{
 "cells": [
  {
   "cell_type": "markdown",
   "id": "a3648bac-0bfd-4d9a-bfdf-8a35340b0fe8",
   "metadata": {},
   "source": [
    "# Travel Assured"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "93c8fb45-5c13-4dd2-98a7-b25898d5589d",
   "metadata": {},
   "source": [
    "## Introduction\n",
    "Travel Assured is a travel insurance company. Due to the COVID pandemic they have had to cut their marketing budget by over 50%. It is more important than ever that they advertise in the right places and to the right people.\n",
    "\n",
    "Travel Assured has data on their current customers as well as people who got quotes but never bought insurance. They want to know if there are differences in the travel habits between customers and non-customers - they believe they are more likely to travel often (buying tickets from frequent flyer miles) and travel abroad."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "6781d9fa-1fc2-4713-83d1-659389fe09f1",
   "metadata": {},
   "source": [
    "## The Data\n",
    "- `age`: Numeric, the customer’s age\n",
    "- `Employment Type`: Character, the sector of employment\n",
    "- `GraduateOrNot`: Character, whether the customer is a college graduate\n",
    "- `AnnualIncome`: Numeric, the customer’s yearly income\n",
    "- `FamilyMembers`: Numeric, the number of family members living with the customer\n",
    "- `ChronicDiseases`: Numeric, whether the customer has any chronic conditions\n",
    "- `FrequentFlyer`: Character, whether a customer books frequent tickets\n",
    "- `EverTravelledAbroad`: Character, has the customer ever travelled abroad\n",
    "- `TravelInsurance`: Numeric, whether the customer bought travel insurance"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "baf53c82-1c29-449d-a5e6-4489aafc7d9a",
   "metadata": {},
   "source": [
    "## Import Libraries\n",
    "Let us set up all the objects (packages, and modules) that we will need to explore the dataset."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "f34546be-a752-480b-82a1-455179ecf4e3",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Import necessary modules\n",
    "import numpy as np\n",
    "import pandas as pd\n",
    "%matplotlib inline\n",
    "import matplotlib.pyplot as plt\n",
    "import seaborn as sns\n",
    "plt.style.use('ggplot')"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "edd0872f-3483-46fc-b0e0-b82cfe3e9833",
   "metadata": {},
   "source": [
    "## Load the Data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "954c282f-d80d-4ead-87d3-bb0d517f998b",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Age</th>\n",
       "      <th>Employment Type</th>\n",
       "      <th>GraduateOrNot</th>\n",
       "      <th>AnnualIncome</th>\n",
       "      <th>FamilyMembers</th>\n",
       "      <th>ChronicDiseases</th>\n",
       "      <th>FrequentFlyer</th>\n",
       "      <th>EverTravelledAbroad</th>\n",
       "      <th>TravelInsurance</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>31</td>\n",
       "      <td>Government Sector</td>\n",
       "      <td>Yes</td>\n",
       "      <td>400000</td>\n",
       "      <td>6</td>\n",
       "      <td>1</td>\n",
       "      <td>No</td>\n",
       "      <td>No</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>31</td>\n",
       "      <td>Private Sector/Self Employed</td>\n",
       "      <td>Yes</td>\n",
       "      <td>1250000</td>\n",
       "      <td>7</td>\n",
       "      <td>0</td>\n",
       "      <td>No</td>\n",
       "      <td>No</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>34</td>\n",
       "      <td>Private Sector/Self Employed</td>\n",
       "      <td>Yes</td>\n",
       "      <td>500000</td>\n",
       "      <td>4</td>\n",
       "      <td>1</td>\n",
       "      <td>No</td>\n",
       "      <td>No</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>28</td>\n",
       "      <td>Private Sector/Self Employed</td>\n",
       "      <td>Yes</td>\n",
       "      <td>700000</td>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "      <td>No</td>\n",
       "      <td>No</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>28</td>\n",
       "      <td>Private Sector/Self Employed</td>\n",
       "      <td>Yes</td>\n",
       "      <td>700000</td>\n",
       "      <td>8</td>\n",
       "      <td>1</td>\n",
       "      <td>Yes</td>\n",
       "      <td>No</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   Age               Employment Type GraduateOrNot  AnnualIncome  \\\n",
       "0   31             Government Sector           Yes        400000   \n",
       "1   31  Private Sector/Self Employed           Yes       1250000   \n",
       "2   34  Private Sector/Self Employed           Yes        500000   \n",
       "3   28  Private Sector/Self Employed           Yes        700000   \n",
       "4   28  Private Sector/Self Employed           Yes        700000   \n",
       "\n",
       "   FamilyMembers  ChronicDiseases FrequentFlyer EverTravelledAbroad  \\\n",
       "0              6                1            No                  No   \n",
       "1              7                0            No                  No   \n",
       "2              4                1            No                  No   \n",
       "3              3                1            No                  No   \n",
       "4              8                1           Yes                  No   \n",
       "\n",
       "   TravelInsurance  \n",
       "0                0  \n",
       "1                0  \n",
       "2                1  \n",
       "3                0  \n",
       "4                0  "
      ]
     },
     "execution_count": 2,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Read file\n",
    "df = pd.read_csv('data/travel_insurance.csv')\n",
    "\n",
    "# Split the data in two subgroups, to compare data with/without Travel Insurance\n",
    "insured_df = df[df['TravelInsurance'] == 1]\n",
    "uninsured_df = df[df['TravelInsurance'] == 0]\n",
    "df.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "9f82c3de-6926-4b14-a48d-ab69d4448e34",
   "metadata": {},
   "source": [
    "## Data Size and Structure\n",
    "\n",
    "- Dataset comprises of 1987 observations and 9 columns.\n",
    "- 8 independent variables: `Age`, `Employement Type`, `GraduateOrNot`, `AnnualIncome`, `FamilyMembers`, `ChronicDiseases`, `FrequentFlyer`, `EverTravelledAbroad`\n",
    "    - 3 numerical features, 5 categorical features\n",
    "- 1 dependent variable: `TravelInsurance`\n",
    "- Complete dataset with no missing/null values."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "c1eae5b1-4014-4da1-a72f-f5f54e6fced9",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Shape: (1987, 9)\n",
      "\n",
      "\n",
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 1987 entries, 0 to 1986\n",
      "Data columns (total 9 columns):\n",
      " #   Column               Non-Null Count  Dtype \n",
      "---  ------               --------------  ----- \n",
      " 0   Age                  1987 non-null   int64 \n",
      " 1   Employment Type      1987 non-null   object\n",
      " 2   GraduateOrNot        1987 non-null   object\n",
      " 3   AnnualIncome         1987 non-null   int64 \n",
      " 4   FamilyMembers        1987 non-null   int64 \n",
      " 5   ChronicDiseases      1987 non-null   int64 \n",
      " 6   FrequentFlyer        1987 non-null   object\n",
      " 7   EverTravelledAbroad  1987 non-null   object\n",
      " 8   TravelInsurance      1987 non-null   int64 \n",
      "dtypes: int64(5), object(4)\n",
      "memory usage: 139.8+ KB\n",
      "None\n"
     ]
    }
   ],
   "source": [
    "# Check the size and datatypes of the DataFrame\n",
    "print(f\"Shape: {df.shape}\")\n",
    "print('\\n')\n",
    "print(df.info())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "b74920c3-4f94-4aa9-9b84-913a1e83d6e4",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Private Sector/Self Employed    1417\n",
      "Government Sector                570\n",
      "Name: Employment Type, dtype: int64\n",
      "\n",
      "\n",
      "Yes    1692\n",
      "No      295\n",
      "Name: GraduateOrNot, dtype: int64\n",
      "\n",
      "\n",
      "0    1435\n",
      "1     552\n",
      "Name: ChronicDiseases, dtype: int64\n",
      "\n",
      "\n",
      "No     1570\n",
      "Yes     417\n",
      "Name: FrequentFlyer, dtype: int64\n",
      "\n",
      "\n",
      "No     1607\n",
      "Yes     380\n",
      "Name: EverTravelledAbroad, dtype: int64\n",
      "\n",
      "\n",
      "0    1277\n",
      "1     710\n",
      "Name: TravelInsurance, dtype: int64\n",
      "\n",
      "\n"
     ]
    }
   ],
   "source": [
    "# For all categorical data columns\n",
    "# Print categories and the number of times they appear\n",
    "# ChornicDiseases and TravelInsurance at categorical because 1 and 0 represent yes and no\n",
    "CAT_COLS = ['Employment Type', 'GraduateOrNot', \"ChronicDiseases\", \"FrequentFlyer\", \"EverTravelledAbroad\", \"TravelInsurance\"]\n",
    "\n",
    "for col in df[CAT_COLS]:\n",
    "    print(df[col].value_counts())\n",
    "    print('\\n')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "0e4e6159-47ac-42ab-80de-7c4d0bb688bd",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Age</th>\n",
       "      <th>AnnualIncome</th>\n",
       "      <th>FamilyMembers</th>\n",
       "      <th>ChronicDiseases</th>\n",
       "      <th>TravelInsurance</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>1987.000000</td>\n",
       "      <td>1.987000e+03</td>\n",
       "      <td>1987.000000</td>\n",
       "      <td>1987.000000</td>\n",
       "      <td>1987.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>29.650226</td>\n",
       "      <td>9.327630e+05</td>\n",
       "      <td>4.752894</td>\n",
       "      <td>0.277806</td>\n",
       "      <td>0.357323</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>2.913308</td>\n",
       "      <td>3.768557e+05</td>\n",
       "      <td>1.609650</td>\n",
       "      <td>0.448030</td>\n",
       "      <td>0.479332</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>25.000000</td>\n",
       "      <td>3.000000e+05</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>28.000000</td>\n",
       "      <td>6.000000e+05</td>\n",
       "      <td>4.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>29.000000</td>\n",
       "      <td>9.000000e+05</td>\n",
       "      <td>5.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>32.000000</td>\n",
       "      <td>1.250000e+06</td>\n",
       "      <td>6.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>35.000000</td>\n",
       "      <td>1.800000e+06</td>\n",
       "      <td>9.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "               Age  AnnualIncome  FamilyMembers  ChronicDiseases  \\\n",
       "count  1987.000000  1.987000e+03    1987.000000      1987.000000   \n",
       "mean     29.650226  9.327630e+05       4.752894         0.277806   \n",
       "std       2.913308  3.768557e+05       1.609650         0.448030   \n",
       "min      25.000000  3.000000e+05       2.000000         0.000000   \n",
       "25%      28.000000  6.000000e+05       4.000000         0.000000   \n",
       "50%      29.000000  9.000000e+05       5.000000         0.000000   \n",
       "75%      32.000000  1.250000e+06       6.000000         1.000000   \n",
       "max      35.000000  1.800000e+06       9.000000         1.000000   \n",
       "\n",
       "       TravelInsurance  \n",
       "count      1987.000000  \n",
       "mean          0.357323  \n",
       "std           0.479332  \n",
       "min           0.000000  \n",
       "25%           0.000000  \n",
       "50%           0.000000  \n",
       "75%           1.000000  \n",
       "max           1.000000  "
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "e2f89fbf-3cfe-4da2-8c22-26c145dc43c8",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Age                    0.0\n",
       "Employment Type        0.0\n",
       "GraduateOrNot          0.0\n",
       "AnnualIncome           0.0\n",
       "FamilyMembers          0.0\n",
       "ChronicDiseases        0.0\n",
       "FrequentFlyer          0.0\n",
       "EverTravelledAbroad    0.0\n",
       "TravelInsurance        0.0\n",
       "dtype: float64"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Percentage of dataset is null\n",
    "df.isnull().sum() / len(df) * 100"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "a146f1c2-bb78-43db-b1dd-e4c2bd38de71",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "EverTravelledAbroad\n",
       "No     412\n",
       "Yes    298\n",
       "dtype: int64"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "insured_df.value_counts('EverTravelledAbroad')"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "24364e63-0b11-4606-b7d5-8fc315681d74",
   "metadata": {},
   "source": [
    "## What are the travel habits of the insured and uninsured?\n",
    "Now let's first look at the how many people have travel insurance and their travel habits."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "07d79030-5872-424a-882b-371763a0f4be",
   "metadata": {},
   "outputs": [],
   "source": [
    "LABELS = ['No', 'Yes'] \n",
    "EMPLOYMENT_LABELS = ['Private Sector/Self Employed', 'Government Sector'] \n",
    "\n",
    "def label_fontsize(fontsize):\n",
    "    for item in ([ax.title, ax.xaxis.label, ax.yaxis.label] + ax.get_xticklabels() + ax.get_yticklabels()):\n",
    "        item.set_fontsize(fontsize)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "32526865-afe8-4b53-8a1d-ee78ec0629b1",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1080x576 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "insured_travelled = insured_df.value_counts('EverTravelledAbroad').values\n",
    "uninsured_travelled = uninsured_df.value_counts('EverTravelledAbroad').values\n",
    "\n",
    "x = np.arange(len(LABELS))  # the label locations\n",
    "width = 0.35  # the width of the bars\n",
    "\n",
    "fig, ax = plt.subplots(figsize=(15, 8))\n",
    "rects1 = ax.bar(x + width/2, insured_travelled, width, label='Insured', color='forestgreen')\n",
    "rects2 = ax.bar(x - width/2, uninsured_travelled, width, label='Uninsured', color='firebrick')\n",
    "\n",
    "# Add some text for labels, title and custom x-axis tick labels, etc.\n",
    "ax.set_xlabel('Ever travelled abroad')\n",
    "ax.set_ylabel('Count')\n",
    "ax.set_title('Count of people that have travelled abroad by whether they are insured')\n",
    "ax.set_xticks(x)\n",
    "ax.set_xticklabels(LABELS)\n",
    "ax.legend(prop={'size': 20})\n",
    "\n",
    "ax.bar_label(rects1, padding=3, fontsize=20)\n",
    "ax.bar_label(rects2, padding=3, fontsize=20)\n",
    "\n",
    "label_fontsize(20)\n",
    "\n",
    "fig.tight_layout()\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "dc393b0e-39d9-4bd4-8855-a64eb2680384",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1080x576 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "insured_freqfly = insured_df.value_counts('FrequentFlyer').values\n",
    "uninsured_freqfly = uninsured_df.value_counts('FrequentFlyer').values\n",
    "\n",
    "x = np.arange(len(LABELS))  # the label locations\n",
    "width = 0.35  # the width of the bars\n",
    "\n",
    "fig, ax = plt.subplots(figsize=(15, 8))\n",
    "rects1 = ax.bar(x + width/2, insured_freqfly, width, label='Insured', color='forestgreen')\n",
    "rects2 = ax.bar(x - width/2, uninsured_freqfly, width, label='Uninsured', color='firebrick')\n",
    "\n",
    "# Add some text for labels, title and custom x-axis tick labels, etc.\n",
    "ax.set_xlabel('Frequent Flyer')\n",
    "ax.set_ylabel('Count')\n",
    "ax.set_title('Count of people that are frequent flyers by whether they are insured')\n",
    "ax.set_xticks(x)\n",
    "ax.set_xticklabels(LABELS)\n",
    "ax.legend(prop={'size': 20})\n",
    "\n",
    "ax.bar_label(rects1, padding=3, fontsize=20)\n",
    "ax.bar_label(rects2, padding=3, fontsize=20)\n",
    "\n",
    "label_fontsize(20)\n",
    "\n",
    "fig.tight_layout()\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "a7827cc9-cec4-41bc-a709-03f73adc1940",
   "metadata": {},
   "source": [
    "The countplot shows that a large proportion of people that have never travelled abroad are uninsured. However for those that have, a significant proportion have gotten travel insurance. This pattern is also seen for people that aren't/are frequent flyers. Indicating that people that have travelled abroad or are frequent flyers are more likely to get travel insurance."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "8bd5290e-8780-4425-b4ff-6643fb3c6595",
   "metadata": {},
   "source": [
    "## Other demographic information\n",
    "Now that we have an understanding of their travel habits, let's explore other demographic information."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "ceaace25-1fda-4740-abc3-c69d0eb84059",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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kWwkwsur1WLlypZFkihcv7vB9f/HiRRMREWEPz4DrIcAAMqFs2bJGksuD+PU88cQTRpIZNWqU07Rdu3YZd3d3U7RoUYf2mw0wChcu7PLLulChQiZPnjwObTcbYNzM9txKgPHvE1Bjrnw5pl9VsX//fmPMlRN2Dw8PU716dZfL27x5s5FkBg4caG9r3LixkWTmzZvn1H/hwoVGkomMjHRoT/9Rtm/fPqd50rfz6hM7VycN6bVER0e7rDX9h8D//vc/l9P/LSMnnJnZN67nueeeM5Kc/sXvVgOMBQsWOE1Lv+IgI/86a4wxGzZsMJLM22+/7dCe/q+8M2fOdGgfPny4kWQ+++wze1tWvzeZrfVa0sPByZMn37Bv+j5Xt25dp2nJycnGZrM5/Qtm+r774osvOs1z8OBBI8kUKlTI4YdVuh49ehhJ5ocffrC3pX9+u3fv7tR/3759Ln+guDq2Pfvss0aSGTFixA23+1r++OMPI8k89NBDTtOuPpZkNMB4+OGHb7jOF1980eU+l65du3bGw8PD6V+AXUlLSzM5c+Z0OBadOXPGSDJ16tQxaWlp150/PaT54osvrlvrv8MNV9L3rX+HFOnSP89Lly61t82cOdP+I+dq6T/IMvoZ+P77750+ryEhIaZz585mwIABxtPT05w/f94YY8zvv//uMkxPDw/27NnjtPz0UPHqoOVm3seMBhh3ooaM/pi+WlZ/n1SuXNnYbDaXV3empKSYPHnymBo1aji0V69e3dhsNofwOiUlxRQsWNAEBAQ4hKU3w9Vnypj/f9y6ViCQO3duExoa6hSGG3Ml4HZzczOdOnXKUA3XCzAyeuzOzDHpVgKMrHo90s8fx4wZc836CDBwIwziCdwBGzdulCQ1bNjQaVqpUqVUsGBBxcbG6syZM8qVK9ctraty5cry8PBwag8PD7/uvd6ZcSe3R5Lq16/v1Obh4aEHH3xQe/fu1aZNm1S4cGHFxMQoNTX1mvd4Xr58WZIc7r3fuHGj3N3dXd6fXL9+fXl4eGjTpk1O0woVKqSiRYs6tTdo0EBvv/22y3mulv5enDlzxmWtJ06ccKr1VmV231i5cqU+++wzrV69WnFxcU5jURw+fDhLByGrXr26y9okOd2rfPLkSQ0fPlyzZ8/Wvn37dOHCBafarvbYY4/p22+/1Q8//KCWLVva23/44Qd5eno6PBknq9+bzNZ6LWvWrJGbm5uaNWuWof6S69fU09NT+fLlc3n/tyTVrFnTqS19f65Xr548PT2dpjds2FA//fSTNm3apJ49e96whmu9r66sWbNGktS8efMb9r2W9GPWjY4lN1KuXDlVrlxZEyZM0D///KO2bdvqwQcfVPXq1eXl5eXQN30/WrZsmWJiYpyWFRcXp9TUVO3evVvVqlWTdOUYNWrUKP3yyy/avn27zpw543AP+dX7Ss6cOdW6dWvNmDFDlStXVseOHVWvXj3VqlXL6ckp6bVs2bLF5T69e/duSVf26XLlyt3wdZCu7Avu7s5DqTVo0EDLli3Tpk2b7K938+bNVbRoUY0bN04ffPCBvb5vvvlGNptNTzzxRIbWmf6ds2jRIj3//PPaunWr4uLi1KhRI4WHh+ujjz7S8uXL1bx5c/u4F66+p3LlyqUSJUo4tbvaL2/mfcyIO1WDq89zVsjo90liYqK2bNmi4OBgffrppy6X5e3t7XQ8feaZZ9S7d2+NGTNGr732miRp9uzZOnTokJ5++mn5+/tnqM7MfKau5up12717t06dOqWSJUvqvffeczlfjhw5suR7O6PH7swck25FVr0e1zsWP/jggy73KeDfCDCATMifP7927NiR4R8c6dIHJrrW6Pr58+fXgQMHdPr06Vv+wR8YGOiy3Waz3dSAga7cye2RpHz58rlsDw0Ndajn5MmTkqSYmBiXJ3npzp8/b//vM2fOKHfu3C6/6G02m4KDgxUXF3fTNV1Leq0LFizQggULMlTrrcrMvjFlyhRFR0fLx8dHTZo0UfHixeXn5yd3d3ctXbpUy5Ytczn4bFbXZ7Nd+ZpKTU21t50+fVo1atRQbGysatasqZ49eyp37tyy2Ww6ffq0PvvsM6fa6tSpo1KlSmn69OlKSEhQUFCQNm7cqK1bt6pdu3YKDg62983K9+Zmar3esoKCgpQjR44M9Zeu/55f/ZpeLX0fvlpGPvPpNWakBlfv67WkL/NWHq2aXv+NPrc34uHhocWLF+udd97Rb7/9pldeeUWSFBAQoEcffVRDhw61/7BK34+GDx9+3WVevR916dJFU6ZMUbFixdS2bVuFhobK29tbkvTpp5867SsTJ07UBx98oPHjx9sHSvbx8VF0dLQ++ugj+/am1/Ltt99muJYbycwx0N3dXX369NGrr76qiRMnqlevXtqwYYM2btyodu3aZfjpVOHh4SpZsqSWLVum1NRULVq0SJLUqFEjhYaGytPTU4sWLVLz5s21aNEi5cyZUzVq1HBazvU+F5Ljfnkz72NG3KkaMrpvZ1ZGv08SEhJkjNGJEyf09ttvZ3j5Xbt2Vf/+/fXtt9/q1Vdflbu7u7755htJUp8+fTK8nMx+ptK5et3S34c9e/Zcd1uy4ns7o8fuzByTbkVWvR7XOxann3MBN0KAAWTCgw8+qMWLF2vRokV6/PHHMzxf+o/4Y8eOqXjx4k7Tjx496tBPujJ6eUpKisvlufqRcCfdzPbciuPHj7tsP3bsmMN60v//pZde0ogRIzK07Fy5cunUqVO6fPmy078sp6SkKD4+Xjlz5rzpmq63Xkn67LPP9Pzzz2eo1jvpzTfflJeXl9avX6+yZcs6TOvTp499lP3s8N133yk2NlaDBw92+tfk1atX67PPPnM5X8+ePfXGG29o4sSJeuqpp+xPRXj00Ucd+mXle3OztboSGBiokydP6uLFi5kKMTLr36PiS46feVey+jN/tfQT+cOHD9/0Y03T67rR5zYjgoKC9Mknn+iTTz7R33//rWXLlmnUqFH64osvdPr0aY0bN85hnWfOnHF5DPm39evXa8qUKWrcuLHmzJlj/yErXXlS0ocffug0T44cOTRkyBANGTJEBw8e1PLly/X999/rp59+0v79++1P5UivZcuWLapYsWKGt/V6MnsM7N27twYPHqxRo0apV69eGjVqlKTM/RiVrlxRMWrUKMXExGjRokUqXLiw/XuoZs2aWrhwoY4cOaKdO3eqVatWt/wvupl9H2+HW6nB1ef5TkqvvUqVKvZ/fc+IHDly6LHHHtMnn3yi+fPnq3z58pozZ45q1aqlSpUqZWgZN/OZSne942D79u01efLkDG/L7ZbRY1L6FVPXO7e8VnCSVa/H1cfiYsWKOUxLP+cqWLBghpaF+xePUQUyoVevXvL09NTvv/+u7du3X7fv1al+lSpVJMnlY0P//vtvHTp0SEWLFnX44ggKCtLBgwed+qempmrz5s03Vf+/pZ/YZeRfQa92M9tzK1z9WE5NTdUff/zhUE/NmjXl7u7u9Ci966lSpYrS0tK0fPlyp2nLly9Xamqqqlat6jTtwIEDLh/1mP6apNd0LREREZKUqVqvx93dPdPv4/X8/fffKleunFN4kZaWZn/db3cN16tNkjp27Og07XrBSs+ePeXu7q4ffvhBly9f1oQJExQcHOxwS4mUte/NzdbqSkREhIwxLh/RfLul789//PGHy5PfJUuWSJLLz8qtSn8/5syZc9PLSK/rRseSzCpRooQef/xxLVu2TP7+/po2bZp9Wmb3o/R9pU2bNg4/tKQrj5C9ePHidecPDw9X9+7dNW/ePJUoUUJ//PGH/V9Is/p4I13ZF1xd1XetY2DevHkVHR2ttWvXauXKlZowYYKKFi2qpk2bZmq9jRo1kiTNmzdPy5cvt/+dPu3PP//UxIkTHfreipt57W72uzUra7gVWXks9/f3V/ny5bVt2zadOnUqU/M+/fTTcnNz06hRozR69GilpqZmKvC61c/Uv5UpU0aBgYFas2aN/ZZUq7neMSkoKEiSXJ5b/v3335l+jOnNvB7XOxb/8ccfd+QcAnc/AgwgE4oUKaIhQ4YoOTlZLVu2vOYzyOfOnetwv3bv3r0lSe+99579/nnpysnNgAEDlJaW5nRFR82aNXXgwAHNnz/fof29997TP//8kyXbkydPHklXfoxnxs1sz61YvHix07Plv/jiC+3du1eRkZEqXLiwJCkkJETdu3fX+vXr9e6777r8Ity7d69iY2OdtmXQoEFKTEy0tycmJurVV1+VJJfbkpqaqldeecXhBD42NlYjR46UzWZTjx49rrtN1atXV7169TR58mSNGTPGZZ+//vrL5e0rruTJk8flScnNKlKkiPbs2aMjR47Y24wxGjJkyDXDu6yu4Xq1Sc4B2qZNmzR06NBrzhceHq6GDRtqzZo1+uyzz3TixAl169bN6cqbrHxvbrZWV5577jlJUv/+/V3expbZW9syo2DBgmrSpIn279/vdB/72rVrNX78eAUFBal9+/ZZvu6nn35aNptN7777rst979ChQzdcRp06dVS6dGktX77c4YRe+v/HkoyIjY3Vvn37nNoTEhKUlJTkcGXMs88+K09PT7300kv2MSaulpyc7PCD9Fr7SlxcnPr27es0/4kTJ/TXX385tV+4cEHnz5+XzWaz3xrXq1cvBQYG6u2339a6deuc5klLS3MZSF/Pnj179OWXXzq0TZs2TcuWLVOJEiVUr149p3mefvppSVcu6z9//rz+85//uBxH43oiIyPl5uamL7/8UmfOnHEIKRo2bChjjIYNG2b/+1Zl9n2Ubv67NStruBVZfSzv16+fkpOT1bt3b5dXkCYkJLi8OqNkyZJq1KiRZs6cqa+//lqBgYHq2rVrhteb2c/UjdhsNj333HM6evSonn/+eZcByNGjR2/4D1xZKTPHpDJlyihnzpyaNm2aw/fXxYsXb+pqw5t5PR577DFJ0vvvv+8QaF26dEmDBg3KdA24P3ELCZBJr732mlJSUvT222+rRo0aqlOnjqpXry5/f38dP35cy5cv1549exwGYKpTp45efvllffjhh6pQoYKio6Pl5+enOXPmaOvWrXrwwQc1cOBAh/UMGDBA8+bNU9u2bdWlSxflzp1bq1atUmxsrBo0aJDpk01XateuLV9fX3366ac6efKk/R7H55577rqXgt/M9tyK1q1bq3379mrfvr1KlCihzZs3a86cOcqdO7fTCfQXX3yhPXv26K233tK4ceP04IMPKl++fDpy5Ih27NihmJgY+7/8SVK3bt00bdo0TZo0SeXLl1e7du3k5uamqVOnKjY2Vl26dFH37t2daqpYsaLWrl2ratWqqWnTpjp9+rQmTZqk06dP68MPP3R5a82/jR8/Xg0bNtTjjz+ukSNHqlatWgoMDNShQ4f0559/auvWrVq9erVCQkJuuKxGjRrpl19+UevWrVW1alV5enrqoYce0kMPPZTBV9nRSy+9pKeeekpVqlRRx44d5enpqZUrV2r79u32gQNvdw3X0rNnTw0fPlwvvviilixZopIlS2rPnj2aOXOmOnToYP/XV1ceffRRLVy40D4o3L9vH0mXVe/NrdT6b02bNtUbb7yh9957T2XLllW7du0UHh6u48eP648//lBERIS+//77DC8vs77++mvVrVtXAwcO1Pz581W9enUdPHhQv/76q9zd3TV27FgFBARk+XrLlSunL7/80r4/tm3bViVLltTJkycVExOjnDlz2q8AuRY3NzeNHj1aTZo0UceOHdWhQwf7sWTRokVq1qxZhq5s2bJlizp06KAaNWqobNmyCgsL04kTJzRt2jRdvnzZfv+5dOXHwpgxY9S7d2+VL19ezZo1U6lSpXT58mUdOHBAK1asUN68ebVz505JUo0aNVS3bl1NnjxZderU0YMPPqjjx49rzpw5Kl26tNM4EYcPH1aVKlX0wAMPqGLFigoPD9fZs2c1c+ZMHTt2TM8//7z9/ciTJ49+++03tW/fXhEREWrUqJHKly8vNzc3HTx4UKtXr9bJkyd16dKlDL8vzZo1U//+/TVnzhxVqlRJf//9tyZPniwfHx+NGTPGZTBRt25dVapUSVu2bJGnp6c9QM6M4OBgVaxYUVu2bJHkGFKkf6fFxcUpb968euCBBzK9/H/L7PsoXTkW/vrrr+rQoYNatGihHDlyqHDhwnrkkUfuWA23IquP5b1799aGDRv05Zdfqnjx4oqKilKhQoV06tQpxcbGavny5erVq5e+/vprp3mfeeYZLVy4UMePH9dzzz2XqdvnMvuZyog333xTW7Zs0ddff60ZM2aoYcOGKlCggOLi4rRnzx6tXLlS77//foYHw71VmTkmeXp66oUXXtC7776rKlWqqH379kpJSdGCBQsUFhZ2R16PunXr6rnnntPnn39uP3/09PTUtGnTFBQUdM1xlgAH2fsQFODutX37dvPss8+a8uXLm4CAAOPp6WlCQ0NNs2bNzHfffWcuXbrkNM+ECRNM3bp1jb+/v/H29jblypUz7733nrl48aLLdUybNs1Uq1bNeHt7m9y5c5suXbqY/fv3X/cxqo8++qjLZV3rsW5z5swxERERxs/Pz+j/nj1+9XKvJzPbcyuPUR07dqyZMWOGiYiIML6+viZXrlymQ4cOZteuXS7nS0pKMp9//rmpXbu2yZkzp/Hy8jLh4eGmYcOG5pNPPjHx8fEO/VNTU83//vc/U61aNZMjRw6TI0cOU7VqVfPFF1+4fEyg/u8Rc4cPHzbdu3c3efPmNd7e3qZKlSrm559/dup/vUeXnT171rz//vumatWqxs/Pz/j4+JgiRYqYFi1amFGjRtkfCXgjx48fNw8//LAJCQkx7u7uDuu72X1j7NixplKlSsbX19fkyZPHtGvXzvz555/XfC+vV8P1XO+Rg9d61O+2bdtM69atTd68eY2vr6+pWrWq+fbbb2+4rRcuXDA5c+Y0kkyFChWuW1dWvTc3W+u1zJo1y0RFRZmgoCDj5eVlChYsaNq1a2cWLVpk73Mzj8vLyGf00KFD5qmnnjKFChUynp6eJk+ePKZt27Zm3bp1Tn1v9Jjm9M/R1Vwd29KtWrXKdOjQweTNm9d4enqa/Pnzm6ioKPPrr79es95/W79+vYmKijL+/v7G39/fNGrUyKxateqa2/7vGg8ePGgGDRpk6tSpY/Lly2e8vLxMgQIFTLNmzczs2bNdrvPPP/80jz76qClUqJDx8vIyQUFBpnz58ubJJ590eM+MMebkyZPm6aefNoULFzbe3t6mWLFiZtCgQebChQtO71lCQoJ5++23TWRkpAkLCzNeXl4mNDTU1K9f34wfP97lo1VjY2NN3759TYkSJYy3t7cJCAgwpUuXNj169DBTpkzJ0Gt49b61atUq06hRIxMQEGD8/f1NkyZNXO4LV0t/pOu1HlOcEf369TOSTLly5ZymNW3a1EgynTt3djmvq30/3fU+A5l5H1NSUsygQYNM0aJFjc1mc9qP7kQNN3qU6/Xcju8TY4yZMWOGadmypf0znC9fPlOjRg3z+uuvmx07dricJyUlxQQHBxtJZuvWrZnelsx8pozJ2OPl09LSzI8//mgaNmxogoKCjKenpwkLCzN169Y177//vjlw4ECGarveY1QzeuzO7DEpLS3NDB061BQrVsx4enqa8PBwM3DgwDv6eqSlpZnPP//clClTxnh5eZn8+fObZ555xpw+ffq6nw0gnZsxxtyucAQAbsX333+vXr16aezYsfbLDq3Azc1N9evXz5KrYADgfvLYY4/phx9+0MKFC7NkjArc2/bt26cSJUqobt26d2wMEADWxhgYAAAAuO0OHjyoX375RWXLls2S8Slw7/voo49kjNGzzz6b3aUAsAjGwAAAAMBtM378eO3evVu//PKLkpKS9O6772b74z1hXQcOHND48eO1Z88ejR07VpUqVVKnTp2yuywAFkGAAQAAgNvmm2++0fLlyxUeHq5PPvnE5WOFgXT79u3ToEGD5OvrqyZNmuirr77K9NNqANy7GAMDAAAAAABYHnEmAAAAAACwPG4huQlHjhzJ7hIAXCU4OFjx8fHZXQYAALjLcA4BWFNYWJjLdq7AAAAAAAAAlkeAAVzFGKOff/5ZrVq1UsmSJVWiRAk1b95cP/74o9LS0hz67tixQwMGDFDTpk31wAMPqGjRoqpevbq6dOmi2bNn61rDy6Smpuqbb75R48aNVbx4cZUvX16PPPKIYmJi7sQmAgAAAMBdiUE8bwK3kNy7nn32WU2ZMkXBwcFq2rSpcuTIoeXLl2vPnj3q2LGjRo4cae87ceJEvfvuu6pataoKFiyogIAAnThxQgsWLNCpU6ec+ktXApI+ffpo1qxZKl68uJo0aaLTp09r+vTpSkpK0rfffquoqKg7vdl3PS7/BAAAN4NzCMCarnULCQHGTSDAuDfNmTNHTzzxhAoVKqRZs2Ypd+7ckqTk5GT95z//0cKFC/Xtt9+qRYsWkqSkpCR5e3s7LefcuXNq3bq19uzZo5kzZ6pKlSr2aVOnTlXfvn1VvXp1TZw4UT4+PpKkzZs3q3379goICNCqVavk7+9/B7b43sHJBwAAuBmcQwDWxBgYwA3MmTNHktSnTx97eCFJXl5eGjhwoCRp7Nix9nZX4YUkBQQEqH79+pKk2NhYh2k//vijJOnll1+2hxeSVLlyZbVu3VonT57UrFmzsmBrAAAAAODeQoAB/J8TJ05IkgoVKuQ0rXDhwpKkdevWKTk5+brLuXjxolauXClJKlOmjL390qVLWr9+vXLkyKFatWo5zdewYUNJ0h9//HFzGwAAAAAA9zAeowr8n/SrLg4cOOA07Z9//pEkpaSk6MCBAypRooR9WmxsrCZPnqzU1FTFx8dr0aJFOnbsmJ599lmVK1fOYRmpqakqXLiwbDbnj17RokXtywMAAAAAOCLAAP5Po0aNNHXqVH377bdq27atgoKCJEmXL1/Wxx9/bO93+vRph/n279+vESNG2P/28vLSm2++qT59+jj0O3v2rKQrt5i4kt5+5syZW94WAAAAALjXEGAA/6dt27b6/ffftXTpUkVGRqpp06by8fHRihUrFBcXpwIFCujw4cNyd3e88yoyMlKHDx/W5cuXdfjwYU2ZMkXDhg3T6tWr9e2338rLyyubtggAAAD3MmOMkpKSlJqaKklyc3PL5ooAR+nPDPHw8JC3t/ct76MEGMD/8fDw0Pfff69vvvlGkydP1m+//SZvb2/Vrl1b3377rZ588klJV0ardsXT01NFihTRSy+9JE9PTw0dOlRjxozRU089JUnKmTOnpCtPKXElvT1XrlxZvWkAAAC4xxhjdOHCBfn4+GTJD0PgdjHGKDU1VRcuXJCfn98t7asM4glcxdPTU3379tWiRYu0b98+7dixQ2PGjFHBggUVGxur3Llzuxzk898iIyMlSatWrbK3FS5cWB4eHvrnn3+UkpLiNE/62BfpY2EAAAAA15KUlCQfHx/ZbDbCC1iam5ubbDabfHx8bvhAhBshwAAyYNq0aUpOTla7du0y1P/YsWOS5DBYp4+Pj6pXr66LFy9q7dq1TvMsXrxYkvTggw/eesEAAAC4p6WmpsrDwyO7ywAyzMPDw+U/5GYGAQZwFVe3d2zdulXvvfeeAgMD1bdvX3v7li1bXC7j5MmTGjp0qKQrA4NerWfPnpKkDz/8UJcuXbK3b968WTNmzFCePHnUokWLW94OAAAA3Pu48gJ3k6zYXxkDA7jKww8/LB8fH5UuXVr+/v7as2ePFi1aJB8fH33//fcKDQ219x0wYIASEhJUpUoVhYWFycPDQwcPHtTixYt16dIlNWvWTF27dnVYftu2bTV79mzNmjVLUVFRatKkiRISEjR9+nSlpqZq+PDh13xKCQAAAJCO8AJ3o1vdb91M+rCgyLAjR45kdwm4Tb766itNmzZN//zzjy5duqTQ0FBFRkbq2WefVVhYmEPf33//XXPnztXWrVs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\n",
      "text/plain": [
       "<Figure size 1080x576 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "insured_chronic = insured_df.value_counts('ChronicDiseases').values\n",
    "uninsured_chronic = uninsured_df.value_counts('ChronicDiseases').values\n",
    "\n",
    "x = np.arange(len(LABELS))  # the label locations\n",
    "width = 0.35  # the width of the bars\n",
    "\n",
    "fig, ax = plt.subplots(figsize=(15, 8))\n",
    "rects1 = ax.bar(x + width/2, insured_chronic, width, label='Insured', color='forestgreen')\n",
    "rects2 = ax.bar(x - width/2, uninsured_chronic, width, label='Uninsured', color='firebrick')\n",
    "\n",
    "# Add some text for labels, title and custom x-axis tick labels, etc.\n",
    "ax.set_xlabel('Has a chronic disease')\n",
    "ax.set_ylabel('Count')\n",
    "ax.set_title('Count of people that have a chronic disease by whether they are insured')\n",
    "ax.set_xticks(x)\n",
    "ax.set_xticklabels(LABELS)\n",
    "ax.legend(prop={'size': 20})\n",
    "ax.bar_label(rects1, padding=3, fontsize=20)\n",
    "ax.bar_label(rects2, padding=3, fontsize=20)\n",
    "label_fontsize(20)\n",
    "\n",
    "fig.tight_layout()\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "f3c8b46d-560a-4c6e-8970-fd032a272a98",
   "metadata": {},
   "source": [
    "This countplot shows that between people who do and don't have a chronic disease, most do not have travel insurance."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "16b511b5-1d75-41d3-99b6-2ca5af89978f",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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wsr6vWrVqZdfeqFEjw8PDw+6LV1ZWllGhQgUjICDA7j1wMxEREUaZMmVs5/eTTz4xJBkbNmww3N3d7V7DDzzwgOHn52dkZmba2qyv28cee8xh20eOHHEIWs6dO2e4u7sbjRo1clrPzp07DUnGSy+95HAObhVg3I0a6tWr53Sdm8lrgPHVV185LPvmm28cQlXrF9MXX3zR6fY+/PBDQ5KxcOFCW1t0dLQtcLve559/bkgyxo0bl+/jutEHH3xgSDJWr15t1279wu4sELDW+vHHHzvdpjU0u/HLvDO3CjCWLVvmsM4bb7xhSDIWLFhga7MGGHkJq+8kwCiI83HixAlbQO8sgLLWR4AB3Du4hQRAkWHkcr/79R588EGntzds2LBBnp6emj17tmbPnu2wPDMzU2fPntX58+dVpkwZbd++XZL08MMPO/QNDAxUvXr1tHr16ts4iv+Tnp6uXbt2KTg42HYbxI28vb2d3r9+//33293uYlWxYkVJ1+YJ8Pf3lyRt3LhRFotFnTp1umVNZcqUUb9+/TRt2jStX79ezZs3lyR9+eWXkvI2xFqS7fy1adPGYVmpUqVUv359rVmzRvv379eDDz6Yp23ejuPHj+udd97R8uXLdfz4cV2+fNlu+alTp5yu5+zWA+vkog0bNnRYVr58eUnX7smvXLlyvuv08PDQM888o/Hjx+vnn3/WwIEDJUnTp0/X5cuXNWTIkFzvM79e//799Z///Ec9e/ZUnz591K5dO0VFRalatWr5runixYvq1q2bTp06pZkzZ9peC5IUFxen5ORk3X///U5vM5CkEiVK5Dr3Qm6cvd8eeughubu7a8eOHXbtzz77rAYPHqxvvvnG9tSaRYsW6eTJkxo2bJjt9X8rbdq00TfffKOdO3eqfv36WrFihcLCwhQZGamGDRvabic5e/as9uzZow4dOji9DcfZa+b696PVli1blJ2dnet8OdZ5SvJ77u5WDU2aNMl3XXmV1/o3bNggSTp27JjT+q1zmuzbt892G0nnzp1VpUoVTZ8+Xe+8847tdqgvv/xSHh4eTm/7yM3evXv13nvvac2aNUpISNCVK1fsljv7XPHx8XF666H1WHbt2uX0WOLi4mzHUrt27TzX6Exez+9jjz2mOXPmqGnTpurfv79at26tqKgoVahQ4Y72f72COh/WzwXr58SNWrVqdcf/VgO4uwgwABQr5cqVc9p+/vx5ZWVlOUxAdqOLFy+qTJkytknjypYtm6/95EdKSooMw9DZs2dvWdeNrp/n4XrWuTKys7NtbampqSpVqpRKlCiRp20/++yzmjZtmr744gs1b95cGRkZmjp1qkJDQ9WrV688bcN6/nKbs8HantvkqQXhyJEjatKkiVJSUtSiRQt16NBBgYGBcnd319GjRzV16tRcJyN19uhT67m92bLrJ8nMryFDhuitt97SF198YQswvvzyS3l5eeU6seqNmjRporVr1+qtt97STz/9ZJuDokaNGhozZoz+/Oc/52k72dnZ6t+/v3bs2KGJEyeqf//+dsvPnz8v6doXxZu9di9evJin/Vk5e795eHgoODhYSUlJdu0DBgzQCy+8oK+++kqvvvqq3NzcbEHb0KFD87zPtm3b6ptvvtHy5cv14IMPauXKlbYvvW3bttW7776rP/74QytWrJBhGE7nv5CcvyedvR+t527Lli0Ok01eL7/n7m7VUBCffbnJb/3OwujrXV+/m5ubhg4dqldffVU//vijBg0apG3btmn79u3q2bNnnp9+tHHjRrVp08Y2F0qPHj1UsmRJubm52ebxcfa5Ehoa6jSEtB7L9ZME3+pYbldez++jjz6q6OhoffDBB/rmm29sc4Q0bNhQEydOdJh35nYU1Pm4G/9WA7i7eAoJgGIlt79SBwYGqlSpUjKu3VqX63/Wv55bv6QmJiY63d6ZM2cc2tzcrn3kZmVlOV3nxi/r1n3Ur1//lnXdiaCgIKWkpDiMPshN06ZNVb9+fc2aNUspKSm2yTsHDRp00wkgr2c9NmfnSZLtaRrOwoCCMnnyZJ0/f17/+9//tGrVKv33v//Vm2++qbFjx6pjx46Ftt/bVb58efXo0cM2MsU6eWevXr0UEhKS5+00a9ZM0dHRSklJ0bp16/T6668rMTFRAwcOtD1x5laGDx+uRYsW6ZlnntGrr77qsNx63Xr16nXT1218fHye65acv9+ysrJ07tw5hxFHJUqU0FNPPaWjR49q6dKltsk7mzZtmq9RPdZRQjExMdq5c6eSk5NtIUWbNm2UnZ2tlStX2kZiOBtVlB/Wc/fPf/7zpudu5cqVd7SfwqohLyOBCpu1/l9++eWm9d842eTgwYPl7e1t+0Ju/d/8BF4TJkzQ5cuXtXTpUv3666/68MMPNX78eI0dO1ZNmzbNdb2b/dskXRtxcLNjcTb5aGHq2rWrVqxYoZSUFC1fvlz//Oc/tXfvXnXr1k2xsbG2fjf7d+9mAXVBnY/b+bcagLkRYACApMjISKWkpGjv3r156m+d4dzZ0NM//vhDO3fudGgvVaqUpGtPQbjRoUOHHB4F6O/vrzp16mjv3r1KTk7OU123IzIyUoZhaPHixXle59lnn9WVK1c0bdo0ffnll7JYLBoyZEie169fv74kOX3MY2pqqnbu3Gl7XGxhOXTokCSpd+/eDsvMOqT42WeflXTti9XtjCa4nre3t5o3b67x48frv//9r6RrX/hu5YMPPtBnn32mDh066NNPP3Xap2bNmran5dzJqJMbObsuv/32m7Kzs22vqesNGzZMFotFX3zxhf73v/8pOzs73+erXLlyql27ttauXWt7j1gDjKioKHl7e2v58uVasWKF7fanO9GkSRO5ublp7dq1eV7HOjT++r+S3+0a7kRB1x8ZGSlJ+a4/JCREffr00aZNm7Ru3TrNnDlTVapUUYcOHfK8jUOHDql06dJOn9ZzO58rt3ssd4ufn5/atGmjyZMn61//+pcyMzPtHhd7s3/3bvUYY2fyez6s70fr58SNnP0bBMDcCDAAQNf+0ihJzzzzjE6fPu2w/NKlS9q4caPt50ceeUSlSpXSjBkzHH4JGzt2rEMYIV37UleyZEn98ssvdsPdL1++rL///e9O6xo5cqQyMzM1ePBgp3+tSklJsc0ncbuGDx8uSXrhhRec3pvtrG3gwIEKDAzUu+++q9WrV6t9+/aqWrVqnvf5+OOPy9PTUx999JEtSLB6/fXXlZaWpscffzxPj2O9XdbHNd74C+ySJUv09ddfF9p+70Tbtm1VvXp1TZ06VbNmzVKNGjXUunXrPK+/fv16pyNtrH+dvP4xqM7MmTNHL7/8sh544AHNnj3b4fG9Vh4eHho+fLgSEhL097//3ek+ExIS7P5Smxdvvvmm3b34V65c0ahRoyTJ6W00999/v9q2bavo6Gh9/vnnCgoK0oABA/K1T+naqIr09HT95z//0f3332+bF6BEiRJq1qyZZs2apcOHD6tVq1a2vzjfrtDQUD322GPaunWr3nzzTadfug4fPmw3eqVMmTKSrs3pUhBup4Y7UdD1P/LII6pWrZo++eQTLVq0yGmfDRs2KD093aF92LBhkq7NF3Px4kU988wz+bqmERERSk5O1u7du+3a//e//2nJkiX5OIprBg0apKCgII0bN06bN292WJ6Tk3PXv4SvWbPG6YgKZ58j1jlRbrzlY/ny5Zo5c2a+953f81GhQgW1b99e8fHx+vjjj+36/vLLL6YNqwHkjjkwAEDXvhhOmjRJo0aN0v33368uXbqoSpUqunjxoo4dO6bVq1froYcesv0F1t/fX19++aX69++vFi1aqH///goLC9Nvv/2mPXv2qGXLllqzZo3dPjw9PfWPf/xDb775purXr69evXopKytLy5YtU3h4uNN7rAcPHqxt27bp008/VbVq1dSxY0dVqlRJycnJio+P15o1azRo0CB9/vnnt33sHTp00OjRozVhwgTVqlVLPXv2VMWKFZWYmKjffvtNkZGR+vbbb+3W8fX11ZNPPmn7y31+/6odERGhDz/8UM8995waNGigfv36KSQkRKtXr9aGDRtUs2ZNvfPOO7d9THnx7LPPasqUKerbt6/69Omj8PBw7dmzR4sXL1a/fv30448/Fur+b4fFYtHf/vY3jRw5UpLyNepFkt59912tWLFCLVq0UJUqVeTv76+9e/fq119/ValSpW65vccff1w5OTlq3LixJk+e7LA8IiJCTz31lKRrQdSuXbv0+eefa8GCBWrTpo3Kly+vpKQkHTx4UOvWrdNbb72Vr4kHa9WqpTp16qhPnz7y9PTUL7/8osOHD6tr1676y1/+4nSdZ599VjExMUpMTNTw4cPzPNfL9dq2bauPP/5YSUlJevTRRx2WWb8w5Tb/RX59/PHHOnjwoN544w1Nnz5dDz30kMqWLavTp09r37592rJli210gHRtDpPy5cvrhx9+kKenpypXriyLxaK//OUvtzVp7O3UcCdat24tNzc3jRo1Snv27LH91X706NG3tT1PT0/NmTNHHTt2VNeuXdW8eXPVq1dPvr6+OnHihLZs2aIjR44oISHBIbSLiorSgw8+qF27dsnT01ODBw/O175HjBihJUuW6KGHHlK/fv0UGBiorVu36rffflOfPn30008/5Wt7ZcqU0U8//aRevXopMjJSbdu2VZ06dWSxWHTixAlt2LBB58+fd5gotDD9/e9/16lTpxQVFaWIiAh5eXlp27ZtWrFihSpXrmwXEg4aNEjvvfeeJk6cqF27dql27dqKi4vTr7/+ql69eunnn3/O175v53x88sknatasmUaMGKGlS5fqwQcf1KFDhzR37lx1795dCxYsKLBzA+AuKIhHmQCAK+n/PzL1ZnJ7ZOWN1q5da/Tt29cICwszPD09jeDgYOPBBx80/vnPfxpbtmxx6L906VIjKirKKFGihBEUFGT06NHD2Ldvn9NHYRqGYeTk5BgTJ040qlatanh6ehoVK1Y0XnrpJePSpUs3fdznggULjK5duxohISGGp6enUbZsWaNx48bGa6+9Zuzbt8/hfFgfH3uj3OoyDMNYuHCh0bFjR6NUqVKGl5eXUaFCBaNnz57G8uXLnW7L+ijFsLAwp4/KzIslS5YY7du3N4KCggwvLy+jWrVqxksvveT0sbGF8RjVdevWGa1btzaCgoIMf39/Iyoqypg7d+4tHzfozM3OrbPHRObnMarXS05ONtzc3AwfHx/j3LlzuRy1c0uWLDGeeuopo1atWkbJkiUNX19fo3r16sbw4cPtHvGaW83W91pu/934usvJyTGmTZtmtGnTxihVqpTh6elphIeHG1FRUcZbb71lHD9+PE91W8/7lStXjNdee82IiIgwvLy8jCpVqhhjx451+mhkq6ysLCM4OPimj0m+lZSUFMPNzc2QZMyaNctu2fr1623HHxsb67DurV63ub1fMzIyjI8++sho1qyZUbJkScPLy8uoWLGi0aZNG+Pf//63w7XfvHmz0aZNG6NkyZKGxWKxu3Z3o4ZbPcr1VqZPn248+OCDho+Pj8Nn+s3eEzfbb2JiovHKK68YderUMUqUKGH4+fkZ9913n9G7d29j+vTpuX5uWR/V2adPn9s6lgULFhhNmzY1/P39jcDAQKN9+/bG6tWrb/rY0Fs96jk+Pt547rnnjPvuu8/w9vY2AgICjBo1ahiPP/64MXfu3DzVdTufa85q/vHHH40BAwYY9913n+Hn52cEBAQYderUMf71r38ZSUlJDtvYs2eP0blzZ8Pf39/w8/MzHn74YWPVqlV39XwcPHjQ6N27txEYGGj4+voakZGRRnR09G3/uwLAdSyGcYezvwEAHDz11FOaOnWq4uPjbbcqFDXffvutBg0apNGjR+vNN990dTnFxqpVq9S6dWs9/vjjtqeIIHdHjhzRfffdp6ioKNPOIwBzsX5+x8TEFNioGgBAwWAODABAvmVlZWny5Mny8PC47UkkcXveffddSdLzzz/v4kruDe+//74Mw+B8IU9OnDihH374QbVq1brjJ8oAAAoec2AAAPLst99+0+rVq7Vq1Sr9/vvvev7551WhQgVXl1Xk/f7774qOjta2bdv066+/qlu3bjd9JGNxd/z4cc2YMUMHDx7UlClT9OCDD6pv376uLgsmNmPGDMXFxemHH35QRkaG3nzzTVM8EhYAYI8AAwCQZzExMRo3bpxKly6tZ555xjYaAIVr27Zt+te//qWSJUuqb9++uT6+FNccOXJEo0aNkq+vr9q3b6/PPvvsjp8OgqLtyy+/1Jo1a1SxYkX9+9//dvp4ZQCA6zEHBgAAAAAAMD3+HAEAAAAAAEyPW0huw+nTp11dAgAAAAAARVJ4eLjTdkZgAAAAAAAA0yPAAAAAAAAApkeAAQAAAAAATI8AAwAAAAAAmB4BBgAAAAAAMD0CDAAAAAAAYHoEGAAAAAAAwPQIMAAAAAAAgOl5uLoAAAAAAAAKgmEYysjIUHZ2tiTJYrG4uKLizTAMSZK7u7u8vb3v+HqYIsD47rvvdOTIESUkJCgtLU1eXl4KCQlR48aN1alTJwUEBNj6JiUl6fnnn891W82bN9eIESOcLlu1apWWLFmikydPys3NTVWqVFH37t3VsGHDgj4kAAAAAMBdZBiGLl26JB8fnwL5soyCYRiGsrOzdenSJfn5+d3RdTFFgLFw4UJVrVpVDzzwgAIDA5WRkaGDBw9q9uzZiomJ0VtvvaXg4GC7dSpXrqzGjRs7bKtSpUpO9zFt2jRFR0erTJkyatu2rbKysrR+/Xq98847Gjx4sDp16lQoxwYAAAAAKHwZGRny8fGRh4cpvubi/7NYLPLw8JCPj48yMzPl7e1929syxZWdOnWqvLy8HNpnzpypuXPnat68eXr66aftlkVERKhfv3552v6BAwcUHR2tsmXLauLEifL395ck9ejRQ6+++qqmT5+uBg0aKDQ09M4PBgAAAABw12VnZ9/Rl2MULnd3d2VkZNzRNTLFJJ7OwgtJatasmSQpISHhjra/bNkySdKjjz5qCy8kKTQ0VB07dtTVq1e1atWqO9oHAAAAAMC1uG3EvAri2pgiwMjNtm3bJF27XeRGKSkpWrZsmebMmaNly5bp2LFjuW5nz549kqR69eo5LKtfv75dHwAAAADAvYfwwvyKxCSeVvPnz9eVK1eUnp6uI0eOaP/+/apcubJ69uzp0Hf37t3avXu3XVudOnX03HPP2c2XceXKFSUnJ8vHx0elSpVy2E65cuUk3fkoDwAAAAAAUHhMFWAsWLBAf/zxh+3nevXq6dlnn1XJkiVtbd7e3urdu7caN26ssmXLSpKOHTum2bNna+/evRo/frzeffdd+fj4SJLS09MlSb6+vk73aW2/dOlSrnXFxMQoJiZGkjRp0iSHCUUBAAAAAK6VmJjIBJ4m5+3tfUffpy2G9cGsJpKamqq4uDh9//33unLlil555RVVrVr1putkZ2frjTfe0MGDB/XUU0+pS5cukqTk5GT97W9/U+nSpfX55587rJeVlaWBAwfKw8NDM2bMyFN9p0+fzv9BAQAAAAAKTXp6eq5/uIY55PUahYeHO2035RwYQUFBatKkiUaPHq0LFy7ok08+ueU67u7uatOmjSQpNjbW1m49OdaRGDeytvv5+d1p2QAAAAAAFCl9+vRR+fLlXV2GJJMGGFYhISGqUKGCTpw4obS0tFv2t95qkpGRYWvz8fFR6dKldeXKFaWkpDisc+bMGUlSWFhYAVUNAAAAALgXlS9f3jRf1uHI1AGGJFvo4OZ261IPHjwoSba5Mazq1q0rSdq5c6fDOjt27LDrAwAAAAAAzMflAcbp06ed3t6Rk5OjmTNn6o8//lCNGjXk7+8vSTpy5IhycnIc+v/+++9auHChJKlFixZ2y9q3by9JmjNnji5evGhrT0pK0pIlS+Tp6alWrVoV1CEBAAAAAIAC5vIpWnfs2KEZM2aoZs2aCg0NVUBAgFJTU7Vv3z4lJiYqKChIQ4cOtfWfNm2aEhISVKNGDZUuXVqSdPz4ce3Zs0eS1L9/f9WoUcNuHzVq1FC3bt0UHR2tl156SU2bNlVWVpY2bNigixcvavDgwQoNDb17Bw2gSMiMinJ1CXfMa906V5cAAABgaidOnFBkZKT69u2rF154QW+//bbWrl2r9PR01ahRQyNHjrT90dwqMzNT06dP16xZs3TixAllZGQoODhYtWvX1qBBg9SyZUuHbX/44YcO++7Tp482bNigU6dO2drWr1+vvn37auTIkWrTpo0mT56s7du3KzU1VRs3blTFihUlSfPmzdN3332nvXv3KiMjQxUrVlSvXr00bNgweXt7O+zrl19+0WeffaaDBw/Kz89PDz/8sF577bUCPJN3zuUBxgMPPKA2bdpo//79Onr0qC5duiRvb2+Fh4erRYsW6tKli230hSS1bNlSmzdv1uHDh7Vjxw5lZ2crMDBQzZo1U6dOnVSrVi2n+3niiSdUqVIlLVmyRMuXL5fFYlGVKlXUo0cPNWzY8G4dLgAAAADgHnTy5El17dpVlSpVUu/evZWamqoFCxZo8ODB+uGHHxR13R+3/vnPf2revHmqWbOm+vTpIx8fH505c0ZbtmzRqlWrbAHGndi2bZs+/vhjNW7cWP3791dycrI8PT0lSSNHjtSPP/6osLAwdenSRSVLltT27dv13nvv6bffftMPP/xg98jZL7/8UuPGjVNgYKB69+6twMBArVq1So888ogCAgLuuNaC4vIAo1KlSvrrX/+a5/5t2rSxPW0kv1q1asWtIgAAAACAfNuwYYNeeOEFjRw50tbWq1cvPfbYY/r8889tAUZaWpp++eUX/elPf1J0dLTc3d3ttpOcnFwg9axevVqTJk3SX/7yF7v2H3/8UT/++KM6d+6sjz76SCVKlLAt++CDDzR58mR9++23evrppyVdGwXy9ttvKygoSIsXL7aN4Bg1apSGDh2qRYsWFUi9BcHlc2AAAAAAAGB2FSpU0D/+8Q+7tlatWql8+fK2h0NIksVikWEY8vLycvowCutUCHeqTp06DuGFJP3vf/+Th4eHPvjgA7vwQpJGjBihUqVKae7cuba2OXPm6OrVqxo0aJAtvJCuPUhj9OjReXqgxt3i8hEYAAAAAACYXZ06dRxGU0hSeHi4tm3bZvs5ICBA7du317Jly9S+fXt17dpVTZo0UYMGDRwChTtRr149h7bLly8rNjZWpUuX1tdff+10PW9vb9sTPCXZ5pOMjIx06Fu5cmWFh4fr5MmTBVP0HSLAAAAAAADgFkqWLOm03cPDw+FJmZ999pk+/fRTzZ07V++//74kycfHR126dNEbb7yhkJCQO67H2YMoUlNTZRiGzp8/r8mTJ+dpO2lpaZKUa00hISEEGAAAAAAAFEUlSpTQCy+8oBdeeEGnTp3Spk2bNGvWLM2ZM0cnT5603cJhvT0jOzvb6Xb++OOPXPdhsVgc2qwhS926dbVkyZI81Wpd5+zZsw5P9LS2m4V5bmYBAAAAAKCIKV++vB599FHNmDFDERER2rx5s20iz8DAQEnS6dOnHda7cOGCjhw5kq99+fn5qUaNGoqLi1NKSkqe1qlbt64kaePGjQ7Ljh075rQ2VyHAAAAAAACggJw/f1779u1zaE9PT1d6ero8PDzk5eUlSfL399d9992nLVu2KC4uztY3Oztb48aN05UrV/K9/yFDhigzM1MvvPCC0xEcqamp+v33320/P/roo/L09NSUKVN04sQJW3tOTo4mTJjgcHuMK3ELCQAAAAAABSQhIUEdO3ZUrVq1VKtWLYWHh+vChQuKiYlRUlKS/vrXv8rf39/Wf9iwYXrhhRfUs2dPdevWTd7e3lq/fr2uXr2q2rVrKzY2Nl/7HzBggHbv3q2pU6eqefPmtielpKam6vjx49q0aZP69eund955R5JUsWJFjRo1SuPHj1fHjh3VvXt3lSxZUqtWrVJaWppq1arlNJBxBQIMAAAAAAAKSMWKFfXiiy9q/fr1Wr9+vZKTkxUUFKRq1arpX//6lx555BG7/gMGDJBhGPryyy81e/ZsBQYGqkOHDnr11Vc1ZMiQ26rh7bffVuvWrTV9+nStXbtWaWlpCgoKUvny5fW3v/1NvXv3tus/dOhQlS1bVp999plmz54tPz8/tWrVSq+99pqef/752z4XBc1iGIbh6iLuNWa6BwiA62RGRbm6hDvmtW6dq0sAAAAoEOnp6fL19XV1GbiJvF6j8PBwp+3MgQEAAAAAAEyPAAMAAAAAAJgeAQYAAAAAADA9AgwAAAAAAGB6BBgAAAAAAMD0CDAAAAAAAIDpEWAAAAAAAADTI8AAAAAAAACmR4ABAAAAAABMjwADAAAAAACYHgEGAAAAAAAwPQIMAAAAAABgegQYAAAAAADA9DxcXQAAAAAAAIUtMyrK1SXclNe6da4uwfQYgQEAAAAAQBFRvnx5lS9fXk2aNNGVK1ec9mnatKnKly+vrKysu1zdnSHAAAAAAACgiDl16pS+/vprV5dRoAgwAAAAAAAoQoKCghQUFKRPPvlEycnJri6nwBBgAAAAAABQhPj4+GjEiBFKS0vT5MmT87Xu/Pnz9eijj6pmzZqqVq2a2rZtq48++kgZGRmFVG3eEWAAAAAAAFDEPPXUU4qIiNB3332nI0eO5GmdiRMnatiwYTp48KB69uypp556SoZhaNKkSXrssceUmZlZyFXfHAEGAAAAAABFjKenp0aNGqWrV6/q7bffvmX/rVu36uOPP1Z4eLiWL1+uSZMm6fXXX9fSpUvVrl07bdiwQZ9//vldqDx3BBgAAAAAABRB3bp1U8OGDfXrr79q8+bNN+37448/SpL+8Y9/KDQ01Nbu4eGhN954Q25ubpo5c2ah1nsrBBgAAAAAABRRb7zxhiRp/PjxN+33+++/S5KioqIcllWrVk1hYWE6fvy40tLSCr7IPCLAAAAAAACgiGrUqJG6du2qHTt26Jdffsm134ULFyRJZcuWdbrcOiqDAAMAAAAAABSKUaNGydPTU5MmTcp1Is6AgABJUlJSktPl1nZrP1cgwAAAAAAAoAirUqWKnnjiCR0/flzffPON0z5169aVJG3YsMFhWXx8vBISElSpUiUFBgYWaq03Q4ABAAAAAEAR989//lOBgYH66KOPdOnSJYfl/fv3lyT95z//0fnz523t2dnZevPNN5WTk6MBAwbctXqd8XDp3gEAAAAAQKErVaqUhg8frgkTJjhd3rhxYz377LP69NNP1aZNG3Xt2lW+vr5auXKl9u/fryZNmmjYsGF3uWp7FsMwDJdWcA86ffq0q0sAYAKZTmZovtd4rVvn6hIAAAAKRHp6unx9fV1dhsuVL19e5cqV07Zt2xyWZWRk6OGHH9aJEyckSceOHZOHh/24hl9++UVTpkxRbGyssrKyVLlyZfXs2VNDhw6Vj4/PHdWW12sUHh7utJ0A4zYQYACQCDAAAADMhADD/O40wGAODAAAAAAAYHoEGAAAAAAAwPQIMAAAAAAAgOkRYAAAAAAAANMjwAAAAAAAAKZHgAEAAAAAAEyPAAMAAAAAAJgeAQYAAAAAADA9AgwAAAAAAGB6BBgAAAAAAMD0CDAAAAAAAIDpEWAAAAAAAADTI8AAAAAAAACm5+HqAgAAAAAAKGx13qvj6hJuau9Le11dgumZIsD47rvvdOTIESUkJCgtLU1eXl4KCQlR48aN1alTJwUEBDisc+DAAc2ZM0dxcXHKzMxUWFiYWrdurc6dO8vNzfnAkm3btmnBggWKj49XTk6OKlasqA4dOqhVq1aFfIQAAAAAABSup59+Wr/++qveeOMNDR061GmfnTt36pFHHlFYWJiWLVvm9Pu2WZniFpKFCxcqIyNDDzzwgLp06aIWLVrI3d1ds2fP1osvvqhz587Z9d+yZYvGjBmj2NhYNWnSRJ06dVJWVpamTp2qDz/80Ok+Fi9erHfeeUcnTpxQixYt1LZtW6WkpOjTTz/VtGnT7sJRAgAAAABQeN59912VLVtW77zzjvbt2+ew/PLlyxo+fLhycnL03//+954KLySTjMCYOnWqvLy8HNpnzpypuXPnat68eXr66aclSenp6friiy/k5uamsWPHqlq1apKk/v37a/z48dq4caPWrVunqKgo23aSkpI0ffp0+fv7a+LEiQoNDZUk9enTR6NGjVJ0dLQiIyNVvXr1u3C0AAAAAAAUvNKlS2vy5Ml6/PHHNXz4cC1cuFDe3t625ePGjdORI0c0fPhwNWnSxIWV3h5TjMBwFl5IUrNmzSRJCQkJtraNGzcqLS1NzZs3t4UX1m0MGDBAkrR06VK77axcuVJXr15Vp06dbOGFJPn7+6tXr15O1wEAAAAA4F7TqlUrDRo0SPv27dPEiRNt7cuXL9f06dP1pz/9SS+88IKysrL07bffqlu3bqpRo4aqVaumDh06aMqUKcrJyXHY7tKlS9WvXz/Vr19fVapUUYMGDdS7d299++23d+3YTBFg5Gbbtm2SpMqVK9va9uzZI0mqV6+eQ/9atWrJ29tbcXFxunr1ap7WqV+/viRp714mTAEAAAAA3Ptee+01Va9eXV9//bXWrl2r5ORkvfDCCypRooQ++ugjSdKTTz6p1157TWlpaerZs6cee+wx5eTkaPTo0frHP/5ht73vvvtOgwYN0sGDB9W+fXsNGTJEbdq00ZUrVzRr1qy7dlymuIXEav78+bpy5YrS09N15MgR7d+/X5UrV1bPnj1tfayjMcLDwx3Wd3d3V2hoqE6cOKHExERVqFBBknT69GlJUlhYmMM6pUqVkre3t86fP6+MjAy74TUAAAAAANxrfHx89NFHH6l79+4aMWKEateurbNnz+rtt9/Wfffdpw8++ECrVq3SoEGDNG7cOLm7u0uSsrOz9fLLL+uHH35Qt27d1LFjR0nXAgwvLy8tW7ZMwcHBdvtKTk6+a8dlqgBjwYIF+uOPP2w/16tXT88++6xKlixpa0tPT5ck+fr6Ot2Gtd3aL6/rZGRkKD093WmAERMTo5iYGEnSpEmTHC4YgOLptKsLKAB8ngEAgKIiMTFRHh6m+oqbLwVde7169fTKK6/ozTff1JkzZ9SuXTv99a9/VU5OjqZMmaLQ0FBNmDDBbr8eHh4aP368fvzxR82bN09du3aVJFksFrm7u8vHx8ehzuunabgVb2/vO/r901RX96uvvpIkpaamKi4uTt9//71eeeUVvfLKK6patarL6mrXrp3atWtn+/nGp6IAwL2KzzMAAFBUZGRk2EYS3IuysrIKfJtDhgzRF198oaSkJI0ePVpZWVk6dOiQUlJSVKVKFX3wwQdO1/Px8VFcXJytpp49e2r8+PFq0aKFevTooWbNmqlx48YqU6ZMvurJyMjI0++fzu64kEwWYFgFBQWpSZMmqlKliv7xj3/ok08+sZ1YZyMsrudstIWvr68uXLig9PR0p4+JudUIDQAAAAAA7jVubm62h2b4+PhIklJSUiRJ8fHxmjx5cq7rXrp0yfb/hw4dqtKlS2vq1Kn65ptv9PXXX8tisSgyMlKvv/66HnzwwUI8iv9j6kk8Q0JCVKFCBZ04cUJpaWmS/m8eC+u8FtfLzs5WUlKS3N3dVbZsWVu7Nb25/mkmVikpKcrIyFCZMmWY/wIAAAAAUKRZ/6jfuXNnnTp1Ktf/Nm7caLde3759FR0drT179mjatGn685//rE2bNmngwIE6f/78Xand1AGG9H/pkJvbtVLr1q0rSdq5c6dD33379ikjI0PVq1eXp6enrf1m6+zYsUOSVKdOnYIsGwAAAAAA07nvvvsUGBio7du32z29M68CAwPVtm1bvffee+rXr59SU1Mdwo7C4vIA4/Tp005vB8nJydHMmTP1xx9/qEaNGvL395ckRUZGKiAgQOvXr9fhw4dt/TMzM/XDDz9Ikjp06GC3rdatW8vT01OLFy9WUlKSrf3ixYuaO3eu03UAAAAAAChqPDw8NGjQICUmJur111/X5cuXHfokJiYqLi7O9vO6detkGIZDP+t8FiVKlCi8gq/j8jkwduzYoRkzZqhmzZoKDQ1VQECAUlNTtW/fPiUmJiooKEhDhw619ff19dXQoUM1efJkjR07VlFRUfL399fWrVt1+vRpRUZGqnnz5nb7CA0N1eOPP64pU6Zo1KhRatasmTw8PLRp0yadP39e3bp1U/Xq1e/2oQMA7hGZUVGuLqFAeK1b5+oSAACACYwYMUKxsbGaPn26li1bpqioKIWFhencuXOKj4/Xli1b9Morr9i+Jz/99NPy9fVVgwYNVLFiRRmGoc2bN2vnzp3605/+pBYtWtyVui2GsxjlLjp+/LiWLVum/fv3Kzk5WZcuXZK3t7fCw8NVv359denSxTb64nr79+/X3LlzFRcXp8zMTJUrV06tW7dWly5dbLeb3Gjr1q1asGCB4uPjZRiGKlSooI4dO6pVq1b5qtnZ/BsAip+i8KWWL7R5UxSutcT1BgAUbenp6TyYwYmmTZvq5MmT2rhxoypWrGhrNwxDP//8s2bNmqW9e/fq0qVLKlOmjCpWrKg2bdqod+/eKl++vCRp2rRpWr16tWJjY5WUlCQfHx+VL19ePXv21BNPPOH0O7szeb1GuT2FxOUBxr2IAAOAVDS+1PKFNm+KwrWWuN4AgKKNAMP87jTAcPkcGAAAAAAAALdCgAEAAAAAAEyPAAMAAAAAAJgeAQYAAAAAADA9AgwAAAAAAGB6BBgAAAAAAMD0CDAAAAAAAPc8wzBcXQJu4U6vEQEGAAAAAKBIIMQwr4K4NgQYAAAAAIB7nru7u7Kzs11dBnKRnZ0tDw+PO9oGAQYAAAAA4J7n7e2tK1euKCsri5EYJmIYhrKysnTlyhV5eXnd0bbuLP4AAAAAAMAELBaL/Pz8lJmZqYyMDFsbXMcaJHl4eMjPz++OrwcBBgAAAACgSLBYLPL29pa3t7erS0Eh4BYSAAAAAAB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\n",
      "text/plain": [
       "<Figure size 1080x576 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig, ax = plt.subplots(figsize=(15, 8))\n",
    "ax = sns.countplot(x=\"FamilyMembers\", data=df, hue=\"TravelInsurance\", palette=[\"red\", \"green\"])\n",
    "ax.set_xlabel('Number of family members')\n",
    "ax.set_title('Frequency of family size by whether they are insured')\n",
    "ax.legend(title=\"Insured\", labels=LABELS, fontsize=20, title_fontsize=20)\n",
    "label_fontsize(20)\n",
    "fig.tight_layout()\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "ef148018-7e06-43fa-ab8e-7fe4397733cf",
   "metadata": {},
   "source": [
    "In this countplot fort each family size there are more people uninsured than insured. However the gap between the two counts seem to decrease as the family size increases. Let's look at this closer using proportions."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "id": "82f4e026-d6d5-4e90-871d-2609aa036aa0",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1080x576 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "insured_fam_prop = insured_df.value_counts('FamilyMembers') / df.value_counts('FamilyMembers')\n",
    "\n",
    "fig, ax = plt.subplots(figsize=(15, 8))\n",
    "ax.bar(insured_fam_prop.index, insured_fam_prop, color='royalblue')\n",
    "\n",
    "ax.set_xlabel('Number of family members')\n",
    "ax.set_ylabel('Proportion insured')\n",
    "ax.set_title('Proportion of the insured by family size')\n",
    "label_fontsize(20)\n",
    "fig.tight_layout()\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "abf113f8-3da9-454b-a3db-6e06c34dd159",
   "metadata": {},
   "source": [
    "This bar chart shows the proportion of people insured for each family size. The proportion does increase as the family size increases, however the margin does not increase significantly. Further exploration may be needed to identify whether this increase is statistically significant."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "id": "7945dd1b-b644-436b-8389-a76be36d09d1",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1152x504 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig, ax = plt.subplots(figsize=[16,7])\n",
    "\n",
    "# plot histogram comparing the density distribution of the Annual Income in each groups\n",
    "_ = plt.hist(insured_df['AnnualIncome'], \n",
    "             histtype='step', \n",
    "             hatch='/', \n",
    "             density=True, \n",
    "             color='green', \n",
    "             bins=10, \n",
    "             linestyle='--', \n",
    "             linewidth=2,\n",
    "            )\n",
    "_ = plt.hist(uninsured_df['AnnualIncome'],\n",
    "             density=True,\n",
    "             color='red', \n",
    "             bins=10,\n",
    "            )\n",
    "\n",
    "# put a title, label the axes, define the legend and display\n",
    "_ = plt.suptitle(\"PDF of Annual Income by whether they are insured\", \n",
    "                 fontsize=20, \n",
    "                 color='black',\n",
    "                 x=0.52,\n",
    "                 y=0.92,\n",
    "                )\n",
    "_ = plt.xlabel('Annual Income')\n",
    "_ = plt.ylabel('Probability Density Function (PDF)')\n",
    "_ = plt.legend(['Insured', 'Uninsured'])\n",
    "_ = plt.ticklabel_format(axis=\"x\", style=\"plain\")\n",
    "_ = plt.ticklabel_format(axis=\"y\", style=\"plain\")\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "67669b43-96a9-45e2-a1d7-a4fbcbbcbd58",
   "metadata": {},
   "source": [
    "The PDF of the insured group is further to the right than the PDF of the uninsured group, meaning that the people with higher incomes are more likely to get travel insurance. This is expected because people with higher incomes are more able to afford insurance.\n",
    "\n",
    "But histograms are affected by a binning bias (i.e. the visualization depends on the number of bins chosen). In this case, the histogram has only 10 bins to make it easier to understand."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "id": "df7d6815-ef43-4e0d-bc23-8a5fd8a01b02",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1080x576 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "insured_grad = insured_df.value_counts('GraduateOrNot').values\n",
    "uninsured_grad = uninsured_df.value_counts('GraduateOrNot').values\n",
    "\n",
    "x = np.arange(len(LABELS))  # the label locations\n",
    "width = 0.35  # the width of the bars\n",
    "\n",
    "fig, ax = plt.subplots(figsize=(15, 8))\n",
    "rects1 = ax.bar(x + width/2, insured_grad, width, label='Insured', color='forestgreen')\n",
    "rects2 = ax.bar(x - width/2, uninsured_grad, width, label='Uninsured', color='firebrick')\n",
    "\n",
    "# Add some text for labels, title and custom x-axis tick labels, etc.\n",
    "ax.set_xlabel('College Graduate')\n",
    "ax.set_ylabel('Count')\n",
    "ax.set_title('Count of people that are college graduates by whether they are insured')\n",
    "ax.set_xticks(x)\n",
    "ax.set_xticklabels(LABELS)\n",
    "ax.legend(prop={'size': 20})\n",
    "\n",
    "ax.bar_label(rects1, padding=3, fontsize=20)\n",
    "ax.bar_label(rects2, padding=3, fontsize=20)\n",
    "\n",
    "label_fontsize(20)\n",
    "\n",
    "fig.tight_layout()\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "083fdb71-a6ba-40c5-8a04-19712170c7fa",
   "metadata": {},
   "source": [
    "This countplot shows that between people who are and aren't have a college graduate, most do not have travel insurance."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "id": "c1b46887-4250-4ad5-a15b-303566d3d30b",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1080x576 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "insured_employ_type = insured_df.value_counts('Employment Type').values\n",
    "uninsured_employ_type = uninsured_df.value_counts('GraduateOrNot').values\n",
    "\n",
    "x = np.arange(len(EMPLOYMENT_LABELS))  # the label locations\n",
    "width = 0.35  # the width of the bars\n",
    "\n",
    "fig, ax = plt.subplots(figsize=(15, 8))\n",
    "rects1 = ax.bar(x + width/2, insured_employ_type, width, label='Insured', color='forestgreen')\n",
    "rects2 = ax.bar(x - width/2, uninsured_employ_type, width, label='Uninsured', color='firebrick')\n",
    "\n",
    "# Add some text for labels, title and custom x-axis tick labels, etc.\n",
    "ax.set_xlabel('Employment Type')\n",
    "ax.set_ylabel('Count')\n",
    "ax.set_title('Count of employment type by whether they are insured')\n",
    "ax.set_xticks(x)\n",
    "ax.set_xticklabels(EMPLOYMENT_LABELS)\n",
    "ax.legend(prop={'size': 20})\n",
    "\n",
    "ax.bar_label(rects1, padding=3, fontsize=20)\n",
    "ax.bar_label(rects2, padding=3, fontsize=20)\n",
    "\n",
    "label_fontsize(20)\n",
    "\n",
    "fig.tight_layout()\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "id": "cdddea73-f132-4312-8951-1cf4499e4bd9",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1152x504 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig, ax = plt.subplots(figsize=[16,7])\n",
    "\n",
    "# plot histogram comparing the density distribution of the Annual Income in each groups\n",
    "_ = plt.hist(insured_df['Age'], \n",
    "             histtype='step', \n",
    "             hatch='/', \n",
    "             density=True, \n",
    "             color='green', \n",
    "             bins=10, \n",
    "             linestyle='--', \n",
    "             linewidth=2,\n",
    "            )\n",
    "_ = plt.hist(uninsured_df['Age'],\n",
    "             density=True,\n",
    "             color='red', \n",
    "             bins=10,\n",
    "            )\n",
    "\n",
    "# put a title, label the axes, define the legend and display\n",
    "_ = plt.suptitle(\"PDF of Age by whether they are insured\", \n",
    "                 fontsize=20, \n",
    "                 color='black',\n",
    "                 x=0.52,\n",
    "                 y=0.92,\n",
    "                )\n",
    "_ = plt.xlabel('Age')\n",
    "_ = plt.ylabel('Probability Density Function (PDF)')\n",
    "_ = plt.legend(['Insured', 'Uninsured'])\n",
    "_ = plt.ticklabel_format(axis=\"x\", style=\"plain\")\n",
    "_ = plt.ticklabel_format(axis=\"y\", style=\"plain\")\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "78d0b1b0-cf4a-4e80-934b-a45224ce0454",
   "metadata": {},
   "source": [
    "The PDF of the insured group is similar to the PDF of the uninsured group, it is unclear whether there are ages where will more likely to get travel insurance. The ages span from 25 to 35."
   ]
  }
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