.stats.proportion as sp\n",
    "import numpy as np"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "3a3a27f3",
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 206
    },
    "id": "3a3a27f3",
    "outputId": "4a83db81-2137-46b5-dd43-c0fbbe9d55b6"
   },
   "outputs": [
    {
     "data": {
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       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
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       "    }\n",
       "\n",
       "    .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>id</th>\n",
       "      <th>Gender</th>\n",
       "      <th>Age</th>\n",
       "      <th>Driving_License</th>\n",
       "      <th>Region_Code</th>\n",
       "      <th>Previously_Insured</th>\n",
       "      <th>Vehicle_Age</th>\n",
       "      <th>Vehicle_Damage</th>\n",
       "      <th>Annual_Premium</th>\n",
       "      <th>Policy_Sales_Channel</th>\n",
       "      <th>Vintage</th>\n",
       "      <th>Response</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1</td>\n",
       "      <td>Male</td>\n",
       "      <td>44</td>\n",
       "      <td>1</td>\n",
       "      <td>28.0</td>\n",
       "      <td>0</td>\n",
       "      <td>&gt; 2 Years</td>\n",
       "      <td>Yes</td>\n",
       "      <td>40454.0</td>\n",
       "      <td>26.0</td>\n",
       "      <td>217</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2</td>\n",
       "      <td>Male</td>\n",
       "      <td>76</td>\n",
       "      <td>1</td>\n",
       "      <td>3.0</td>\n",
       "      <td>0</td>\n",
       "      <td>1-2 Year</td>\n",
       "      <td>No</td>\n",
       "      <td>33536.0</td>\n",
       "      <td>26.0</td>\n",
       "      <td>183</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>3</td>\n",
       "      <td>Male</td>\n",
       "      <td>47</td>\n",
       "      <td>1</td>\n",
       "      <td>28.0</td>\n",
       "      <td>0</td>\n",
       "      <td>&gt; 2 Years</td>\n",
       "      <td>Yes</td>\n",
       "      <td>38294.0</td>\n",
       "      <td>26.0</td>\n",
       "      <td>27</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>4</td>\n",
       "      <td>Male</td>\n",
       "      <td>21</td>\n",
       "      <td>1</td>\n",
       "      <td>11.0</td>\n",
       "      <td>1</td>\n",
       "      <td>&lt; 1 Year</td>\n",
       "      <td>No</td>\n",
       "      <td>28619.0</td>\n",
       "      <td>152.0</td>\n",
       "      <td>203</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>5</td>\n",
       "      <td>Female</td>\n",
       "      <td>29</td>\n",
       "      <td>1</td>\n",
       "      <td>41.0</td>\n",
       "      <td>1</td>\n",
       "      <td>&lt; 1 Year</td>\n",
       "      <td>No</td>\n",
       "      <td>27496.0</td>\n",
       "      <td>152.0</td>\n",
       "      <td>39</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   id  Gender  Age  Driving_License  Region_Code  Previously_Insured  \\\n",
       "0   1    Male   44                1         28.0                   0   \n",
       "1   2    Male   76                1          3.0                   0   \n",
       "2   3    Male   47                1         28.0                   0   \n",
       "3   4    Male   21                1         11.0                   1   \n",
       "4   5  Female   29                1         41.0                   1   \n",
       "\n",
       "  Vehicle_Age Vehicle_Damage  Annual_Premium  Policy_Sales_Channel  Vintage  \\\n",
       "0   > 2 Years            Yes         40454.0                  26.0      217   \n",
       "1    1-2 Year             No         33536.0                  26.0      183   \n",
       "2   > 2 Years            Yes         38294.0                  26.0       27   \n",
       "3    < 1 Year             No         28619.0                 152.0      203   \n",
       "4    < 1 Year             No         27496.0                 152.0       39   \n",
       "\n",
       "   Response  \n",
       "0         1  \n",
       "1         0  \n",
       "2         1  \n",
       "3         0  \n",
       "4         0  "
      ]
     },
     "execution_count": 2,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data = pd.read_csv('train_hi.csv')\n",
    "data.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "a53e83e8",
   "metadata": {
    "id": "a53e83e8"
   },
   "source": [
    "# 1. Descriptive Statistics"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "4ae8027c",
   "metadata": {
    "id": "4ae8027c"
   },
   "source": [
    "## Kesesuaian Tipe Data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "eb636e24",
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "eb636e24",
    "outputId": "490f7169-dd9d-4607-9048-b81a6ffab2a6"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 381109 entries, 0 to 381108\n",
      "Data columns (total 12 columns):\n",
      " #   Column                Non-Null Count   Dtype  \n",
      "---  ------                --------------   -----  \n",
      " 0   id                    381109 non-null  int64  \n",
      " 1   Gender                381109 non-null  object \n",
      " 2   Age                   381109 non-null  int64  \n",
      " 3   Driving_License       381109 non-null  int64  \n",
      " 4   Region_Code           381109 non-null  float64\n",
      " 5   Previously_Insured    381109 non-null  int64  \n",
      " 6   Vehicle_Age           381109 non-null  object \n",
      " 7   Vehicle_Damage        381109 non-null  object \n",
      " 8   Annual_Premium        381109 non-null  float64\n",
      " 9   Policy_Sales_Channel  381109 non-null  float64\n",
      " 10  Vintage               381109 non-null  int64  \n",
      " 11  Response              381109 non-null  int64  \n",
      "dtypes: float64(3), int64(6), object(3)\n",
      "memory usage: 34.9+ MB\n"
     ]
    }
   ],
   "source": [
    "data.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "a47bf231",
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "a47bf231",
    "outputId": "29e3e7ee-76f2-44c1-f19b-d33a5ca97d5e"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(381109, 12)"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data.shape"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "24f6a6ac",
   "metadata": {
    "id": "24f6a6ac"
   },
   "source": [
    "#### Semua tipe data setiap kolom sudah sesuai"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "c0920df0",
   "metadata": {
    "id": "c0920df0"
   },
   "source": [
    "## Cek Missing Values"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "3efa85f9",
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "3efa85f9",
    "outputId": "7d6a4644-5ba3-471a-b8ca-452d3fef8711"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Kolom yang memiliki missing value adalah :\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "[]"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "print(\"Kolom yang memiliki missing value adalah :\")\n",
    "data.columns[data.isnull().any()].tolist()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "6d9b0e31",
   "metadata": {
    "id": "6d9b0e31"
   },
   "source": [
    "#### Tidak ada kolom yang memiliki missing values"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "1d502510",
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 300
    },
    "id": "1d502510",
    "outputId": "708bd7af-c06d-4074-e02a-98e35c72cdf7"
   },
   "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>id</th>\n",
       "      <th>Age</th>\n",
       "      <th>Driving_License</th>\n",
       "      <th>Region_Code</th>\n",
       "      <th>Previously_Insured</th>\n",
       "      <th>Annual_Premium</th>\n",
       "      <th>Policy_Sales_Channel</th>\n",
       "      <th>Vintage</th>\n",
       "      <th>Response</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>381109.000000</td>\n",
       "      <td>381109.000000</td>\n",
       "      <td>381109.000000</td>\n",
       "      <td>381109.000000</td>\n",
       "      <td>381109.000000</td>\n",
       "      <td>381109.000000</td>\n",
       "      <td>381109.000000</td>\n",
       "      <td>381109.000000</td>\n",
       "      <td>381109.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>190555.000000</td>\n",
       "      <td>38.822584</td>\n",
       "      <td>0.997869</td>\n",
       "      <td>26.388807</td>\n",
       "      <td>0.458210</td>\n",
       "      <td>30564.389581</td>\n",
       "      <td>112.034295</td>\n",
       "      <td>154.347397</td>\n",
       "      <td>0.122563</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>110016.836208</td>\n",
       "      <td>15.511611</td>\n",
       "      <td>0.046110</td>\n",
       "      <td>13.229888</td>\n",
       "      <td>0.498251</td>\n",
       "      <td>17213.155057</td>\n",
       "      <td>54.203995</td>\n",
       "      <td>83.671304</td>\n",
       "      <td>0.327936</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>1.000000</td>\n",
       "      <td>20.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>2630.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>10.000000</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>95278.000000</td>\n",
       "      <td>25.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>15.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>24405.000000</td>\n",
       "      <td>29.000000</td>\n",
       "      <td>82.000000</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>190555.000000</td>\n",
       "      <td>36.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>28.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>31669.000000</td>\n",
       "      <td>133.000000</td>\n",
       "      <td>154.000000</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>285832.000000</td>\n",
       "      <td>49.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>35.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>39400.000000</td>\n",
       "      <td>152.000000</td>\n",
       "      <td>227.000000</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>381109.000000</td>\n",
       "      <td>85.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>52.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>540165.000000</td>\n",
       "      <td>163.000000</td>\n",
       "      <td>299.000000</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                  id            Age  Driving_License    Region_Code  \\\n",
       "count  381109.000000  381109.000000    381109.000000  381109.000000   \n",
       "mean   190555.000000      38.822584         0.997869      26.388807   \n",
       "std    110016.836208      15.511611         0.046110      13.229888   \n",
       "min         1.000000      20.000000         0.000000       0.000000   \n",
       "25%     95278.000000      25.000000         1.000000      15.000000   \n",
       "50%    190555.000000      36.000000         1.000000      28.000000   \n",
       "75%    285832.000000      49.000000         1.000000      35.000000   \n",
       "max    381109.000000      85.000000         1.000000      52.000000   \n",
       "\n",
       "       Previously_Insured  Annual_Premium  Policy_Sales_Channel  \\\n",
       "count       381109.000000   381109.000000         381109.000000   \n",
       "mean             0.458210    30564.389581            112.034295   \n",
       "std              0.498251    17213.155057             54.203995   \n",
       "min              0.000000     2630.000000              1.000000   \n",
       "25%              0.000000    24405.000000             29.000000   \n",
       "50%              0.000000    31669.000000            133.000000   \n",
       "75%              1.000000    39400.000000            152.000000   \n",
       "max              1.000000   540165.000000            163.000000   \n",
       "\n",
       "             Vintage       Response  \n",
       "count  381109.000000  381109.000000  \n",
       "mean      154.347397       0.122563  \n",
       "std        83.671304       0.327936  \n",
       "min        10.000000       0.000000  \n",
       "25%        82.000000       0.000000  \n",
       "50%       154.000000       0.000000  \n",
       "75%       227.000000       0.000000  \n",
       "max       299.000000       1.000000  "
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data.describe()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "471cd9d2",
   "metadata": {
    "id": "471cd9d2"
   },
   "source": [
    "## Data Numerik dan Kategori"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "c2d78792",
   "metadata": {
    "id": "c2d78792"
   },
   "outputs": [],
   "source": [
    "numerical = ['Age', 'Annual_Premium', 'Vintage']\n",
    "categorical = ['Gender', 'Vehicle_Age', 'Vehicle_Damage']\n",
    "categorical_int = ['Driving_License', 'Region_Code', 'Previously_Insured', 'Policy_Sales_Channel', 'Response']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "878c1eb7",
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 300
    },
    "id": "878c1eb7",
    "outputId": "a13243a4-f3ad-4e97-d211-6ef97b52fad0"
   },
   "outputs": [
    {
     "data": {
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Age</th>\n",
       "      <th>Annual_Premium</th>\n",
       "      <th>Vintage</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>381109.000000</td>\n",
       "      <td>381109.000000</td>\n",
       "      <td>381109.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>38.822584</td>\n",
       "      <td>30564.389581</td>\n",
       "      <td>154.347397</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>15.511611</td>\n",
       "      <td>17213.155057</td>\n",
       "      <td>83.671304</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>20.000000</td>\n",
       "      <td>2630.000000</td>\n",
       "      <td>10.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>25.000000</td>\n",
       "      <td>24405.000000</td>\n",
       "      <td>82.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>36.000000</td>\n",
       "      <td>31669.000000</td>\n",
       "      <td>154.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>49.000000</td>\n",
       "      <td>39400.000000</td>\n",
       "      <td>227.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>85.000000</td>\n",
       "      <td>540165.000000</td>\n",
       "      <td>299.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                 Age  Annual_Premium        Vintage\n",
       "count  381109.000000   381109.000000  381109.000000\n",
       "mean       38.822584    30564.389581     154.347397\n",
       "std        15.511611    17213.155057      83.671304\n",
       "min        20.000000     2630.000000      10.000000\n",
       "25%        25.000000    24405.000000      82.000000\n",
       "50%        36.000000    31669.000000     154.000000\n",
       "75%        49.000000    39400.000000     227.000000\n",
       "max        85.000000   540165.000000     299.000000"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data[numerical].describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "bf3ae11a",
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 175
    },
    "id": "bf3ae11a",
    "outputId": "49440cf4-6d8f-4f2c-a630-5f5a8d9c3de3"
   },
   "outputs": [
    {
     "data": {
      "text/html": [
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       "\n",
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       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Gender</th>\n",
       "      <th>Vehicle_Age</th>\n",
       "      <th>Vehicle_Damage</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>381109</td>\n",
       "      <td>381109</td>\n",
       "      <td>381109</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>unique</th>\n",
       "      <td>2</td>\n",
       "      <td>3</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>top</th>\n",
       "      <td>Male</td>\n",
       "      <td>1-2 Year</td>\n",
       "      <td>Yes</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>freq</th>\n",
       "      <td>206089</td>\n",
       "      <td>200316</td>\n",
       "      <td>192413</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "        Gender Vehicle_Age Vehicle_Damage\n",
       "count   381109      381109         381109\n",
       "unique       2           3              2\n",
       "top       Male    1-2 Year            Yes\n",
       "freq    206089      200316         192413"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data[categorical].describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "f33ece81",
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "f33ece81",
    "outputId": "289d1adc-0528-4818-da9e-91517a9e04a9"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Male      206089\n",
       "Female    175020\n",
       "Name: Gender, dtype: int64"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data['Gender'].value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "9ad25e71",
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "9ad25e71",
    "outputId": "23e8c41a-d773-4cea-fbc8-75741bb8d23d"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "28.0    106415\n",
       "8.0      33877\n",
       "46.0     19749\n",
       "41.0     18263\n",
       "15.0     13308\n",
       "30.0     12191\n",
       "29.0     11065\n",
       "50.0     10243\n",
       "3.0       9251\n",
       "11.0      9232\n",
       "36.0      8797\n",
       "33.0      7654\n",
       "47.0      7436\n",
       "35.0      6942\n",
       "6.0       6280\n",
       "45.0      5605\n",
       "37.0      5501\n",
       "18.0      5153\n",
       "48.0      4681\n",
       "14.0      4678\n",
       "39.0      4644\n",
       "10.0      4374\n",
       "21.0      4266\n",
       "2.0       4038\n",
       "13.0      4036\n",
       "7.0       3279\n",
       "12.0      3198\n",
       "9.0       3101\n",
       "27.0      2823\n",
       "32.0      2787\n",
       "43.0      2639\n",
       "17.0      2617\n",
       "26.0      2587\n",
       "25.0      2503\n",
       "24.0      2415\n",
       "38.0      2026\n",
       "0.0       2021\n",
       "16.0      2007\n",
       "31.0      1960\n",
       "23.0      1960\n",
       "20.0      1935\n",
       "49.0      1832\n",
       "4.0       1801\n",
       "34.0      1664\n",
       "19.0      1535\n",
       "22.0      1309\n",
       "40.0      1295\n",
       "5.0       1279\n",
       "1.0       1008\n",
       "44.0       808\n",
       "42.0       591\n",
       "52.0       267\n",
       "51.0       183\n",
       "Name: Region_Code, dtype: int64"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data['Region_Code'].value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "229de53d",
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "229de53d",
    "outputId": "3c44a3a7-9906-42a2-f67f-4fe801b9174a"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "1    380297\n",
       "0       812\n",
       "Name: Driving_License, dtype: int64"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data['Driving_License'].value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "id": "5264eb5f",
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "5264eb5f",
    "outputId": "5bae29c2-471d-45af-d230-a9fd5f6e1498"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "1-2 Year     200316\n",
       "< 1 Year     164786\n",
       "> 2 Years     16007\n",
       "Name: Vehicle_Age, dtype: int64"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data['Vehicle_Age'].value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "id": "d7fee567",
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "d7fee567",
    "outputId": "bd23efbb-7cbc-4c7c-e513-ae308709c8f0"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0    206481\n",
       "1    174628\n",
       "Name: Previously_Insured, dtype: int64"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data['Previously_Insured'].value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "id": "bb843362",
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "bb843362",
    "outputId": "1907f1e7-af6d-4d84-e5c6-75c06f2b6f0b"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Yes    192413\n",
       "No     188696\n",
       "Name: Vehicle_Damage, dtype: int64"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data['Vehicle_Damage'].value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "id": "65e46b01",
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "65e46b01",
    "outputId": "1884c678-4196-4988-b3e0-ab2c7b786d85"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "152.0    134784\n",
       "26.0      79700\n",
       "124.0     73995\n",
       "160.0     21779\n",
       "156.0     10661\n",
       "          ...  \n",
       "149.0         1\n",
       "43.0          1\n",
       "144.0         1\n",
       "143.0         1\n",
       "41.0          1\n",
       "Name: Policy_Sales_Channel, Length: 155, dtype: int64"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data['Policy_Sales_Channel'].value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "id": "7e699808",
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "7e699808",
    "outputId": "19b5a893-5e4b-49e7-84ef-0be9dbf66c69"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0    334399\n",
       "1     46710\n",
       "Name: Response, dtype: int64"
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data['Response'].value_counts()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "f0b5aa41",
   "metadata": {
    "id": "f0b5aa41"
   },
   "source": [
    "### Interpretasi:"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "98e55e96",
   "metadata": {
    "id": "98e55e96"
   },
   "source": [
    "Berdasarkan data di atas,\n",
    "\n",
    "- Semua tipe data setiap kolom sudah sesuai\n",
    "- Tidak ada kolom yang memiliki missing values\n",
    "- Terdapat data numerical yang berbeda jauh antara nilai min/max dengan mean nya yaitu `Age`, `Annual_Premium` dan `Vintage`.\n",
    "\n",
    "    - Untuk data Age, nilai min = 20.00, max = 85.00 dan mean = 38.82.\n",
    "    - Untuk data Annual_Premium, nilai min = 2630.00, max = 540165.00, dan mean = 30564.38\n",
    "    - Untuk data Vintage, nilai min = 10.00, max = 299.00, dan mean = 154.34\n",
    "    \n",
    "   Sedangkan untuk data categorical, data dengan frekuensi yang timpang adalah `Vehicle_Age`, yaitu `1-2 Year` sebanyak `200316` dengan `2 Years` sebanyak      `16007`\n",
    "   Pada categorical_int, juga terdapat data dengan frekuensi yang timpang : `Region_Code` dengan kode `28` dan `Policy_Sales_Channel` dengan kode `152.0`\n",
    "- Target output: response"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "8e1a1517",
   "metadata": {
    "id": "8e1a1517"
   },
   "source": [
    "### Kesimpulan:"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "9bf829c4",
   "metadata": {
    "id": "9bf829c4"
   },
   "source": [
    "- Semua tipe data pada kolom sudah sesuai dan tidak terdapat missing values sehingga tidak berlu diubah\n",
    "- Terdapat beberapa kolom yang diduga ada keanehan timpang sehingga perlu diolah terlebih dahulu atau tidak digunakan\n",
    "- Target output dari project ini adalah memprediksi respons user apakah tertarik atau tidak untuk mendaftar asuransi kendaraan"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "ad281f40",
   "metadata": {
    "id": "ad281f40"
   },
   "source": [
    "# Exploratory Data Analysis"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "4a14fa94",
   "metadata": {
    "id": "4a14fa94"
   },
   "source": [
    "## 2. Univariate Analysis"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "id": "ff918023",
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 206
    },
    "id": "ff918023",
    "outputId": "b84372e0-d6c1-4bfe-8939-0594ceddf8c9"
   },
   "outputs": [
    {
     "data": {
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>id</th>\n",
       "      <th>Gender</th>\n",
       "      <th>Age</th>\n",
       "      <th>Driving_License</th>\n",
       "      <th>Region_Code</th>\n",
       "      <th>Previously_Insured</th>\n",
       "      <th>Vehicle_Age</th>\n",
       "      <th>Vehicle_Damage</th>\n",
       "      <th>Annual_Premium</th>\n",
       "      <th>Policy_Sales_Channel</th>\n",
       "      <th>Vintage</th>\n",
       "      <th>Response</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1</td>\n",
       "      <td>Male</td>\n",
       "      <td>44</td>\n",
       "      <td>1</td>\n",
       "      <td>28.0</td>\n",
       "      <td>0</td>\n",
       "      <td>&gt; 2 Years</td>\n",
       "      <td>Yes</td>\n",
       "      <td>40454.0</td>\n",
       "      <td>26.0</td>\n",
       "      <td>217</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2</td>\n",
       "      <td>Male</td>\n",
       "      <td>76</td>\n",
       "      <td>1</td>\n",
       "      <td>3.0</td>\n",
       "      <td>0</td>\n",
       "      <td>1-2 Year</td>\n",
       "      <td>No</td>\n",
       "      <td>33536.0</td>\n",
       "      <td>26.0</td>\n",
       "      <td>183</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>3</td>\n",
       "      <td>Male</td>\n",
       "      <td>47</td>\n",
       "      <td>1</td>\n",
       "      <td>28.0</td>\n",
       "      <td>0</td>\n",
       "      <td>&gt; 2 Years</td>\n",
       "      <td>Yes</td>\n",
       "      <td>38294.0</td>\n",
       "      <td>26.0</td>\n",
       "      <td>27</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>4</td>\n",
       "      <td>Male</td>\n",
       "      <td>21</td>\n",
       "      <td>1</td>\n",
       "      <td>11.0</td>\n",
       "      <td>1</td>\n",
       "      <td>&lt; 1 Year</td>\n",
       "      <td>No</td>\n",
       "      <td>28619.0</td>\n",
       "      <td>152.0</td>\n",
       "      <td>203</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>5</td>\n",
       "      <td>Female</td>\n",
       "      <td>29</td>\n",
       "      <td>1</td>\n",
       "      <td>41.0</td>\n",
       "      <td>1</td>\n",
       "      <td>&lt; 1 Year</td>\n",
       "      <td>No</td>\n",
       "      <td>27496.0</td>\n",
       "      <td>152.0</td>\n",
       "      <td>39</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   id  Gender  Age  Driving_License  Region_Code  Previously_Insured  \\\n",
       "0   1    Male   44                1         28.0                   0   \n",
       "1   2    Male   76                1          3.0                   0   \n",
       "2   3    Male   47                1         28.0                   0   \n",
       "3   4    Male   21                1         11.0                   1   \n",
       "4   5  Female   29                1         41.0                   1   \n",
       "\n",
       "  Vehicle_Age Vehicle_Damage  Annual_Premium  Policy_Sales_Channel  Vintage  \\\n",
       "0   > 2 Years            Yes         40454.0                  26.0      217   \n",
       "1    1-2 Year             No         33536.0                  26.0      183   \n",
       "2   > 2 Years            Yes         38294.0                  26.0       27   \n",
       "3    < 1 Year             No         28619.0                 152.0      203   \n",
       "4    < 1 Year             No         27496.0                 152.0       39   \n",
       "\n",
       "   Response  \n",
       "0         1  \n",
       "1         0  \n",
       "2         1  \n",
       "3         0  \n",
       "4         0  "
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "asurance = pd.read_csv('train_hi.csv')\n",
    "asurance.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "id": "5ee35a0a",
   "metadata": {
    "id": "5ee35a0a"
   },
   "outputs": [],
   "source": [
    "numericals = ['Age', 'Annual_Premium', 'Vintage']\n",
    "categoricals = ['Gender', 'Vehicle_Damage', 'Region_Code', 'Policy_Sales_Channel', 'Vehicle_Age', 'Driving_License', 'Previously_Insured', 'Response']"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "2b119bf3",
   "metadata": {
    "id": "2b119bf3"
   },
   "source": [
    "# Individuals Boxplots (Numericals)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "id": "4794c01d",
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 297
    },
    "id": "4794c01d",
    "outputId": "af1ca506-598d-414e-b0b1-ff0b9d830e2c"
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x288 with 3 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "features = numericals\n",
    "plt.figure(figsize=(10, 4))\n",
    "for i in range(0, len(features)):\n",
    "    plt.subplot(1, len(features), i+1)\n",
    "    sns.boxplot(y=asurance[features[i]], color='green', orient ='v')\n",
    "    plt.tight_layout()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "f65c17b7",
   "metadata": {
    "id": "f65c17b7"
   },
   "source": [
    "# Individual Distplot (Numericals)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "id": "6e3490c0",
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 380
    },
    "id": "6e3490c0",
    "outputId": "d0f69b58-8572-4837-909e-d4f743f6a678"
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1800x720 with 3 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "features = numericals\n",
    "plt.figure(figsize=(25, 10))\n",
    "for i in range(0, len(features)):\n",
    "    plt.subplot(2, 4, i+1)\n",
    "    sns.kdeplot(x=asurance[features[i]], color='green')\n",
    "    plt.xlabel(features[i])\n",
    "    plt.tight_layout()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "be0ac7b4",
   "metadata": {
    "id": "be0ac7b4"
   },
   "source": [
    "#  Countplot Categorical"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "id": "eedab281",
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 372
    },
    "id": "eedab281",
    "outputId": "f923d8cc-a183-47a2-c897-9fc2d9259d21"
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1800x504 with 8 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "features = categoricals\n",
    "plt.figure(figsize=(25, 7))\n",
    "for i in range(0, len(features)):\n",
    "    plt.subplot(1, len(features), i+1)\n",
    "    sns.countplot(x=asurance[features[i]], color='green')\n",
    "    plt.tight_layout()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "5146acb5",
   "metadata": {
    "id": "5146acb5"
   },
   "source": [
    "# Hasil Observasi"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "e6de9fe5",
   "metadata": {
    "id": "e6de9fe5"
   },
   "source": [
    "1) Terdapat outlier pada varibel Annual_Premium.\n",
    "\n",
    "2) Secara visualisasi tidak ada data yang berdistribusi normal.\n",
    "\n",
    "3) Variabel Age memiliki positively skewed\n",
    "\n",
    "4) Variabel vintage berdistribusi uniform \n",
    "\n",
    "5) Variabel annual_premium berdistribusi bimodal\n",
    "\n",
    "6) Berdasarkan countplot categorical, untuk distribusi response mayoritas customer merespon tidak tertarik (0). pada variabel umur kendaraan (Vehicle_Age) mayoritas < 1 tahun dan 1-2 tahun. kemudian mayoritas customer memiliki surat ijin mengemudi (Driving_License). untuk variabel Vehicle_Age dan Driving_License, perlu diperhatikan dalam langkah selanjutnya karena datanya imbalance"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "354e3ce8",
   "metadata": {
    "id": "354e3ce8"
   },
   "source": [
    "# Solusi"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "5cab2bba",
   "metadata": {
    "id": "5cab2bba"
   },
   "source": [
    "1) Proses selanjutnya perlu dilakukan transformasi agar data nya berdistribusi  normal.\n",
    "\n",
    "2) Penghapusan data outlier"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "86c661ae",
   "metadata": {
    "id": "86c661ae"
   },
   "source": [
    "## 3. Multivariate Analysis"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "id": "d55f1118",
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 206
    },
    "id": "d55f1118",
    "outputId": "0cb2ed42-5168-444d-ed48-edfeff2f84a4"
   },
   "outputs": [
    {
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      "text/plain": [
       "   id Gender  Age  Driving_License  Region_Code  Previously_Insured  \\\n",
       "0   1      1   44                1         28.0                   0   \n",
       "1   2      1   76                1          3.0                   0   \n",
       "2   3      1   47                1         28.0                   0   \n",
       "3   4      1   21                1         11.0                   1   \n",
       "4   5      0   29                1         41.0                   1   \n",
       "\n",
       "  Vehicle_Age Vehicle_Damage  Annual_Premium  Policy_Sales_Channel  Vintage  \\\n",
       "0           2              1         40454.0                  26.0      217   \n",
       "1           1              0         33536.0                  26.0      183   \n",
       "2           2              1         38294.0                  26.0       27   \n",
       "3           0              0         28619.0                 152.0      203   \n",
       "4           0              0         27496.0                 152.0       39   \n",
       "\n",
       "   Response  \n",
       "0         1  \n",
       "1         0  \n",
       "2         1  \n",
       "3         0  \n",
       "4         0  "
      ]
     },
     "execution_count": 23,
     "metadata": {},
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   ],
   "source": [
    "data.loc[data['Gender'] == 'Male', 'Gender'] = 1 \n",
    "data.loc[data['Gender'] == 'Female', 'Gender'] = 0\n",
    "\n",
    "data.loc[data['Vehicle_Age'] == '> 2 Years', 'Vehicle_Age'] = 2 \n",
    "data.loc[data['Vehicle_Age'] == '1-2 Year', 'Vehicle_Age'] = 1 \n",
    "data.loc[data['Vehicle_Age'] == '< 1 Year', 'Vehicle_Age'] = 0 \n",
    "\n",
    "data.loc[data['Vehicle_Damage'] == 'Yes', 'Vehicle_Damage'] = 1 \n",
    "data.loc[data['Vehicle_Damage'] == 'No', 'Vehicle_Damage'] = 0\n",
    "\n",
    "data.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "id": "46b183f5",
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 206
    },
    "id": "46b183f5",
    "outputId": "f2f9938e-bb7b-4746-8ef2-df53c1e07b87"
   },
   "outputs": [
    {
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      "text/plain": [
       "   id  Gender  Age  Driving_License  Region_Code  Previously_Insured  \\\n",
       "0   1       1   44                1           28                   0   \n",
       "1   2       1   76                1            3                   0   \n",
       "2   3       1   47                1           28                   0   \n",
       "3   4       1   21                1           11                   1   \n",
       "4   5       0   29                1           41                   1   \n",
       "\n",
       "   Vehicle_Age  Vehicle_Damage  Annual_Premium  Policy_Sales_Channel  Vintage  \\\n",
       "0            2               1           40454                    26      217   \n",
       "1            1               0           33536                    26      183   \n",
       "2            2               1           38294                    26       27   \n",
       "3            0               0           28619                   152      203   \n",
       "4            0               0           27496                   152       39   \n",
       "\n",
       "   Response  \n",
       "0         1  \n",
       "1         0  \n",
       "2         1  \n",
       "3         0  \n",
       "4         0  "
      ]
     },
     "execution_count": 24,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "for col in data.columns:\n",
    "    data[col] = data[col].astype(np.int32)\n",
    "\n",
    "data.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "id": "05417e51",
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 601
    },
    "id": "05417e51",
    "outputId": "aef35a5d-25d6-48b9-8ae0-d373e70dba0d"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:>"
      ]
     },
     "execution_count": 25,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x576 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(10,8))\n",
    "sns.heatmap(data.corr(), cmap='Blues', annot=True, fmt='.1f')"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "b002ab3f",
   "metadata": {
    "id": "b002ab3f"
   },
   "source": [
    "**Hasil Observasi untuk point A**\n",
    "\n",
    "Berdasarkan heatmap chart diatas, terdapat beberapa feature yang memiliki korelasi dengan label yaitu feature previously_insured, vehicle damage dan policy_sales_channel dimana keterkaitan antara feature dengan label dijabarkan dalam pemaparan beriikut:\n",
    "\n",
    "1) Ada kaitan yang cukup kuat antara feature previously_insured dengan label. Dimana semakin besar nilai previously_insured maka semakin kecil pula nilai label nya. Sehingga feature ini tetap perlu dipertahankan.\n",
    "\n",
    "2) Feature vehicle damage memiliki kaitan yang lumayan kuat dengan label. Dimana feature ini memiliki nilai korelasi sebesar 0.4 (mendekati 0.7). Feature ini dapat dipertahankan untuk pembuatan model.\n",
    "\n",
    "3) Feature policy_sales_channel memiliki korelasi dengan label. Walaupun nilai korelasi anntara feature dengan label ini tidak sebesar feature lainnya. Namun feature ini dapat tetap dipertahankan karena in real life, sales channel dapat mempengaruhi keputusan nasabah untuk tergabung dengan program baru yang ditawarkan."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "9aea66ed",
   "metadata": {
    "id": "9aea66ed"
   },
   "source": [
    "**Hasil Observasi Point B**\n",
    "\n",
    "1) Terdapat korelasi yang cukup kuat antara featured previoulsy insured dengan feature vehicle damage dengan nilai -0.8. Nilai korelasi dari 2 feature ini tidak searah (berbanding terbalik) yang mana semakin besar / banyak nilai previously insured nya maka semakin kecil nilai vehicle damage.\n",
    "\n",
    "2) Terdapat korelasi yang cukup kuat antara featured policy_sales_channel dengan feature age dengan nilai -0.6. Nilai korelasi dari 2 feature ini tidak searah (berbanding terbalik) yang mana semakin besar / banyak nilai policy_sales_channel nya maka semakin kecil nilai age nya.\n",
    "\n",
    "3) Terdapat korelasi yang cukup kuat antara featured vehicle_age dengan featured policy_sales_channel dengan nilai -0.6. Nilai korelasi dari 2 feature ini tidak searah (berbanding terbalik) yang mana semakin besar / banyak nilai vehicle_age nya maka semakin kecil nilai policy_sales_channel nya.\n",
    "\n",
    "4) Terdapat korelasi yang cukup kuat antara featured response dengan featured previously_insured dengan nilai -0.3. Nilai korelasi dari 2 feature ini tidak searah (berbanding terbalik) yang mana semakin besar / banyak nilai response nya maka semakin kecil nilai previously_insured nya.\n",
    "\n",
    "5) Terdapat korelasi yang lemah antara featured vehicle_age dengan featured previously_insured dengan nilai -0.4. Nilai korelasi dari 2 feaature ini tidak searah (berbanding terbalik) yang mana semakin besar / banyak nilai vehicle_age nya maka semakin kecil nilai previously_insured nya.\n",
    "\n",
    "6) Terdapat korelasi yang lemah antara featured previously_insured dengan featured age dengan nilai -0.3. Nilai korelasi dari 2 feature ini tidak searah (berbanding terbalik) yang mana semakin besar / banyak nilai previously_insured nya maka semakin kecil nilai age nya."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "id": "134af654",
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 731
    },
    "id": "134af654",
    "outputId": "a5cd5465-73e2-4202-9013-8c6e98dc6e53"
   },
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 1296x1296 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(18,18))\n",
    "numerical = ['Age', 'Annual_Premium', 'Vintage']\n",
    "categorical = ['Gender', 'Vehicle_Age', 'Vehicle_Damage','Driving_License', 'Region_Code', 'Previously_Insured', 'Policy_Sales_Channel', 'Response']\n",
    "\n",
    "categorical.remove('Region_Code')\n",
    "categorical.remove('Policy_Sales_Channel')\n",
    "features = categorical\n",
    "for i in range(0, len(features)):\n",
    "    plt.subplot(5, 10, i+1) \n",
    "    sns.barplot(data=data, x='Response', y=features[i])\n",
    "    plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "75f8a6fd",
   "metadata": {
    "id": "75f8a6fd"
   },
   "source": [
    "# 4. Analisa, Visualisasi, dan Insight"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "fc561753",
   "metadata": {
    "id": "fc561753"
   },
   "source": [
    "## Gender"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "id": "3c5e6b76",
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 206
    },
    "id": "3c5e6b76",
    "outputId": "4745b742-5a4b-4225-a503-5063eadbfe4c"
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
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       "    }\n",
       "\n",
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       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
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       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>id</th>\n",
       "      <th>Gender</th>\n",
       "      <th>Age</th>\n",
       "      <th>Driving_License</th>\n",
       "      <th>Region_Code</th>\n",
       "      <th>Previously_Insured</th>\n",
       "      <th>Vehicle_Age</th>\n",
       "      <th>Vehicle_Damage</th>\n",
       "      <th>Annual_Premium</th>\n",
       "      <th>Policy_Sales_Channel</th>\n",
       "      <th>Vintage</th>\n",
       "      <th>Response</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1</td>\n",
       "      <td>Male</td>\n",
       "      <td>44</td>\n",
       "      <td>1</td>\n",
       "      <td>28.0</td>\n",
       "      <td>0</td>\n",
       "      <td>&gt; 2 Years</td>\n",
       "      <td>Yes</td>\n",
       "      <td>40454.0</td>\n",
       "      <td>26.0</td>\n",
       "      <td>217</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2</td>\n",
       "      <td>Male</td>\n",
       "      <td>76</td>\n",
       "      <td>1</td>\n",
       "      <td>3.0</td>\n",
       "      <td>0</td>\n",
       "      <td>1-2 Year</td>\n",
       "      <td>No</td>\n",
       "      <td>33536.0</td>\n",
       "      <td>26.0</td>\n",
       "      <td>183</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>3</td>\n",
       "      <td>Male</td>\n",
       "      <td>47</td>\n",
       "      <td>1</td>\n",
       "      <td>28.0</td>\n",
       "      <td>0</td>\n",
       "      <td>&gt; 2 Years</td>\n",
       "      <td>Yes</td>\n",
       "      <td>38294.0</td>\n",
       "      <td>26.0</td>\n",
       "      <td>27</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>4</td>\n",
       "      <td>Male</td>\n",
       "      <td>21</td>\n",
       "      <td>1</td>\n",
       "      <td>11.0</td>\n",
       "      <td>1</td>\n",
       "      <td>&lt; 1 Year</td>\n",
       "      <td>No</td>\n",
       "      <td>28619.0</td>\n",
       "      <td>152.0</td>\n",
       "      <td>203</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>5</td>\n",
       "      <td>Female</td>\n",
       "      <td>29</td>\n",
       "      <td>1</td>\n",
       "      <td>41.0</td>\n",
       "      <td>1</td>\n",
       "      <td>&lt; 1 Year</td>\n",
       "      <td>No</td>\n",
       "      <td>27496.0</td>\n",
       "      <td>152.0</td>\n",
       "      <td>39</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   id  Gender  Age  Driving_License  Region_Code  Previously_Insured  \\\n",
       "0   1    Male   44                1         28.0                   0   \n",
       "1   2    Male   76                1          3.0                   0   \n",
       "2   3    Male   47                1         28.0                   0   \n",
       "3   4    Male   21                1         11.0                   1   \n",
       "4   5  Female   29                1         41.0                   1   \n",
       "\n",
       "  Vehicle_Age Vehicle_Damage  Annual_Premium  Policy_Sales_Channel  Vintage  \\\n",
       "0   > 2 Years            Yes         40454.0                  26.0      217   \n",
       "1    1-2 Year             No         33536.0                  26.0      183   \n",
       "2   > 2 Years            Yes         38294.0                  26.0       27   \n",
       "3    < 1 Year             No         28619.0                 152.0      203   \n",
       "4    < 1 Year             No         27496.0                 152.0       39   \n",
       "\n",
       "   Response  \n",
       "0         1  \n",
       "1         0  \n",
       "2         1  \n",
       "3         0  \n",
       "4         0  "
      ]
     },
     "execution_count": 27,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "fs = pd.read_csv('train_hi.csv')\n",
    "fs.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "id": "4aa97b72",
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 206
    },
    "id": "4aa97b72",
    "outputId": "1817265c-52af-4631-a8d9-de22de75e9a0"
   },
   "outputs": [
    {
     "data": {
      "text/html": [
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       "\n",
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       "\n",
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       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>id</th>\n",
       "      <th>Gender</th>\n",
       "      <th>Age</th>\n",
       "      <th>Driving_License</th>\n",
       "      <th>Region_Code</th>\n",
       "      <th>Previously_Insured</th>\n",
       "      <th>Vehicle_Age</th>\n",
       "      <th>Vehicle_Damage</th>\n",
       "      <th>Annual_Premium</th>\n",
       "      <th>Policy_Sales_Channel</th>\n",
       "      <th>Vintage</th>\n",
       "      <th>Response</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1</td>\n",
       "      <td>Male</td>\n",
       "      <td>44</td>\n",
       "      <td>1</td>\n",
       "      <td>28.0</td>\n",
       "      <td>0</td>\n",
       "      <td>&gt; 2 Years</td>\n",
       "      <td>Yes</td>\n",
       "      <td>40454.0</td>\n",
       "      <td>26.0</td>\n",
       "      <td>217</td>\n",
       "      <td>yes</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2</td>\n",
       "      <td>Male</td>\n",
       "      <td>76</td>\n",
       "      <td>1</td>\n",
       "      <td>3.0</td>\n",
       "      <td>0</td>\n",
       "      <td>1-2 Year</td>\n",
       "      <td>No</td>\n",
       "      <td>33536.0</td>\n",
       "      <td>26.0</td>\n",
       "      <td>183</td>\n",
       "      <td>no</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>3</td>\n",
       "      <td>Male</td>\n",
       "      <td>47</td>\n",
       "      <td>1</td>\n",
       "      <td>28.0</td>\n",
       "      <td>0</td>\n",
       "      <td>&gt; 2 Years</td>\n",
       "      <td>Yes</td>\n",
       "      <td>38294.0</td>\n",
       "      <td>26.0</td>\n",
       "      <td>27</td>\n",
       "      <td>yes</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>4</td>\n",
       "      <td>Male</td>\n",
       "      <td>21</td>\n",
       "      <td>1</td>\n",
       "      <td>11.0</td>\n",
       "      <td>1</td>\n",
       "      <td>&lt; 1 Year</td>\n",
       "      <td>No</td>\n",
       "      <td>28619.0</td>\n",
       "      <td>152.0</td>\n",
       "      <td>203</td>\n",
       "      <td>no</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>5</td>\n",
       "      <td>Female</td>\n",
       "      <td>29</td>\n",
       "      <td>1</td>\n",
       "      <td>41.0</td>\n",
       "      <td>1</td>\n",
       "      <td>&lt; 1 Year</td>\n",
       "      <td>No</td>\n",
       "      <td>27496.0</td>\n",
       "      <td>152.0</td>\n",
       "      <td>39</td>\n",
       "      <td>no</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   id  Gender  Age  Driving_License  Region_Code  Previously_Insured  \\\n",
       "0   1    Male   44                1         28.0                   0   \n",
       "1   2    Male   76                1          3.0                   0   \n",
       "2   3    Male   47                1         28.0                   0   \n",
       "3   4    Male   21                1         11.0                   1   \n",
       "4   5  Female   29                1         41.0                   1   \n",
       "\n",
       "  Vehicle_Age Vehicle_Damage  Annual_Premium  Policy_Sales_Channel  Vintage  \\\n",
       "0   > 2 Years            Yes         40454.0                  26.0      217   \n",
       "1    1-2 Year             No         33536.0                  26.0      183   \n",
       "2   > 2 Years            Yes         38294.0                  26.0       27   \n",
       "3    < 1 Year             No         28619.0                 152.0      203   \n",
       "4    < 1 Year             No         27496.0                 152.0       39   \n",
       "\n",
       "  Response  \n",
       "0      yes  \n",
       "1       no  \n",
       "2      yes  \n",
       "3       no  \n",
       "4       no  "
      ]
     },
     "execution_count": 28,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "fs = fs.replace({'Response': {1 : 'yes', 0 : 'no'}})\n",
    "fs.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "id": "a3ada27f",
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 175
    },
    "id": "a3ada27f",
    "outputId": "4db7a680-42a1-4e0d-e02d-0ead926f3b76"
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Gender</th>\n",
       "      <th>Response</th>\n",
       "      <th>id</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Female</td>\n",
       "      <td>no</td>\n",
       "      <td>156835</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Female</td>\n",
       "      <td>yes</td>\n",
       "      <td>18185</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Male</td>\n",
       "      <td>no</td>\n",
       "      <td>177564</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Male</td>\n",
       "      <td>yes</td>\n",
       "      <td>28525</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   Gender Response      id\n",
       "0  Female       no  156835\n",
       "1  Female      yes   18185\n",
       "2    Male       no  177564\n",
       "3    Male      yes   28525"
      ]
     },
     "execution_count": 29,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "gen = fs.groupby(['Gender', 'Response'])['id'].nunique().reset_index()\n",
    "gen"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "id": "c6aea940",
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 618
    },
    "id": "c6aea940",
    "outputId": "55a878bd-941e-4e2a-978f-1692ee85b55c"
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x432 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "x = gen['Gender']\n",
    "y = gen['id']\n",
    "\n",
    "plt.figure(figsize=(6,6))\n",
    "gender = sns.barplot(x=x, y=y, data=gen,\n",
    "           hue='Response')\n",
    "\n",
    "plt.ylabel('Total User', fontsize=13) \n",
    "plt.xlabel('Gender', fontsize=13)\n",
    "\n",
    "for i in gender.containers:\n",
    "    gender.bar_label(i,)\n",
    "\n",
    "plt.tight_layout()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "537e436b",
   "metadata": {
    "id": "537e436b"
   },
   "source": [
    "Grafik diatas menunjukkan bahwa lebih dari 300.000 pengguna yang terdiri dari laki-laki dan perempuan tidak tertarik untuk menggunakan produk asuransi kendaraan, hanya sekitar 10% dari total keseluruhan pengguna yang memiliki ketertarikan untuk menggunakan produk asuransi kendaraan. Sehingga, dapat ditarik kesimpulan bahwa tidak ada pengaruh yang signifikan jika produk asuransi ditawarkan berdasarkan gender dari pengguna."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "91024a4f",
   "metadata": {
    "id": "91024a4f"
   },
   "source": [
    "## Age"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "id": "81803911",
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 206
    },
    "id": "81803911",
    "outputId": "8ab3d175-c538-42bf-af4e-716554c6c22a"
   },
   "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>id</th>\n",
       "      <th>Gender</th>\n",
       "      <th>Age</th>\n",
       "      <th>Driving_License</th>\n",
       "      <th>Region_Code</th>\n",
       "      <th>Previously_Insured</th>\n",
       "      <th>Vehicle_Age</th>\n",
       "      <th>Vehicle_Damage</th>\n",
       "      <th>Annual_Premium</th>\n",
       "      <th>Policy_Sales_Channel</th>\n",
       "      <th>Vintage</th>\n",
       "      <th>Response</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1</td>\n",
       "      <td>Male</td>\n",
       "      <td>44</td>\n",
       "      <td>1</td>\n",
       "      <td>28.0</td>\n",
       "      <td>0</td>\n",
       "      <td>&gt; 2 Years</td>\n",
       "      <td>Yes</td>\n",
       "      <td>40454.0</td>\n",
       "      <td>26.0</td>\n",
       "      <td>217</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2</td>\n",
       "      <td>Male</td>\n",
       "      <td>76</td>\n",
       "      <td>1</td>\n",
       "      <td>3.0</td>\n",
       "      <td>0</td>\n",
       "      <td>1-2 Year</td>\n",
       "      <td>No</td>\n",
       "      <td>33536.0</td>\n",
       "      <td>26.0</td>\n",
       "      <td>183</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>3</td>\n",
       "      <td>Male</td>\n",
       "      <td>47</td>\n",
       "      <td>1</td>\n",
       "      <td>28.0</td>\n",
       "      <td>0</td>\n",
       "      <td>&gt; 2 Years</td>\n",
       "      <td>Yes</td>\n",
       "      <td>38294.0</td>\n",
       "      <td>26.0</td>\n",
       "      <td>27</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>4</td>\n",
       "      <td>Male</td>\n",
       "      <td>21</td>\n",
       "      <td>1</td>\n",
       "      <td>11.0</td>\n",
       "      <td>1</td>\n",
       "      <td>&lt; 1 Year</td>\n",
       "      <td>No</td>\n",
       "      <td>28619.0</td>\n",
       "      <td>152.0</td>\n",
       "      <td>203</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>5</td>\n",
       "      <td>Female</td>\n",
       "      <td>29</td>\n",
       "      <td>1</td>\n",
       "      <td>41.0</td>\n",
       "      <td>1</td>\n",
       "      <td>&lt; 1 Year</td>\n",
       "      <td>No</td>\n",
       "      <td>27496.0</td>\n",
       "      <td>152.0</td>\n",
       "      <td>39</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   id  Gender  Age  Driving_License  Region_Code  Previously_Insured  \\\n",
       "0   1    Male   44                1         28.0                   0   \n",
       "1   2    Male   76                1          3.0                   0   \n",
       "2   3    Male   47                1         28.0                   0   \n",
       "3   4    Male   21                1         11.0                   1   \n",
       "4   5  Female   29                1         41.0                   1   \n",
       "\n",
       "  Vehicle_Age Vehicle_Damage  Annual_Premium  Policy_Sales_Channel  Vintage  \\\n",
       "0   > 2 Years            Yes         40454.0                  26.0      217   \n",
       "1    1-2 Year             No         33536.0                  26.0      183   \n",
       "2   > 2 Years            Yes         38294.0                  26.0       27   \n",
       "3    < 1 Year             No         28619.0                 152.0      203   \n",
       "4    < 1 Year             No         27496.0                 152.0       39   \n",
       "\n",
       "   Response  \n",
       "0         1  \n",
       "1         0  \n",
       "2         1  \n",
       "3         0  \n",
       "4         0  "
      ]
     },
     "execution_count": 31,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "asurance = pd.read_csv('train_hi.csv')\n",
    "asurance.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "id": "ac8bfc44",
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 352
    },
    "id": "ac8bfc44",
    "outputId": "da99c008-59cc-4126-e337-fa1ae98e33a5"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:xlabel='Age', ylabel='Count'>"
      ]
     },
     "execution_count": 32,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1800x360 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(25,5))\n",
    "plt.subplot(1,2,2)\n",
    "sns.histplot(data=asurance, x='Age', hue='Response', binwidth=10)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "26981fa4",
   "metadata": {
    "id": "26981fa4"
   },
   "source": [
    "Grafik diatas menunjukkan bahwa rentang usia 40 tahun sampai 50 tahun memiliki ketertarikan yang lebih tinggi untuk menggunakan  jasa asuransi kendaraan sedangkan customer pada rentang usia 20 tahun sampai 30 tahun cenderung tidak tertarik menggunakan jasa asuransi kendaraan. Dari data tersebut dapat diambil kesimpulan untuk lebih memfokuskan promosi asuransi kendaraan kepada customer dengan rentang usia 40 tahun sampai 50 tahun.\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "6f6da233",
   "metadata": {
    "id": "6f6da233"
   },
   "source": [
    "## Driving Licence"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "id": "fa66fb1c",
   "metadata": {
    "id": "fa66fb1c"
   },
   "outputs": [],
   "source": [
    "DL = data.copy()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "id": "fb7a73b3",
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 175
    },
    "id": "fb7a73b3",
    "outputId": "8003d968-eee3-4fbf-f8f1-d871c2830381"
   },
   "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>Driving_License</th>\n",
       "      <th>Response</th>\n",
       "      <th>id</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>771</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>41</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>333628</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>46669</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   Driving_License  Response      id\n",
       "0                0         0     771\n",
       "1                0         1      41\n",
       "2                1         0  333628\n",
       "3                1         1   46669"
      ]
     },
     "execution_count": 34,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "DL_group = DL.groupby(['Driving_License', 'Response'])['id'].nunique().reset_index()\n",
    "DL_group"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "id": "2655c075",
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 887
    },
    "id": "2655c075",
    "outputId": "925d74f9-fc93-49c5-9db8-f5d17f56cdc8"
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x720 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "x = DL_group['Driving_License']\n",
    "y = DL_group['id']\n",
    "\n",
    "plt.figure(figsize=(10,10))\n",
    "g = sns.barplot(x=x, y=y, data=DL_group, \n",
    "                hue='Response', \n",
    "                color='deeppink',\n",
    "                edgecolor= 'maroon', \n",
    "                linewidth = 2)\n",
    "\n",
    "plt.title('Comparison of the Effect of\\nDriving License Ownership on User Response', \n",
    "          color='purple', \n",
    "          loc='center',\n",
    "          fontsize=23,\n",
    "          fontweight='bold')\n",
    "plt.xlabel('Driving License', fontsize=14)\n",
    "plt.ylabel('Total User', fontsize=14)\n",
    "\n",
    "for i in g.containers:\n",
    "    g.bar_label(i,)\n",
    "\n",
    "plt.ylim(0, 350000)\n",
    "plt.tight_layout()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "6d6206fa-f37a-4b44-8540-b91128da93ac",
   "metadata": {
    "id": "6d6206fa-f37a-4b44-8540-b91128da93ac"
   },
   "source": [
    "### Interpretasi:"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "90944f5e-15d1-49af-a07a-bf9ec4ffa944",
   "metadata": {
    "id": "90944f5e-15d1-49af-a07a-bf9ec4ffa944"
   },
   "source": [
    "Berdasarkan grafik dapat dilihat bahwa banyak konsumen yang memiliki Driving License namun belum tertarik untuk mengambil Asuransi Kendaraan."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "e79c4f14-ba67-4f49-a7a5-4055772b692b",
   "metadata": {
    "id": "e79c4f14-ba67-4f49-a7a5-4055772b692b"
   },
   "source": [
    "### Business Insight:"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "43a5a8db-5cf5-497b-b7b3-67599bc8ff81",
   "metadata": {
    "id": "43a5a8db-5cf5-497b-b7b3-67599bc8ff81"
   },
   "source": [
    "Konsumen yang sudah memiliki Driving License dapat didorong dengan memberikan informasi keuntungan memiliki Asuransi Kendaraan atau promosi asuransi."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "1f452da1",
   "metadata": {
    "id": "1f452da1"
   },
   "source": [
    "## Previously Insured"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "id": "6cd68f97",
   "metadata": {
    "id": "6cd68f97"
   },
   "outputs": [],
   "source": [
    "p = data.copy()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "id": "9ee5eeed",
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 334
    },
    "id": "9ee5eeed",
    "outputId": "c84354fd-7bb2-40e7-a091-50106962b42c"
   },
   "outputs": [
    {
     "data": {
      "text/html": [
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       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
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       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
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       "\n",
       "    .dataframe thead th {\n",
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       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Previously_Insured</th>\n",
       "      <th>Response</th>\n",
       "      <th>id</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>159929</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>46552</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>174470</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>158</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   Previously_Insured  Response      id\n",
       "0                   0         0  159929\n",
       "1                   0         1   46552\n",
       "2                   1         0  174470\n",
       "3                   1         1     158"
      ]
     },
     "execution_count": 37,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "p_group = p.groupby(['Previously_Insured','Response'])['id'].nunique().reset_index()\n",
    "p_group"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "id": "8ebce460-d0e3-4718-b1b4-ab63989eae8d",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x720 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "x = p_group['Previously_Insured']\n",
    "y = p_group['id']\n",
    "\n",
    "plt.figure(figsize=(10,10))\n",
    "g = sns.barplot(x=x, y=y, data=p_group, \n",
    "                hue='Response', \n",
    "                color='deeppink',\n",
    "                edgecolor= 'maroon', \n",
    "                linewidth = 2)\n",
    "\n",
    "plt.title('Comparison of the Effect of\\nPreviously Insured on User Response', \n",
    "          color='purple', \n",
    "          loc='center',\n",
    "          fontsize=23,\n",
    "          fontweight='bold')\n",
    "plt.xlabel('Vehicle Damage', fontsize=14)\n",
    "plt.ylabel('Total User', fontsize=14)\n",
    "\n",
    "for i in g.containers:\n",
    "    g.bar_label(i,)\n",
    "\n",
    "plt.ylim(0, 200000)\n",
    "plt.tight_layout()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "ff8f7b23",
   "metadata": {
    "id": "ff8f7b23"
   },
   "source": [
    "berdasarkan plot diatas mayoritas customer yang pernah mengasuransikan kendaraannya merespon tidak tertarik (0) ketika ditawarkan asuransi kendaraan. \n",
    "kemudian untuk customer yang tertarik (1) dengan asuransi kendaraan pun mayoritas merupakan customer yang sebelumnya belum pernah mengasuransikan kendaraannya. "
   ]
  },
  {
   "cell_type": "markdown",
   "id": "35ab0f62",
   "metadata": {
    "id": "35ab0f62"
   },
   "source": [
    "secara bisnis kita dapat menggali informasi lebih dalam melalui customer yang pernah mengasuransikan kendaraannya namun tidak tertarik ketika ditawarkan kembali, kita dapat menggali faktor apa saja yang menyebabkan customer tidak tertarik lagi. apakah dari proses claimmnya yang tidak memuaskan? atau cakupan manfaatnya kurang sesuai?. kemudian dari informasi tersebut dapat kita gunakan untuk mengembangkan produk kita agar lebih sesuai dengan kebutuhan customer."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "ce1f5976",
   "metadata": {
    "id": "ce1f5976"
   },
   "source": [
    "## Vehicle Age"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "id": "390846aa",
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 462
    },
    "id": "390846aa",
    "outputId": "5f537b3d-40c6-4acb-c25d-6a99024c9078"
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\Hafiz\\anaconda3\\lib\\site-packages\\seaborn\\_decorators.py:36: FutureWarning: Pass the following variable as a keyword arg: x. From version 0.12, the only valid positional argument will be `data`, and passing other arguments without an explicit keyword will result in an error or misinterpretation.\n",
      "  warnings.warn(\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 576x432 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(8,6))\n",
    "ax = sns.countplot(data['Vehicle_Age'], hue = data['Response'])\n",
    "plt.title(\"Response in Vehicle Age\")\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "caca2abb",
   "metadata": {
    "id": "caca2abb"
   },
   "source": [
    "**Interpretasi**\n",
    "\n",
    "Dari data diatas menunjukkan perbandingan data customer yang seimbang berada pada vVehicle Age yang nomor 2, bisa diperkirakan 50:50."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "5de5f9fe",
   "metadata": {
    "id": "5de5f9fe"
   },
   "source": [
    "**Business Insight**\n",
    "\n",
    "Pihak asuransi dapat menambah jumlah kendaraan yang baru untuk meningkatkan ketertarikan masyarakat untuk berlangganan asuransi kendaraan. Dengan menggunakan kendaraan yang baru dapat menurunkan tingkat kerusakan kendaraan dan masyarakat juga merasa aman dalam menggunakan kendaraan tersebut."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "5835ee48",
   "metadata": {
    "id": "5835ee48"
   },
   "source": [
    "## Vehicle Damage"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "id": "07835962",
   "metadata": {
    "id": "07835962"
   },
   "outputs": [],
   "source": [
    "z= data.copy()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "id": "957e2773",
   "metadata": {
    "id": "957e2773"
   },
   "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>Vehicle_Damage</th>\n",
       "      <th>Response</th>\n",
       "      <th>id</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>187714</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>982</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>146685</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>45728</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   Vehicle_Damage  Response      id\n",
       "0               0         0  187714\n",
       "1               0         1     982\n",
       "2               1         0  146685\n",
       "3               1         1   45728"
      ]
     },
     "execution_count": 41,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "z_group = z.groupby(['Vehicle_Damage','Response'])['id'].nunique().reset_index()\n",
    "z_group"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "id": "85c1eff8",
   "metadata": {
    "id": "85c1eff8"
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x720 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "x = z_group['Vehicle_Damage']\n",
    "y = z_group['id']\n",
    "\n",
    "plt.figure(figsize=(10,10))\n",
    "g = sns.barplot(x=x, y=y, data=z_group, \n",
    "                hue='Response', \n",
    "                color='deeppink',\n",
    "                edgecolor= 'maroon', \n",
    "                linewidth = 2)\n",
    "\n",
    "plt.title('Comparison of the Effect of\\nVehicle Damage on User Response', \n",
    "          color='purple', \n",
    "          loc='center',\n",
    "          fontsize=23,\n",
    "          fontweight='bold')\n",
    "plt.xlabel('Vehicle Damage', fontsize=14)\n",
    "plt.ylabel('Total User', fontsize=14)\n",
    "\n",
    "for i in g.containers:\n",
    "    g.bar_label(i,)\n",
    "\n",
    "plt.ylim(0, 200000)\n",
    "plt.tight_layout()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "322754f2",
   "metadata": {
    "id": "322754f2"
   },
   "source": [
    "### Perbandingan Respon User Berdasarkan Ada atau Tidaknya Kerusakan Kendaraan"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "79d8b7b8",
   "metadata": {
    "id": "79d8b7b8"
   },
   "source": [
    "### Interpretasi:"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "653d089b",
   "metadata": {
    "id": "653d089b"
   },
   "source": [
    "Berdasarkan grafik di atas dapat diketahui bahwa total user yang kendaraannya sudah pernah mengalami kerusakan, memberi respon tertarik (1) lebih banyak dibandingkan dengan user yang kendaraannya tidak terdapat kerusakan. Jika dibandingkan, total user dengan respon tidak tertarik (0) dengan kendaraan yang tidak mengalami kerusakan lebih banyak dibandingkan dengan yang mengalami kerusakan.\n",
    "\n",
    "Hal ini bisa disebabkan karena user dengan kendaraan yang sudah mengalami kerusakan lebih aware dibandingkan yang belum mengalami kerusakan, sehingga katertarikannya lebih tinggi. Hal ini bisa dioptimalkan dengan memberikan informasi mengenai pentingnya mengasuransikan kendaraan ataupun meningkatkan promosi, sehingga user yang tidak tertarik bisa lebih yakin dan mau mendaftar asuransi kendaraan."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "77c75820",
   "metadata": {
    "id": "77c75820"
   },
   "source": [
    "### Insight"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "e8354be2",
   "metadata": {
    "id": "e8354be2"
   },
   "source": [
    "Bisnis: keadaan ini bisa dioptimalkan dengan memberikan informasi mengenai pentingnya mengasuransikan kendaraan ataupun meningkatkan promosi, sehingga user yang tidak tertarik bisa lebih yakin dan mau mendaftar asuransi kendaraan."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "b2836a58",
   "metadata": {
    "id": "b2836a58"
   },
   "source": [
    "## Annual Premium"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "id": "13af4f47",
   "metadata": {
    "id": "13af4f47"
   },
   "outputs": [],
   "source": [
    "AP = data.copy()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "id": "a738fa6a-7944-4a82-9b3c-cfc24e78d24c",
   "metadata": {
    "id": "a738fa6a-7944-4a82-9b3c-cfc24e78d24c"
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.boxplot(data=AP, x='Response', y='Annual_Premium', color='green')\n",
    "plt.xlabel('Response', fontsize=14)\n",
    "plt.ylabel('Annual Premium', fontsize=14)\n",
    "plt.tight_layout()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "ba15cf46-f3a6-40e6-a0a8-44c20ca3f93d",
   "metadata": {
    "id": "ba15cf46-f3a6-40e6-a0a8-44c20ca3f93d"
   },
   "source": [
    "### Interpretasi"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "e80903a4-a0a7-4310-b898-fa34e0e99613",
   "metadata": {
    "id": "e80903a4-a0a7-4310-b898-fa34e0e99613"
   },
   "source": [
    "Berdasarkan grafik di atas dapat dilihat bahwa Annual Premium tidak berpengaruh terhadap Response konsumen karena jumlah Response Yes dan No terhadap Annual Premium berimbang."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "a099cbca-1bfa-42ff-9b01-cc6633d0dc4f",
   "metadata": {
    "id": "a099cbca-1bfa-42ff-9b01-cc6633d0dc4f"
   },
   "source": [
    "### Business Insight"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "009c97f7-cf44-49c5-962e-9db2bc3b2d0d",
   "metadata": {
    "id": "009c97f7-cf44-49c5-962e-9db2bc3b2d0d"
   },
   "source": [
    "Berdasarkan interpretasi dan grafik maka dapat disimpulkan bahwa Annual Premium tidak dapat dijadikan sebagai faktor untuk menarik konsumen mengambil asuransi kendaraan"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "ec8d86a3",
   "metadata": {
    "id": "ec8d86a3"
   },
   "source": [
    "## Vintage"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "id": "e1b51da9",
   "metadata": {
    "id": "e1b51da9"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(0.0, 6000.0)"
      ]
     },
     "execution_count": 45,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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PfeVPp3Rav53SAnQTpS/l+/vZxRmUaXqupgT+iylB6p8ofalaO/cfQmmdfKyf+n6H0rL4raqOT1A+D5cDHwZ2y8yuTGmRmT+ktJCfwpIJsBdUj8+hTCf0X93Ydz/1uYnSAvVJlvSp+zvle+IiyuCPf66t0vFnoMUJlAmir6q2fzdlFOurswyI6KtPt9+bS6v9XkIZbPF4VZ+bKT8kpmfmY1VdnsrMN1EuQZ/HklH686rXcTJlipU7q22fR5kK6grK9+WTlM/8tZS+oi+tv1b1Nu9FK42iiNiD0vIGcHtmbjJulZE0IhFxIaUlG+BtmTl7/GojDY0teJIkSQ1jgCdJktQwBniSJEkNYx88SZKkhrEFT5IkqWEM8CRJkhrGAE+SRHUz+ayW2eNdH0kj463KJI1YRKxNuZ/l/pQJg9egTB57J/Az4LTMHO171vZXlyOBNaunszPztrHYr5aIiE0od1wBeDgzjx+3ykjLKQdZSBqRiHg58L+UG57356zMfM0Y1ec2yg3XAV6SmReOxX4nuojYGNi4enpfdYeI4W5rD5zwWxpXtuBJGrbqH/kPgRWrpHuAL1Nuy/QM4PmU+7ZqlEXEqpk5f7S2l5l3UG7vJakB7IMnNUBEfK7Wf+qkNvk31/L3rtKeFxHfjog7I+LxiFgQEbdFxDkR8b4O9rkC8N8sCe5up9wj9vOZ+fPMPC8zv5CZ2wMfq613Ya0uM1u2mbVlk1r6LhFxVkTcExFPRMSj1Ws6MyIOrsrMiohkSesdwK9q25tV294/RsQXIuLaiPhbRCyMiOsj4ksRsWFLnWbWtnFhRLwwIi6q1ptT7XdStc3TIuLBiJgfET+JiH9qc9zWjohPRcSfq3ILI+Iv1XZWHWTfO0fEBRHxKEvuH9ruvdm3tt71bfK/Ucv/TP34tfbBa1OHrav34pHqnPlpRGxWK38bS1rvAJ7T7n2t9veLiLijOg6PR8TdEfHD6odDa50nRcTREXFrRCyKiGsi4tDW+rVZ740RcX5EzKv2cU9EnBER2/R3/KRGyEwXF5cJvgCbA1ktDwDPrOXtUsu7jfLDbp2qXPazXN/BPndpWeeQDut6YW2dmS159e1tUqU9j3JD+/7qel5VbtYAZRKYVZXbErhvgHJzgW1qdZpZy7sTWNBmnZOAv7ZJ/wuwQm1bm1Xb6G/fVwNr97PvOcDfas8fHuAYr0AJuPvK7ljLWwl4uEp/CvinNsdvdj91uBuYP9DrpJxjA70Pfe/rvQOUeQo4oOU1faOfspfXHl/Ycgy+PcA+FgGvHO/ProtLtxZb8KQGyNJf6qLq6drAfrXs+iXS/8nMp4CXsKTP3K+AVwD7AocCXwfu6mC3O7Y8P3+I1e7UKyhBCcD3KK/tZcDhlH/g86q8bwAzKIFDn/dVaTOqfIBvAetXj28CDgLeAFxbpa0LfLtqoWw1BbgEeCWl9bLP4ZSBHYcChwALq/StgL1r5b5VbQPKcX9tta1fV2nTgOPb7Bfg2ZSBK4cB+wD/3k85qvd4di3p4Nrjl1MGwQD8OjP/2t922ngWcAPwOuBI4Ikqvf46X0857n3uZcl7MINyGR/K63xrVZ89KOffR6u8AD7Vt4GImAG8rbbN/6GcA8cA2/dT18NZcu7PA95T1fHTlABvJeC0iFhr4JcsTVDjHWG6uLiMzgK8hSWtE9+t0iaxpKVkMbBxlb53rey3KS1Lk4a4v6NZukXkGR2ud2FtnZktee1aeg6rpX2RMhAgBtj+bbXye7TkbdOyjx1qeVu35O1cpc+spS2kamED1msp/67ats6ppR9RpU2rpT1OCdJ2q5bXteSt2mbfT1FrWezgOG9SrdPX8tbXwnZmbZtvrpWfVUufXUuf2VK3DWt557a+zip9j1r6bf3Ubyvgm8At9N9Cu1pV9iu1tCtbtvO9Wt6FtfTLaunH1o71bsAVtbzDx/uz6+LSjcUWPKk5vg88Wj1+ZUSsBuwF/GOV9vMsHekBfkO5rAalleMm4G9Vv6b/ioitOtjfwy3P1xl2zQd2Fkta5T5IufS4ICKuiIgvRsRGQ9jW82qPF2bmFX1PMvMvLP2a6mX7XJ+ZD1aPH2jJu6T2eF7tcV9Laf2YrkiZPuY31fL9lrznttn3zZl5VZv0trJMD/PL6umzgD0jYnVKyxeU13pmp9urXJ+Zd9ee14/BQKOolxIRzwf+QPlRMpUlLbSt+lrXNq+lXdxS5rf9rFs/3h9iybH+DUu3+m3dQZWlCccAT2qIzPwb8J3q6crAASx9efbrtbKLgF2BfwF+Quk/Nonyz+5dwMVRps0YyOUtz/fqtKq1x0+P5I+I9doWzrwf2IFySfICykjPyZR/0h8EflMFLp2IfurRqUdq9XqqJe/hDvbZqdXapN3TJm0wp9YeH0xpKZxcPT89Mxcuu8qAHmx5/mTt8VBe5xHAKtXjm6q67U5p+avr+x9Vf6+G874NpN2xliY8AzypWer/0N9B6eMFpaXlrL6MiIjMfCQz/zMzX5GZmwGrs6RFZw2WtPT05w/AdbXnn4qI9dsVjIh6K8lDtcdTao9f2c+6kZn3ZOanMnOfzHwOpbWor8XsOcCLaqvUA6/W77h6ff8hIp5uyalaLdes5S8z+nSE6vteCKyZmdG6AKtn5q/brD+cwOYHLDneB1D6CPY5ddnio2ag9wCWzLcHcEJmnp6Zv6F0I2inPiffLi15M/pZp368D+/nWK9MmaBbahznwZMaJDP/EBHXUPp77VbLOi0zH689f0FEfI0yh90NlEuga7P05arJDCAzn4qId1MGV6xIudT2p4g4HvgT5R/71pTWmTnAa6pVb6xt5qiIeIwSXP5LP7t6Q0R8gBKg3gLcD2xY7a9dXR+o5b01Ip6itDRdlZlXRcQVlBZBgDMi4j8ogcV/1LZxDcu2UI5IZl4dEX8EdqYEFr+MiBMoo2rXAzalDDh4HNhzlPb594j4NvBeyjHuOyeurF+e7oL6pdsNI+ItlPduYWZeXj3u845qapW1KQMg2vk/SqsfwA4RcTLwI0pwd0A/65zKkvf5P6sW4j8CzwQ2orRgv4rSL/O2Tl+YNGGMdydAFxeX0V0ooxtbO6tPaynTOsVJ6/Io8JwO9/dyBp5yJYEf1cpPBf7epszVLc83qcofOMi276TqjF+V/2w/5Xar8rdi4GlS5tH/NCkXtrz2Zepbpc+upc+qpW/OwNOktA4U6HffQzgftmuzj/e2KTerlj+7w9ff3+uc1M/rvLnKfz4lkG3N/9UAx/TUfo7Xn/o5disApw9yrJfah4tLkxYv0UrN8y3KP88+f8jMa1rK3Ax8hjI9x92UgOsJSv+2bwEvzMzbO9lZZv6EErh8hNKBfV61rfsprWCfBf6tVv5WSmvelVU97wW+Sv+X2i6ljJ69pCr7eFXfmynzz03PzMdq5T8NnFztf5nLmpl5LaXV5j8pl2EXVcuNlLtwbJNDGMwwFFmms9kG+CQlMJlfvZY7KNPcHA388yjv80rKqNE+iygjp7smMxdTugdcRJm7rzX/aspI7kso8wr2nQNtL9NX3kmZMPt2yjlwXZX2zVqZBbV9PJWZbwL+H3AeZX7DJynn51WUc+RlDDBhtDSReS9aSVLPq/piLvMPKyLOZMll2uMz86ixrZnUm8a0BS8inhUR34yIudWtZq6NiBfX8qO6fc3d1e17LmzpnE1ErBQRX6luO7MgIs6OiCktZdaKcsugR6rltIhYc4xepiRp9B0d5fZyL46IjSJi24g4liXBXQKnjWP9pJ4yZgFeFWD9jjKU/uWU2wUdQbmM0ufDlGkPjqB0RL4fuKCaz6vP8ZSh/gdRLumsDpwTEZNqZU6ndK7dnzLr/Q74wZekiWxVykCcCymXtK+kzG8HJbj7t+zuwBFpQhmzS7QR8VngxZm5az/5QekL9NXM7Lv59cqUIO9fMvPkiFiD0o/ibZn57arMRpQ+Gftn5s8iYkvKLYd2y8zfVWV2o/QNel5m3tDVFypJGnURsRflFmjbUW4ntwJlbsDfAf+Vma0TIEvLtbG8RPsa4NKI+G5E3B8RV0bEe6vADsrIug2o3c8yyyScF7FkjqsdKdMx1MvcSels21dmOqXjcv3D/jtK59v6XFmSpAkiM3+ema/KzI0z8x8yc3JmTs3MNxvcScsaywBvU+DdlPmP9qWMVjuGcgNoKMEdlOkL6u6r5W1Ama9q3iBl5tY741aP76+VkSRJaqyxnOh4BeCyzPxI9fxPEbE5JcD7aq1c6zXjaJPWqrVMu/L9bici3kk1m/kqq6yy4/Oe1+4WlJIkSb3l8ssvn5eZy9zqcSwDvHsofePqrgPeXz3uu5n4Biw9L9H6LGnVu5cygea6lL549TIX1cqsXx9SX10GXo9lWwcByMxTgFMAdtppp7zsssuG9MIkSZLGQ0S0nbN0LC/R/g7YoiXtuZQBEgC3UoKzvfsyI2IyZaRsX/+KyykTqNbLTKGMyO0rcwlltNX02n6mU25sbT8NSZLUeGPZgvcl4OKIOBr4LrA9ZUTUR6H0k6vuYXl0RFxPmVX+Y5QBE6dXZR6JiFOBL0TE/ZTbIx1HmZX851WZ6yLiPODkiDiMcmn2ZOAcR9BKkqTlwZgFeJn5x4h4DeW2RR+nzGP0ceC/asWOpdyE+0RgLcotivZpuQ3RUZTbzXy3KvsL4C3VrXH6HAycwJLRtmdTbrYtSZLUeN6qrIV98CRJmpieeOIJ5syZw6JFi8a7KqNu8uTJTJkyhRVXXHGp9Ii4PDN3ai0/lpdoJUmSumbOnDmsttpqbLLJJiyZZnfiy0weeOAB5syZw9SpUztaZ0zvRStJktQtixYtYp111mlUcAcQEayzzjpDapk0wJMkSY3RtOCuz1BflwGeJElqrEmTJrHddtsxbdo0XvnKV/Lwww+Pd5XGhAGeJElqrJVXXpkrr7ySa665hrXXXpsTTzxxvKs0JgzwJEnScmH69OncddddAPz1r39lv/32Y8cdd2TGjBlcf/31AHzve99j2rRpbLvttuy+++4AzJ49m1e/+tXst99+bLHFFnziE594epvHHXcc06ZNY9q0aRx//PEA3HbbbWy55ZYcdthhbL311uyzzz4sXLgQgBNOOIGtttqKbbbZhgMPPBCABQsWcOihh7Lzzjuz/fbbc9ZZZ438xWamS23ZcccdU5IkTTzXXnvtMmmrrLJKZmY++eST+frXvz7PPffczMzcc88988Ybb8zMzN///vf5kpe8JDMzp02blnPmzMnMzIceeigzM//nf/4nN9hgg5w3b17+7W9/y6233jr/+Mc/5mWXXZbTpk3L+fPn52OPPZZbbbVVXnHFFXnrrbfmpEmT8k9/+lNmZr7hDW/I0047LTMzn/WsZ+WiRYuW2v5HPvKRp/Mfeuih3HzzzXP+/PkdvT7gsmwTz9iCJ0mSGmvhwoVst912rLPOOjz44IPsvffezJ8/n4svvpg3vOENbLfddhx++OHcc889AOy6667MnDmTr33tayxevOQeCnvvvTfrrLMOK6+8MgcccAC//e1v+e1vf8trX/taVlllFVZddVUOOOAAfvOb3wAwdepUtttuOwB23HFHbrvtNgC22WYbDj74YL71rW/xjGeU2erOP/98jjnmGLbbbjv22GMPFi1axB133DGi1+08eJIkqbH6+uA98sgjvOIVr+DEE09k5syZrLnmmlx55ZXLlD/ppJO49NJL+clPfsJ22233dJnWUawRQQ5ws4iVVlrp6ceTJk16+hLtT37yEy666CLOPvtsPvWpT/GXv/yFzOTMM89kiy22GPkLrtiCJ0mSGm+NNdbghBNO4Itf/CIrr7wyU6dO5Xvf+x5Quqv9+c9/BkrfvBe+8IV88pOfZN111+XOO+8E4IILLuDBBx9k4cKF/OhHP2LXXXdl991350c/+hF/+9vfWLBgAT/84Q+ZMWNGv3V46qmnuPPOO3nJS17Csccey8MPP8z8+fPZd999+cpXvvJ0wPinP/1pxK/XFjxJkrRc2H777dl22235zne+w7e//W3e9a538elPf5onnniCAw88kG233ZYPfehD3HTTTWQmL33pS9l222258sor2W233TjkkEO4+eabedOb3sROO5W7g82cOZMXvOAFALzjHe9g++23f/pybKvFixfz5je/mUceeYTM5KijjmLNNdfk4x//OEceeSTbbLMNmckmm2zCOeecM6LX6r1oW3gvWkmSJqbrrruOLbfcctS3O3v2bC677DK++tWvjvq2h6Ld6+vvXrReopUkSWoYL9FKkiQNYObMmcycOXO8qzEktuBJkiQ1jAGeJElSwxjgSZIkNYwBniRJUsMY4EmSJI2B8847jy222ILNNtuMY445pqv7MsCTJEnLnY02fg4RMWrLRhs/Z8D9LV68mPe85z2ce+65XHvttZxxxhlce+21XXt9TpMiSZKWO3PuvIPjzr9h1Lb3gX0Gvo/sH/7wBzbbbDM23XRTAA488EDOOussttpqq1GrQ50teJIkSV121113sdFGGz39fMqUKdx1111d258BniRJUpe1uzVsRHRtfwZ4kiRJXTZlyhTuvPPOp5/PmTOHDTfcsGv7M8CTJEnqsp133pmbbrqJW2+9lccff5zvfOc7vOpVr+ra/hxkIUmS1GXPeMYz+OpXv8q+++7L4sWLOfTQQ9l66627t7+ubVmSJKlHTdlo40FHvg51e4N52ctexste9rJR2+dADPAkSdJy5847bh/vKnSVffAkSZIaxgBPkiSpYQzwJEmSGsYAT5IkqWEM8CRJkhrGAE+SJGkMHHrooay//vpMmzat6/sywJMkScudTTaeQkSM2rLJxlMG3efMmTM577zzxuDVOQ+eJElaDt1+513kLz87atuLPT86aJndd9+d2267bdT2ORBb8CRJkhrGAE+SJKlhDPAkSZIaxgBPkiSpYQzwJEmSxsBBBx3E9OnTueGGG5gyZQqnnnpq1/blKFpJkrTcec5Gz+5o5OtQtjeYM844Y9T2NxgDPEmStNy57Y45412FrvISrSRJUsMY4EmSJDWMAZ4kSWqMzBzvKnTFUF+XAZ4kSWqEyZMn88ADDzQuyMtMHnjgASZPntzxOg6ykCRJjTBlyhTmzJnD3Llzx7sqo27y5MlMmTKl4/IGeJIkqRFWXHFFpk6dOt7V6AleopUkSWoYAzxJkqSGMcCTJElqGAM8SZKkhjHAkyRJahgDPEmSpIYxwJMkSWoYAzxJkqSGMcCTJElqGAM8SZKkhjHAkyRJahgDPEmSpIYxwJMkSWqYMQvwImJWRGTLcm8tP6oyd0fEwoi4MCK2btnGShHxlYiYFxELIuLsiJjSUmatiDgtIh6pltMiYs0xepmSJEnjbqxb8G4AnlVbnl/L+zDwQeAIYGfgfuCCiFitVuZ44HXAQcAMYHXgnIiYVCtzOrADsD+wX/X4tC68FkmSpJ70jDHe35OZeW9rYkQEcCRwTGaeWaW9lRLkvQk4OSLWAN4OvC0zL6jKHALcDuwF/CwitqQEdbtl5sVVmcOB30TEFpl5Q7dfoCRJ0ngb6xa8TSPiroi4NSK+ExGbVulTgQ2A8/sKZuZC4CLgRVXSjsCKLWXuBK6rlZkOzAcuru3zd8CCWhlJkqRGG8sA71JgJuXS6WGUgO7iiFinegxwX8s699XyNgAWA/MGKTM3M7Mvs3p8f63MMiLinRFxWURcNnfu3CG+LEmSpN4yZpdoM/Pc+vOI+D1wC/BW4Pd9xVpWizZprVrLtCs/4HYy8xTgFICddtppsP1JkiT1tHGbJiUz5wN/ATYH+vrltbayrc+SVr17gUnAuoOUWb/q0wc83b9vPZZtHZQkSWqkcQvwImIy8DzgHuBWSnC2d0v+DJb0p7sceKKlzBRgy1qZS4BVKX3x+kwHVmHpfnmSJEmNNWaXaCPii8CPgTsorW4fpwRe38zMjIjjgaMj4nrgRuBjlAETpwNk5iMRcSrwhYi4H3gAOA64Cvh5Vea6iDiPMur2MMql2ZOBcxxBK0mSlhdjOU3KFOAMyiXWuZR+d7tk5u1V/rHAysCJwFqUQRn7ZOZjtW0cBTwJfLcq+wvgLZm5uFbmYOAEloy2PRt4bzdekCRJUi+K2oBTUQZZXHbZZeNdDUmSpEFFxOWZuVNruveilSRJahgDPEmSpIYxwJMkSWoYAzxJkqSGMcCTJElqGAM8SZKkhjHAkyRJahgDPEmSpIYxwJMkSWqYsbxVmSRJGmcH/L+DmPvAQ23z1ltnLX7wf2eMcY3UDQZ4kiQtR+Y+8BAH/NsJbfN+cMz7xrg26hYDPGkI/OUrSZoIDPCkIfCXr0aDPxQkdZsBniSNMX8oSOo2Azw11li3klx//XXMeOl+Y7Y/Sd1hC6uawACvIfxCWtZwW0kGOpbX33hjv+s98VTaKjNG+nuPltdzfTw0+TvHFlY1gQFeQwz0hfTZmXsPq2WpyV/gAxnoWH7iLXuNcW1GXxPe1/7eI//5jp1u/ICaKOff8sr3bmIxwFsODLdlyV+x468bX6jdeF/94lfdQN0Vrr/xRj76jfPa5vm90tsmyv8Ev48KAzyph3WjZbYbbM1R3UA/KgdqBbcf6/gbLDifCCZKINptBnjLuYnyYbbP1bLGus/fcPsmDsQvYtU1uR9rN37MdGObww3O1XsM8JZzE+XDbJ+r8TfWfRMnyo8PqRPd+DHT9B9ItvCPjAGepJ40lj8+BvpHcsstN7Ppppu1zfOfjHpVE34gNT2A7TYDPA3LQF8e/kPURDNY6+RE/ydjS0hv60bfw7G+OjPc19CNrh+jbaL+ADTA07AM9uXR6/8Qm/DrdiC99Pp6qS6jbaIMCrAlpLc1oe9hN2Zr6JVuQhP1B6ABnpZLE6Xv4XD10uvrpbqMtib8Y9bomQitUVp+GOBpTE2UFo/Rtry+7rHWS8e5Gy2XXmodPWM9Knyi/5hpiv4+l038/BjgjYPl+Ut6eW3xWF5f91jrpePcjbngBpokeKB5EQcKWHopKB5Lww3GmtzlYHnQ3+dyuJ+fXmaANw7sD9PecL44m/Bl24TXMBFMlOM83MCwG+stz99H/Wlyl4PlWRPfVwO8HrO8/pqG4X3AmvChbMJrmAg8ztL4mCg/rprGAK/H+GtakoZmONNYTJTAognBkT+uxocBniRpQhvONBYTJbAwONJwGeBNIE34JSdJkrrPAG8C8ZecpLHkj0pp4jLAkyS15Y9K9Sp/fAzOAE+SJE0o/vgY3ArjXQFJkiSNLgM8SZKkhjHAkyRJahgDPEmSpIYxwJMkSWoYAzxJkqSGMcCTJElqGAM8SZKkhjHAkyRJahgDPEmSpIYxwJMkSWoYAzxJkqSGMcCTJElqGAM8SZKkhuk4wIuI/SPinIi4NiI2qtLeEREv7V71JEmSNFQdBXgRcTDwf8BNwFRgxSprEvDh7lRNkiRJw9FpC96HgcMy8yjgyVr674HtRrtSkiRJGr5OA7zNgUvapM8HVh+96kiSJGmkOg3w7gae2yZ9d+Cvo1cdSZIkjVSnAd4pwAkRsWv1fKOIeCtwLPDfXamZJEmShuUZnRTKzGMjYg3gAmAy8Cvg78AXM/PELtZPkiRJQ9RRgAeQmUdHxGeArSgtf9dm5vyu1UySJEnDMqSJjjPzb5l5GXA1sEtEPKc71ZIkSdJwdToP3uyIeHf1+JnApcD5wA0RsX8X6ydJkqQh6rQFb1/KnHcArwLWADYAZlWLJEmSekSnAd5awP3V4/2A72fm/cB3KH3yJEmS1CM6DfDuBaZFxCRKa97Pq/RVgSeGs+OI+GhEZER8tZYWETErIu6OiIURcWFEbN2y3koR8ZWImBcRCyLi7IiY0lJmrYg4LSIeqZbTImLN4dRTkiRpouk0wPsG8F3gGmAx8Isq/YXA9UPdaUTsAhwGXNWS9WHgg8ARwM6UVsMLImK1WpnjgdcBBwEzKHfSOKcKPvucDuwA7E9pcdwBOG2o9ZQkSZqIOp0H75MR8RdgY+B7mfl4lfUk8Pmh7LCaT+/bwNuBf6+lB3AkcExmnlmlvZUS5L0JOLla9+3A2zLzgqrMIcDtwF7AzyJiS0pQt1tmXlyVORz4TURskZk3DKW+kiRJE03H06Rk5pmZ+aXMnFNL+2ZmnjXEfZ5C6cP3y5b0qZSBG+fXtr8QuAh4UZW0I7BiS5k7getqZaZT7pF7cW3bvwMW1MpIkiQ1VscTHUfERpRLouvTEhhm5nEdbuMwYDPgkDbZG1R/72tJvw94dq3MYmBemzIb1MrMzcys1S8j4v5aGUmSpMbqKMCLiIMp/fCeBOYCWctOYNAALyK2AD4LzKhd4m0nW55Hm7RlNt+mToOVqdftncA7ATbeeONBdiVJktTbOr1E+0ngP4HVM3OTzJxaWzbtcBvTgXWBayLiyYh4Engx8O7q8QNVudZWtvVZ0qp3LzCp2s5AZdav+vQBT/fvW49lWwcByMxTMnOnzNxpvfXW6/DlSJIk9aZOA7x/BL6emYtHsK8fAc8Htqstl1Hm0tsOuJESnO3dt0JETKZcFu7rT3c5ZVqWepkpwJa1MpdQpm+ZXtv3dGAVlu6XJ0mS1Eid9sH7KWVKlFuGu6PMfBh4uJ4WEQuABzPzmur58cDREXE9JeD7GGXAxOnVNh6JiFOBL1R96h6gXB6+impuvsy8LiLOo4y6PYxyafZk4BxH0EqSpOVBpwHeBcDnq0mHr6ZlcuPM/MEo1edYYGXgRMrdMy4F9snMx2pljqL0BfxuVfYXwFtaWhcPBk5gyWjbs4H3jlIdJUmSelqnAd7J1d+PtslLSr+4IcvMPVqeJ4Pc3zYzF1EmQj5igDIPAm8eTp0kSZImuk4nOu54vjxJkiSNLwM3SZKkhuk4wIuIl0fERRExLyLmRsSvI+Jl3aycJEmShq6jAC8i3gH8EPgr8K/AvwG3Aj+MiEO7Vz1JkiQNVaeDLP4V+EBmfrWWdmpEXE4J9r4x6jWTJEnSsHR6iXZj4Lw26ecCzxm96kiSJGmkOg3w7qB294iafYDbR686kiRJGqlOL9F+EfhKROxAud1XArsBhzDAfHSSJEkae53Og3dydWuwDwIHVMnXAf8vM8/qVuUkSZI0dJ224JGZP6SMpJUkSVIP6zjAA4iIPYGtqqfXZuYvR79KkiRJGomOAryImAr8AHg+cHeVvGFEXA28LjNv6VL9JEmSNESdjqI9FXgU2DQzN87MjYFNgYeBr3epbpIkSRqGTi/RTgd2ycw7+hIy846IOAq4pCs1kyRJ0rAMZR68ldukTwbuHL3qSJIkaaQ6DfA+CJwQEbtExKRq2QU4vsqTJElSj+j0Eu0ZwErA74CnqrQVgMXAtyPi6YKZufpoVlCSJElD02mA996u1kKSJEmjptM7WXyz2xWRJEnS6OioD15EbBURW9Se7x0R34qIj0TEpO5VT5IkSUM1lHnwtgeIiCnAWcDawHuAT3enapIkSRqOTgO8LYErqsdvAC7NzJcBhwAHdaNikiRJGp5OA7xJwOPV45cCP60e/xX4x9GulCRJkoav0wDvGuBdETGDEuCdV6U/G5jXjYpJkiRpeDoN8P4VOAy4EDgjM6+u0l8F/KEL9ZIkSdIwdTpNykURsR6wemY+VMs6GfhbV2omSZKkYem0BY/MXAxMiogXRsRKVdptmXl/12onSZKkIet0HrzVIuJ7wP3AxZS+d0TESRExq3vVkyRJ0lB12oL3eWBDYAdgYS39HOC1o10pSZIkDV+n96J9FfDazLwyIrKWfh2w6ehXS5IkScPVaQveWsADbdJXAxaPXnUkSZI0Up0GeH+ktOL16WvFO5zSJ0+SJEk9otNLtB8FfhYRW1frfKB6/AJg925VTpIkSUPX6Tx4F0fEi4B/odye7KWUe9NOr016LGmCu/7665jx0v3a59144xjXRpI0XJ224FEFcm9tTY+IAzPzO6NaK0nj4omnkgP+7YS2eZ94y15jXBtJ0nAN2gcvIp4REVtHxHNb0l8TEVcB3+xa7SRJkjRkAwZ4EbEVcCNwFXBdRPwgItaPiF8Cs4Hzgc26XktJkiR1bLBLtMcAtwLvAw4G3ghsBZwOvDozH+tu9SRJsn+oNFSDBXgvAF6WmVdExG8pAd4XM/Pr3a+aJEmF/UOloRmsD976wF0Amfkw8Dfgoi7XSZIkSSMwWICXwFO1508BT3SvOpIkSRqpwS7RBnBL7f6zqwJXtdyPlsxcvRuVkyRJ0tANFuC9bUxqIUmSpFEzYICXmc5xJ0mSNMEMOtGxJEmSJhYDPEmSpIYxwJMkSWoYAzxJkqSGGTTAi4gVI+LSiNhiLCokSZKkkRk0wMvMJ4CplEmPJUmS1OM6vUT7TeCwblZEkiRJo2OwiY77rAIcHBF7A5cDC+qZmfm+0a6YJEmShqfTAG9L4Irq8aYteV66lSRJ6iEdBXiZ+ZJuV0SSJEmjY0jTpETEuhHxwohYqVsVkiRJ0sh0FOBFxGoR8T3gfuBi4NlV+kkRMat71ZMkSdJQddqC93lgQ2AHYGEt/RzgtaNdKUmSJA1fp4MsXgW8NjOvjIj6oIrrWHbQhSRJksZRpy14awEPtElfDVg8etWRJEnSSHUa4P2R0orXp68V73BKnzxJkiT1iE4v0X4U+FlEbF2t84Hq8QuA3btVOUmSJA1dRy14mXkx8CLgmcBfgZcCdwPTM/OKgdaVJEnS2Op4HrzMvDoz35qZ0zJzq8x8c2Ze3en6EfGeiLgqIh6tlksi4uW1/IiIWRFxd0QsjIgLq1bC+jZWioivRMS8iFgQEWdHxJSWMmtFxGkR8Ui1nBYRa3ZaT0mSpImu03nwtoqILWrP946Ib0XERyJiUof7mgP8K2WqlZ2AXwI/iohtqvwPAx8EjgB2psy5d0FErFbbxvHA64CDgBnA6sA5LXU4vdrH/sB+1ePTOqyjJEnShNdpC96pwPYAVYvZWcDawHuAT3eygcw8KzPPzcybM/PGzDwaeAyYHhEBHAkck5lnZuY1wFspo3TfVO13DeDtwIcy84Lq0vAhwDbAXlWZLSlB3Tsz8+LMvIQyEOQV9QBVkiSpyToN8LYE+vravQG4NDNfRgmwDhrqTiNiUkQcCKxKGYU7FdgAOL+vTGYuBC6i9P0D2BFYsaXMnZS5+PrKTAfms/TI3t8BC2plJEmSGq3TAG8S8Hj1+KXAT6vHfwX+sdOdRcTzI2I+8HfgJMrkyVdTgjuA+1pWua+WtwFlzr15g5SZm5lPT8ZcPb6/VqZdvd4ZEZdFxGVz587t9OVIkiT1pE4DvGuAd0XEDEqAd16V/myWDbgGcgOwHbAL8N/ANyNiWi0/W8pHm7RWrWXalR9wO5l5SmbulJk7rbfeeoPsTpIkqbd1GuD9K3AYcCFwRm307KuAP3S6s8x8vOqDd1lmfgS4EjgKuLcq0trKtj5LWvXupbQkrjtImfWrPn1AGZ0LrMeyrYOSJEmN1Ok8eBdRgqR1M/PQWtbJwLtGuP+VgFspwdnefRkRMZkyUravP93lwBMtZaZQ+gf2lbmE0q9vem0f04FV8I4bkiRpOdHpnSzIzMXAQy1pt3W6fkQcA/wEuJMlo2P3AF6emRkRxwNHR8T1wI3AxygDJk6v9vVIRJwKfCEi7qfcG/c44Crg51WZ6yLiPODkiDiMcmn2ZOCczLyh07pKkiRNZAMGeBFxNe37rj1C6U/3pcz8S4f72gD4VvX3EUpgtn9m/qzKPxZYGTgRWAu4FNgnMx+rbeMo4Engu1XZXwBvqYLPPgcDJ7BktO3ZwHs7rKMkSdKEN1gL3vf7SV+TMoHwFRGxZ2b+brAdZebMQfITmFUt/ZVZRJkI+YgByjwIvHmw+kiSJDXVgAFeZn5ioPyI+AzwKWDP0ayUJEmShq/je9H249vA80ejIpIkSRodIw3wkjKQQZIkST1ipAHe64BOB1lIkiRpDAw2ivYD/WStQbk37L7A/qNdKUmSJA3fYKNo+xut+ihlmpTdM/OS0a2SJEmSRmKwUbRTx6oikiRJGh0j7YMnSZKkHmOAJ0mS1DAGeJIkSQ1jgCdJktQw/QZ4EfGNiFiterx7RAw24laSJEk9YKAWvDcDq1SPfwWs3f3qSJIkaaQGapW7DTgiIs6n3I5sekQ81K5gZl7UhbpJkiRpGAYK8D4EfA34COWesz/sp1wCk0a5XpIkSRqmfgO8zDwLOCsi1gQeBLYG7h+jekmSJGmYBh04kZkPR8RLgJsy88kxqJMkSZJGoKORsZn564hYKSLeAmxFuSx7LXB6Zv69mxWUJEnS0HQ0D15EbAXcCBwHvBDYBfgScGNEbNm96kmSJGmoOp3o+MvAlcDGmTkjM2cAGwN/Bo7vTtUkSZI0HJ1OXrwrsHNmPtqXkJmPRsTRwO+7UjNJkiQNS6cteIuANdukr1HlSZIkqUd0GuD9GPhaROwaEZOqZTfgZODs7lVPkiRJQ9VpgPd+4CbgN5QWu0XArykDL47sSs0kSZI0LJ1Ok/Iw8OqI2AzYknLrsmsz8+Yu1k2SJEnD0OkgCwCqgM6gTpIkqYd1eolWkiRJE4QBniRJUsMY4EmSJDXMoAFeRDwjIt4dERuORYUkSZI0MoMGeJn5JPAFYMXuV0eSJEkj1ekl2t8DO3SzIpIkSRodnU6T8jXgPyPiOcDlwIJ6ZmZeMdoVkyRJ0vB0GuCdXv09rk1eApNGpzqSJEkaqU4DvKldrYUkSZJGTae3Kru92xWRJEnS6Oh4HryI2D8izomIayNioyrtHRHx0u5VT5IkSUPVUYAXEQcD/wfcRLlc2zdlyiTgw92pmiRJkoaj0xa8DwOHZeZRwJO19N8D2412pSRJkjR8nQZ4mwOXtEmfD6w+etWRJEnSSHUa4N0NPLdN+u7AX0evOpIkSRqpTgO8U4ATImLX6vlGEfFW4Fjgv7tSM0mSJA1Lp9OkHBsRawAXAJOBXwF/B76YmSd2sX6SJEkaok4nOiYzj46IzwBbUVr+rs3M+V2rmSRJkoal4wCvksCi6vHiUa6LJEmSRkGn8+CtFBHHAw8CfwauAh6MiC9HxOQu1k+SJElD1GkL3n8D+wDvYMl0KdOBzwGrAYeOftUkSZI0HJ0GeG8ADsjMC2ppt0TE/cCZGOBJkiT1jE6nSVkA3NUm/S5g4ehVR5IkSSPVaYD3FeA/ImLlvoTq8cerPEmSJPWIfi/RRsTZLUl7AHdFxFXV8+dX66/SnapJkiRpOAbqg/dAy/MzW57fOsp1kSRJ0ijoN8DLzLeNZUUkSZI0OjrtgydJkqQJoqNpUiJiLWAW8BJgfVoCw8xcf9RrJkmSpGHpdB68/wW2Br4J3Ee5ZZkkSZJ6UKcB3h7AizPzii7WRZIkSaOg0z54fx1CWUmSJI2jToO29wOfi4htI2JSNyskSZKkken0Eu3NwMrAFQARsVRmZhr0SZIk9YhOA7wzgDWA9+EgC0mSpJ7W6SXanYCDMvPEzPx+Zp5ZXzrZQER8JCL+GBGPRsTciPhxRExrKRMRMSsi7o6IhRFxYURs3VJmpYj4SkTMi4gFEXF2RExpKbNWRJwWEY9Uy2kRsWaHr1WSJGlC6zTAuxZYfYT72gP4L+BFwJ7Ak8DPI2LtWpkPAx8EjgB2Bu4HLoiI1WpljgdeBxwEzKjqdU5L38DTgR2A/YH9qsenjbD+kiRJE0Knl2g/BhwXER8DrgaeqGdm5oODbSAz960/j4hDgEeAXYEfR+nYdyRwTF+rYES8lRLkvQk4OSLWAN4OvC0zL6ht53ZgL+BnEbElJajbLTMvrsocDvwmIrbIzBs6fM2SJEkTUqcteD8FXgCcD9wNzK2WedXf4Vit2v9D1fOpwAbVPgDIzIXARZRWP4AdgRVbytwJXFcrMx2YD1xc29fvgAW1MpIkSY3VaQveS7qw7y8DVwKXVM83qP7e11LuPuDZtTKLKYFla5kNamXmZubTA0EyMyPi/loZSZKkxuoowMvMX4/mTiPiOGA3ymXUxa27ay3eJm2ZTbaUaVe+3+1ExDuBdwJsvPHGg+xKkiSpt3UU4EXEDgPlD+UWZhHxJeBA4CWZeUst697q7wbAnbX09VnSqncvMAlYl6UvDa9PuZTbV2b9iIi+Vryqf996LNs62Ff/U4BTAHbaaSengJEkSRNap5doL6O0ftVnOK4HQh1NdBwRX6YEd3tk5vUt2bdSgrO9gT9W5SdTRsp+qCpzOWWAx96UkbJUU6RsyZI+d5cAq1L64vWlTQdWYel+eZIkSY3UaYA3teX5isD2wNHARzrZQEScCBwCvAZ4KCL6+sPNz8z5VT+544GjI+J64EbK6N35VMFcZj4SEacCX6j61D0AHAdcBfy8KnNdRJxHGXV7GCUoPRk4xxG0kiRpedBpH7zb2yTfHBGPAP8BnNvBZt5d/f1FS/ongFnV42Mpt0Q7EVgLuBTYJzMfq5U/ijKH3nersr8A3tLSl+9g4ASWjLY9G3hvB3WUJEma8DptwevPrcB2nRTMzOigTFKCvVkDlFlEmQj5iAHKPAi8uZN6SZIkNU2ngyzWbk0CnkUJxLzsKUmS1EM6bcGbR/vpS+4E3jiqNZIkSdKIDHei46co05TcnJlPjm6VJEmSNBLjMtGxJEmSumfAAK9N37u2qkENkiRJ6gGDteC163vXKjvYjiRJksbIYIFZa9+7uv2A91PmpJMkSVKPGDDAa9f3rrov7eeB3Sl3iPhUd6omSZKk4Vih04IRMTUiTqfcXeJBYKvMfF9mzu1a7SRJkjRkgwZ4EbFORHwZuB7YAJiemW/MzL92vXaSJEkasgEDvIj4KPBX4MXAqzNzz8y8bExqJkmSpGEZbJDFp4GFwBzg3RHx7naFMvNVo10xSZIkDc9gAd7/Mvg0KZIkSeohg42inTlG9ZAkSdIo6XgUrSRJkiYGAzxJkqSGMcCTJElqGAM8SZKkhjHAkyRJahgDPEmSpIYxwJMkSWoYAzxJkqSGMcCTJElqGAM8SZKkhjHAkyRJahgDPEmSpIYxwJMkSWoYAzxJkqSGMcCTJElqGAM8SZKkhjHAkyRJahgDPEmSpIYxwJMkSWoYAzxJkqSGMcCTJElqGAM8SZKkhjHAkyRJahgDPEmSpIYxwJMkSWoYAzxJkqSGMcCTJElqGAM8SZKkhjHAkyRJahgDPEmSpIYxwJMkSWoYAzxJkqSGMcCTJElqGAM8SZKkhjHAkyRJahgDPEmSpIYxwJMkSWoYAzxJkqSGMcCTJElqGAM8SZKkhjHAkyRJahgDPEmSpIYxwJMkSWoYAzxJkqSGMcCTJElqGAM8SZKkhjHAkyRJahgDPEmSpIYZ0wAvInaPiLMj4q6IyIiY2ZIfETErIu6OiIURcWFEbN1SZqWI+EpEzIuIBdX2prSUWSsiTouIR6rltIhYs/uvUJIkafyNdQveqsA1wPuBhW3yPwx8EDgC2Bm4H7ggIlarlTkeeB1wEDADWB04JyIm1cqcDuwA7A/sVz0+bTRfiCRJUq96xljuLDN/CvwUICJm1/MiIoAjgWMy88wq7a2UIO9NwMkRsQbwduBtmXlBVeYQ4HZgL+BnEbElJajbLTMvrsocDvwmIrbIzBu6/TolSZLGUy/1wZsKbACc35eQmQuBi4AXVUk7Aiu2lLkTuK5WZjowH7i4tu3fAQtqZSRJkhqrlwK8Daq/97Wk31fL2wBYDMwbpMzczMy+zOrx/bUyS4mId0bEZRFx2dy5c4f/CiRJknpALwV4fbLlebRJa9Vapl35freTmadk5k6ZudN6663XcUUlSZJ6US8FePdWf1tb2dZnSavevcAkYN1Byqxf9ekDnu7ftx7Ltg5KkiQ1Ti8FeLdSgrO9+xIiYjJlpGxff7rLgSdaykwBtqyVuYQyWnd6bdvTgVVYul+eJElSI43pKNqIWBXYrHq6ArBxRGwHPJiZd0TE8cDREXE9cCPwMcqAidMBMvORiDgV+EJE3A88ABwHXAX8vCpzXUScRxl1exjl0uzJwDmOoJUkScuDMQ3wgJ2AX9Wef6JavgnMBI4FVgZOBNYCLgX2yczHauscBTwJfLcq+wvgLZm5uFbmYOAEloy2PRt47yi/FkmSpJ401vPgXUhpUesvP4FZ1dJfmUWUiZCPGKDMg8Cbh1lNSZKkCa2X+uBJkiRpFBjgSZIkNYwBniRJUsMY4EmSJDWMAZ4kSVLDGOBJkiQ1jAGeJElSwxjgSZIkNYwBniRJUsMY4EmSJDWMAZ4kSVLDGOBJkiQ1jAGeJElSwxjgSZIkNYwBniRJUsM8Y7wrIC0PFi1cyBmnfaPfPEnd0d9nz8+dms4AbxzcdOP1E+Kf/XCDkm4EMxP9SzozOWjGc9vm/fmsHOPaqJUBeHP199n70w8XT/j3fKzPWz8nE4sB3jh48sknJsQ/++EGJd0IZvrb5kDb88to7IxlUD/QNodbj+Ges55jo2usj+dEf8/H+oejP1QnFgO8CWSs/4k+9dRTw9pmr/DLaOyMZVAP/be+LF68eNTr8dRTT/X7GenG/gbSS8HFQHVZMH/+mAbaY2ki1HEi6ZUfh8M10L7uuPH6Ud3XUBngTSDD+YcHA3/ZDvQP6k8/7L8uA/3TGygwHGi9idDi0Uv/YAfSS/Uc6D0f6Nwc7AdG+8tuQ69fJ4bzGRnIWLc0DrcuI/nuGM531UT/UTnc77fl+VLrWP847G+b3fhMfuaCJwapaXcZ4DXI8L5su7O/4a039C/+4QaaAxnogz7WLTbDNdwfA936ch/rc7PX9VIr0MDnSm99d/Snl65E9Eq3loH00vnXK4b7ndnLP0oM8HpMNwKWiWQ4X/yj3QI53FbNbujWL+3R7ns0Ec7Nsf5sdaOVu8mt4yMxWJDaC3rpfBjr83245183Wv/72+ZwrhhA75xf7Rjg9aCJeCJNRKN9nLvxhdpLv7Qnwj/RwYx1/Ue/lXt46y2vrTLD1a3gqFfOh7E/34ffAtaN1uWx7N4xngzwpFHUK/0ZJ0KLmsaO58rQTfQfM8PVa8Gths8AT+oBE+UXuiYuzxV1ynOlGbxVmSRJUsMY4EmSJDWMAZ4kSVLDGOBJkiQ1jAGeJElSwziKVpLU85zqRRoaAzxJ0oTg9B1S5wzwJD3NVhJJagYDPElLsZVEkiY+B1lIkiQ1jAGeJElSwxjgSZIkNYwBniRJUsMY4EmSJDWMAZ4kSVLDGOBJkiQ1jAGeJElSwxjgSZIkNYwBniRJUsMY4EmSJDWMAZ4kSVLDGOBJkiQ1jAGeJElSwxjgSZIkNYwBniRJUsMY4EmSJDWMAZ4kSVLDGOBJkiQ1jAGeJElSwxjgSZIkNYwBniRJUsMY4EmSJDWMAZ4kSVLDGOBJkiQ1jAGeJElSwxjgSZIkNUyjA7yIeHdE3BoRiyLi8oiYMd51kiRJ6rbGBngR8Ubgy8Bnge2Bi4FzI2Ljca2YJElSlzU2wAM+AMzOzK9l5nWZeQRwD/Cuca6XJElSVzUywIuIZwI7Aue3ZJ0PvGjsayRJkjR2GhngAesCk4D7WtLvAzYY++pIkiSNncjM8a7DqIuIDYG7gN0z8ze19P8ADsrM57WUfyfwzurpFsANA2x+XWDe6NZYFY9td3l8u8vj210e3+7x2HZXt4/vczJzvdbEZ3Rxh+NpHrCYZVvr1mfZVj0y8xTglE42HBGXZeZOI66hluGx7S6Pb3d5fLvL49s9HtvuGq/j28hLtJn5OHA5sHdL1t6U0bSSJEmN1dQWPIDjgNMi4g/A74B/BjYEThrXWkmSJHVZYwO8zPxuRKwDfAx4FnAN8LLMvH2Em+7oUq6GxWPbXR7f7vL4dpfHt3s8tt01Lse3kYMsJEmSlmeN7IMnSZK0PDPAkyRJahgDvA5FxLsj4taIWBQRl0fEjPGu00QTEbMiIluWe2v5UZW5OyIWRsSFEbH1eNa5l0XE7hFxdkTcVR3LmS35gx7PiFgpIr4SEfMiYkG1vSlj+kJ6VAfHd3ab8/n3LWU8vm1ExEci4o8R8WhEzI2IH0fEtJYynr/D1OHx9fwdpoh4T0RcVR3fRyPikoh4eS2/J85dA7wORMQbgS8DnwW2p0y1cm5EbDyuFZuYbqAMeulbnl/L+zDwQeAIYGfgfuCCiFhtrCs5QaxKGTz0fmBhm/xOjufxwOuAg4AZwOrAORExqXvVnjAGO74AP2fp8/llLfnH4/FtZw/gvyi3jtwTeBL4eUSsXSvj+Tt8ezD48QXP3+GaA/wrsAOwE/BL4EcRsU2V3xvnbma6DLIAlwJfa0m7CfjceNdtIi3ALOCafvICuAc4upa2MvAYcPh4173XF2A+MHMoxxNYA3gcOLhWZiPgKWDf8X5NvbS0Ht8qbTZwzgDreHw7P76rUianf2X13PO3i8e3SvP8Hd1j/CBweC+du7bgDSIingnsCJzfknU+5deRhmbT6pLXrRHxnYjYtEqfSrnzyNPHOTMXAhfhcR6OTo7njsCKLWXuBK7DY96p3SLi/oi4MSK+FhHr1/I8vp1bjXJF6aHquefv6Go9vn08f0coIiZFxIGUIPpieujcNcAb3LrAJJa9xdl9LHsrNA3sUmAmsD9wGOX4XRxlvsK+Y+lxHh2dHM8NKL/qW++R6DHvzHnAW4CXUi7HvAD4ZUSsVOV7fDv3ZeBK4JLquefv6Go9vuD5OyIR8fyImA/8nXIDhddm5tX00Lnb2ImOu6B1wsBok6YBZOa59edVh95bgLcCfZ17Pc6jazjH02Pegcz8Tu3p1RFxOXA78HLgBwOs6vGtiYjjgN2A3TJzcUu25+8I9Xd8PX9H7AZgO2BNSl+6b0bEHrX8cT93bcEb3DxKpN0aVa/PshG6hiAz5wN/ATYH+kbTepxHRyfH815K6/S6A5RRhzLzbkrn682rJI/vICLiS5RO5ntm5i21LM/fUTDA8V2G5+/QZObjmXlzZl6WmR+htJAeRQ+duwZ4g8jMx4HLgb1bsvamXG/XMEXEZOB5lA6pt1JO+r1b8mfgcR6OTo7n5cATLWWmAFviMR+yiFgXeDblfAaP74Ai4svAmyjBx/Ut2Z6/IzTI8W1X3vN3ZFYAVqKXzt3xHnkyERbgjZQRL++o3oAvU0bVPWe86zaRFuCLwIspnVBfCJwDPNp3HCnDzh8FDgCmAd8B7gZWG++69+JC6dS7XbX8Dfj36vHGnR5P4L+Bu4C9KFMA/YryS3TSeL++8V4GOr5V3heB6cAmlGkpLqG0gHh8Bz+2J1bn5p6Ulo6+ZdVaGc/fLh1fz98RH99jKAHbJpSpvj5HGQG7f5XfE+fuuB+oibIA7wZuo3SovBzYfbzrNNGW2kn+eHVinwlsVcsPylQq9wCLgF8D08a73r26VF/K2WaZ3enxBCYDXwEeoAQxPwY2Gu/X1gvLQMeXMu3BzyjzWz1O6bs0u/XYeXz7PbbtjmsCs2plPH+7dHw9f0d8fGdXx+zv1TH8ObXpTXrl3I1qR5IkSWoI++BJkiQ1jAGeJElSwxjgSZIkNYwBniRJUsMY4EmSJDWMAZ4kSVLDGOBJ0gAiYo+IyGqmf0maEAzwJC23IuLHEfHzfvK2jIik3H7oWZQJSTvd7qyIuGaUqilJQ2aAJ2l59nVgz4jYpE3e2ymz1V+Qmfems8JLmkAM8CQtz34C3Ae8rZ4YESsChwDfAHavX6KNiJkRMT8iXhoR10TEgoj4VURM7csH/gPYulovqzQi4gMRcVW1zl0R8fWIWLNl34dGxB0R8beqhfHdVUtivcwrI+LyiFgUEbdGxGci4pndOECSJiYDPEnLrcx8EvgmMDMi6t+HrwTWBf6nn1VXAj4CHEq5YfuawElV3neB/wRuoFzafVaVBuWG5EcCWwNvAl5AuR8lABExndKqeCKwHXA28In6jiNiX+DbwFer7RwKvB74bMcvXFLjeS9aScu1iNgcuJFys/Dzq7SfACtk5v4RsQfwK2C9zJxXtcb9D/C8zLyhKn9wlTY5M5+KiFnA6zNz2iD73g84C1i5Wu8MYK3M3K9W5hTgsMyM6vlFlMvGn6qVeQ3wLWA1LyVLAlvwJC3nMvMm4CJKSxgRsSGwL6UlrT9/7wvuKncDK1Ja8voVEXtGxAURMSciHgN+ADwT2KAq8jzgDy2rXdryfEfg6Ooy8fyImA+cDqxS246k5dwzxrsCktQDvg58LSLWBmYCD1Iuj/bnyZbnfa1m/f5ojojnUPr8fQ34d8qo3B2AMyhBHkDUttWfFSiXbb/XJm/uIOtKWk4Y4EkSfJ/SF+7NlJa8/83MJ0awvceBSS1pO1ECuaMyczFARLyipcx1lH55da3Pr6BcHr55BPWT1HAGeJKWe5m5MCJOB2YBawGnjnCTtwHPiYgdgDuAx4CbKK1vR0bED4BdKAMu6k4AfhsRHwJ+BOwOvLalzCeBcyLiduD/KK2J04AXZOaHR1hvSQ1hHzxJKr5OCe4uzszrRritM4GfAr+gXDY9KDOvAt4PfAC4FngH8C/1lTLzEuAw4H3AVcBrgM8Di2plfga8HHgJpb/eH4B/owSSkgQ4ilaSelpEfAnYKzOfP951kTRxeIlWknpIdXn2AmA+sBfwz8BHx7VSkiYcW/AkqYdExHeBPYA1gFuBk4EvO7+dpKEwwJMkSWoYB1lIkiQ1jAGeJElSwxjgSZIkNYwBniRJUsMY4EmSJDWMAZ4kSVLD/H+U4I6NYlK9vAAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<Figure size 720x576 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(10,8))\n",
    "sns.histplot(x='Vintage', hue='Response', data=data)\n",
    "\n",
    "plt.title(\"Comparison of Customer responses\\nvs Customer vintage\",color='black',fontsize=18,fontweight='bold')\n",
    "plt.ylabel('Number of User Response', fontsize=14) # y axis label\n",
    "plt.xlabel('Vintage', fontsize=14) # x axis label\n",
    "plt.tick_params(axis = 'both', which = 'major', labelsize = 14)\n",
    "plt.ylim(0,6000)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "6c72dd0e",
   "metadata": {
    "id": "6c72dd0e"
   },
   "source": [
    "**Business Insight**\n",
    "\n",
    "Grafik histogram diatas menunjukan distribusi persebaran customer vintage terhadap user response.  Berdasarkan grafik tersebut, dapat ditarik kesimpulan bahwa lama masa langganan customer (vintage) tidak mengindikasikan ketertarikan customer untuk ikut serta berlangganan jasa layanan asuransi kendaraan bermotor. Hal ini bisa disimpulkan juga bahwa vintage tidak bisa digunakan sebagai salah satu variable untuk approaching customer agar tertarik menggunakan layanan jasa asuransi kendaraan bermotor yang disediakan oleh perusahaan."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "3f523d68",
   "metadata": {
    "id": "3f523d68"
   },
   "source": [
    "## Response"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "id": "e8fb3a89",
   "metadata": {
    "id": "e8fb3a89"
   },
   "outputs": [],
   "source": [
    "yes = p[p['Response'] == 1]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 50,
   "id": "42afb6a8",
   "metadata": {
    "id": "42afb6a8"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "Int64Index: 46710 entries, 0 to 381097\n",
      "Data columns (total 12 columns):\n",
      " #   Column                Non-Null Count  Dtype\n",
      "---  ------                --------------  -----\n",
      " 0   id                    46710 non-null  int32\n",
      " 1   Gender                46710 non-null  int32\n",
      " 2   Age                   46710 non-null  int32\n",
      " 3   Driving_License       46710 non-null  int32\n",
      " 4   Region_Code           46710 non-null  int32\n",
      " 5   Previously_Insured    46710 non-null  int32\n",
      " 6   Vehicle_Age           46710 non-null  int32\n",
      " 7   Vehicle_Damage        46710 non-null  int32\n",
      " 8   Annual_Premium        46710 non-null  int32\n",
      " 9   Policy_Sales_Channel  46710 non-null  int32\n",
      " 10  Vintage               46710 non-null  int32\n",
      " 11  Response              46710 non-null  int32\n",
      "dtypes: int32(12)\n",
      "memory usage: 2.5 MB\n"
     ]
    }
   ],
   "source": [
    "yes.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 51,
   "id": "5b726576",
   "metadata": {
    "id": "5b726576"
   },
   "outputs": [],
   "source": [
    "yes_nums = ['id', 'Age', 'Region_Code', 'Annual_Premium', 'Policy_Sales_Channel', 'Vintage']\n",
    "yes_cats = ['Gender', 'Vehicle_Damage', 'Vehicle_Age', 'Driving_License', 'Previously_Insured']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 52,
   "id": "cf8b9ca3",
   "metadata": {
    "id": "cf8b9ca3"
   },
   "outputs": [],
   "source": [
    "from matplotlib import rcParams\n",
    "\n",
    "rcParams['figure.figsize'] = 12, 4\n",
    "rcParams['lines.linewidth'] = 3\n",
    "rcParams['xtick.labelsize'] = 'x-large'\n",
    "rcParams['ytick.labelsize'] = 'x-large'"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 56,
   "id": "fa8830b4",
   "metadata": {
    "id": "fa8830b4"
   },
   "outputs": [
    {
     "data": {
      "image/png": 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zMUuB8cDfSVkC7ySt59wrVwMLUn2re4EbSKMP/YBxufcaSOvDfgFcnrVpM1Kto3k0iL322gsp3X+SxN57713fBvUBktbMbgJsRbrW1s/2N80eP13SHpI2zf4OfQc4jFRrr6CSmwFmtpKcRdCsvGNIC4B/DHwQmE8qGLqsIKyLxpr1TtWqV5alXx9XJmYeaQ1nZzFvkD4UH9ZJTBupuPBJnZ2rngp1sBYvXsyAAQNcB6s6WoB7cvtHZdt9wE7AIFJnaX3gHVJmzP0jYlnG24h4U9IYUqHhWVncdcDxPdB+s6bhDpZZGdkI1GQ6zyrmorFmZpmhQ4ey7777cs0117Dffvs5RXsVRMS9LJ+WXurx46mgo1TJzQAzWzmeImhmZmZV19LSQkTwqU99qnywmVkf4g6WmZmZVd0ZZ5wBwHe+8506t8TMrGe5g2VmZmZVdf/997No0SIAXn/9dWbNmlXnFpmZ9Rx3sMzMzKyqjjvuuHb7xx57bJ1aYmbW89zBMjMzs6oqjF4VvP7663VqiZlZz3MHy8zMzKpq0KBB7fbXXnvtOrXEzKznuYNlZmZmVXXeeee127/gggvq1BIzs57nDpaZmZlV1Q477LBsFGvttddmu+22q3OLzMx6jjtYZmZmVnXnnXce/fr18+iVmTWdD9S7AWZmZtb37LDDDjz55JP1boaZWY/zCJaZmZlV3fz58zn44INZsGBBvZti1pQkTZY0S9Krkl6TdL+k3YpiviIpSmxjiuI2l3SHpLclLZR0qaQ1evYn6j3cwTIzM7Oqu/jii5k9ezYXX3xxvZti1qx2AX4O7AyMAh4EpksaXRS3BBhWtP2+8KCkNYG7gfeB7YH9gd2AK2rc/l7LHSwzMzOrqvnz5/Pb3/6WiOD666/3KJZZHUTE7hFxWUQ8EhGtEXEi8CSwT4nYl4u2xbmHDwIGAwdl5/odcBRwgKSNe+SH6WXcwTIzM7Oquvjii1m6dCkAS5cu9SiWWQOQ1A9YC1hY9FB/Sc9KmifpXknjix4fDcyKiHzF8BnA0uwxK+IOlpmZmVXVLbfcQltbGwBtbW3cfPPNdW6RmQHfAtYBpuaOtQJfJo1q7QM8Atwi6dBczDDg5fyJIqINeCV7bAWSJkqaLWl2M45gu4NlZmZmVfX5z3+eAQMGADBgwAD23HPPOrfIrLlJmkTqYO0XES8WjkfErIj4ZTb1b1ZEfIPUAftmhaeOkgcjpkRES0S0DBkyZGWb3+u4g2VmZmZVNWnSJPr1Sx8x+vXrx6RJk+rcIrPmJelE4Bxgz4i4q4KnPABslNufB6xfdM4BwLoUjWxZ4jpYZmZmVlVDhw5ln3324ZprrmHfffelGe9gN5uhtw6tdxOsBEnfBY4D9oiI+yp82tbAC7n9mcAFkgZFxKLs2FjSQM3MqjW2D3EHy8zMzKpu0qRJPPPMMx69MqsTSecDhwMHAq2SCqNQ7xQSVkg6HXgIeBpYBdgPOAw4JneqacBpwDRJp5JGri4Cro2IubX/SXofd7DMzMys6oYOHcrVV19d72aYNbNjs683FB2/CvhK9v0gUmdpfeAd4Clg/4i4vhAcEW9mhYcvBGZlcdcBx9es5b1czTpYkiYA3wA+AqwKPAdcDpwbEZHFjALOA7YBXgWuBL4dEUty59mc9A/6GeBt0j/oCRHxVi5mGHABqegZwK3AMRExPxezFnAuKUPKqqQCal+PiL/lYgYA3wcmkLKszAGOjYg5K/8bMTMzM+ub5u8xv3xQnTXbNMaIUAUxx1NBRykiWoFx1WhXM6hlkov5wJmkis8jgbOA75INOUraELiTlB5yW+BI0jDm9wsnqKRydJbTfzqwMWk+6Dhgc+BGSfkLayrwOdLQ5w6AgDslrZaLOQc4NGvHJ4FngbtyQ6pmZmZmZmYdqtkIVkTcUXToWUl7AzuRRpuOBBYBh0bEUuBxSRsAZ0s6MxuhyleOLswVPQqYLumUbN7nGNII2BZZ77owevYYsCNwbzYKthewa0Tck8UcSMp8cgBwZTbCdQRp5OvmLOYQ4KXs+OlV/hWZmZmZmVkf0yNp2pV8ilTt+Z7s8GhgRta5KrgdWJ2UvaQQU65y9GhgbqFzBRARjwMvkkaqCjFtpNGwQsyrpEV9hZgW0uK+23MxS0ijbIUYMzMzMzOzDtW0gyVpbUlvAu+RFsX9NCJ+kj28QlXo3P6wjmJKVI4udZ7CufIxC/NruzqIybehVMwKmr1SdTOQ9JykKLE9nosZJekBSe9Kmifph5L617Pd1lgk7SJpiaRnio772jEzM+tDaj2C9QawFWl06CjgOEmHdRIfRV870xAxzV6pukl8ktTJLmybkjLoXAOVrSe05iZpPVLWpjuLjvvaMbOKSPqspJskPZ/d5Pt20eOHSLpH0gJJb0iaI+k/imJ26uCGYWefzcysi2qapj2b/le4W/uopH8BvkfKJrhCVejcfmEUaR6wYT6gROXoeaR1WMXWK4oZLKl/0SjWeqS8/4WYQhv+3sF5rAlFRLuhSUlfAwawPNlKJesJrUlliXh+RUqDuyqpg17ga8fMKrUm8ASpJtH5JR7/HHAzcBIpM/O/A1MlvR8R1xbFbsPyzz0Ar2NmVdMja7CKXm+V7PuZwNjsw0fBbqRU7A/nYraTNCgXU1w5eiawsaTNCgGStiR1zO7PxQwAdsnFrAOMysXMIU1l3DUX04/UeSvEmEEaYbglIv6R7VeyntCa12mkUfCzSzzma8fMKhIRt0bEKVln6b0Sjx8cEedFxJ8i4pmIOAf4H1IG5mILIuLl3PZOrdtv1kxq1sGSdIakMZI+ImlEdtf/m8Avs5BLgLWByySNlLQnKa37hbm7ttOAhaTK0Z+QtDMrVo6+C/gzcLWkT2W1taYCDwL3AUTE08BNwCWSdpS0VXbul4Brs5hFwKXADySNlzQS+DmwGvCzmvySrNeR1EKaypW/JipZT1h8Hq/dawLZe9YRwISiTlSBrx0zq6W1SZ+jit0vaX62/vPLRWVtzGwl1XIEaxCpw/I4qbNzBHAKWTGziHiBVLNqS9Lo0ZRsO7Vwgoh4kzSCNJCUJOM6UhbBQ3MxS4HxpGl9d5PWM/wN2KtQ0DgzAbiXVM36AdLPPq7ors1k4BekKYxzgM2AsRGRH0a35nY4MJd0HXam0/WEXrvX90kaDFwNfDUiujLN2NeOma00SQcDn6b9DcF5pKnJ+wF7AHcAl5HqlJY6h2/omHVDLetgHQccVybmQVIB4c5iylaOzjpAXygT8wZwWLZ1FNNGmrt8UmfnsuaUTVU9EPheUee9kvWE1nz+DfggcEvu5nA/UuWK94Ev4WvH+rAnnniCCRMm8Ktf/Yotttii3s1pKpL2InWcDo2IPxeOZ5+pWnOhs7OspcdL+m72OYhcfOHmNy0tLZUkBTMzapzkwqyPOZg0mvqLouMzgQmS+uWmgRWvJ7Tm8yfgY0XHJpFG3PcAXiB1wnztNBBJu5BmQsyNiE1zx0cB55GSA7wKXAl8O584KStqfyHwGdK/4XXACflkJZKGAReQ/p0BbiUVuJ+fi1kLOBfYh5QY5ffA1yPib7mYAaRskxOAdUizLo6NiDnV+D1Uw+TJk3nzzTc58cQTmT59er2b0zQkfZF0fX4tIqZW8JQHSGtFhwD/KBNrttJGjBjRa1+jtbW1fBDuYNVUM1xATeZw4MaI+L+i45cAXyetJzwX2IQV1xNak8n+7R/LH5M0H1gcEY9l+752GkhROv1856qQTv964Guk6eM/BwScnMWsSZqm/ihpZsa6Wcw6wBezmH7AdGApKWGTgIuBGyWNzo2MTwU+TprG9RrwQ+BOSSNz09rPIXWuDgGeJc28uEvSll2ckloTTzzxBM88k5II//Wvf+Wpp57yKFYPyNa7Xwh8uUTmwI5sTSo9Umqtlpl1Q09nETTrlSR9mvSBZ4WEJ5WsJzQrxddO4yhKp/9g0cP5dPqPR8SNpDv+R0taI4s5CBgMHBQRj0TE70j1Hw+QtHEWM4Y0AnZwRPwxmyY/AdgO2DFrx+bAXsAREXFPRDxMmpq8AXBAFrMW2brmiLg567AfQsosd0Q1fy/dNXny5Hb7J554Yp1a0ndIWlPSVlmiroHA+tn+ptnjx5Fu+B0L3Cdp/WxbN3eO4yTtK2mLLAHZMaRr+aKIWNzzP5VZ3+QRLLMKZB+EOsyyVMl6QrOIOB04veiYr53GkE+n/59Fj3WUTv+npLv/92cxsyIiX09oBmm0ajQpOc5o0tTDZVMEIuJxSS8CO5ASMY0G2kijYYWYVyU9lMVcCbSQSp7cnotZIunOLKbuCqNXBX/961/r1JI+pQW4J7d/VLbdB+xE6lj1JyUYuzQXV3gc0ue+H5BK2bSRapUey/K6jmY9av4e88sH1dnQW4d2+TnuYPWQvnoBmZn1drl0+ltHxNISGauHsbz2YkFxOv0VUu5HRJukVzqLyZ0rH7Mwv7arg5h8G/Ix25Q4P5ImAhMBhg8fXiqkqgYMGEBb2/J8CQMHDqz5a/Z1EXEvnd/o26iCc5xDml5qZjXkKYJmZta0apVOv4PYusX0dHr/fOcKYPFizz4zs+bhDpaZmTWzfDr997MU+v8JbJLtH0Rl6fRXiMky/a3bWUxmvaKYwVnq7M5i8m0oFVNXm266abv9zTbbrE4tMTPree5gmZlZMyuk098qt11KSqO/FfA/pOmBY7NEGAXF6fRnAttl9fIKxpL+zs7MxWwsaVlvQ9KWpPUw9+diBgC75GLWAUblYuaQElrsmovpR0qiUYipq3POaT8L7Uc/+lGdWmJm1vPcwTIzs6YVEW9FxGP5DViWTj9LWnEJsDYpnf5ISXuyYjr9aaQ019MkfSJb13URcG1EzM1i7gL+DFwt6VNZba2ppKyF92XteRq4CbhE0o5ZxrhpwEvAtVnMIlIn8AeSxksaSUoJvxolMp3Ww0c/+tFlo1ibbbaZU7SbWVNxB8vMzKwTlaTTj4g3SSNIA4FZpCLDM4BDczFLSYWm/07KEngn8Ddgr1wNLEip2+8FbiAVge0HjMvVwAKYTCp6fnnWps2AsRExjwZxzjnnsOaaa3r0ysyajrMImpmZ5XQ3nX6Wfn1cmZh5wBfKxLwBHJZtHcW0kYoLn9TZuerpox/9KHPmzKl3MxqGpAvpJFFJRBzTg80xsxpyB8vMzMys9mbXuwFm1jPcwTIzMzOrsYi4Kr8vaY3cGj4z60O8BsvMzMysh0jaTtITwJPZ/ickXVznZplZFbmDZWZmZlU3ffp0RowYwW233VbvpjSa80kp9v8JEBH/C3y2ng0ys+pyB8vMzMyq7uSTTwbgpJMaNg9H3WSZKfOW1KUhZlYT7mCZmZlZVU2fPp22tjYAFi9e7FGs9l6QtD0QkgZKOpFsuqCZ9Q3uYJmZmVlVFUavCjyK1c4RwFHABsCLwFbZvpn1Ec4iaGZmZlVVGL0qWLx4cZ1a0ngiYiHwH/Vuh5nVjjtYZmZ9xIgRI3rta7S2ttbkvFYfAwYMaNfJGjhwYB1b01gkDQG+BmxE7nNYRHy1Xm2yvknSZGAfYAtAwGPA9yLi9qK4UcB5wDbAq8CVwLcjYkkuZnPgQuAzwNvAdcAJLjVQmqcImpmZWVWdddZZ7fbPPvvsOrWkId0ErA3cBfxPbjOrtl2AnwM7A6OAB4HpkkYXAiRtCNwJtALbAkcChwPfz8WsCdwNvA9sD+wP7AZc0SM/RS/kESwzM+v1JK0DfIkVRwWOqVOTmtr48eM5+eSTaWtrY+DAgey+++71blIjWT0ivlnvRljfFxHF//FOlLQraVRrZnbsSGARcGhELAUel7QBcLakM7MRqoOAwcBBEfE6gKSjSJ21UyJibk/8PL2JO1hmZn3Q/D3m17sJZQ29dWg1T3cr6e7sX4Cl1Tyxdc9ZZ53FCSec4NGrFU2XtEdE3FrvhlhzkdQPWAtYmDs8GpiRda4Kbgd+CmwN3J/FzCp0rjIzSO+1owF3sIq4g2VmZn3BqhFxfL0bYVaBY4FvSXoPaCOtjYmIGFTfZlkT+BawDjA1d2wYy0ezCl7OPVb4+nI+ICLaJL2Si2lH0kRgIsDw4cNXqtG9kddgmZlZXzBV0tckDZO0bmGrd6OamQsNlxYRa0VEv4hYLSIGZfvuXFlNSZpE6mDtFxEvlgmPoq+VxLY/GDElIloiomXIkCFdaGnf4A6WmZn1BYuBc4BZwJxsm13XFjUxFxrunKQNJG0v6bOFrd5tsr4rK2Z9DrBnRNxV9PA8YP2iY4X9lzuKkTQAWJeikS1LPEXQzMz6guOBTbMaQ1ZnpQoNO9FFIum/gAOAJ4BCGuwAfl+3RlmfJem7wHHAHhFxX4mQmcAESf1y67B2I6VifzgXc4GkQRGxKDs2ljRQUzy90HAHy8zM+obHSR8IrAG40HCn9gZGRMR79W6I9W2SzielXD8QaJVUGIV6J5ew4hLg68Blks4FNgHOBC7M1biaBpwGTJN0Kmnk6iLgWmcQLM0dLDMz6wuWAI9IugdY9sHVadrro3///ixZsqTdvi3zLDCA3HVqViPHZl9vKDp+FfAVgIh4QdI44FzS1OrXgCnAtwvBEfGmpDGkQsOzgHdIhYadWKgD7mCZVUDSYNIdnb2AfwX+AfxXRFyaiylbCd3MaubGbLMG8IEPfKBdB+sDH/DHjZy3STcD7sY3A6yGIkIVxj1IKiDcWUwrMK4a7WoGfsczKyOrYP574CXSMPvzpLSkA3IxhUro1wNfAzYjVU8XcDJmVlMRcVW922DLvffee53uN7mbs61LskQYJwBbAcOB0yLie0UxZW/0SdqcNBLxGVJn7zrghNx0MDNbSe5gmZU3GVgdGJ+bM/9cUUwlldCtl8o+kFwCrBcR/ybp46RsTN8r81TrIZLmUiJdcER8pA7NMevQStwMWJOUGGMacH7xg5Xc6MtuGN4NPEoasVg3i1kH+GI322VmRZym3ay8fUmVzM+TNE/SU5LOkbR6LqajSuirkyqhW+92GXAKqSgoEfEo/jDSaFqAT2bbZ4CfAFfXtUVmJUjaTNJ1kp6Q9GxhK/e8iLg1Ik6JiGspvX4rf6Pv8Yi4kZSY4GhJa2QxBwGDgYMi4pGI+B1wFHCApI2r8fOZmTtYZpXYBNgPWAP4PHASKcXuZbmYFaqcs2Il9GUkTZQ0W9LsBQsWVL/FVm2rR8RDRcfer0tLrKSI+Gdueykizgd2qXe7zEr4BWlE/H1gZ+CXwNQqnLeSG32jgVm5DHIAM4Cl2WNmVgXuYJmV1w/4J+mu4OyIuJmUOecgSet28rwOK6E3e4XzXmihpE3I/i0l7UcqvGgNQtI2ua1F0hHAWvVuV7Pacsst2+2PHDmyTi1pSKtFxN2AIuL5iDid6twMqORG3woxEdEGvIJvBppVjddgmZU3D3guIvIjFo9nXz9M+sNUSSV0672OIqWt3ULSS8Bc4OD6NsmK/Dj3/fukdZL716cp9sILL7Tbf/755+vUkob0rqR+wF8lfZ2UQGlojV6rwxt9ncQuPxAxhfTeR0tLSyXnMDPcwTKrxB+AnST1z2ViGpF9fS77WkkldOulIuJZYEy2jqFfRLxR7zZZexGxc73bYMt9/vOf59e//vWy/T333LOOrWk43yBN2zuGVP5jF+DLVThvJTf65gEb5gMkDSAlu/DNQLMqcQfLrLwfke6E/zSriv7B7NgvI+LVLKaSSujWy0gqWURRSqVFIuLcHm2QrUDSwRFxdUf/Vv43qo/999+/XQfrgAMOqGNrGktE/Cn79k3gkCqeupIbfTOBCyQNiohF2bGxpKnwM6vYFrOm5jVYZmVExP8Ce5CylP0vaYHyDaSMTYWYF0gF+LYkVUIvTKs4tafba1W1Vra1kP69N8i2I4CPlnuypAmS5kh6VdI7kp6UdIIKPbQUM0rSA5LezbJU/lBS/xr9PH1RITvaWh1sVgdXXdU+E/mVV15Zn4Y0IEmbS7pM0gxJvytsFTxvTUlbSdoKGAisn+1vmoVcAqxNutE3UtKerHijbxqwEJgm6ROSdgYuAq6NiLlV/lHNmpZHsMwqkC1I/mSZmLKV0K13iYgzACTNALYpTA2UdDrw3xWcYj7pA04rKa3yZ4CLSWuELnCB6pUXET/Lvp5R77bYcjfddFO7/RtvvJGzzjqrTq1pOP8NXErKRLukTGxeC3BPbv+obLsP2CkiXpA0DjiXdKPvNdKNvm8XnhARb0oaQyo0PAt4h1RouOQIsJl1jztYZmblDQcW5/YXAxuVe1JE3FF06FlJewM7ARfgAtVVk9XwOZr077Lsb1tEePFPHUREp/tN7v2IuKSrT4qIe0k3XzqLKXujLyJaSTMuzKxG3MEyMytvKvCQpBuy/b2BqzoOX1E2LfCTpFoz38sOd1S35qekujX3r0Sbm82NwBXALaSaPmYNJVfW4xZJk0hTzZcVDI6IV+rSMDOrOnewzMzKiIjvS7qNNMUvgEMioqLskJLWJqVhHgj0B86IiJ9kDw9jxYXlHRaozs43EZgIMHz48K78GH3du7nfq1kjmkN6/yiMQk3OPRbAR3q8RWZWE+5gmZlVZglpZCTo2gjJG8BWpLTM2wM/lPSPiLi8g/hO69a4Lk2HLpD0HWAG7UcF/ly/JpktFxEb17sNZtYznEXQzKwMSccCvwIGkwqCXi3p6EqeGxFLI+KZiHg0Ii4Fzmb5FEEXqK6ej5EShZxFKjr8Y1I5hU5VK9NjlhnuDklvS1oo6dKsblo+Zpik30halG3XSBpaFLNWlmHun5LeknSbpE2KYgZIOjtryzuS7pe0bZd/YzWU+/WV3G9Gkg6WNKHE8a9JOqgebbLeQdJ6kq7IZlIg6aOSDq13u6xj7mCZmZV3KDAqIr4TEf8JfJr0Yb47+gGrZN/PBMZKyr8Xu0B19/w78JGI2DEids62XSp4XiHT4/bASFIH7bukIrDkMj22AtuSEpMcDny/cAJJawJ3k7JDbk+qm7cbaU1YIaYfMB3YmFR3aBywOXCj2vc+pgKfA/YDdiBNJ7tT0mq5mHNI1+ThpHV9zwJ3SSrurNfNhz70oXb7G264YQeRTeUE0lrBYtdmj5l15ErgDlIdToCnSQWrrUG5g2VmVp5on055CWWyeQFIOkPSGEkfkTRC0teAbwK/zEIqqVtjlflfYJ2uPiki7oiIGyPiyYh4NiKuIk0z3CkLyWd6fDwibgROA47OjVAdRBrdPCgiHomI35HSZx+QZTcEGANsAxwcEX/Msr1NALYDdoQ0CgbsBRwREfdk6/wOJNVeOyCLWYtUh+2UiLg5Ih4jFat9LzveEF588cVO95tU/0Kph7ys4O+AOrTHeo/BEfEbsunpEfE+XUvxbz3Ma7DMzMr7BfDHoiyCV3QcvswgUr2bDYB3SSMNp2THqKRujVVsPeApSX+i/RqsitO0r0Smx9HArIh4PRczg/RhaDQwt/A1S5FdaNvjkl4kjVTdm8W0kUbDCjGvSnooi7mSVAtplawNhZglku7MYjr62Xo0OUpxWvalS53YERggaY3imydZp3lgndpkvcNbkv6VbG2upE8Dr3f+FKsnd7DMzMqIiHMl3cvyKVsVZRGMiOOA48rEuEB1dXynu0+sQqbHYRStmYuINkmvdBaTO1c+ZmFEFN+ZLo7JtyEfs02J8xfa4+Qo9XcFcJ2kIyPiOQBJGwEXUdkNG2texwM3A5tImgkMIU0jtgblDpaZWQckfZI0NeO2LBvdn7Pje0rqFxFz6ttCK4iI+yR9GNgsIu6StDqps1SJqmZ67CC2UWKsTiLiR5LeBO7L1u0F8BZwVncKD1vziIg/S9oRGEG6ydcaEW11bpZ1omYdLEmTgX2ALUgXw2PA9yLi9qK4UcB5pDtvr5KmQHw7fwcvm5d+IakGzdvAdcAJ+WF2ScOAC0gLiwFuBY6JiPm5mLVIU3H2AVYFfg98PSL+losZQFq8PIE0n38OcKw/SJk1pXOAr5Q4/gRpNKCSJAp1MfTWoeWD+pBsfdtEYF1gE9K0zEtJCSM6lU3/eybbfVTSv5CmCF5OZZke5wHtsjhkf0vWLYoZU+Ll1yuKGSypf9Eo1nqkRe2FmEIb/t7BeaxBZZlEL806WCq1JkvSl7O1gGYASNqn6NDmkl4H/pL/nGuNo5ZJLnYBfg7sDIwCHgSmSxpdCHB2JjNrcP9amMqTFxHPAP/a882xThxFWsO0CCAi/kpKqd8dXc30OBPYTtKgXMzY7DwzczEbS9qsECBpS1LH7P5czAByHXdJ65D+hhZi5pDWmO2ai+lH6rwVYqzBRcSbpTpXmWN7tDHWGxxKuuHzH9l2GWna4MxSqf+t/mrWwYqI3SPisiyjUmtEnAg8SRo9KnB2JjNrZKt18tganTxmPe+9iFhc2JH0ASqYMlelTI/TgIXANEmfkLQzaV3NtRExN4u5izTF9GpJn8pmb0wl3Xy8DyAingZuAi6RtKOkrbJzv0RK5V3IOHcp8ANJ4yWNJN3MXA34WVd/adaQXDTMii0FtoyIfSNiX+CjpM+mo0jvV9ZgemwNVnaHbS3SH6GCPpGdyXoHSesBPwA+GBG7S/oosF1EeHGxdeQuSd8nTVte9mFd0hnA7+rXrPLm79H4s0aqPI3xPknfAlaTNBaYBNxSwfNWOtNjRLwpaQxpKvss4B3SVPbjczFLJY0HfkL6OxTAbcDR+WuLdHPwPOAGlk9lHxcR7+RiJgOLSXe018naNTYi5tEgBg4cyOLFi9vtW8W8ls6KbRQR/5fbnw9sHhGvSPJarAbUk0kuvkX6QzA1d6zXZ2fq6dS3tlKuJKXbPjXbf5p0V9gdLOvICaQPsc9IeiQ79glgNnBYvRplJX2T9G/yF9IU71tJ/3adqlamx+wG37gyMfOAL5SJeYP0c3R4fWWL20/Ktoa077778utf/3rZ/n77OeFZF3gEy4r9QdJ04L+z/X2B32ezvV6rW6usQz3SwZI0idTB2jMiylUb7FXZmZz6tlcZHBG/kXQKpEJ9klyozzqUTQE7UNJHgJHZ4ccj4tl8nKSREfF4jzfQgGUzJB6NiH8jrU2wOps0aRLXXnstS5cupX///kyaNKneTepNim88mx1F6lSNJnXAfwlcn41+71zPhllptUxyAYCkE0mJI/aMiLuKHq40O1O7mA6yM5VKQlEyO1OZmHwbSsVY7+VCfdYtEfFsRNySbc+WCJla4pj1kGya+f9K8jSCBjF06FAOOOAAJHHAAQcwZMiQejepYUg6VtIgJVdI+nM2DRWAiPh6PdtnjSeS6yLiuIj4Rva9b+o3sJp2sCR9l1T8cY8SnStwdibrWcWF+n4JHF3fJlkf4Sk99TcMeFzS3ZJuLmz1blQzGzNmDJIYN67TmZPN6KtZspJxpIKxhwBn1bdJ1sgkfVrSnyS9KWmxpCWSFtW7XdaxTqcIlsi7305E/LaT555Pmgd/INCaS3P+Ti5hxSXA10nZmc4l1S4plZ3pNFJ2plNJI1edZWc6mvRh5yKKsjNJKmRnOpQ0cvEDirIzSSpkZ5pHSqAxGWdn6hNcqM9qyHcS6++MejfA2jvzzDNZunQpZ5xxBrfffnv5JzSPwg2ZPYBfRMT/FpWUMSv2U+CLpDVYLcCXgE3r2iLrVLk1WJ/Pvg4lLfAtZM3amZSZr8MOFsvrONxQdPwqssKdzZSdqdmKfjYiF+oz63skrUoqo7EpKcHFFRHxfn1bZU888QTPPfccAHPnzuWpp55iiy22qG+jGsccSTNItTtPyUrELC3zHGtyEfFMrgj5LyQ9UO82VUNf/XzcaQcrIg4ByDKXfLTQyZA0jDRC1NlzK7ob4+xM1oMOJdVGuyfb34k0yrm5pO9GhNfRWHctLh9iNXIVqQTHH4DdSfVhXKi1zo47rn1ixm984xsexVruUGAr4NmIeDtbG3xIfZtkDe5tSQOBRySdTcoZUFEtRkmfJWXE3QoYDpwWEd/LPf4VUoblYmPzy3uyerIXAp8hLeW5DjghN+PMcirNIrhR0QjO/wGb16A9ZrVUKNT3f7CsLtYlpHV4v8eJCqwTkjYAPkzufTMifp99/XS92mV8NCI+BiDpCuChOrfHYNnoVcHcuXNLBzanIN0IGA98l/RBedW6tsga3QRS7oGvk8pKbAh0uownZ03gCdKSm/M7iFkCfKjo2CuFbyStSZoh9ihpUGRdUoHzdUhTF61IpR2seyXdAfya9MbwRZaPAlgFmrDoZyNyoT7rFkn/BRxA+iNVSO0fpI651dey/7tZ6YV6tsUyksjP0O/Xr+ZJi3uTi0k3/HYhdbDeAK4HPlnPRllD2zsiLiAVQz8DUjZK4IJyT4yIW0l1AQt/yzqK6yxb9kHAYOCgQh4FSUcB0yWdksuJ0GV99fNxRR2siPh6tn7lM9mhKRFRvLbKrNG5UJ91197AiIh4r94NsRV8IpdNS8Bq2b5I2Y0HdfxUq5Vdd9213ZTAXXfdtZPopjMqIraR9DBARLyaTf8y68iXWbEz9ZUSx7qrv6RnSUndWoEfRcT03OOjgVm5JHUAM0g3CkaTksK1I2kiMBFg+PDmq6BRcaHhLGNgZ0ktzBrdUaQh9R2y/YeAYdn8YRfqs848Syrz4A5Wg4mI4tqGJUn6l4h4tdbtseTUU09t18E69dRT69iahtOW1eQs1GQcgpNcWAmSDiSNHm1cVHZiLeCfVXqZVlIH7lFSB+sA4BZJh0XEFVnMMIrqwUZEm6RXssdWEBFTSInraGlpabpMu+XStN8fETtIeoP2aYh9Z9B6nYgISX8jrbnan3TH5fpyz5N0OqmeW7HNIuKZLGYUKUPlNsCrwJXAt7NsP9b7vU1aXHw3uU5WRBxTvyZZF91N+v9pPWDo0PZTalxouJ2fkLIZD5X0fWA/ctmTzXIeICW0GAz8OHf8DVKHaKVFxCxSlu6CWZLWBb4JXFH6We1PUY129DXlsgjukH1dq2eaY1Z9WeabL5Jqsv2TVPdMEdGVUavnSBkI8xZk598QuJPUWfsasBlp8aeAk1em7dYwbs426728OKsH/exn7UtHXn755Rx2WIcJfJtKRPxK0hzgc6Trcu+IeHJlzyvpOVIinmJPRMTISrPFWeOIiOeB51nx80etPUD75BXzSIk1lpE0gJTsorO1W02r4imCZr3YU6QUzp/PjTgd1/lTVrCkkwWgRwKLgEMjYinweJZx7mxJZzqFae8XEVdlayQK2VNdpLr38V3WHnTuuee22z/nnHOavoOVjQoUzCclDlv2WES8suKzuuSTQH7K7Bqk2nDX5I51mi3OGlOWB+G/SHVpRe1nkm0NvJDbnwlcIGlQRBTWvI4lZTacWaM29GruYFkz2Jcs86Wk20l/bLp6N/tDkl7Mvv8LcGZEFIr8jQZmZJ2rgttJlde3Bu7vdsutIUjaiVRv6TnStbOhpC8X0rSbmVVgDqmjn//7U9gP4CMrc/KIWJDfl/Q10trRK4riPOLQ+5xNuknc5ZHOLMX6ptnuQGB9SVsBb2bFi08nrUl/GliFNGX1MCA/BX4acBowTdKppJGri4BrVyaDYF/mDpb1eVnGyxuybIF7k2pIrCfpEuCGiJhR5hR/BL5EGglbmzRi9QdJu0XEnaQFnsV3cAp/wEou/mz27Dq90I+BcVnR88K0018D29a1VdYVniJodRURG/fwSx4O3BIR/8gdK5ctzhrT/63ENNIW2pdWOirb7gN2AgaROkvrA++QPuvsHxHL1qhHxJuSxpAKDc/K4q4Dju9mm/o8d7CsaWRT9X4F/CqbqvEF0hqpTjtYEXFb0aE/ZFMAJ5PWXpV8WtHX4nM2dXadXmhAoXMFEBFPZ/PPrc6Kpl2tIDft6nM90BzLbLnlljz55PLPgyNHjqxjaxqPpH8hrdddVmC4miPiklpIN4Dy6RsryRZXfB7fDGwMsyVdC9xI+0RLZbN7R8S9dHKDKSKOp4KOUvY3cFwFbTXcwbImlX3o+lm2dccslldRn0e685NX2O/WVIwRI0Z0s1n1f43W1tbyQb3PbElXAFOz/f8gTfex+is17apg2bSrKqxvsS54+umn2+0/9dRTdWpJ45F0GHAsaS3UI8CnSX9TdqniyxxOypS77AZid7LF+WZgwxhEymab7+AELp/UsNzBMuue/ALQmcAESf1y67B2I70ZPlyPxlnVHUmaUnEM6YP874GL69oiA+oy7coqsGTJkk73m9yxpIQUD0bEzpK2AM6o1sklDSJlzf1eRJTrFBVni7MGFBGH1LsN1jXuYJmVIelcYDopwcEgUir2scBeWcglwNeBy7LYTYAzgQudQbBviIj3gHOzzRpUraddmVXJuxHxriQkrRIRT0mq5pSCg0nJDEqlZC9WnC3OGoikkyLibEkXUmLJgWsxNi53sMzKGwb8EhgCvE6avz4mIn4HEBEvSBpH+vA9B3iNNKWiKoUj5+8xvxqnqamhtw4tH9QLSfpNROwv6S+U/uP28To0y0rooWlXZtXwoqR1SOtp7pT0KvCPTp/RNYcDN0bE/+UPVpgtzhrL4ZJmsnwqtPUS7mCZlRERB1YQ8yCwfQ80x3rWsdnX8XVthVWiptOuzKolIv49+/Z0SfeQstPeXo1zS/o08HFKJy0omy3OGs6FwI9IN3qvBX4dEY/UtUVWEXewzMw6EBHzsq/P17stVlatp12ZrRRJqwNthSLl2fW5DfB8RCyuxmtkN/tKZoyrNFucNY6IOB84X9KHSWvlfiFpVVKZkF9HxF/r2T7rWL96N8DMrNFJekPSoqLtBUk3SFqp4qBWNcXTrm6iutOuzFbW7cBGAJI2JU1h/QhwlKSz6tgua3AR8XxE/FdEbA0cBPw7aQTSGpRHsMzMyjuX9GF9Gunu8BdJ02xagZ+TijVaHdVy2pVZlfxLbsThy6QRiKMlDSStsTm5fk2zRpbVXdyN9Lfnc6QiwZ4C3cDcwTIzK2+3iBiV258i6cGI+K6kb9WtVbaMpHwV1LnZ1/WBv9ehOWal5JMU7AKcAxARiyUtLf0Ua2aSxpJS7v8/UoKSa4CJzlDc+NzBMjMrb6mk/YHrsv39co85s1Nj+B+WFxxeFdiYNMI4sp6Nalbrrrsur7yyvLbzv/7rv9axNQ3jUUk/Al4CNiUrApxNbTUr5VukmRMnulh67+IOlplZef8BXEAqLhzAg8DBklYj1UCzOouIj+X3JW1DSldtddC/f/92+/36eck3qYbisaR1WOMi4u3s+EdJmeLM2omInevdBused7DMzMqIiGeBz3fw8P092RarTET8WdIn692OZrVgwYJO95tRRLwDrJDMIiIeAB4o7Eu6PiL27cm2mVl1+ZaSmVkZkjaXdLekx7L9j0vqtJC0pMmSZkl6VdJrku6XtFuJuFGSHpD0rqR5kn4oqX+pc1rHJB2f206UNA3wp3rrjZyZ1KyXcwfLzKy8y4BTgDaAiHiUlM2pM7uQMgzuDIwiTSucLml0IUDShsCdpLVC2wJHkqa1fb/K7W8Ga+W2VUhrsvaqa4vMusfrOs16OU8RNDMrb/WIeEhqV7/z/c6eEBG7Fx06UdKuwD7AzOzYkcAi4NCIWAo8LmkD4GxJZzpTVOUiwimLzcysIbiDZWZW3kJJm5DdWZa0HzCvKyeQ1I80urIwd3g0MCPrXBXcDvwU2Bqv76qYpM2BE0kJBJb9bYuIXerVJrNuUvkQM2tkniJoZlbeUcDPgC0kvQR8Aziii+f4FrAOMDV3bBjwclHcy7nHViBpoqTZkmY7cUA7/w08DHwbmJzbOlXNtXLZWr07JL0taaGkSyWtURQzTNJvJC3KtmskDS2KWUvSZZL+KektSbdlHfx8zABJZ2dteSdr97YV/7ZqrGi0d4X9ZiZpfHbDpSPf7LHGmFlNuINlZlZGRDwbEWOAIcAWwE7ADpU+X9IkUgdrv4h4sdzLFX0tbsuUiGiJiJYhQ4ZU2oRm8H5EXBIRD0XEnMJWwfOqslZO0prA3aSpo9sD+wO7AVfkYvoB00k1usYC44DNgRvVvgcyFfgcqd7aDqQRjTuzsgAF5wCHZu34JPAscJek9Sv4mWtuwIABne43uS8Cf806yFsWPxgRM+rQJjOrInewzMw6IGmQpFMk/VTSWOBt4MvAM6QP0JWc40TSh+E9I+KuoofnAcUfiAv7xSNb1rlbJE3KRojWLWzlnhQRu0fEZRHxSES0RsSJwJOktXIF+bVyj0fEjcBpwNG5EaqDgMHAQdm5fkca+TxA0sZZzBhgG+DgiPhjRDwITAC2A3aEZVMd9wKOiIh7IuJh4EBgA+CALGYt0gjqKRFxc0Q8BhwCvEfXR1ZrYvXVV2+3v8Yaa3QQ2Xwi4mDSFOC/Ab/IRlAnZv+uZtYHuINlZtaxqcAI4C+kIqEzgC8Ae0dE2Qx1kr4LfAfYo0TnClKyi7FF04V2I3XkHl7JtjebL5OmBD4AzMm22V09SRfXyq1O+qBciJkVEa/nYmYAS7PHCjFzI6K1EBARjwMvsnxEdDQpW+XduZhXgYdyMS2kTIm352KWkEbZKh5ZraXXXnut3f6rr75an4Y0qIhYBFwPXEOaDvzvwJ8lHV3XhplZVTjJhZlZxz4SER8DkHQ56UP38Ih4o9wTJZ1Pmr51INCam7r1Tu5D+CXA14HLJJ0LbAKcCVzoDIJdExEbl4+qSEdr5WYWxRWvlVthPV1EtEl6pbOY3LnyMQuzDlNnMfk25GO2KXF+JE0EJgIMHz68VIj1EEmfB75K+v8+FfhURMyXtDpp9PTCerbPzFaeO1hmZh1rK3wTEUskza2kc5U5Nvt6Q9Hxq4CvZOd8QdI44FzSiMtrwBRSogbrIknbs2IWwV924fmFtXJ7ruxauQ5i6xYTEVNI1xYtLS2us1RfXwDOi4jf5w9GxNuSvlqnNplZFbmDZWbWsU9IWpR9L2C1bF9ARMSgjp4YERWlTcvW4Wy/0i1tcpKmkkYEHgEKoz8BVNTBytbKnUH318rNAzYsOucAYN2imDElXn69opjBkvoXjWKtBzydiym04e8dnMca13fIlXnIkpesFxHPRcTdHT/NzHoLr8EyM+tARPSPiEHZtlZEfCD3fYedK6uLFmB0REyKiKOz7ZhKnliltXIzge0k5a+LsaS/szNzMRtL2iz32luSOmb352IGkLIbFmLWIWU4LMTMISW02DUX04/UeXPttMb336S1eQVLsmNm1ke4g2VmZn3BY6w4ylRWtlZuMimbX6uk9bNt7VzYJcDapLVyIyXtyYpr5aaR1uhNk/QJSTsDFwHXRsTcLOYu4M/A1ZI+JWkUaQ3Og8B9ABHxNHATcImkHSVtlZ37JeDaLGYRcCnwg6ym0khSqvnVSPXarLF9ICIWF3ay7wfWsT1mVmWeImhmZn3BYOAJSQ+RRncgTeMsl+2xKmvlIuJNSWNICQpmAe8A1wHH52KWShoP/ISUJTCA24CjIyK/LmoCcF7WplWB3wPjIuKdXMxkYDFwOSkpxxxgbETMwxrdAkl7RsTNAJL2on3WSjPr5dzBMjOzvuD03PcipSs/sNyTqrlWLku/Pq5MzDxSkoPOYt4ADsu2jmLagJOyzXqXI4BfSfop6Vp9AfhSfZtkZtXkDpaZmfV6EXFfNp3uIFIR6LmkaXRmDSUi/gZ8WtKagLqQmdTMegl3sMzMrNeStDnwRdJo1T9J65QUETvXtWFmRSQdHBFXSzq+6DgAEXFuXRpmZlXnJBdmZtabPQV8Dvh8ROwQEReyPE271ckqq6zS6X6TWiP7ulYHm1nVSfqspJskPS8pJK1QZ1HSKEkPSHpX0jxJP5TUvyhmc0l3SHpb0kJJl0pao/hclngEy6yLJO0C3AnMjYhNc8dHkRambwO8ClwJfLuolo2ZVde+pBGseyTdDlxDWtdidfTee+91ut+MIuJn2dcz6t0WayprAk+QspGeX/ygpA1Jn2muB74GbEbKSirg5CxmTVJinkdJa1HXzWLWIb3/WhF3sMy6QNJ6pOxidwL5zlXZNygzq76IuAG4IbuTujdwHLCepEuAGyJiRj3bZ1Yg6SedPV5p3bZOzn86qZ5bsc0i4pksxjcCm0xE3ArcCiDpv0qEHAksAg6NiKXA45I2AM6WdGZWiuIgUqbWgyLi9excRwHTJZ2SK0VhGXewzCqUFfL8Fam2zarkOlhU9gZlZjWS/R/7FSk727qkTH0nA+5gWaOY0wOv8RywXdGxBeAbgdah0cCM7LNLwe3AT4GtScXLRwOzCp2rzAxSwezRpKRCluMOllnlTiPVrTkb+M+ixyp5gzKzHhARr5AK7rrorjWMiLgqvy9prXQ43qziyyyJiJc7eMw3Aq2UYcDMomMv5x4rfG13XUVEm6RXcjHtSJoITAQYPnx41RrbWzjJhVkFJO1Mql0yoagTVbDCmw8rvkHlzzdR0mxJsxcsWFDdxpqZWcOS9G+SHgYeIxXHniNpZJVO/yFJL2bbbZLytds6uhG4OulGoFlBFH2tJLb9wYgpEdESES1DhgypXst6CXewzMqQNBi4GvhqJ3cGS+nwDarZ33jMzJrYFOD4iPhwRAwHTgAuq8J5/0gqWLwHqWzBq8AfJI3NHu/SjUDwzcAmMQ9Yv+hYYf/ljmIkDSAlu+jK56Km4SmCZuX9G/BB4JZCvRLSzQlJep/0B62SNygzM7M1IuKewk5E3FuNdNcRcVvRoT9kUwAnk9ZelXxa0dfic04hdQhpaWmpZDTDep+ZwARJ/XKjm7sBbwMP52IukDQoIhZlx8aSPgsVTy803MEyq8SfgI8VHZsEjCfdKXyB1Akr9wZl1mOG3jq03k0ws9KelXQaMDXbP5jaJQmYBeyTfe8bgU0oS7FeSMo1EFhf0lbAm1l2yUuArwOXSToX2AQ4E7gwty5vGmkd+jRJp5JGri4CrnUGwdI8RdCsjIh4KyIey2/AfGBxtv866Q1qbdIb1EhJe7LiG5SZmdlXgSHAb7NtMHBIjV5ra9JNQEgjDWOzjLgFvhHY97WQ/n0fJk0FPSr7/nKAiHgBGAdsScp0WRi1PLVwgiwRyxhSB20WcB0pi+ChPfVD9DYewTKrgoh4QdI44FzSG9RrpDeoFSqmm5lZ85G0KilZ0qbAX4ATIqKtiuc/F5hOStU+iJSKfSywVxZSyUiF9TERcS9liq9HxIOkAsKdxbSSOmJWgZp2sCR9lrR4cytgOHBaRHyvKKZs0TtJmwMXAp8h3Wm5jvTG9FYuZhhwAeluDKSiasdExPxczFqkD8D7kOoY/R74ekT8LRczAPg+MIFUoXoOcGxE9ET9CuslIuJ04PSiY2XfoMxqqbW1tSbnHTFiRM1fw6wJXAW0AX8AdieNGHyjiucfBvySNDr2OvAoMCYifge+EWjWk2o9grUm8ARp7ub5xQ9WUvQumzt6N+mNYnvSvM+fkzo/X8xi+pHu2iwl3a0RcDFwo6TREVFYmDkV+DiwH+mN5YfAnZJGRsQ7Wcw5pM7VIcCzwEnAXZK27GIGOTMzM7OCj0bExwAkXQE8VM2TR8SBFcT4RqBZD6hpBysibiWNJCHpv0qEVFL07iDS/OSDChWkJR0FTJd0Sra4bgxpBGyLbAgTSRNINSZ2BO7NRsH2AnYtZO+RdCBpYecBwJXZCNcRpJGvm7OYQ4CXsuOnV/UXZGZmZs1i2XTAiHg/l5XWzPqYeie5qKTo3WhgVqFzlZlBGq0anYuZW+hcAUTE48CLwA65mDbSaFgh5lXSHaRCTAuwStaGQswS0ihbIcbMzMysqz4haVG2vQF8vPC9pEVln21mvUa9k1wMY8X8+cVF71YojBcRbZJe6Swmd658zML82q4OYvJtyMdsU+oHkDQRmAgwfPjwUiFmZmbW5CKif73bYGY9o94jWKV0WvSug9i6xUTElIhoiYiWIUOGVHAaMzMzMzPrq+o9glVJ0bt5wIb5gCzT37pFMWNKnH+9opjBkvoXjWKtBzydiym04e8dnKdbXPTTzMzMzGy5vvr5uN4drJnABEn9cuuwiovezQQukDQoIgpzlMeSRt9m5mL+U9JmEfFXAElbkjpm9+diBgC7kNZUIWkdYBQpKyGktKXvAbsCl2Ux/UidtynV+7HNKtdX33zMzMzM+qKaThGUtKakrSRtRar+vH62v2kWcgmwNqno3UhJe7Ji0btpwEJgmqRPSNoZuAi4NssgCHAX8GfgakmfymprTQUeBO4DiIingZuASyTtmLVpGilD4LVZzCLgUuAHksZLGknqfK0G/KwWvyMzMzMzM+s7aj2C1QLck9s/KtvuA3aqpOhdRLwpaQyp0PAs4B1SoeHjczFLJY0HfkLKEhjAbcDRuRpYkOpbnQfcwPJCw+NyNbAAJgOLgctZXmh4bETMo4tc9NPMzMzMbLlm+Hxc6zpY95KK/nYWU7boXZZ+fVyZmHnAF8rEvAEclm0dxbSRiguf1Nm5zHrK/D3m17sJZXkao5mZmVnSiFkEzczMzMzMeiV3sMzMzMzMzKrEHSwzMzMzM7MqcQfLzMzMzMysStzBMjMzMzMzqxJ3sMzMzMzMzKrEHSwzsxqR9FlJN0l6XlJI+naJmFGSHpD0rqR5kn4oqX892mtmZmYrzx0sM7PaWRN4glRX7+XiByVtCNwJtALbAkcChwPf78E2mpmZWRW5g2VmViMRcWtEnBIR1wLvlQg5ElgEHBoRj0fEjcBpwNGS1ujBpja1ao00Stpc0h2S3pa0UNKlxf+OkoZJ+o2kRdl2jaShRTFrSbpM0j8lvSXpNkmbFMUMkHR21pZ3JN0vadtq/l7MzKx73MEyM6uf0cCMiFiaO3Y7sDqwdaknSJooabak2QsWLOiJNjaDlR5plLQmcDfwPrA9sD+wG3BFLqYfMB3YGBgLjAM2B26UpNxLTgU+B+wH7AAIuFPSarmYc4BDs3Z8EngWuEvS+t38HZiZWZW4g2VmVj/DWPED/cu5x1YQEVMioiUiWoYMGVLTxjWLKo00HgQMBg6KiEci4nfAUcABkjbOYsYA2wAHR8QfI+JBYAKwHbAjpFEwYC/giIi4JyIeBg4ENgAOyGLWAo4ATomImyPiMeCQrO1HVO83Y2Zm3eEOlplZY4mir1Z/lYw0jgZmRcTruZgZwNLssULM3IhoLQRExOPAi6SRqkJMG2k0rBDzKvBQLqYFWCVrQyFmCWmUrRBjZmZ14g6WmVn9zAOKp3QV9leYqmZ1U8lI4woxEdEGvNJZTO5c+ZiFWYeps5h8G0rFtOOppWZmPecD9W6AmVkTmwlMkNQvNzqyG/A28HD9mmUV6MpIY91jImIKMAWgpaXFo6NWdUNvHVo+yKxJeATLzKxGJK0paStJWwEDgfWz/U2zkEuAtYHLJI2UtCdwJnBhRLxVn1ZbCZWMNK4QI2kAsG5nMZn1imIGl6iFVhyTb0OpGDMzqxN3sMzKkDRB0hxJr2bpkJ+UdEI+65eLxVoHWkgjUQ+Tpm4dlX1/OUBEvEDKJLclMIc0wjAFOLUejbUOzQTGZlkAC4pHGmcC20kalIsZS/o7OzMXs7GkzQoBkrYENgTuz8UMAHbJxawDjMrFzCEltNg1F9OPlESjEGNmZnXiKYJm5c0njSq0kj7UfAa4mJSO+YJcCufrga8BmwE/J6VWPrkeDbbGEBH3kq6DzmIeJKX1tjrJUqwXRhWXjTQCb0bEM6SRxq+TRhrPBTZhxZHGaaTMgtMknUoauboIuDYi5mYxdwF/Bq6WdDTp2rgIeBC4DyAinpZ0E3CJpEOB14EfAC8B12YxiyRdCvxA0jxgLjAZWA34WbV/P9Y7SJoM7ANsQbq2HgO+FxG352K+AvyixNPHRsRdXX3N1tbW8kHdMGLEiJq/hlktuYNlVkZE3FF06FlJewM7ARfQPoXzUuBxSRsAZ0s601O9zBpeC3BPbv+obLsP2CkiXpA0DjiXNHr0GmmkcVlB4oh4U9IY4EJgFvAOcB1wfC5mqaTxwE9IWQIDuA04OiLy66ImAOcBNwCrAr8HxkXEO7mYycBi0mjoOlm7xkbEPKxZ7UK6ufcn0vX3NWC6pB0jYmYubgnwoaLnvtIzTTRrDu5gmXVBNi3wk6RUyt/LDneUwvmnpBTOK0zZkTQRmAgwfPjwWjbZzMqo1khjln59XJmYecAXysS8ARyWbR3FtJEKI5/U2bmseUTE7kWHTpS0K2lUa2ZRrNfq2TKSTge+U+KhzbJRfCSNIt342QZ4FbgS+HaJjKeG12CZVUTS2pLeJE0RnAX8NCJ+kj3sYrFmZtZQsnV5awELix7qL+nZbL3wvdmoqtlzpM8s+W0uQG4pRCuwLWnmzuHA9+vR0N7AI1hmlXkD2IpUWHR74IeS/hERl3cQ72KxZmZWT98iTR+dmjvWCnwZeJS0Zu8A4BZJh0XEFcUn8GyLprKkk5FNL4XoIo9gmVUgIpZGxDMR8WhEXAqczfIpgi4Wa2ZmDUPSJFIHa7+IeLFwPCJmRcQvI+KR7PtvkDpg3yx1Hs+2aCofkvRitt0mKT8luqOlEKuTlkJYEXewzLqnH7BK9n0lKZzNzMxqTtKJwDnAnhVmBnwA2KimjbJG90fgS8AewIGkNVZ/kDQ2e7zLSyEkTZQ0W9LsBQsW1KDJjc1TBM3KkHQG8AfgWVJ9ms+S7vYVUt1WksLZzMyspiR9FzgO2CMi7qvwaVsDL9SuVdboIuK2okN/yKYATiatvSr5tKKvxecs1HWkpaWl6ZZLuINlVt4g4FJgA+BdUkfrlOwYlaRwNjMzqyVJ55MSDxwItEoqTFV/JyJez2JOBx4CnibNwtiPlK3ymJ5urzW8WaQMlOClEF3mDpZZGRFxHOmOYGcxLhZrZmb1dGz29Yai41cBX8m+H0Qqbr0+qVbWU8D+EXF9TzTQepX8yOZMYIKkfrl1WF4K0Ql3sMzMzMx6uYjotJZbFnM8ueLXZgDZ8obppFTtg0hFqscCe2UhXgrRRe5gmZmZmZk1r2HAL4EhwOukNP5jIuJ34KUQ3eEOlpmZmVVkxIgRNX9ua2trt1/DzLouIg6sIMZLIbrAadrNzMzMzMyqxB0sMzMzMzOzKvEUQTMzM6tIpdP3Sk0H9NQ/M2sWHsEyMzOzqiruTLlzZWbNxB0sMzMzMzOzKnEHy8zMzMzMrEq8BsuswQ29dWi9m2BmZmZmFfIIlpmZmZmZWZV4BMvMzKyJrUzx4Hq/hpNnmFkjcgfLrAHV6kND/kOOP5iYmZmZVZ+nCJqZmZmZmVWJR7DMzMwMgPl7zK93E8py4h8za3TuYJmZmRngzouZWTV4iqCZmZmZmVmVuINlZmZmZmZWJe5gmZmZmZmZVYk7WGZmZmZmZlXiDpaZmZmZmVmVOIugmZlZE3NhczOz6vIIlpmZmZmZWZW4g2VmZmZmZlYl7mCZmZmZmZlViTtYJUjaQ9Ijkt6T9Jyk4+vdJqsfSZMlzZL0qqTXJN0vabcScaMkPSDpXUnzJP1QUv96tNl6F7/nmJmZ9R3uYBWR1ALcBNwObAWcDvxA0hF1bJbV1y7Az4GdgVHAg8B0SaMLAZI2BO4EWoFtgSOBw4Hv93hrrVfxe451lzvm1h2+bsxqz1kEV3Q88KeIODnbf1LSSOCbwKW1etF8tqVaPc9ZnLonInYvOnSipF2BfYCZ2bEjgUXAoRGxFHhc0gbA2ZLOjIi3atU+Xzu9nt9zrMtyHfMfAweSbv5cKuntiPB1YyXV67oBXzvWPb31uvEI1opGk+4k590ObCTpQ8XBkiZKmi1p9oIFC3qkgVZfkvoBawELc4dHAzOyzlXB7cDqwNYlzuHrxgr8nmPdsaxjHhFPRsSVwIWkjrlZR3zdmPUAd7BWNAx4uejYy7nH2omIKRHREhEtQ4YMqXnjrCF8C1gHmJo75uvGusvXjnWHO+bWHV26bsysezxFsGuiVif2sHbvIGkSqYO1Z0S8WCY8ir7WhK+dPs3vOdaRch3zdu9PETEFmALQ0tLS7evK102v16XrRtJEYCLA8OHDV+qFfe1Yd/TW68YjWCuaB6xfdGy97Gvxm5I1EUknAueQOld3FT1c6rop7Pu6sc74PceqraY3dazPWuG68Yi5Wfe4g7WimcCuRcd2A56vYMTC+ihJ3wW+A+xRonMF6boZm63PKtgNeBt4uAeaaL2X33OsO9wxt+7wdWPWA9zBWtF5wKckfV/SFpK+BBwNnFXndlmdSDofmAxMAFolrZ9ta+fCLgHWBi6TNFLSnsCZwIW1zCBofYLfc6w73DG37vB1Y93mFP+VcwerSET8CdgbGA/8L+lD8qm1Tl9qDe1YYFXgBtLdv8J2QSEgIl4AxgFbAnNIax2mAKf2dGOtd/F7jnWTO+bWHb5urFtcs7FrnOSihIj4H+B/6t0OawwRoQrjHgS2r3FzrA/ye451VUT8SdLewA+AE0nTu9wxt075urGVUJeajb2VO1hmZma9kDvm1h2+bqybRgNXFB27HThR0oeKp5hWMwNlb+QpgmZmZmZm1hnXbOwCd7DMzMzMzKy7XBqiiDtYZmZmZmbWGaf47wJFuNNZLZIWAM/30MsNBhb20GsBfDgimm+Mtwf08HUDPXvt+LqpIb/nWHf4urHu8N+q5iZpGun3NDp37Gxg/4jYqMxzm+49xx2sXkrS7IhoqXc7rPfxtWPd4evGusPXjXWXr53GIumTwAPA2cBU4FPAz4DjGikLZaNcN54iaGZmZmZmHXLNxq5xmnYzMzMzM+uUU/xXziNYvdeUejfAei1fO9Ydvm6sO3zdWHf52rHuaIjrxmuwzMzMzMzMqsQjWGZmZmZmZlXiDpaZmZmZmVmVuINlZmZmZmZWJe5gmZmZmZmZVYk7WGZmZmZmZlXiDpaZmZmZmVmV/H9eDPVd+Lo/PAAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<Figure size 864x288 with 6 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "features = yes_nums\n",
    "for i in range(0, len(features)):\n",
    "    plt.subplot(1, len(features), i+1)\n",
    "    sns.boxplot(y=p[features[i]], color='green', orient='v')\n",
    "    plt.tight_layout()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 55,
   "id": "1738bddf",
   "metadata": {
    "id": "1738bddf"
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\Hafiz\\AppData\\Local\\Temp/ipykernel_10112/754702108.py:4: MatplotlibDeprecationWarning: Passing non-integers as three-element position specification is deprecated since 3.3 and will be removed two minor releases later.\n",
      "  plt.subplot(2, len(yes_nums)/2, i+1)\n",
      "C:\\Users\\Hafiz\\AppData\\Local\\Temp/ipykernel_10112/754702108.py:4: MatplotlibDeprecationWarning: Passing non-integers as three-element position specification is deprecated since 3.3 and will be removed two minor releases later.\n",
      "  plt.subplot(2, len(yes_nums)/2, i+1)\n",
      "C:\\Users\\Hafiz\\AppData\\Local\\Temp/ipykernel_10112/754702108.py:4: MatplotlibDeprecationWarning: Passing non-integers as three-element position specification is deprecated since 3.3 and will be removed two minor releases later.\n",
      "  plt.subplot(2, len(yes_nums)/2, i+1)\n",
      "C:\\Users\\Hafiz\\AppData\\Local\\Temp/ipykernel_10112/754702108.py:4: MatplotlibDeprecationWarning: Passing non-integers as three-element position specification is deprecated since 3.3 and will be removed two minor releases later.\n",
      "  plt.subplot(2, len(yes_nums)/2, i+1)\n",
      "C:\\Users\\Hafiz\\AppData\\Local\\Temp/ipykernel_10112/754702108.py:4: MatplotlibDeprecationWarning: Passing non-integers as three-element position specification is deprecated since 3.3 and will be removed two minor releases later.\n",
      "  plt.subplot(2, len(yes_nums)/2, i+1)\n",
      "C:\\Users\\Hafiz\\AppData\\Local\\Temp/ipykernel_10112/754702108.py:4: MatplotlibDeprecationWarning: Passing non-integers as three-element position specification is deprecated since 3.3 and will be removed two minor releases later.\n",
      "  plt.subplot(2, len(yes_nums)/2, i+1)\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1440x360 with 6 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "features = yes_nums\n",
    "plt.figure(figsize=(20, 5))\n",
    "for i in range(0, len(yes_nums)):\n",
    "    plt.subplot(2, len(yes_nums)/2, i+1)\n",
    "    sns.kdeplot(x=p[features[i]], color='green')\n",
    "    plt.xlabel(features[i])\n",
    "    plt.tight_layout()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 57,
   "id": "ef0ef80b",
   "metadata": {
    "id": "ef0ef80b"
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 864x288 with 5 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "df_sample = yes.sample(1000, random_state=1000)\n",
    "for i in range(0, len(yes_cats)):\n",
    "    plt.subplot(2, 3, i+1)\n",
    "    sns.countplot(x = df_sample[yes_cats[i]], color='green', orient='v')\n",
    "    plt.tight_layout()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "e8524062",
   "metadata": {
    "id": "e8524062"
   },
   "source": [
    "dari total 381109 customer terdapat 46710 customer yang tertarik dengan asuransi kendaraan. mayoritas customer yang tertarik adalah pria, kendaraannya pernah mengalami kerusakan, kemudian mayoritas umur kendaraannya 1-2 tahun dan sebelumnya belum pernah diasuransikan."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "1686b2e9",
   "metadata": {
    "id": "1686b2e9"
   },
   "source": [
    "dengan mengetahui karakteristik customer yang tertarik terhadap asuransi kendaraan (0), kita dapat memprioritaskan target marketing kepada customer-customer dengan karakteristiknya yang sama."
   ]
  }
 ],
 "metadata": {
  "colab": {
   "collapsed_sections": [],
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