{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# U.S. Medical Insurance Costs\n",
    "## Aim\n",
    "The aim of this project is to explore the U.S. Medical Insurance Costs data set and understand how different factors impact the charges.\n",
    "## Project Goals\n",
    "The goals of this project are:\n",
    "- Determine the relationship between sex and charges\n",
    "- Determine the relationship between region and charges\n",
    "- Determine the relationship between BMI and charges"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Analysis\n",
    "This section will analyse the data included in the data set."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 63,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 1338 entries, 0 to 1337\n",
      "Data columns (total 7 columns):\n",
      " #   Column    Non-Null Count  Dtype  \n",
      "---  ------    --------------  -----  \n",
      " 0   age       1338 non-null   int64  \n",
      " 1   sex       1338 non-null   object \n",
      " 2   bmi       1338 non-null   float64\n",
      " 3   children  1338 non-null   int64  \n",
      " 4   smoker    1338 non-null   object \n",
      " 5   region    1338 non-null   object \n",
      " 6   charges   1338 non-null   float64\n",
      "dtypes: float64(2), int64(2), object(3)\n",
      "memory usage: 73.3+ KB\n"
     ]
    }
   ],
   "source": [
    "import pandas as pd\n",
    "import matplotlib.pyplot as plt\n",
    "import numpy as np\n",
    "from IPython.display import display, HTML\n",
    "from shared.palette import palette\n",
    "from shared.MetricsHTML import style, metrics_html\n",
    "path = '../data/insurance.csv'\n",
    "\n",
    "df = pd.read_csv(path)\n",
    "\n",
    "df.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 64,
   "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>sex</th>\n",
       "      <th>bmi</th>\n",
       "      <th>children</th>\n",
       "      <th>smoker</th>\n",
       "      <th>region</th>\n",
       "      <th>charges</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>1338.000000</td>\n",
       "      <td>1338</td>\n",
       "      <td>1338.000000</td>\n",
       "      <td>1338.000000</td>\n",
       "      <td>1338</td>\n",
       "      <td>1338</td>\n",
       "      <td>1338.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>unique</th>\n",
       "      <td>NaN</td>\n",
       "      <td>2</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2</td>\n",
       "      <td>4</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>top</th>\n",
       "      <td>NaN</td>\n",
       "      <td>male</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>no</td>\n",
       "      <td>southeast</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>freq</th>\n",
       "      <td>NaN</td>\n",
       "      <td>676</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1064</td>\n",
       "      <td>364</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>39.207025</td>\n",
       "      <td>NaN</td>\n",
       "      <td>30.663397</td>\n",
       "      <td>1.094918</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>13270.422265</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>14.049960</td>\n",
       "      <td>NaN</td>\n",
       "      <td>6.098187</td>\n",
       "      <td>1.205493</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>12110.011237</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>18.000000</td>\n",
       "      <td>NaN</td>\n",
       "      <td>15.960000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1121.873900</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>27.000000</td>\n",
       "      <td>NaN</td>\n",
       "      <td>26.296250</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>4740.287150</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>39.000000</td>\n",
       "      <td>NaN</td>\n",
       "      <td>30.400000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>9382.033000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>51.000000</td>\n",
       "      <td>NaN</td>\n",
       "      <td>34.693750</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>16639.912515</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>64.000000</td>\n",
       "      <td>NaN</td>\n",
       "      <td>53.130000</td>\n",
       "      <td>5.000000</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>63770.428010</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                age   sex          bmi     children smoker     region  \\\n",
       "count   1338.000000  1338  1338.000000  1338.000000   1338       1338   \n",
       "unique          NaN     2          NaN          NaN      2          4   \n",
       "top             NaN  male          NaN          NaN     no  southeast   \n",
       "freq            NaN   676          NaN          NaN   1064        364   \n",
       "mean      39.207025   NaN    30.663397     1.094918    NaN        NaN   \n",
       "std       14.049960   NaN     6.098187     1.205493    NaN        NaN   \n",
       "min       18.000000   NaN    15.960000     0.000000    NaN        NaN   \n",
       "25%       27.000000   NaN    26.296250     0.000000    NaN        NaN   \n",
       "50%       39.000000   NaN    30.400000     1.000000    NaN        NaN   \n",
       "75%       51.000000   NaN    34.693750     2.000000    NaN        NaN   \n",
       "max       64.000000   NaN    53.130000     5.000000    NaN        NaN   \n",
       "\n",
       "             charges  \n",
       "count    1338.000000  \n",
       "unique           NaN  \n",
       "top              NaN  \n",
       "freq             NaN  \n",
       "mean    13270.422265  \n",
       "std     12110.011237  \n",
       "min      1121.873900  \n",
       "25%      4740.287150  \n",
       "50%      9382.033000  \n",
       "75%     16639.912515  \n",
       "max     63770.428010  "
      ]
     },
     "execution_count": 64,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.describe(include='all')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "\n",
    "### Data"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Sex and charges\n",
    "This section explores the relationship between sex and charges in the dataset.\n",
    "\n",
    "#### Date Representation\n",
    "This section aims to compare the number of records where the sex is male versus female.\n",
    "\n",
    "Though the dataset has an almost equal split of male and female records, there are `1%` more records where the sex is female versus male:\n",
    "- There are 676 records where the sex is male\n",
    "- There are 662 records where the sex is female\n",
    "\n",
    "The bar chart below shows the count of records where the value was either male or female in the sex column."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 65,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sex_and_charges = df[['sex', 'charges']]\n",
    "sex_data = sex_and_charges['sex'].value_counts()\n",
    "sex_plot = sex_data.plot.bar(x='sex', color=palette[\"Pink Lace\"], title='The Count of Male vs Female records in the dataset', xlabel='Sex', ylabel='Count');\n",
    "labels = sex_plot.bar_label(sex_plot.containers[0])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### What's the average charge per sex?\n",
    "This section aims to determine the average charge per sex.\n",
    "\n",
    "##### Average charge for Females versus Males"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 66,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "\n",
       "<style> \n",
       "#metrics {\n",
       "    width: 95%;\n",
       "    margin: 20px auto;\n",
       "    font-family: arial, sans-serif;\n",
       "    display: flex;\n",
       "    flex-direction: column;\n",
       "    text-align: center;\n",
       "}\n",
       "h1 {\n",
       "    font-size: 2em;\n",
       "    font-weight: 400;\n",
       "    margin: 20px auto;\n",
       "    color: ##32292f;\n",
       "}\n",
       "h2 {\n",
       "    font-size: 1.5em;\n",
       "    font-weight: 400;\n",
       "    color: #32292f;\n",
       "}\n",
       "h3 {\n",
       "    font-size: 1.2em;\n",
       "    font-weight: 400;\n",
       "    color: #32292f;\n",
       "}\n",
       ".metrics-display {\n",
       "    display: flex;\n",
       "    flex-direction: row;\n",
       "    justify-content: center;\n",
       "}\n",
       ".metric-box {\n",
       "    margin: 0 auto;\n",
       "    background-color: #99e1d9;\n",
       "    border-radius: 15px 50px 30px;\n",
       "    padding: 20px;\n",
       "    margin: 10px 10px 10px 10px;\n",
       "    text-align: center;\n",
       "    width: 200px;\n",
       "    height: 150px;\n",
       "}\n",
       "</style>\n",
       "<div id=\"metrics\">\n",
       "<h1>Average Charge for Females vs Males</h1>\n",
       "<div class=\"metrics-display\">\n",
       "<div>\n",
       "    <div class=\"metric-box\">\n",
       "        <h3 class=\"sub-text\">Female Charges</h3>\n",
       "        <h2 class=\"head\">$12569.58</h2>\n",
       "    </div>  \n",
       "</div>\n",
       "\n",
       "<div>\n",
       "    <div class=\"metric-box\">\n",
       "        <h3 class=\"sub-text\">Male Charges</h3>\n",
       "        <h2 class=\"head\">$13956.75</h2>\n",
       "    </div>  \n",
       "</div>\n",
       "</div></div> "
      ],
      "text/plain": [
       "<IPython.core.display.HTML object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "female_charges_display = '$' + format(sex_and_charges.query(\"sex == 'female'\")['charges'].mean(), '.2F')\n",
    "male_charges_display = '$' + format(sex_and_charges.query(\"sex == 'male'\")['charges'].mean(), '.2F')\n",
    "\n",
    "metrics = [{'title': 'Female Charges', 'metric': female_charges_display}, {'title': 'Male Charges', 'metric': male_charges_display}]\n",
    "display(HTML(metrics_html('Average Charge for Females vs Males', metrics)))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Region and charges\n",
    "This section explores the relationship between region and charges in the dataset.\n",
    "#### Date Representation\n",
    "This section aims to compare the number of records for each region.\n",
    "\n",
    "Though the dataset has an almost equal split between region, the Southeast has the most records:\n",
    "- There are 364 records for Southeast\n",
    "- There are 325 records for Southwest\n",
    "- There are 325 records for Northwest\n",
    "- There are 324 record for Northeast\n",
    "The bar chart below shows the count of records for each region."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 67,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "region_and_charges = df[['region', 'charges']]\n",
    "region_data = region_and_charges['region'].value_counts()\n",
    "region_plot = region_data.plot.bar(x='region', color=palette[\"Pink Lace\"], title='The Count of records in the dataset for each region', xlabel='Region', ylabel='Count')\n",
    "labels = region_plot.bar_label(region_plot.containers[0])\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### What's the average charge per region?\n",
    "This section aims to determine the average charge per region.\n",
    "\n",
    "##### Average charge per region\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 68,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "\n",
       "<style> \n",
       "#metrics {\n",
       "    width: 95%;\n",
       "    margin: 20px auto;\n",
       "    font-family: arial, sans-serif;\n",
       "    display: flex;\n",
       "    flex-direction: column;\n",
       "    text-align: center;\n",
       "}\n",
       "h1 {\n",
       "    font-size: 2em;\n",
       "    font-weight: 400;\n",
       "    margin: 20px auto;\n",
       "    color: ##32292f;\n",
       "}\n",
       "h2 {\n",
       "    font-size: 1.5em;\n",
       "    font-weight: 400;\n",
       "    color: #32292f;\n",
       "}\n",
       "h3 {\n",
       "    font-size: 1.2em;\n",
       "    font-weight: 400;\n",
       "    color: #32292f;\n",
       "}\n",
       ".metrics-display {\n",
       "    display: flex;\n",
       "    flex-direction: row;\n",
       "    justify-content: center;\n",
       "}\n",
       ".metric-box {\n",
       "    margin: 0 auto;\n",
       "    background-color: #99e1d9;\n",
       "    border-radius: 15px 50px 30px;\n",
       "    padding: 20px;\n",
       "    margin: 10px 10px 10px 10px;\n",
       "    text-align: center;\n",
       "    width: 200px;\n",
       "    height: 150px;\n",
       "}\n",
       "</style>\n",
       "<div id=\"metrics\">\n",
       "<h1>Average Charge per Region</h1>\n",
       "<div class=\"metrics-display\">\n",
       "<div>\n",
       "    <div class=\"metric-box\">\n",
       "        <h3 class=\"sub-text\">Northeast</h3>\n",
       "        <h2 class=\"head\">$13406.38</h2>\n",
       "    </div>  \n",
       "</div>\n",
       "\n",
       "<div>\n",
       "    <div class=\"metric-box\">\n",
       "        <h3 class=\"sub-text\">Northwest</h3>\n",
       "        <h2 class=\"head\">$12417.58</h2>\n",
       "    </div>  \n",
       "</div>\n",
       "\n",
       "<div>\n",
       "    <div class=\"metric-box\">\n",
       "        <h3 class=\"sub-text\">Southeast</h3>\n",
       "        <h2 class=\"head\">$14735.41</h2>\n",
       "    </div>  \n",
       "</div>\n",
       "\n",
       "<div>\n",
       "    <div class=\"metric-box\">\n",
       "        <h3 class=\"sub-text\">Southwest</h3>\n",
       "        <h2 class=\"head\">$12346.94</h2>\n",
       "    </div>  \n",
       "</div>\n",
       "</div></div> "
      ],
      "text/plain": [
       "<IPython.core.display.HTML object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "metrics = []\n",
    "region_and_charges_means = region_and_charges.groupby('region').mean()\n",
    "\n",
    "for row_index, row in region_and_charges_means.iterrows():\n",
    "    metrics.append({'title': row_index.capitalize(), 'metric': '$' + format(row['charges'], '.2F')})\n",
    "display(HTML(metrics_html('Average Charge per Region', metrics)))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### BMI and charges\n",
    "This section explores the relationship between BMI and charges in the dataset.\n",
    "#### Date Representation\n",
    "This section aims to compare the number of records for each BMI.\n",
    "The data set has:\n",
    "- The least amount of records for the BMI interval of 50 to 55\n",
    "- Tme most amount of records for the BMI interval of 30 to 35\n",
    "\n",
    "The histogram chart below shows the count of records for each BMI interval."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 69,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "bmi_and_charges = df[['bmi', 'charges']]\n",
    "values, bins, bars = plt.hist(df['bmi'], edgecolor='white', color=palette[\"Pink Lace\"], bins=[15,20,25,30,35,40,45,50,55])\n",
    "plt.xlabel(\"BMI\")\n",
    "plt.ylabel(\"Frequency\")\n",
    "plt.bar_label(bars, fontsize=15, color=palette[\"Black Coffee\"])\n",
    "plt.margins(x=0.01, y=0.1)\n",
    "plt.title(label=\"BMI Distribution in the dataset\")\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### What's the average charge per BMI interval?\n",
    "This section aims to determine the average charge per BMI interval.\n",
    "\n",
    "##### Average charge per BMI interval"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 71,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
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       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
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       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>charges</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>(15, 20]</th>\n",
       "      <td>14772.24</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>(20, 25]</th>\n",
       "      <td>12638.73</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>(25, 30]</th>\n",
       "      <td>20758.45</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>(30, 35]</th>\n",
       "      <td>14695.22</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>(35, 40]</th>\n",
       "      <td>21781.64</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>(40, 45]</th>\n",
       "      <td>8849.19</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>(45, 50]</th>\n",
       "      <td>12100.10</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>(50, 55]</th>\n",
       "      <td>24089.37</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "           charges\n",
       "(15, 20]  14772.24\n",
       "(20, 25]  12638.73\n",
       "(25, 30]  20758.45\n",
       "(30, 35]  14695.22\n",
       "(35, 40]  21781.64\n",
       "(40, 45]   8849.19\n",
       "(45, 50]  12100.10\n",
       "(50, 55]  24089.37"
      ]
     },
     "execution_count": 71,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "bmi_and_charges.set_index('bmi')\n",
    "bmi_as_bins = pd.cut(bmi_and_charges.index, bins=[15,20,25,30,35,40,45,50,55])\n",
    "bmi_and_charges_means = bmi_and_charges.groupby(bmi_as_bins).mean()\n",
    "bmi_and_charges_means['charges'] = bmi_and_charges_means['charges'].apply(lambda x: round(x, 2))\n",
    "bmi_and_charges_means[['charges']]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
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