Medical_insurance_prediction_model.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "id": "28fa9766",
   "metadata": {},
   "source": [
    "# Medical insurance dataset"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "84e0e070",
   "metadata": {},
   "source": [
    "###### Submitted by: Leen Alnajjar"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "ec0af10d",
   "metadata": {},
   "source": [
    "# Data science part "
   ]
  },
  {
   "cell_type": "markdown",
   "id": "d399c316",
   "metadata": {},
   "source": [
    "## Step one: Data understanding and reading:"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "c2d9afa6",
   "metadata": {},
   "source": [
    "### 1. Libraries importing: "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 273,
   "id": "dff9b4be",
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "import pandas as pd\n",
    "import matplotlib.pyplot as plt\n",
    "from pandas.api.types import infer_dtype\n",
    "import warnings\n",
    "from sklearn.preprocessing import LabelEncoder\n",
    "from tabulate import tabulate\n",
    "from sklearn.model_selection import train_test_split\n",
    "from sklearn.linear_model import LinearRegression\n",
    "import statsmodels.api as sm\n",
    "import sklearn.metrics as acc\n",
    "warnings.filterwarnings('ignore')"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "49450e0e",
   "metadata": {},
   "source": [
    "### 2. Data presentation:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 176,
   "id": "ba7a2856",
   "metadata": {},
   "outputs": [],
   "source": [
    "cols=['patientid','age','sex','bmi','children','smoker','region','charges']\n",
    "dataset=pd.read_csv('meddical_insurance.csv',usecols =[col for col in cols if col != 'patientid'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "c3ae8573",
   "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>0</th>\n",
       "      <td>19</td>\n",
       "      <td>female</td>\n",
       "      <td>27.9</td>\n",
       "      <td>0</td>\n",
       "      <td>yes</td>\n",
       "      <td>southwest</td>\n",
       "      <td>16884.92</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>18</td>\n",
       "      <td>male</td>\n",
       "      <td>33.8</td>\n",
       "      <td>1</td>\n",
       "      <td>no</td>\n",
       "      <td>southeast</td>\n",
       "      <td>1725.55</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>28</td>\n",
       "      <td>male</td>\n",
       "      <td>33.0</td>\n",
       "      <td>3</td>\n",
       "      <td>no</td>\n",
       "      <td>southeast</td>\n",
       "      <td>4449.46</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>33</td>\n",
       "      <td>male</td>\n",
       "      <td>22.7</td>\n",
       "      <td>0</td>\n",
       "      <td>no</td>\n",
       "      <td>northwest</td>\n",
       "      <td>21984.47</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>32</td>\n",
       "      <td>male</td>\n",
       "      <td>28.9</td>\n",
       "      <td>0</td>\n",
       "      <td>no</td>\n",
       "      <td>northwest</td>\n",
       "      <td>3866.86</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   age     sex   bmi  children smoker     region   charges\n",
       "0   19  female  27.9         0    yes  southwest  16884.92\n",
       "1   18    male  33.8         1     no  southeast   1725.55\n",
       "2   28    male  33.0         3     no  southeast   4449.46\n",
       "3   33    male  22.7         0     no  northwest  21984.47\n",
       "4   32    male  28.9         0     no  northwest   3866.86"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "dataset.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "a2bf91d8",
   "metadata": {},
   "source": [
    "### 3. Data understanding:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "id": "098bec3f",
   "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": [
    "#Check the info of the dataset:\n",
    "info=dataset.info() #Four obejects are in the file\n",
    "\n",
    "#Check the null values:\n",
    "null=dataset.isnull().sum()\n",
    "\n",
    "#Check the duplicated values:\n",
    "duplicate=dataset.duplicated().sum() \n",
    "\n",
    "#Check the mixed data type:\n",
    "mixed=dataset.apply(lambda x: 'mixed' in infer_dtype(x)) \n",
    "\n",
    "#Check unique values\n",
    "unique=dataset.iloc[:,:].nunique()\n",
    "\n",
    "#Check the white spaces\n",
    "whitespaces=dataset.columns.tolist() "
   ]
  },
  {
   "cell_type": "markdown",
   "id": "2f378437",
   "metadata": {},
   "source": [
    "### 4. conclusion of step one:"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "0085e187",
   "metadata": {},
   "source": [
    "Based on the above analysis, it can be concluded that the data contains 1339 records, seven features, and one output label data\n",
    "the following information was extracted from the data:\n",
    "1. Non null or duplicated data was found through the analysis. \n",
    "2. There were three columns with string values (region, sex,and smoker).\n",
    "3. No mixed data type was found.\n",
    "4. No problem regarding white spaces in the headers was found. \n",
    "5. The only problem that need to be solved is the categorical data problem."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "38a64669",
   "metadata": {},
   "source": [
    "## Step two: Statistical analysis"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "634b733e",
   "metadata": {},
   "source": [
    "As a part of the analysis, the data were statistically analyzed, and then to illustrate the outliers, the box plot was used. "
   ]
  },
  {
   "cell_type": "markdown",
   "id": "b0af6937",
   "metadata": {},
   "source": [
    "### 1. Describe and sumarize the data statistically:"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "c999069c",
   "metadata": {},
   "source": [
    "The data were statistically analyzed in order to find the maximum, minimum, and average of each feature, the following observations were obtained:\n",
    "\n",
    "1.The average recorded age was 39 years.\n",
    "\n",
    "2.The average BMI was 30, the maximum and the minmum were 53, 16 respectively. \n",
    "\n",
    "3.The average charges was 13270 and the maximum was 63770. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "id": "87d3354d",
   "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>bmi</th>\n",
       "      <th>children</th>\n",
       "      <th>charges</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>1338.000000</td>\n",
       "      <td>1338.000000</td>\n",
       "      <td>1338.000000</td>\n",
       "      <td>1338.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>39.207025</td>\n",
       "      <td>30.665471</td>\n",
       "      <td>1.094918</td>\n",
       "      <td>13270.422414</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>14.049960</td>\n",
       "      <td>6.098382</td>\n",
       "      <td>1.205493</td>\n",
       "      <td>12110.011240</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>18.000000</td>\n",
       "      <td>16.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>1121.870000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>27.000000</td>\n",
       "      <td>26.300000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>4740.287500</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>39.000000</td>\n",
       "      <td>30.400000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>9382.030000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>51.000000</td>\n",
       "      <td>34.700000</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>16639.915000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>64.000000</td>\n",
       "      <td>53.100000</td>\n",
       "      <td>5.000000</td>\n",
       "      <td>63770.430000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "               age          bmi     children       charges\n",
       "count  1338.000000  1338.000000  1338.000000   1338.000000\n",
       "mean     39.207025    30.665471     1.094918  13270.422414\n",
       "std      14.049960     6.098382     1.205493  12110.011240\n",
       "min      18.000000    16.000000     0.000000   1121.870000\n",
       "25%      27.000000    26.300000     0.000000   4740.287500\n",
       "50%      39.000000    30.400000     1.000000   9382.030000\n",
       "75%      51.000000    34.700000     2.000000  16639.915000\n",
       "max      64.000000    53.100000     5.000000  63770.430000"
      ]
     },
     "execution_count": 24,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "dataset.describe() "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "id": "d3b95410",
   "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>sex</th>\n",
       "      <th>smoker</th>\n",
       "      <th>region</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>1338</td>\n",
       "      <td>1338</td>\n",
       "      <td>1338</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>unique</th>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>top</th>\n",
       "      <td>male</td>\n",
       "      <td>no</td>\n",
       "      <td>southeast</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>freq</th>\n",
       "      <td>676</td>\n",
       "      <td>1064</td>\n",
       "      <td>364</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "         sex smoker     region\n",
       "count   1338   1338       1338\n",
       "unique     2      2          4\n",
       "top     male     no  southeast\n",
       "freq     676   1064        364"
      ]
     },
     "execution_count": 33,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "dataset.describe(include=[np.object])"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "4e565780",
   "metadata": {},
   "source": [
    "### 2. Describe and sumarize the data using boxplot:"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "f7fbb29c",
   "metadata": {},
   "source": [
    "To extensively illustrate the information regarding the data, the boxplot was used to exhibit the outliers and provide a clear comparison between each group."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 108,
   "id": "b3ee4acd",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.boxplot(x=\"sex\", y=\"charges\", data=dataset , width=0.5)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 109,
   "id": "3cdd9bff",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.boxplot(x=\"smoker\", y=\"charges\", data=dataset, width=0.5)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 112,
   "id": "f014c53d",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.boxplot(x=\"children\", y=\"charges\", data=dataset, width=0.5)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 127,
   "id": "31bae990",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.boxplot(x=\"region\", y=\"charges\", data=dataset, width=0.5)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "45e2ce05",
   "metadata": {},
   "source": [
    "### 3. conclusion of step two:"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "58ddf1e9",
   "metadata": {},
   "source": [
    "Based on the above results, it can be concluded that the region has insignificant effect on the medical charges, on the other hand the smokers has fascinating impact of increasing the charges.\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "cf62e915",
   "metadata": {},
   "source": [
    "## Step three: Data pre-processing "
   ]
  },
  {
   "cell_type": "markdown",
   "id": "422387f4",
   "metadata": {},
   "source": [
    "As mentioned earlier, the data contains three features of data type categorical, and this problem need to be solved in order to build machine learning model and visualize the data. "
   ]
  },
  {
   "cell_type": "markdown",
   "id": "8e9ab987",
   "metadata": {},
   "source": [
    "### 1. Solve the categorical data problem:"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "ce580608",
   "metadata": {},
   "source": [
    "The gender, region and smoker all of them has categorical data type , both gender and smoker can be solved using label encoder, while on the other hand the region problem was solved using get dummies. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 142,
   "id": "d132e060",
   "metadata": {},
   "outputs": [],
   "source": [
    "#Label encoding the sex column:\n",
    "labelencoder=LabelEncoder() #Object defined\n",
    "dataset['sex']=labelencoder.fit_transform(dataset['sex'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 143,
   "id": "922bf94c",
   "metadata": {},
   "outputs": [],
   "source": [
    "#Label encoding the smoker column:\n",
    "labelencoder=LabelEncoder() #Object defined\n",
    "dataset['smoker']=labelencoder.fit_transform(dataset['smoker'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 144,
   "id": "b81c8a35",
   "metadata": {},
   "outputs": [],
   "source": [
    "#Encoding the region column:\n",
    "data_encoded=pd.get_dummies(data=dataset['region'],drop_first=True)\n",
    "merged = pd.concat([dataset,data_encoded],axis=1)\n",
    "data = merged.drop(['region'], axis=1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 145,
   "id": "d395fe22",
   "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>charges</th>\n",
       "      <th>northwest</th>\n",
       "      <th>southeast</th>\n",
       "      <th>southwest</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>19</td>\n",
       "      <td>0</td>\n",
       "      <td>27.9</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>16884.92</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>18</td>\n",
       "      <td>1</td>\n",
       "      <td>33.8</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1725.55</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>28</td>\n",
       "      <td>1</td>\n",
       "      <td>33.0</td>\n",
       "      <td>3</td>\n",
       "      <td>0</td>\n",
       "      <td>4449.46</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>33</td>\n",
       "      <td>1</td>\n",
       "      <td>22.7</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>21984.47</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>32</td>\n",
       "      <td>1</td>\n",
       "      <td>28.9</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>3866.86</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1333</th>\n",
       "      <td>50</td>\n",
       "      <td>1</td>\n",
       "      <td>31.0</td>\n",
       "      <td>3</td>\n",
       "      <td>0</td>\n",
       "      <td>10600.55</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1334</th>\n",
       "      <td>18</td>\n",
       "      <td>0</td>\n",
       "      <td>31.9</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>2205.98</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1335</th>\n",
       "      <td>18</td>\n",
       "      <td>0</td>\n",
       "      <td>36.9</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1629.83</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1336</th>\n",
       "      <td>21</td>\n",
       "      <td>0</td>\n",
       "      <td>25.8</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>2007.95</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1337</th>\n",
       "      <td>61</td>\n",
       "      <td>0</td>\n",
       "      <td>29.1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>29141.36</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>1338 rows × 9 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "      age  sex   bmi  children  smoker   charges  northwest  southeast  \\\n",
       "0      19    0  27.9         0       1  16884.92          0          0   \n",
       "1      18    1  33.8         1       0   1725.55          0          1   \n",
       "2      28    1  33.0         3       0   4449.46          0          1   \n",
       "3      33    1  22.7         0       0  21984.47          1          0   \n",
       "4      32    1  28.9         0       0   3866.86          1          0   \n",
       "...   ...  ...   ...       ...     ...       ...        ...        ...   \n",
       "1333   50    1  31.0         3       0  10600.55          1          0   \n",
       "1334   18    0  31.9         0       0   2205.98          0          0   \n",
       "1335   18    0  36.9         0       0   1629.83          0          1   \n",
       "1336   21    0  25.8         0       0   2007.95          0          0   \n",
       "1337   61    0  29.1         0       1  29141.36          1          0   \n",
       "\n",
       "      southwest  \n",
       "0             1  \n",
       "1             0  \n",
       "2             0  \n",
       "3             0  \n",
       "4             0  \n",
       "...         ...  \n",
       "1333          0  \n",
       "1334          0  \n",
       "1335          0  \n",
       "1336          1  \n",
       "1337          0  \n",
       "\n",
       "[1338 rows x 9 columns]"
      ]
     },
     "execution_count": 145,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "c352774d",
   "metadata": {},
   "source": [
    "### 2.Data visualization:"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "5748c71b",
   "metadata": {},
   "source": [
    "Through the data visualization, the light was spot on the most important factors that led to an increase in insurance charges. Although gender and the region were not critical factors, but they were taken into account with the smoking and number of children. They were studied in order to give the company the insight to know which people slip they need to focus on in the future."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "ff0d6776",
   "metadata": {},
   "source": [
    "###### 1. Smoker people vs nonsmoker people:"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "1ba0395c",
   "metadata": {},
   "source": [
    "The analysis was implemented on both male and female, it was found that the male smoker presents 58% of the overall smokers."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 112,
   "id": "2d4b037d",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAnoAAAFNCAYAAACXPcKEAAAAOXRFWHRTb2Z0d2FyZQBNYXRwbG90bGliIHZlcnNpb24zLjUuMSwgaHR0cHM6Ly9tYXRwbG90bGliLm9yZy/YYfK9AAAACXBIWXMAAAsTAAALEwEAmpwYAABV0ElEQVR4nO3dd5hjVf3H8fd32vbeKLsQehEQEKSIslJUCAgWitRFBUGpAhqwMDQNCoKKCAgCAtJ/ohKQJmUBUUCK9JrtvWT77pTz++PcYbPZmdkpmTnJzef1PHl2c3Nz7ydlbr73nnPPNeccIiIiIhI/VaEDiIiIiEjPUKEnIiIiElMq9ERERERiSoWeiIiISEyp0BMRERGJKRV6IiIiIjGlQk96lJlNMLOXzGyxmS0ws5fN7Fe9uP7xZubMbLveWmfcmNkPzGx86BzFYGZZM7s87/7hZjahlfmeNLN7ezVcCTGzLc2s3syGdmDeVt/DcmdmL5rZzaFziHSXCj3pMWZ2HnAD8DDwVeA44K/Al0Pmkk77ATA+dIgi+Qrwm7z7hwMTwkQpaVsCFwBDOzCv3kORElYTOoDE2qnAdc658/Om/d3MLgwVqFjMrBZods419fJ6+znnlvfmOuPEOfdyb6/TzPo651b09nqlNOjzl9B0RE960lBgZuFEl3c5FjNLRE2rR5rZTWa2yMymmtkx0eM/MLPpZjbHzC4zszW+s2a2j5n928xWmNksM7vGzAa2Fypa1yozOzm6X2VmKTN738xWmtm7ZnZ8wXOeNLN7zewkM/sAWAFs0Mby8+fNmtlyM8uY2YYF8/U1s1+Y2ZRova+a2YEF82TN7Aoz+4mZTQUWRdOrzey8KOvK6D27ueC5h0TNTyvMbGa0rtq8x+vNbK6Z7WRmz5vZsqhp/bP56wdGABdEn5NracY1s7PN7AUzy0Xv/d/NbPOCDGZmF5vZ7Oiz/WP0/jszS3TmvWjlfX7azK7Lu//FaLlX5E37WvRZ9897Py+P/n8z8DVg77zXVl+wjqOi78UiM3vIzMauI9OEaDmfjr4Hy4Fzo8e2i74Hi6PbPWa2Xt5za83scjObHL0H083sL2ZWV7DsXc1sYvS9etfMvtJKjnY/+2ieHaLPbKGZLTGz/5jZ/tHn+/doto+idWbbeL3tvodmdqqZvRe9nvfN7Kz23r/oOUX7zrR83mZ2lvm/kQVmdqcVNElHn82z0fv1lpm12upgZnuZ2VPm/1bmmdkfzGxQ3uNtfv4iwTjndNOtR27ARGA2cDwwoo15EoADJgE/A/YH7gCagCuAe4EvAT+K5jsy77nbAquADJAETgYWAv/Im2d89LztovsToudMyJvnd8ASfBPlfsBl0foPypvnSWAG8DLwdeBAYHAbr+lJYBrwP3yT9VHAFOCFgvkeiN6fU4Av4Ju5G4Ed8+bJRut9DN/k/dVo+g3R67gkes+OAO7Je97h0Wu4Jlr2KdF7c3nePPXAMuA14ATgAOB5YC7QP5pnp+h5NwC7R7fB0WNXRp/t+Cjbg8AsYEjeOs6KclwU5fgdMDn6TBKdeS9aeZ8vAd7Ku38psDz/fQZ+DTxf8H5eHv1/M+CfwH/zXtvYvM9wCvAccAhwZPTaHlzHd35C9No+AM4BPh+9h5sDOeBx4FB8cfQm8AJg0XN/Gn3WxwOfiz7Dm4F+Bcv+MFr2AcB90fv0yU5+9lsDi4EXo9e2P3Ae8E1gMHB2tK6vRO/LTm283vbewxOjZVwR5fg50Ayk1vEeFu07E33ek6N5DwROwv+tX5M3Tz/83+ur+L/Xo6P3eDZwc958nwFWAndFyzo2et696/r8Q2+LdavsW/AAusX3BuwQbTBdtIF/I9p4D86bJxE9flPetMFAA/AeUJ03/T/AXXn372xlnsOj5e0R3R8f3d8OXwiuZM1icfMo2/EF2f/EmgXDk/giYr0OvO4no/wb5037TJTjS9H9faP7exc892nWLNiy+B//vnnTto6ee3ob6zd84XxTwfRvRq9hRHS/PlrOPnnz7JifM5o2F6hfx2uujn4wFwPH5U2bAfyuYN4HyfvR7uh70co6vxg9b1Te/Ffjf+wHRtP+C/yy4P3ML3juBZ5s4zPMAcPypp0Zra9fO5kmRPOcUTD9VuAdoC5v2hb4giYZ3X8AuKIDyz4/b1oV8DZwZyc/+zuAqW29FuAgCgqrdnKt9R5Guaa1kuOa6H3t28ayivqdiT7vD4CavGlXATPz7n8X//c6tpW/15vzpk0EnihY3z6svSO51uevm24hb2q6lR7jnHsN2AZ/tOca/I/QT4AXbe3m1cfznrcImAM85dbsA/c+kN/8+WngLwXztBzh2Ktg+afjN/BHOufuzJu+L77Q+4uZ1bTcojw7mll13rwvOefWaopuw3+dc5PyXtOz+CMEn44m7Ydv1n62lfXuUrCsx92afXw+H/17cxvr3hLYCLi7YNn/BPrii94WDfiipsWb0b/tNlECmNnuZvaomc3Dv+fLgIHR+gHGAesBfyt4auH9zrwX+Z7DF0p7mVkf/Ht7A74w3cPMBuN3Niau67W04QXn3IK8+y3vzYatzVwgU3B/P+AvQHPe6/sIX4i0vMZXgAnmuyvsYGbWxrL/0vIf51wz/gSnlu9VRz/7ffA7TT3V33MsvmvDPQXT78LvyG3fxvN64jvzhHOuMe/+m8DoliZx/Hv3knNuassMeX+vAJhv+t+Dtd/XZ/B/Q58qWGfh5y8SjE7GkB7lnFuJ7+/zdwAz+xb+x/hb+Ga1FgsLnrqqjWl98+6vj29Oy19fU1R4DC947tfwheJjBdNH4o8i5Np4Cevjj3xQuK51mN3GtPXz1rse/keiUOEJHoXrHQEsjQri1oyM/n2wjcfH5f1/UVQsAOCcWxXVF33XelYeM9sIeAR/lPU7wHRWN6O3PLel/9mcgqcX3u/Me/Ex59xiM3sF+Cy+uFuOb4Z+JppWgz+y9Gx7r6UdCwvur4r+bfe9iRR+ZiOBH0a3Qi2fxyX4nY7v4rsPTDOzXzrnfl0wf+F3q/B7Bev+7Efgj5z1lJY8he9Dy/3Cv88WPfGdWVhwfxV+p7Mu+v96tP332mIYfjtxTXQrNK7gfme2FSI9SoWe9Crn3I1m9gt882N3zQBG50+IjsCNAOYXzHs08Hv8Wb8H5B3JmI8/GvUZ/I9sofyNvetEttFtTGv5cZ2Pb9o6tAPLKlzvPGCAmQ1uo9hree0n4fsUFvqoA+tcly8B/YFDnHNLAaIjHPk/4C1HP0cVPLfwfmfei0IT8UXdPOBZ51yzmU2MllULvOmcm9eF5XZX4Wc2H38k7oZW5p0LEB21/SnwUzPbAt/V4Coze8c594+8+UfjX2/+/fzvFaz7s5/H6mKsJ7TkKfw7GBP9W/j32aI3vjOtrbO17VF+9oX4z7Se1ovo6QX3O7OtEOlRKvSkx5jZaOfc7IJpo4AhFGeP99/AV8zs/Lzm26/iv9fPFMw7Fd9MOxG418wOdc414Ju0qvEnEDxahEwtdjazjZxzkwHM7DP4H47/RI8/ju/wvsQ593Ynl/3P6N/j8H3SCr2D/xFMOOf+0Onkays8kgq+P14zvkhucThrblOm4H9ED8GPpdii8IzG7rwXE4HToox/jaY9je/4P4B1N9u29tp6wuP4ZtOXnHPrLAKcc++Z2TnA9/AnHeUXel8B3gJ/xjj+/W35XnX0s38cONzMfuRaH/qjM0cvW3sPp+KLn8OAh/KmH44/c/x/bSyrN74zhV4AjjazsS3Nt3l/rwA455aa2fPAVs65i7q5PpFepUJPetL/zOyv+Ca+2cDG+DPRlgG3FGH5l+CPWtxvZr/H9wu6DHjYOfevwpmdcx+a2X74QuA2M/uGc+4dM7sWuDM60vgi/kfrE8CWzrlvdzHbbOCBaKiJvlGu/+YdmXkU/0P2qJldhj9RZTD+ZIi+zrnz2lpwlPl64AozGx29nqHA151zR0ZHtc4Gbo36qT2E/zHeFH8E5OvOuWWdeC1vA0kz+wf+jMV3WF0g32RmN+Lfr3PIayaLmtF/CfzSzObgm1C/zOr+WS1HULv8XuALuWpgT/wPP/izJxuAXfH9Mtf12g4xs0OJihPnXOHRmWKoxxdjGTP7I/4o3ob4s11vds49aWZ/AV7Cf6eX48/ursF/vvm+bWargNfxZ7ZuDnwDfJ+9Dn72F+ILnKfND0czD3928Dzn3B/xnzHAd8zsTmCZc66t4qzV9zD67l8XdaV4FNgbf4bs+W0Ul731nSl0E/Bj/GdTj9+JuZjoSGueHwCPm1kz/gSUxfj+kEngR865dzuxTpHeE/psEN3ie8MfjXgEv2e/At/x/M/A1nnzJPDNHAcVPDdL3tmR0bSbgRcLpu2LP7K3Al9cXUN0xmX0+HjyzoqLprUMGXIjvq+O4c+ofAN/Vu4c4Cmis0ej5zxJ3jAK63jdT+J/CE7GD+2wHP+DO65gvj74H9z38T/GM/FHbpLtvQ/R9GrgfPxZzavwP7A3FcxzAL4QWoo/ivIKvjiuiR6vB+a2smwHnJp3/1P4YVeWRo+Nj6Yfhz+jcXn0+G6FeaP39uLoPV0M3I7/sXfA0M68F+28329F2Wrzpj0UrWOj9r5X+L5ef8E3BTqis4tb+7xb+y61kmVCNM/AVh7bOvpezI/es/eB61g9HMm5+B2NXPRe/RvfNF647E/jC6AV0TK+1sq62v3so3l2wDdDLs5b3755j5+NP4O3Eci285pbfQ+jx07N+0w/BM7qwOdZtO9M4efd1mcUvRfP4f/+38EXxS+Sd9ZtNN9u0ToWRe/tm8CviIYUau/z1023ULeW8ZtEpEjM7El8AfX10FlKkZndAOzvnNs4dJZyYv56sjcBg5xzSwLH6VX6zoh0nZpuRaTHmNl2+MGcn8M3ux2AH5y5tbNPRfSdESkyFXoi0pOW4sc0PBV/csQk/A/2Fe09SSqavjMiRaSmWxEREZGY0pUxRERERGJKhZ6IiIhITKnQExEREYkpFXoiIiIiMaVCT0RERCSmVOiJiIiIxJQKPREREZGYUqEnIiIiElMq9ERERERiSoWeiIiISEyp0BMRERGJKRV6IiIiIjGlQk9EREQkplToiYiIiMSUCj0RERGRmFKhJyIiIhJTKvREREREYkqFnoiIiEhMqdATERERiSkVeiIiIiIxpUJPREREJKZU6ImIiIjElAo9ERERkZhSoSciIiISUyr0RERERGJKhZ6IiIhITKnQExEREYkpFXoiIiIiMaVCT0RERCSmVOiJiIiIxJQKPREREZGYUqEnIiIiElMq9ERERERiSoWeiIiISEyp0BMRERGJKRV6IiIiIjGlQk9EREQkplToiYiIiMSUCj0RERGRmFKhJyIiIhJTKvTKnJklzMyZWU3oLK0p9XwiUrpKfftR6vlEQIVeUGaWNbNVZjayYPor0cYjESiaFFH0WW4eOodIZ0XbqOVmtiTvtkHoXFI6tH0rfSr0wvsI+EbLHTPbHugXLk5p6uk9Zu2Ri7TpYOfcwLzb9NCB4kbbN+lJKvTCuxU4Lu/+8cCf8mcws6SZvWxmi8xsipnVt7UwMxtiZjea2Qwzm2Zml5hZdRvzftrMXoyWO8vMfhVNb2mOOCFa3wIzO9nMdjWz18xsoZldnbecKjP7sZlNMrPZZvYnMxvSxjq/Fh0l2C56XsrMPjCzeWZ2t5kNL8jwLTObDPyzlWWNN7OpZna+mc2Nlnt03uN9zOxyM5scvb5rzaxfwXN/aGYzgZvMrDpa1gdmttjMXjKzcdH8W5vZo2Y238zeMbPD89Zzs5n9zswy0fP+bWabRY89Hc32anQ05AgzG2ZmD5jZnOi9fcDMxuYtbxMzezpa1mPRsm/Le3x3M3su+hxeNbPxbX0fRHpCe9sZM5tgZs+a2ZXRd/RDM9szmj4l2kYcn7csbd+0fdP2rSc553QLdAOywH7AO8A2QDUwBdgYcEAimm88sD2+MN8BmAUcGj2WiOatie7fD1wHDABGA/8BvtPG+v8FHBv9fyCwe8EyrwX6Al8AVkTLHg1sCMwG9o7m/ybwPrBptJz/A24tzAecEM23efTYmcDzwFigT5T7joLn/Sl6Lf1ayT8eaAR+FT1/b2ApsFX0+FXA34DhwCDg78DPC557WfTcfsC5wP+ArQADPgmMiNY/JcpfA+wMzAU+ES3rZmA+8Ono8duBO/NyupbXHN0fAXwN6B/luge4v+BzuRyoA/YCFgG3RY9tCMwDDsR/H/aP7o8K/X3WLX43om1UK9Pvp43tDDAh+ts6Ab9NuwSYDPwu+lv7ArAYGBjNPx5t37R9c9q+9djfcegAlXxjdaH3Y+DnwJeAR6M/po8LvVaedxVwZfT//A3NGGBl/kYD3yz8RBvLeRq4EBhZML1lmRvmTZsHHJF3/z7gzOj/jwPfzXtsK6AhytSyrHOAN4GxefO9Beybd3/9Vp63aTvvX8vGbEDetLuBn0QbsqXAZnmP7QF8lPfcVUDfvMffAQ5pZT1HABMLpl0HXBD9/2bghrzHDgTezru/xoawleXvCCyI/r9R9Jr65z1+W96G8IdEPzJ5jz8MHB/6+6xb/G7RNmoJsDC63b+u7Qy+0Hsv77Hto7+BMXnT5gE7trHOq9D2rWUbpe2btm/dvqndvjTcit8obUJBsy2Ame0GpIHt8HtBffB7SYU2BmqBGWbWMq0Kv7fWmm8BFwFvm9lHwIXOuQfyHp+V9//lrdwfGP1/A2BS3mOTWL1hbnEucJFzbmpB3r+YWXPetKaC57WVvcUC59zSgnVvAIzC71G+lPdeGP4IQ4s5zrkVeffHAR+0so6Ngd3MbGHetBr859ZiZt7/l7H6vVmLmfUHrsQX9sOiyYOiJqgNgPnOuWV5T5kSZWvJcpiZHZz3eC3wRFvrE+mmQ51zj7XcMbNPs+7tTOG2Audcq9sPbd/ape2btm/dpkKvBDjnJkUbogPxG6dCfwauBg5wzq0ws6uAka3MNwW/xzvSOdfYgfW+B3zDzKqArwL3mtmILryE6fg/0BYte22z8M0W4JtH/mFmM51z9+Xl/aZz7tnCBdrqM47dOtY9zMwG5G0MNwJexzc9LMc3P0xr47mFy54CbBY9v3D6U865/deRpaPOxh8V2M05N9PMdgRexm+oZwDDzax/3sZwXN5zp+D3eE8sUhaRzurUdqYDtH1rm7Zv0m06GaN0fAvYp2DvrcUg/F7Qimhv+qjWFuCcmwE8AlxhZoOjzsCbmdnerc1vZseY2SjnXDO+WQb8Hmdn3QGcFXWyHQj8DLirYGP8Bn4P73dm9uVo2rXApWa2cZRnlJkd0oX1X2hmdWb2WeAg4J7oNf0BuNLMRkfL39DMvtjOcm4ALjazLczbIfpheADY0syONbPa6LarmW3TwXyz8P17WgzCb6QXmu+cfUHLA865ScCLQH30mvYA8vdubwMONrMvRp2r+0Ydr8ci0gs6u53pAG3f2qftm7Zv3aJCr0Q45z5wzr3YxsPfBS4ys8XAT/H9NNpyHL75401gAXAvvm9Ia74EvGFmS4BfA0cWHOrvqD+yuvn5I3zH5tMKZ3LOvYrfUP3BzA6I1vk34JHotT0P7NbJdc/Ev87p+E7CJzvn3o4e+yG+c/TzZrYIeAy/p9mWX+Hf20fwHYRvxPcHWozfYz8yWs9MVndy7oh64BbzZ5Edju+D1A+/V/488I+C+Y/G97eZh+/Ifhf+SAbOuSnAIcD5wBz8HvC56G9ZeldntjProu1b27R90/at28y5dR05FilN5k+7v805F+u9PTO7C9/5+YJ1ziwisaDtmxSLqmSREhM1m2wWNU19Cb+He3/gWCIi3abtW+/TyRgipWc9/FhdI4CpwCnOuZfDRhIRKQpt33qZmm5FREREYkpNtyIiIiIxpUJPREREJKZU6ImIiIjElAo9ERERkZhSoSciIiISUyr0RERERGJKhZ6IiIhITKnQExEREYkpFXoiIiIiMaVCT0RERCSmdK1b6TWJVGYQMBoYFd1G5v2/5X4//A6ItfGvA5YAubzbfGBO3m0aMCmbTjb10ksTkQqQSGUM2ADYBL8tGwYMjf4dAgwA+uO3Y/3x262V0W1FG/8uwW+zprbcsunkqt56TRJ/utatFFUilakCNga2AbaObi3/H9mLUVYBHwHvAu8V3KZk00l98UVkLYlUph+wGbBpdMv/fwLo28MRHH6HdQqri79JwP+A/2bTydk9vH6JGRV60mWJVKYG+CSwe3TbAdgCvzdbypYB/wX+BTwH/CubTs4KG0lEelu0Y7otsBt+G7Yb8AlKu1vTdPz267/Ay/jib3LYSFLKVOhJh0V7unsAe0e3XfHNE3HwEb7wa7m9oqZfkXhJpDKjWL1juht+GzY4aKjimIsv/CYCjwEvaPslLVToSbsSqczmwFeAg/Abx7qwiXrNfOAh4O/AQ9l0clHgPCLSBYlU5pPAl6Pbp/D95uIuBzwJPAw8mE0nJ4WNIyGp0JO1JFKZnfDF3VeA7QLHKQUNwNPA34C/ZdPJbNg4ItKWRCpTC4zHF3YH4/sMV7rXgQzwAPBcNp1sDpxHepEKPWk5k2wv4KvAofgOx9K214H/A/6UTSc/CB1GpNJF3UoOxe+cfpF4NMf2lCnArcAt2XTy3dBhpOep0KtgiVRmPeCbwLfxwwVI5z0D3ALcreZdkd6VSGX2AE4AjkDFXVf8G7/9ujObTi4IHUZ6hgq9ChMdvdsfOAnftFEbNlFsLAPuBK7LppP/CR1GJK4SqcxwYAJwIn7YJum+lfhm3VvwfZIbA+eRIlKhVyESqcwYVh+92zRwnLh7BbgG37S7MnAWkVhIpDK7Aafgj9719Fh2lWwm8Gvg99l0Mhc6jHSfCr2Yi86aPR84Bh29620zgF8B12bTySWhw4iUo0QqcyDwE/xZ/9J7FgHXAVdl08npocNI16nQi6lEKrMV8GPgG0B14DiVbj7wW+A32XRyfugwIuUgkcp8GV/g7RI6S4VbhT9545fZdPKd0GGk81ToxUwilfkEvsA7nNIe3b0SLcHvIV+RTSdnhA4jUmqiPsRfwRd4O4ZNIwWagb8Cl2XTyX+HDiMdp0IvJhKpzA7AT/FDpFTCgKDlbCW+4KvXmW4iH1+K7DD8TqrG7ix9DwBna3iW8qBCr8wlUpn1gZ8Dx6ECr9zMBy7A9+HTWW5SkRKpzD74zv8q8MpLA/6kswu1w1raVOiVqUQq0xc4G0gBAwPHke55E/h+Np18OHQQkd6SSGXGAlfgu5lI+ZoP1OPP0tUOawlSoVeGEqnMwfg9YA1yHC8P4gs+dXiW2EqkMnXAWfh+eAMCx5HieRs4J5tOZkIHkTWp0CsjiVRmE+A3wEGhs0iPaQB+B/w4m04uDR1GpJgSqcz++DPQtwqdRXrMI8Bp6r9XOlTolYGoo/LZwIVAv8BxpHd8AJyQTScnhg4i0l2JVGYj/JiSXwudRXrFCvzJgb/KppNNocNUOhV6JS6RyiTwl6X5XOAo0vua8U3052fTyRWhw4h0RSKVmYBviRgUOIr0vv/gd1jfDB2kkqnQK2GJVOYE/A+9NpCV7R3geI1dJeUkkcoMxQ8jpJMtKttK/LA5v8qmk82hw1QiFXolKJHKjAKuBw4NHEVKRxPwC/zYe6tChxFpTyKV+RxwGzAudBYpGU8Cx2XTySmhg1QaFXolJpHKHATcAIwJnUVK0v+AI7Lp5Fuhg4gUSqQyNcBFwA/RlXlkbQuB72XTyT+HDlJJVOiViGgDeQVweugsUvKWAN/OppN3hQ4i0iKRymwO/BnYNXQWKXnXAGdm08mG0EEqQewLPTN7ErjNOXdD6CxtSaQyI4B7gM+HziJl5bf4yxBpYylBJVKZI4E/oMHbpeOeBr6eTSfnhA4Sd+s8tG5mWTObZWYD8qZ9OyqgpJuia9S+gIo86bzTgCcSqczo0EGkMiVSmapEKvNz4A5U5EnnfA54MZHK7Bw6SNx1tA9FDXBGTwYpZeYVvb9JIpX5GvAcusKFdN1n8BvLT4UOIpUlkcoMAv6KvwyjSFdsBDyTSGWOCh0kzjpavPwSOMfMhhY+YGZ7mtkLZpaL/t0z77EnzexiM3vWzBab2SNmNjJ6rK+Z3WZm88xsYfTcMXnPu8TMnjOzJWb2dzMbYWa3m9miaN5ERzIUZF3fzF4zs3Oi+7tH61hoZq+a2fiC7Jea2bPAMmDTDr5X65RIZSyRylyMb67VJYCku8YBExOpzDdCB5HKEI3v+Ty6So90Xz/g9kQqc0UilakOHSaOOlrovYg/Nfqc/IlmNhzI4AfDHIEf+TxjZiPyZjsKOAEYDdTlLeN4YAj+R2oEcDKwPO95RwLHAhsCmwH/Am4ChgNvARd0IgNRYfgUcLVz7nIz2zB63iXRMs8B7jOzUXlPOxY4CT+O3aQOvE/rlEhl+gN/wY8rZMVYpgirN5ZnhQ4i8ZZIZXbFF3nbhs4isfJ94KFo/EUpos40R/4UOK2gEEoC7znnbnXONTrn7sBf2PjgvHlucs6965xbDtwN7BhNb8AXZps755qccy855xYVPO8D51wOeAj4wDn3mHOuEX8kbKdOZNgWX6he4Jy7Ppp2DPCgc+5B51yzc+5RfEF7YN7zbnbOvREtt9sd3hOpzBD8dQAP6e6yRFphwK8SqczPQgeReEqkMofgt6Ua/kl6wv7AP6OxZKVIOlzoOedeBx5gzf4YG7D2ka5J+KNwLWbm/X8Zqzvs3go8DNxpZtPN7BdmVps376y8/y9v5X7LcjqS4WhgGnBv3rSNgcOiZtuFZrYQ2AtYP2+eog3sGH1xn8D3qRLpSeclUpnromskixRFIpU5Gfg/oH/oLBJrOwFPJVKZDdc5p3RIZ38ILgBOZHURNR1fMOXbCF9Utcs51+Ccu9A5ty2wJ76vx3GdzNPRDPXAXODPZtbSB2AKcKtzbmjebYBzLp0fswt51hJ9YZ9m9VFIkZ52EnBXIpWpCx1Eyl8ilTkV+D0aBFl6xzb4fsc6UbEIOvVH65x7H7iL1YP6PghsaWZHmVmNmR2BbyZ9YF3LMrPPm9n2UeG1CN+U29Sp9B3P0AAchj/x4dboDNrbgIPN7ItmVh2dHDLezMZ2IUObEqnMZsAzwNbFXK5IB3wdyCRSGQ17IV2WSGVOx4/ZKNKbNsEXe/rt7Kau7J1dRHSmqHNuHv5I3NnAPOAHwEHOubkdWM56+KbURfiTK57CF1+d0tEMzrlVwFfxJ4X8EX/E7xDgfGAO/gjfuRRxjzWRynwCmAgkirVMkU7aD3hcHZylKxKpzJnAr0PnkIq1IfB0IpXZMXSQchb7K2OEkkhldgIexZ9wIhLac8D+2XRyWeggUh6iM7h/FTqHCP4auQdk08nnQwcpRyr0ekB0qHkiMDJ0FpE8jwAHZ9PJVaGDSGlLpDJnA5eHziGSZwkwPptOvhQ6SLlRx9oiS6QyG+OP5KnIk1LzBeA2nY0r7UmkMueiIk9Kz0B8n+OiXbygUmiDX0TRNUcfBYp6QodIER0GXBc6hJSmRCrzTeAXoXOItGEM8LDG2escFXpFkkhlBuCvtLFF6Cwi6/DtRCrzy9AhpLQkUpl9gGtD5xBZh83xR/Z0+dAOUqFXBIlUpgZ/tY5dQmcR6aBzEqnM+aFDSGlIpDLbAPcBteuaV6QE7ArcE/32yjqo0CuO64ADQocQ6aRLE6nM4aFDSFhRM1gGGBo4ikhnHAD8IXSIcqBCr5uis9O+GTqHSBfdlEhldggdQsJIpDJ9gb/iB6cVKTcTEqnMJaFDlDoNr9INiVRmPPAYUL2OWUVK2UfALtl0cn7oINJ7EqmMAXcAR4TOItJNx2XTyVtDhyhVOqLXRdH1a+9CRZ6Uv02AOxOpjL7LleViVORJPFyXSGU+GTpEqVKh1wWJVKYWf/LF6NBZRIpkfyAdOkRcmdmTZvbt0DlaJFKZQ4Efhc4hUiT9gPt0qcfWqdDrmiuBPUKHECmycxKpzDdCh2iNmWXNbJaZDcib9m0zezJgrLKUSGXGAjeGziFSZJsBt0ZdEiSPCr1OSqQyxwDfC51DpIfcUMJNIDXAGaFDhGJet7bZ0VVRbgOGFyeVSEk5CDg7dIhSo0KvExKpzPboqgISb/2BP0dnY5aaXwLnmNnQwgfMbE8ze8HMctG/e+Y99qSZXWxmz5rZYjN7xMxGRo/1NbPbzGyemS2Mnjsm73mXmNlzZrbEzP5uZiPM7HYzWxTNm+hIhoKs65vZa2Z2TnR/92gdC83sVTMbX5D9UjN7FlgGdPfyTz8C9u7mMkRK2c8SqcxuoUOUEhV6HRT1y7sN/0MoEmfbUpqXwXoReBI4J3+imQ3HjwP3G2AE8CsgY2Yj8mY7CjgB36+2Lm8ZxwNDgHHRc08Gluc970jgWGBDfNPQv4Cb8EfE3gIu6EQGosLwKeBq59zlZrZh9LxLomWeA9xnZvmXeDoWOAkYBEzqwPvUqkQqs2dLXpEYqwXuSKQyQ0IHKRUq9DruJ4DGG5NKcWoilfli6BCt+ClwWkEhlATec87d6pxrdM7dAbwNHJw3z03OuXedc8uBu4Edo+kN+MJsc+dck3PuJefcooLnfeCcywEPAR845x5zzjXiT8jaqRMZtsUXqhc4566Pph0DPOice9A51+ycexRf0B6Y97ybnXNvRMtt6OwbBhD96P0ZjRIglWET1Pr2MRV6HZBIZXYGzgudQ6QXGX4w5WGhg+Rzzr0OPACk8iZvwNpHuibhj8K1mJn3/2XAwOj/twIPA3ea2XQz+4WZ5V8GbFbe/5e3cr9lOR3JcDQwDbg3b9rGwGFRs+1CM1sI7AWsnzfPFLrv+mhdIpXiiEQq8+XQIUqBCr11SKQydcDN+I7gIpVkfeDXoUO04gLgRFYXUdNZu4jZCF9Utcs51+Ccu9A5ty2wJ74z93FdyNSRDPXAXODPZtZyZG0KcKtzbmjebYBzLn+om26Nap9IZSYAutSdVKJrEqnM4NAhQlOht24/AbYPHUIkkGMTqczB656t9zjn3scPVn56NOlBYEszO8rMaszsCHwz6QPrWpaZfd7Mto8Kr0X4ptymLsTqSIYG4DBgAHBrdAbtbcDBZvZFM6uOTg4Zb2Zju5BhLYlUZgy+v6BIJdoQ+HnoEKGp0GtHIpX5FGs2EYlUoutKrQkXuAhfMOGcm8fqYRXmAT8ADnLOze3ActbDN6Uuwp9c8RS++OqUjmZwzq0Cvoo/KeSP+CN+hwDnA3PwR/jOpXjb5iuBUvvsRHrTKdGJSBVL17ptQ9Rk+yI6micC8NtsOnn6umeTUpFIZb6A738oUuneBHbKppOrQgcJQUf02nYmKvJEWpySSGU+ETqEdEw0DuI1oXOIlIhtqeATKlXotSKRyoxC14EUyVcDXBU6hHTYufhx/0TEOz+RymwTOkQIKvRadxFQ8WfqiBTYL5HKHBo6hLQvkcpsRAUfvRBpQx3w+9AhQlChVyBqnjoxdA6REnVFIpXpEzqEtOsKoF/oECIlaO9EKnNA6BC9TYXe2q5Ao8eLtGVT4PuhQ0jrEqnMeODroXOIlLCfJVIZCx2iN6nQyxNV+qV42SeRUnJ+IpVZf92zSQAXhw4gUuJ2BI4IHaI3qdCLJFKZGuDy0DlEysBAfD9WKSGJVGY//OXTRKR9F0e/+RVBhd5q38Kfgi0i63Z8IpUZFzqErKE+dACRMrE5/je/IqjQAxKpTC06S02kM2qBH4YOIV4ildkf+EzoHCJl5KeJVKYiTlqqmEOX63AMa1+QPIjJv1qzH7VrXMWgnQ5k+P4nA9DcsIIFT/yRZW8/g2tqpG70Jqx39GWtLmvmn1OsnP4OVuXPLakeNIINT7wOgMZFc5hzf5rGBdMYsP1+DN/n2x8/b9bdP2XoZ4+lz/pb9MRLlPj4ViKVuSSbTs4MHUS4IHQAkTKzAXAa8IvQQXpaxRd6iVSmihI6mrfR9+/9+P/Nq1Yw9epj6L/16m438/9xNa65iQ2+/Xuq+g5k1eyP2l3e8P1PZtAn1z6/JPf8PQzcfl8GbLs3M24+g5Xb7E2f9bdg6VtPUzN0PRV50hF9gXOimwSio3kiXfbDRCrz+2w6uTh0kJ6kpls4DCjJqmbZO89S3X8Ifcb6K081zJvKsvf/zYgvnUZ1/yFYVTV91tu8S8tuXDiLvhvtQFWfAdSttwWNC2fSvHIZuefvZdjnjivmy5B4OzmRyowIHaLC1YcOIFKmhgMnhA7R01To+UsFlaQlrz/OgO32wcwP+bNyxjvUDB7NwmduZ8pvjmL6jd9j6TvPtruMhU/dwpTfHMXM285lxeTXPp5eN2pjlmdfpnnFElbNfJ/akeNYOPFWBu9yCFV9B/bo65JYGYC/LrQEEB3N2zN0DpEydmrcx9Wr6EIvkcp8HvhU6BytaVw0m5VTXmfAdvt+PK1p8Twa5k6iqk9/xn7vFobvfzLzMlfSMHdKq8sYNv4ENvzODYz97i0M/OSXmH3fxTQsmAHA4N0PY+XUN5n55xSDdk5CcxOr5mTpt/mnmfO3XzLz9h+y6KW/98prlbJ3WiKVGRI6RIXS4NUi3bMFEOurZVR0oUcJ9y1a8vo/6TN2W2qHrvfxNKupg6oahux5JFZdS9+NtqfvRtuzPPvfVpfRZ4OtqOrTH6upZeD2+9Jnw21Y/uGLAFT3G8SoQ37IBt+8mkG7fJn5j17H8P2+w6Ln76Fu1MaMOfISlrzyEKvmTu6V1ytlbQgV0PxRahKpzCbAF0LnEImB00MH6EkVW+glUpmSruKXvv5PBm63zxrTakclurlUA+fWmrrklX/QZ4OtqBuVYNWcSdSttzlWXUvtqAQNcyZ1c51SIXR96N53IhW8DRcpoi8kUpmtQofoKZW8kfgmUJLt8iumvkXTknn032rNQe77jtuOmsGjyP3rblxzEyumvsmKKa/Tb5Od11pG84olLP/wJVzjKlxzE0veeIKVU9eet2npQhb/N8OQvY4CoGboGFZMfo3mVctZNfM9avKOKIq0Y9tEKqO+Yr0kGvvzm6FziMSE4YdaiaWKHF4lkcpUAyV7aunS1x+n/5Z7UtWn/xrTrbqGUV/9MfP+8RsW/fteagaPZmTyLGpH+AsU5P51NyumvMGYwy/ENTexcOJtNMyfClZF7fCxjPrKj6kdMXaNZS544kaGfOYbVNX5cSOH7H4Yc+7/OUtefogBO+yvYVakM04EngsdokIcAowJHUIkRo5PpDLnZ9PJRaGDFJu5Vpry4i6RyhwIZELnEImZZcAG2XQyFzpI3CVSmceAfdc5o4h0xlnZdPKq0CGKrSKP6KGO4yI9oT9wFPD70EHiLJHKbA7ss84Ze0hjbhbzHrmGVdPehppaBmz1GYbtexKrZrzHwom3sWrW+2BV9N1oe4bt9x1qBg5vc1lL33yKhc/eQdPiOVQPGMaIA8+k77jtdOUeCeUk4KrQIYqt4vroRYO7fjl0DpGY0kkZPe8kAvYvnvfINVT3H8rYU29lgwm/ZcXk11n83wzNK5YwcMcvseHJf2TDU/6I1fVj3oNXtbmc5R+9zIKnbmbkgWcy7qx7GHNU+uM+wS1X7tnw5BtZ/t7zrJzxHoCu3CM9bZtEKrNT6BDFVnGFHnA0UBc6hEhM7ZRIZUpybMo4SKQyNcCEkBkaF85iwNZ7YTV1VA8cRr9NP0XD3Mn022wXBmy9F1V9+lNV25dBOx/Eymlvtbmc3DO3M2TPb9Bnw60xq6Jm0EhqBo38eB26co8EclToAMVWiYWemm1FetaxoQPE2N7AqJABBu/yZZa+9TTNDStoXDyX5R++SL9N1z7zf+WUN6gdsVGry3DNTayc+T7Ny3NMu+5Epv7ueOY/+nuaG1YCunKPBHVk3K6UUVGFXiKV2Q7YMXQOkZj7atw2lCXk0NAB+o7bnoa5k5ly5eFMu2YCdettQb8t9lhjnlWzPyL33B0M+3zr+9VNSxdCcyPL3nmWMUdfxvon/IZVsz4k96+7AF25R4IaC3w2dIhiqqhCD/XNE+kN44BdQ4eIqUNCrty5Zmbd/VP6b7knG33/Psae/meaVyxh4ZM3fTxPw4LpzL7nAobtexJ9x23X6nKstg8Ag3Y+mJqBw6nuP4RBux7K8g905R4pCV8PHaCYKq3QOyh0AJEK8bXQAeIm6vs4LmSG5uWLaVo8h0GfOgirqaW632AGbr/fx5dWbMzNZtadP2bInkeudWWffNV9B1I9aGSHTinRlXskgK/EqVWiYgq9RCozEtgtdA6RCnFo6AAxFPRoHkB1/yHUDBnD4pcfxDU30bxiCUtef5za0ZvQuHgus+48n0E7Jxm004HrXNbA7fdj8UsP0LR0IU0rlrD4xb/Sf7M1DwTryj0SyFhi1CpRSePoHUgFFbYigW2ZSGW2yKaT74UOEiOHhg4AMOorP2L+49ez6Pl7oaqavhttz/B9TmTxyw/SuHAmuWfvIPfsHR/Pv9H37wXWvHIPwJA9j6Rp+SKm/eE7WE0tA7b+LEP2PGKNdenKPRLQV4H/hA5RDBVzZYxEKnM3cFjoHCIV5PvZdPLK0CHiIJHKbAp8EDqHSAX5Xzad3CF0iGKoiCNc0QXAvxA6h0iFUZ/Y4jk0dACRCrNddIGFslcRhR7+VOkhoUOIVJjPJlKZ/qFDxMTBoQOIVBgDPhc6RDFUSqG37p7BIlJstcCnQ4cod4lUpg7YPXQOkQq0d+gAxVAphd5eoQOIVKjPhA4QA7sCfUOHEKlA40MHKIbYF3rR3vCOoXOIVCgVet0Xq1H6RcrI9olUZljoEN0V+0IP2AnoEzqESIXaPU4DjwaiQk8kjCpi0E+vEgo9DZIsEs4wYNvQIcpVVCTvsc4ZRaSnlH0/vUoo9NSJWSQsNd923Rb4YllEwhgfOkB3VUKhpyN6ImHtGTpAGdNZyyJh7ZBIZcr6ZKhYF3qJVGYUsGnoHCIVTkf0uk6FnkhY1cBWoUN0R6wLPXQ0T6QUbJZIZfqFDlGmPhU6gIiwTegA3RH3Qm/H0AFEBMP3NZPO2zp0ABEp7xPK4l7oaSMpUhq2DB2g3ETjdw0PnUNEVOiVsrJuVxeJEf0tdt7moQOICKBCr6Tpx0WkNOhvsfNU6ImUhs0TqUxt6BBdFdtCL5HKjAEGhc4hIoCabrtChZ5IaailjPsZx7bQQ8OqiJQSHdHrPBV6IqWjbJtv41zoJUIHEJGPDU2kMqNDhygzKvRESkfZ/j3GudDbJHQAEVmD/iY7p2ybikRiqGx3VONc6G0cOoCIrGFk6ADlIpHKDAJGhc4hIh8r27/HOBd6+lERKS36m+y4sj16IBJTZbv9inOhNzR0ABFZw4jQAcrIkNABRGQNOqJXgoaGDiAia1Ch13Eq9ERKiwq9EjQ0dAARWUPZNn0EoEJPpLSo0CtBQ0MHEJE16Ihex6nQEykt/RKpTP/QIboiloVeIpUxtKEUKTUq9DpuaOgAIrKWsjyqF8tCD3/ps+rQIURkDSr0Ok47qiKlpyy3YXEt9IaGDiAiaynLZo9AVOiJlJ660AG6Iq6Fnn5QREqPjrJ3nAo9kdJTEzpAV8S10GsOHUBE1lKWG8lAyvLIgUjMleXOalwLvabQAURkLWW5kQykIXQAEVlLWe6slmXoDlChFxPDWDT/qT7fnx46h3RfA9XzYWroGOVChV5MnFr9l2dOqskMDZ1Dum+qG9UIydAxOk2FnpS0xfQfNIhlnzDDQmeRblOV13GrQgeQ4ri9ad9tz665p8aMwaGzSPdsa5PKslVCTbdS0hqpqXXYgtA5pCj0d9lxKvRiYgGDhz/YvNvLoXNIUZTlNkyFnpS8VdTMD51BiqIxdIAyoqbbGPlBw0k7NzvTdqz8lWVtoUJPSt4S+i0KnUGKQoVex+mIXowspd+gu5v2fi10Dum2FaEDdIUKPSl5C9ygsvzjkrWoeOk4vVcxU994/G5NzmaFziHdUpbdiOJa6C0GXOgQUhwz3TA1Y8XD3NAByoi+8zGzgj79bmo64J3QOaRbyrL5PZaFXjadbKRMK29Z2zRXlteRlrXNDB2gjCwLHUCK77LGI3dvdFU6+7w8NQO50CG6IpaFXmR26ABSHJPc6LgOA1RpVOh13JzQAaT4Gqip+23jV7Khc0iX5KjPleVVt+Jc6KkvRExMdmP6hc4gRaFCr+O0/Yqp3zZ9ZY+Vruaj0Dmk08qy2RbiXejpiF5MTHGjBoXOIEWhQq/jVOjFVDNV1Zc1fmNG6BzSaWXbHUyFnpS8aW7k8NAZpChU6HWcCr0Y+2PTl/ZY5up0YkZ50RG9EqQNZUzMY/Bw5zQGWwyo0OugbDo5nzIds0s6wuyCxgll2bG/gpXtNdfjXOjpiF5smDVj80KnkG5Todc5Ojszxu5pGv/pxa7fG6FzSId9EDpAV8W50FMfiBhZQV3Z9o8QwF8VQ+Podc7k0AGkZ53bcNLK0Bmkw94PHaCr4lzole2HImtbTP8loTNIt8wp16EJAlKhF3P/aN5t53lu0Muhc0iH6IheCXofP8ChxMA8N1j9lcrbpNABylA2dADpeWc0nKpxQsuDCr1Sk00nV6ENZWzMcCN0/eLy9mroAGXozdABpOc907z99jPc8BdC55B2LaQ+p7NuS5ROX4+JKW6Uhc4g3fJK6ABl6JXQAaR3fHfVGYOd0/XZS1jZHs2D+Bd6OqMpJia70bWhM0i3qB9S570PqG9qBXjZbbFV1o15PnQOaVNZ9/mPe6H3v9ABpDgmu9H9Q2eQLmsCXgsdotxk00mH3reKcUrDWWOcQ11UStMroQN0R9wLPW0kY2KqGzU4dAbpsveozy0PHaJMqW9jhXjbbbTp226jf4XOIa0q6z6UcS/03gJdUSEOprsRI0JnkC57JXSAMvZK6ADSe05pOGNj52gInUPW4IAXQ4fojlgXetl0ciVqvo2FxQwY7Bw6KlSeXgkdoIy9EjqA9J6sW3/cf90WOqpXWt6lPlfWl6uLdaEXmRg6gBRHI9W6DFp5eiV0gDL2P1C/rUryvVVnbKWd2pJS1s22UBmF3tOhA0hxLKduYegM0iU647aLsunkcjRMVEWZyfAxE5u3/0/oHPIxFXplQIVeTOTcwKWhM0inTac+Nzt0iDL3TOgA0rvOaPje9s6xOHQOAVTolb5sOjkHeDt0Dum+OQzRBcDLz2OhA8TAI6EDSO9awODhDzV/+qXQOYSVxKBFIvaFXkT99GJghhuhkePLz0OhA8TAP1E/vYrzg4aTdm52LAido8JNpD5X9tdZr5RCT823MTDZja6U72tcNKGjUd2WTScXUObDO0jnLaH/4HubPqdxFMP6R+gAxVApP5xPhQ4g3TfZje4TOoN0ygvlfCHwEvNo6ADS+y5oPOHTTc7UxzUcFXrlIptOTqHML0osMNmNGRA6g3SKmm2LR0dGK9By+vS/pemLb/XGuq7+zyp2uX4JfS5ZxIT7V4/ucvtrDQz82aKPb/0vXYRduIiXprffm+C9eU30vWQRx/zf6mVNyTWz+w1LGX7ZIs5+eM0W0S/dtpQX17HMXjaV+twboUMUQ0UUepH7QweQ7pnqRg4NnUE6RYVe8TwPOguzEv288ag9Gl3VtJ5ezwaDjB9/rg/f3LF2jelH71DLkvMHf3y7JtmXTYcZO6/ffvnwvQdXsOuG1WtM+/kzKzn+k7V8dMYg7n+n4ePC7q7XG9h0WBW7bFDd2qJCeTh0gGKppELvvtABpHtmuuG6DFr5mIP6lRVNNp1sAJ4MnUN6XwM1db9rOuTDnl7PV7ep5dCtaxnR39qd75ZXGzhuhzrM2p7vztcbGNrX2HeTNQu3jxY2s88m1Qzpa+y6QTUfLmhm0UpH+tmV/GzfvkV5HUWkQq8MPQ9MDx1Cum4ldX2do6wvRVNBHqY+p7Oki0vNtxXqN41f3WOVq8mGzjFpYTNPT2riuE/WtjnPopWOnz6xkiu+sHbhtt2oah79sImFKxwvTm9i21FV/OSfKzlztzqG9m2/wOxlTcSoX2zFFHrZdNIBfwmdQ7qngRp17i8ParYtvr8AzaFDSO9rorrml41HBD9Q8adXG/jsRtVsMqzt0uEn/1zJt3aqZdyQtec577N9mDi5kb1vXsr3dq2joQlem93EwVvVctR9y/jcTUu5+j+revIldNQT1OcWhg5RLBVT6EXUfFvmltJ3UegMsk7N6OhT0WXTyWnAE6FzSBh/aDpwj+Wu7r2QGf70WgPHt3M075WZTTz2USNn7VHX6uPD+xl3fb0/r548kDN2r+O0h1bw2wP6kn5mJduNruax4/pz7YureHNO8JMybg8doJgqrdB7GpgbOoR03QI3cFnoDLJOD1Of099Zz7g1dAAJxezCxuOCtWg8O7mR6Yub+fq2bRd6T2YbyS5sZqMrl7De5Yu5/LlV3PdWAztft2Stea9/qYHdx1az3ehq/je7iV02qKau2th+TBWvzw564HoF8H8hAxRbRRV62XSyCfhr6BzSdbMZ1hA6g6zTjaEDxNj/AdrZqVB3Nu2z22LXr0eG/GhsdqxodDQ1Q5ODFY2OxubV3WxvebWBr21Ty6A+bfelO+lTdXxw+kBeOXkAr5w8gJN3qSO5RQ0PH9N/jflmL23mdy+son68Hxp1k6FVPPFRI0tWOV6c3sym7TQN94K/U5+LVctRRRV6kXtDB5Cum+ZGqoN/aZsD/C10iLjKppOL0c5qRUs1nNgjl+S65OmV9Lt0MelnV3Hbaw30u3QxlzztLy++otFx9xutN9v+bOJKDrh9KQD9a431BlZ9fBtYB31rjFED1iw1znlkJT/9XB8G1vmi8by9+vDPbCPjrlzMl7esCT3MSqyabQHMucr63UykMlXAR8BGobNI551efd8z36+9b6/QOaRNv6I+d3boEHGWSGUOBDKhc0g4L/c56ZVhtmTH0DliaAGwHvW5kjgjpFgq7oheNp1sBv4QOod0zWQ3puQGW5I13BA6QAV4BNBlsSrYGQ3fq7jf7l5yb9yKPKjAQi9yA9AYOoR03mQ3emCI9a5sdHzrr8vZ+KrFDPr5Ina6bgkPvee7C745p4ldrl/CsMsWMeyyRez3p6XtnjU2/ual9L1k9SWFtrp6dUflMrtEUKF/UZ/rlcs1VbJsOtkI3Bk6h4TzdPMnd5jphmlA8uK7OXSAnlCRhV42nZyJLolWlma4EcNCrLexGcYNMZ6aMIBcahAXf74Ph9+7nOzCZjYYVMW9h/dn/g8GMffcQXx5qxqOvHd5u8u7+sC+H19S6J1TV9euZXaJoEI6CaP33BI6gIR16qrTBzhHZfW96lkvUZ97LnSInlCRhV7k2tABpPNmM3SEc70/aOyAOqN+fF8SQ6uoMuOgLWvZZGgVL01vYmhfIzG0CjPDAdVmvD+/axHL7BJB+ZYAd4UOUSmy6eR/gX+FziHhvOi22maSG/N86Bwx8pvQAXpKJRd6/wTeDR1COqeJ6hqHBb86xqwlzbw7r5lPjF79JzQ0vYi+lyzmtIdWcP5n+7T7/PMeX8nIXyzmM39cypPZ1b0IyugSQYXupj639mBZ0pOuCh1Awjql4czRIXZ8Y2gWMe4OUbGFXnRJtOtC55DOW0ntgpDrb2hyHP1/yzn+k7VsPXJ1U+rC1GByqUFcfWBfdlqv7T+ty/brw4enD2Ta9wdy0s61HHzHMj6IjgCW0SWCCukEp953HzA5dAgJ5y238WbvuHGxbG7sZdfG8SSMFhVb6EVuBtrvTCUlZwn9Fodad7NzHPuX5dRVG1cfuHZT6oA64+Rdajnu/hXMXtr6jvZuY2sY1MfoU2Mcv2MdnxlXzYPv+aN6ZXSJoHxPUp9TE1IviwaA/23oHBLWyQ1nbuwcGki+61YBvw8doidVdKGXTSfnoyMRZWeeGxykOHfO8a2/rWDWUsd9h/ejtrr1ptRmB8saHNMWdayftBm01qe6hC8RVOjC0AEq2PVALnQICSfr1h/3sttc/TW77i7qc7NCh+hJFV3oRX6Jr+ilTMxyYS6DdkpmBW/Naebv3+hPv9rVRd6jHzTy8owmmpodi1Y6vv/wSob1NbYZtfaf18IVjoffb/z48kK3v9bA05Oa+OJmNWvMV+KXCMr3FPW5J0OHqFTZdHIRcE3oHBLWqatO38I5euSKGRXgqtABelrNumeJt2w6OTWRytwCnBg6i3TMVDey189KmLSwmeteaqBPNax3+eqW4+sO6kddNZz20AqmLmqmX60/W/Yfx/Snb42P+bOJK5k4uZGHjh5AQ5Pjx0+s4O25zVQbbD2ymvuP6M9WI9ccNqW1SwR9/Z5lXPvSKr65Y10pDbOio3nhXQWcCfQLG0NCmc7I9Z9t3u6pvapf3zt0ljLzV+pz/w0doqdV3CXQWpNIZTYF3kGFb1n4TvXfnzuv9o49Q+cQJlKf+1zoEAKJVOZq4Huhc0g4I8jNfbHPKX3NCDKofBlqBnakPve/0EF6Wsm0/4SUTSc/JKYjYsfRJDdGRy5Kg47mlY40OrGsos1jyMhHmnfR1TI67u5KKPJAhV6+i1FfvbIwxY0aHDqD8Az1ucdDhxAvm05OBX4VOoeEdU7Dd3ZqdiwMnaMMNAEXhA7RW1ToRbLp5GR0Bm5ZmO5GDA+dQXQ0rwSl8QO/SoVazIAhf2n+7Cuhc5SBP1Gfq5gLJqjQW9Ol+Es5SQlbwKChzunoa0DPUZ97LHQIWVM2nVxCBR2lkNb9pOGEXZuczQmdo4StosJ2VFXo5cmmkzPwTbhS0syaqJoXOkWFcsAPQ4eQNt0AvBE6hISzjL4Dbmva/83QOUrY9dTnJoUO0ZtU6K3tSuDt0CGkfSuoC3oZtAp2E/W5Z0KHkNZFV8s4N3QOCevSxqN3b3RV00PnKEFzgZ+GDtHbVOgVyKaTDcBpoXNI+xbTf2noDBVoLvCD0CGkfdl08iHg0dA5JJxV1Pa5tungD0LnKEE/oj5XcQcJVOi1IptOPoa/YLiUqLluyMrQGSrQD6jPqcm8PJyDHydMKtSVjV/fY5WrrqgmynV4Cd+1oeKo0Gvb94FloUNI66a74U2hM1SYp9FYk2Ujm06+BlwbOoeE00R1zRWNh08NnaNENAOnUJ8rqZ0fM5tgZj3eFUaFXhui4VYuDZ1DWjfFje71y6BVsAb8RlKX0SkvPwSyoUNIONc3JfdY4WrfD52jBFxDfe6FzjzBzPYys+fMLGdm883sWTPbtacC9iQVeu27HHgvdAhZ22Q3pi50hgpyBfU5ncVXZqLhVr6JP1NaKpCjquqixmPnhs4R2HTgR515gpkNBh4AfgsMBzbED8lSkl2GzKzdy7eq0GtHNp1cBUzAj6ItJWSSGz0gdIYK8RFwUegQ0jXZdPIJ4Pehc0g4f27ab/clrm8l76idQn1uUSefsyWAc+4O51yTc265c+4R59xrUXPrs2Z2pZktNLMPzWzPaPoUM5ttZse3LMjMhpjZn8xsjplNMrMfm1mrtZeZ/dLMnomeM8TMbjSzGWY2zcwuMbPqaL78DPOB+vZejAq9dcimk88BPw+dQ9Y01Y0aEjpDhTiV+pyuoVrefgB8GDqEhHNew7cr9W/4D9Tn/taF570LNJnZLWZ2gJkNK3h8N+A1YATwZ+BOYFdgc+AY4GozGxjN+1tgCLApsDdwHHBC/sLMrMrM/gDsAHzBOZcDbgEao2XuBHwB+HZBhg+B0ayjm5kKvY65EOhU+770rJluuC6D1vP+RH3uwdAhpHuy6eRS1IRb0f7evOenFriBr4bO0cveA87qyhOdc4uAvfB/M38A5pjZ38xsTDTLR865m5xzTcBdwDjgIufcSufcI/irb2weHYE7AjjPObfYOZcFrgCOzVtdLXAHvon4YOfcsmg9BwBnOueWOudm48f4PTLvedOdc791zjU659ot5FXodUA2nWwEjgY0dluJWEq/gc7p8+hBbwKnhA4hxZFNJ58Crg6dQ8I5q+G7lXQCm//Nrs91+TfCOfeWc26Cc24ssB2wAXBV9HD+NaWXR/MXThsIjATqgPxhbibh+/y12Bw4BLjQOddyac+N8QXgjKh5eCFwHf7oXYspHX0tKvQ6KJtOvgecHTqHrNZItcZ06xlLgcOoz2l4oXhJARpEt0I92bzjDrPc0BdD5+glF3b2LNv2OOfexg8vtV0nnzoXP2rBxnnTNgKm5d1/C9+U+5CZbRVNm4I/8WOkc25odBvsnPtEfqyOhlCh1wnZdPI64O+hc4i3jD650Bli6rs6yzZ+sunkMnwz0orQWSSMU1edXgknsT1LN/vVm9nWZna2mY2N7o8DvgE835nlRE27dwOXmtkgM9sYP0bvbQXz3QGcDzxmZps552YAjwBXmNngqA/fZma2d1dejwq9zvsWax62lUAWuoFqui2+G6nP/Sl0COkZ2XTyJdQkX7FecFtvM7l5dKeKlTKTA46lPtfdkTIW4092+LeZLcUXeK/TtVa90/CtJB8Cz+BP3vhj4UzOuVvwIxz808wS+JM26vDdaBYA9wLrd2H9mHPqn9tZiVTms8Dj+DZ0CeTeuvqnd6l693Ohc8TI/4DddJZt/CVSmd8B3w2dQ3rftpb9IFN3/iZmsTvQ0wwcrBPI1ha3D7pXZNPJicD3QueodNPcCO2lFM8SfL88FXmV4Ux8E5dUmDddYrN33dh/hc7RA85Xkdc6FXpdlE0n/4DOYgtqihut72/xnER97p3QIaR3ZNPJBuAwYEboLNL7Tmk4c6xzNIbOUUR3UJ+7LHSIUqUfyu45C9+EKwFMdqP7hM4QE9dSn7sjdAjpXdl0cgbwdfyYX1JBPnQbbPyq2ywuR/X+i+87L21QodcN0fh6h6MhC4KY7EYPXPdcsg4P4zsLSwWKrvxzRugc0vu+t+r0zZ0rzWu3dsJs4FB1OWmfCr1uyqaT84EvA529lp500zQ3amjoDGXuZeDr1Ofi1IQjnZRNJ68FrgmdQ3rXNEat/6/mbcv5DNxVwNeoz3V44OBKpUKvCLLp5Jv4MXb0g9mLZrrhI0NnKGOTgAOpzy0JHURKwmn44RukgpzecOq2ZXqFoSbgG9TnngkdpByo0CuSbDr5IL6fgM4E7SUN1NQ1OxaGzlGG5gNfoj43M3QQKQ3ZdLIZfzH2J0Jnkd4zl6GjHmveudyu4+6AE6nP/V/oIOVChV4RZdPJP+GHLZBe0kCtLoPWOUuAA6jPvR06iJSWbDq5En/Nzf+GziK95+yGk3dyjnK6ytDZ1OduCh2inKjQK7JsOvkboD50jkqxhL6LQ2coIyvwA4r+J3SQfGY2wczUBFMCsunkYuCL+NH4pQIsYuCQ+5s/83LoHB10MfW5K0OHKDcq9HpANp28EEiHzlEJFrhBy0JnKBN+3LT63JOdeZKZ7WVmz5lZzszmm9mzZrZrz0SUUpBNJ+cC+wLvhc4ivePHDd/cpdnZ3NA51uFq6nM/DR2iHKnQ6yHZdPI84IrQOeJuthvaEDpDGWgEjqM+90BnnmRmg4EHgN8Cw4ENgQuhNIdkMLOa0BniIptOzsQXe9nAUaQXLKXfwNub9n0jdI523AScHjpEuVKh14Oy6eQ5wK9D54izqW5U6AilbjnwFepzd3bhuVsCOOfucM41OeeWO+cecc69FjW3PmtmV5rZQjP70Mz2jKZPMbPZZnZ8y4LMbIiZ/cnM5pjZJDP7sZm1uv0xs1+a2TPRc4aY2Y1mNsPMppnZJWZWHc2Xn2E+6jJRVNl0cgowHng3cBTpBZc0HrNbk6sqxSul/Ab4FvU5nejYRSr0elg2nTwT/QD1mMlutI7itG0BsF9nj+TleRdoMrNbzOwAMxtW8PhuwGvACODPwJ3ArsDm+DM4rzazlkGtfwsMATYF9gaOA07IX5iZVZnZH4AdgC8453LALfgjkpsDOwFfAL5dkOFDYDRwaRdfp7Qhm05OAvYCXgydRXrWSur6Xt+ULLXm+gupz52hIq97VOj1gqjP3nfwY/9IEU1yY/qGzlCipgGfpT73XFcX4JxbhP+Rd8AfgDlm9jczGxPN8pFz7ibnXBNwFzAOuMg5t9I59wh+QNPNoyNwRwDnOecWO+ey+G4Nx+atrha4A99EfLBzblm0ngOAM51zS51zs4ErgSPznjfdOfdb51yjc06j4/eAbDo5B/g88GjoLNKzrmg8bI8GVz0pdA78Nucs6nP1oYPEgQq9XpJNJ68HvoY/81GKZIobPSh0hhL0DrAn9blu97lxzr3lnJvgnBsLbAdsAFwVPTwrb9bl0fyF0wYCI4E6/CDNLSbh+/y12Bw/tMeFzrmWa69ujC8AZ0TNwwuB6/BH71poVPxekE0nlwBJfDEuMdVITe0VjYdNDRyjCd9Ue1XgHLGhQq8XZdPJvwL74ZvUpAimuRHDQ2coMf8B9qI+N7nYC3bOvQ3cjC/4OmMu/qzfjfOmbYQ/6tjiLXxT7kNmtlU0bQr+xI+Rzrmh0W2wc+4T+bE6mUW6KJtONgBH4/tMSUxd13TQHitc7fuBVr8SOELj5BWXCr1elk0nn8U3h4Xea4qFuQwZ7pyaxCOPAPtQnyvKMAlmtrWZnW1mY6P74/CX+uvU9TGjpt27gUvNbJCZbQx8H7itYL47gPOBx8xsM+fcDPxrusLMBkd9+DYzs727/+qkK7LppMumk2cAPwqdRXqGo6rqksZjQgy1MgMYT33uvgDrjjUVegFE18bdA3g1dJZy56iqasbmh85RAu4ADqI+V8zrVi7Gn+zwbzNbii/wXgfO7sKyTgOW4k+ceAZ/8sYfC2dyzt0CXAT808wS+JM26vAD+C7AX491/S6sX4oom07+DJiAuqLE0m1N++++1PV9qxdX+R9gF+pzndqJlI4x59TyEUoilekH/B44fl3zStve7DPh3f62asvQOQJpAM6lPqdhfKTXJVKZnfDF96ahs0hxHVL17Iu/rvvdLr2wqluBk6jPaaehh6jQKwGJVOYkfL+XPqGzlKN/9/nuS2Ns4adC5whgEnB4qV3STCpLIpUZih8G58uBo0iRvdLnxNeG2tIdemjxTcAPqc/pwgI9TE23JSA6I/czaBT6LpnnBlfinuDfgZ1U5Elo2XRyIXAokEJDSMXK9xtO6akjQfOBpIq83qFCr0Rk08mXgE8BD4XOUm5muuGNoTP0okbgXOAQ6nM6e1tKQnSSxmX4UQVmrWt+KQ//bN75k7PdkJeKvNjHgR2ozz1c5OVKG1TolZBsOun3cuCnaM+4w6a4URY6Qy+ZCuxNfe5yjRQvpSibTj6Jv4LJM4GjSJGctuq0fkVa1Cr8Tur+1OemrWtmKR4VeiUm2jO+GNgTf6ahrMNkN6Y2dIZe8BCwY3eudCHSG7Lp5Az8lTTOR2fllr1/u223ndI88t/dXMybwG7aSQ1DhV6JyqaT/wF2Bn6Gb66TNkx2o4u1x1mKFgGn4vuzzAsdRqQjsulkYzad/DmwIzq6V/a+23DmcOdo7uLTr8YPnfJKESNJJ+is2zKQSGV2Bm7CX+xdCmxjkz58qM95cRze4S789R5nhA4i0lWJVMaA7wI/B3TJwjL1aN05z25RNf0znXjKB8B3qc890lOZpGNU6JWJRCpTi28K+RH++p8SGcKS3Kt9TxoSOkcRfQB8T52VJU4SqcxGwLXAAaGzSOdtZtMmPVZ37oZm1Kxj1pXAZcDPNTZeaVChV2YSqcwOwPX4KxZI5KM+R600K/txCFcBvwQu0QZS4iqRyhwLXAmMCJ1FOudvdT+auEPVR59tZ5ZH8Dupoa6VK61QoVeGoqaQY/BNIRsGjlMS3u9zzLQaay7n9+Ip4GTqc2+HDiLS0xKpzCjgQuAkoDpwHOmgsTZn+sS6M0a0slM9HTiT+tw9IXJJ+1TolbFEKtMf+CFwDtA/cJyg/tfnW28MsuWfCJ2jC2bhR4e/JXQQkd6WSGW2wR/FTobOIh1zZ93FT+1e9dbe0d3lwK+Bn1GfWxwwlrRDhV4MJFKZDYB64JtU6N7xM31O/89Ym/vp0Dk6YSbwC+Ba6nPLQ4cRCSmRyuyLb6HYNXQWad9oFsz5d5/v1ZlxF3Ah9bnpoTNJ+1ToxUgildkaPxzLoUClDCIMwP11P5m4Y9UH7fUdKRUq8ETakEhlDgUuBrYLHEVa1wzccVz1IxdcdOmvPwgdRjpGhV4MJVKZT+BHID+KCjlD9/e1Vz51QPULe697zmBm4s9Eu04FnkjbEqlMFXAkcB4q+EpFM364p4uy6aT6EZcZFXoxlkhlxgJnAScS8/Grzq+5/emTajKfC52jFTNYXeDpTFqRTkikMvsBZwIHUmGtFCViKXA7cFU2nXwrdBjpGhV6FSCRygwFTgFOB9YLm6ZnHF392L8vrf1jKQ058zpwHXCDCjyR7kmkMlvit18TgAFh01SE94BrgJuz6eTCwFmkm1ToVZBEKtMHPyzLt4HdA8cpqs9Wvfb6rXXp0M08S4A78cVdd68NKSIFop3WE/GXBdwobJrYaQYeBH4HPJxNJ1UcxIQKvQoV7SFPAI4FxoZN032b2IwpT/Q5e1yg1T8H3AjcRX1uaaAMIhUjkcpUA4cAR+ObdfuGTVTW5gN/BK7JppMfhQ4jxadCr8JFHZ/3A44HvgL0C5uoa/qxctlbfU/ozbEE5wB/Am6kPqe+KyKBJFKZwfiRBo4E9od1XqJLYB7wV+Be4LFsOtkQOI/0IBV68rFog3kYfk95X8psEOaP+hy12KxHTzqZBjwEPAA8SH1OG0eREpJIZUYAXwO+AXwOqAqbqKTMBu7HF3dPZNPJxrBxpLeo0JNWRf35xuObRZLAZkEDdcC7fY7L1lljooiLbASexfdbeYj63P+KuGwR6UGJVGZ9/JG+ffHbskq8tu5U4G/APcDEbDrZFDiPBKBCTzok6tN3YHTbixJs4n2lz4mvDbWlO3RzMS1H7R4CHqM+t6j7yUQkpOj64DsA+0S3zwGDg4bqGe8BE4GngafV505AhZ50QSKVqQU+CewR3XYDNg0aCniy7qznE1WzOnM28SLgVeCV6PaCjtqJxF90MsengM/ji74dgQ1CZuqCZvwwTk8TFXfZdHJm2EhSilToSVEkUplhwM7RbUdgc3zxN7K3Mtxdd+HTn656p61Bk6eyuqB7BXgZ+Ij6nP4ARIREKjMSvwO7I/AJYGtgG2BouFSAL+imAu/gC7s3on/fzKaTi0MGk/KgQk96VCKVGYQv+Apv6+GbToZEt+6cKbcQmHtJzY3PHFPz+ED8RnEqvhl2KvA29bm53Vi+iFSoRCozBtgSWB8YE91G5/2/5X5nT15biT/7tfA2H3/ixAfA+8CH2XRSg65Ll6nQk5KQSGX6s2bhNwhwQAP+pIiGgv83AsuAeRoaQERCS6QyA/F9l6sKblZwfxV+u6UxN6VXqNATERERiSmNMSQiIiISUyr0RERERGJKhZ6IiIhITKnQExEREYkpFXoiIiIiMaVCT0RERCSmVOiJiIiIxJQKPREREZGYUqEnIiIiElMq9ERERERiSoWeiIiISEyp0BMRERGJKRV6IiIiIjGlQk9EREQkpv4fDw3E6N+LToIAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<Figure size 720x720 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.rcParams['figure.figsize'] = [10, 10]\n",
    "fig = plt.figure()\n",
    "fig.suptitle(\"Smoker percentage with respect to gender\", fontsize=15)\n",
    "\n",
    "ax12 = plt.subplot2grid((2,2), (0,1))\n",
    "ax12.pie( data[data['sex']==0]['smoker'].value_counts(), labels = ['Nonsmoker','Smoker'], autopct='%1.1f%%',radius=12,textprops={'fontsize': 12})\n",
    "ax12.axis('equal')\n",
    "ax12.title.set_text(\"Female smoker percentage\")\n",
    "\n",
    "ax11 = plt.subplot2grid((2,2), (0,0))\n",
    "ax11.pie( data[data['sex']==1]['smoker'].value_counts(),labels = ['Nonsmoker','Smoker'],autopct='%1.1f%%', radius=12,textprops={'fontsize': 12})\n",
    "ax11.axis('equal')\n",
    "\n",
    "ax11.title.set_text(\"Male smoker percentage\")\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 113,
   "id": "5e0fc460",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 720x720 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.rcParams['figure.figsize'] = [10, 10]\n",
    "fig = plt.figure()\n",
    "fig.suptitle(\"Smoker percentage\", fontsize=15)\n",
    "\n",
    "ax12 = plt.subplot2grid((2,2), (0,0))\n",
    "ax12.pie( data['smoker'].value_counts(), labels = ['Nonsmoker','Smoker'], autopct='%1.1f%%',radius=1.5,textprops={'fontsize': 12})\n",
    "ax12.axis('equal')\n",
    "ax12.title.set_text(\"Smoker vs nonsmoker percentage\")\n",
    "ax12.axis('equal')\n",
    "\n",
    "ax11 = plt.subplot2grid((2,2), (0,1))\n",
    "ax11.pie( data[data['smoker']==1]['sex'].value_counts(),labels=['Male','Female'],autopct='%1.1f%%', radius=1.5,textprops={'fontsize': 12})\n",
    "ax11.axis('equal')\n",
    "\n",
    "ax11.title.set_text(\"Male vs Female smoker percentage\")\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "a2c952af",
   "metadata": {},
   "source": [
    "###### 2. Body Mass Index:"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "f20a7a92",
   "metadata": {},
   "source": [
    "The BMI was an extremely important factor because it mainly increases the risk factor against disease and decreases the response to medicine (prognosis) and ultimately increases the insurance expenditure."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "3a8194c4",
   "metadata": {},
   "source": [
    "To analyze the BMI factor, the BMI was categorise into four groups according to ['https://www.cdc.gov/obesity/basics/adult-defining.html']:\n",
    "\n",
    "If your BMI is less than 18.5, it falls within the underweight range.\n",
    "\n",
    "If your BMI is 18.5 to <25, it falls within the healthy weight range.\n",
    "\n",
    "If your BMI is 25.0 to <30, it falls within the overweight range.\n",
    "\n",
    "If your BMI is 30.0 or higher, it falls within the obesity range.\n",
    "\n",
    "Obesity is frequently subdivided into categories:\n",
    "\n",
    "Class 1: BMI of 30 to < 35\n",
    "\n",
    "Class 2: BMI of 35 to < 40\n",
    "\n",
    "Class 3: BMI of 40 or higher"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 122,
   "id": "40489c61",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "BMI is less than 18.5 cost:  18809.825999999997 $\n",
      "BMI is between 18.5 and 25 cost:  19942.223200000004 $\n",
      "BMI is between 25 and 30 cost:  22495.87405405406 $\n",
      "BMI is between 30 and 35 cost:  39889.67014285715 $\n",
      "BMI is between 35 and 40 cost:  42753.62279999998 $\n",
      "BMI is higher than 40 cost:  45467.78761904762 $\n"
     ]
    }
   ],
   "source": [
    "print('BMI is less than 18.5 cost: ',data[(data['bmi']<18.5) & (data['smoker']==1)]['charges'].mean(), '$')\n",
    "print('BMI is between 18.5 and 25 cost: ',data[(data['bmi']>18.5) & (dataset['bmi']<25) & (data['smoker']==1)]['charges'].mean(), '$')\n",
    "print('BMI is between 25 and 30 cost: ',data[(data['bmi']>25) & (dataset['bmi']<30) & (data['smoker']==1)]['charges'].mean(), '$')\n",
    "print('BMI is between 30 and 35 cost: ',data[(data['bmi']>30) & (dataset['bmi']<35) & (data['smoker']==1)]['charges'].mean(), '$')\n",
    "print('BMI is between 35 and 40 cost: ',data[(data['bmi']>35) & (dataset['bmi']<40) & (data['smoker']==1)]['charges'].mean(), '$')\n",
    "print('BMI is higher than 40 cost: ',data[(data['bmi']>40) &  (data['smoker']==1)]['charges'].mean(), '$')\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 131,
   "id": "714a49db",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "BMI is less than 18.5 cost:  5532.993333333333 $\n",
      "BMI is between 18.5 and 25 cost:  7616.209181286548 $\n",
      "BMI is between 25 and 30 cost:  8243.256419354844 $\n",
      "BMI is between 30 and 35 cost:  8540.598446601938 $\n",
      "BMI is between 35 and 40 cost:  9670.244137931035 $\n",
      "BMI is higher than 40 cost:  8179.664714285711 $\n"
     ]
    }
   ],
   "source": [
    "print('BMI is less than 18.5 cost: ',data[(data['bmi']<18.5) & (data['smoker']==0)]['charges'].mean(), '$')\n",
    "print('BMI is between 18.5 and 25 cost: ',data[(data['bmi']>18.5) & (dataset['bmi']<25) & (data['smoker']==0)]['charges'].mean(), '$')\n",
    "print('BMI is between 25 and 30 cost: ',data[(data['bmi']>25) & (dataset['bmi']<30) & (data['smoker']==0)]['charges'].mean(), '$')\n",
    "print('BMI is between 30 and 35 cost: ',data[(data['bmi']>30) & (dataset['bmi']<35) & (data['smoker']==0)]['charges'].mean(), '$')\n",
    "print('BMI is between 35 and 40 cost: ',data[(data['bmi']>35) & (dataset['bmi']<40) & (data['smoker']==0)]['charges'].mean(), '$')\n",
    "print('BMI is higher than 40 cost: ',data[(data['bmi']>40) &  (data['smoker']==0)]['charges'].mean(), '$')\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 189,
   "id": "62559f76",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 576x576 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.rcParams['figure.figsize'] = [8, 8]\n",
    "fig = plt.figure()\n",
    "X = [\"> than 18.5\",\"18.5-25 \",\" 25-30\",\"30-35\",'35-40','40<']\n",
    "Ysmoker = [18809.825999999997,19942.223200000004,22495.87405405406,39889.67014285715,42753.62279999998,45467.78761904762]\n",
    "Znonsmoker = [5532.993333333333,7616.209181286548,8243.256419354844,8540.598446601938,9670.244137931035,8179.664714285711]\n",
    "  \n",
    "X_axis = np.arange(len(X))\n",
    "  \n",
    "plt.bar(X_axis - 0.2, Ysmoker, 0.4, label = 'Smokers')\n",
    "plt.bar(X_axis + 0.2, Znonsmoker, 0.4, label = 'Nonsmokers')\n",
    "  \n",
    "plt.xticks(X_axis, X)\n",
    "plt.xlabel(\"Class of Obesity\")\n",
    "plt.ylabel(\"Number of Students\")\n",
    "plt.title(\"Charges of classes of obesity based on smoking\")\n",
    "plt.legend()\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "61e44ffb",
   "metadata": {},
   "source": [
    "###### 1.As seen from the above bar chart, it is clear that smokers significantly increase the charges in all classes, accordingly the remaining analysis focuses on these slips."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "61a9b82a",
   "metadata": {},
   "source": [
    "###### 2. The bar chart illustrasts that pasients with BMI above 30 has the high charges values, which means that it is important to spot the light on these people."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "45fbb813",
   "metadata": {},
   "source": [
    "###### 3.Smoker people with obesity with respect to gender:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 167,
   "id": "7bdcab07",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 360x360 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.rcParams['figure.figsize'] = [5, 5]\n",
    "fig = plt.figure()\n",
    "fig.suptitle(\"Smoker with obesity percentage \", fontsize=15)\n",
    "plt.pie( data[(data['bmi']>30) & (data['smoker']==1)]['sex'].value_counts(), labels = ['Male','Female'], autopct='%1.1f%%',radius=1.2,textprops={'fontsize': 12})\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "39caf161",
   "metadata": {},
   "source": [
    "###### The male present 64.3% of the obesity patients, accordingly it it importnat to check if this slip lead to increase the charges or not, by cheacking the mean chagrges for male and female:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 190,
   "id": "3b77532e",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "42497.34803921568"
      ]
     },
     "execution_count": 190,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data[(data['bmi']>30) & (data['smoker']==1) & (data['sex']==0)]['charges'].mean()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 206,
   "id": "f2b8098b",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "41337.95934782609"
      ]
     },
     "execution_count": 206,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data[(data['bmi']>30) & (data['smoker']==1) & (data['sex']==1)]['charges'].mean()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 208,
   "id": "bd0153af",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 576x576 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "x_axis = ['Male','Female']\n",
    "y_axis = [41337.95934782609, 42497.34803921568]\n",
    "\n",
    "plt.bar(x_axis, y_axis)\n",
    "plt.title('Male vs Female suffering from obesity and smoking')\n",
    "plt.xlabel('sex')\n",
    "plt.ylabel('Average charges')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "1f9a71f0",
   "metadata": {},
   "source": [
    "###### It can be concluded that gender won't affect the results or lead to a significant change in the insurance cost although the notable difference between the percentage of males and females suffering from obesity and smoking."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "33511321",
   "metadata": {},
   "source": [
    "###### 3.Number of children:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 246,
   "id": "53b9a368",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 576x576 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "x_axis = ['zero','one','two','three','four']\n",
    "y_axis=[data[(data['bmi']>30) & (data['smoker']==1) & (data['children']==i)]['charges'].mean() for i in range(5)]\n",
    "plt.bar(x_axis, y_axis)\n",
    "plt.title('Male vs Female suffering from obesity and smoking')\n",
    "plt.xlabel('sex')\n",
    "plt.ylabel('Average charges')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "487d7813",
   "metadata": {},
   "source": [
    "As shown in the statistical analysis, number of children has slightly impact on increasing the insurance cost , but when tested in the differance was almost negligable."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "552ce571",
   "metadata": {},
   "source": [
    "###### Lastly, based on the analysis, it can be concluded that the body mass index and the smoking only affected on increasing the insurance, it is also important to mention that the patient with BMI above 30 and smoking cost in average 41000 without taking any consideration regarding gender."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "313c4011",
   "metadata": {},
   "source": [
    "# Machine learning part"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "890e7f3e",
   "metadata": {},
   "source": [
    "Building model, linear regression model was selected initally as regression model to build the prediction model. "
   ]
  },
  {
   "cell_type": "markdown",
   "id": "d31a7eed",
   "metadata": {},
   "source": [
    "### step one: Split the data to dependent and independent:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 269,
   "id": "9eb29068",
   "metadata": {},
   "outputs": [],
   "source": [
    "#Now split the data before continue:\n",
    "X=data.loc[:, data.columns != 'charges'].iloc[:,:].values # Independent variables  \n",
    "y=data.iloc[:,5].values #Dependent variables\n",
    "\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "0179bcd1",
   "metadata": {},
   "source": [
    "### Step three: Apply the linear regression model:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 274,
   "id": "aebf48a8",
   "metadata": {},
   "outputs": [],
   "source": [
    "X=np.append(arr=np.ones((1338,1)),values=X,axis=1) # Add the constant of the equation"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 275,
   "id": "b283a1ec",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<table class=\"simpletable\">\n",
       "<caption>OLS Regression Results</caption>\n",
       "<tr>\n",
       "  <th>Dep. Variable:</th>            <td>y</td>        <th>  R-squared:         </th> <td>   0.751</td> \n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Model:</th>                   <td>OLS</td>       <th>  Adj. R-squared:    </th> <td>   0.750</td> \n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Method:</th>             <td>Least Squares</td>  <th>  F-statistic:       </th> <td>   668.3</td> \n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Date:</th>             <td>Wed, 25 Jan 2023</td> <th>  Prob (F-statistic):</th>  <td>  0.00</td>  \n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Time:</th>                 <td>05:47:57</td>     <th>  Log-Likelihood:    </th> <td> -13548.</td> \n",
       "</tr>\n",
       "<tr>\n",
       "  <th>No. Observations:</th>      <td>  1338</td>      <th>  AIC:               </th> <td>2.711e+04</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Df Residuals:</th>          <td>  1331</td>      <th>  BIC:               </th> <td>2.715e+04</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Df Model:</th>              <td>     6</td>      <th>                     </th>     <td> </td>    \n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Covariance Type:</th>      <td>nonrobust</td>    <th>                     </th>     <td> </td>    \n",
       "</tr>\n",
       "</table>\n",
       "<table class=\"simpletable\">\n",
       "<tr>\n",
       "    <td></td>       <th>coef</th>     <th>std err</th>      <th>t</th>      <th>P>|t|</th>  <th>[0.025</th>    <th>0.975]</th>  \n",
       "</tr>\n",
       "<tr>\n",
       "  <th>const</th> <td>-6084.1767</td> <td>  474.763</td> <td>  -12.815</td> <td> 0.000</td> <td>-7015.542</td> <td>-5152.811</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>x1</th>    <td>-6084.1767</td> <td>  474.763</td> <td>  -12.815</td> <td> 0.000</td> <td>-7015.542</td> <td>-5152.811</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>x2</th>    <td>  256.9892</td> <td>   11.889</td> <td>   21.616</td> <td> 0.000</td> <td>  233.666</td> <td>  280.312</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>x3</th>    <td>  338.7383</td> <td>   28.553</td> <td>   11.864</td> <td> 0.000</td> <td>  282.725</td> <td>  394.752</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>x4</th>    <td>  471.7334</td> <td>  137.651</td> <td>    3.427</td> <td> 0.001</td> <td>  201.696</td> <td>  741.771</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>x5</th>    <td> 2.384e+04</td> <td>  411.645</td> <td>   57.921</td> <td> 0.000</td> <td>  2.3e+04</td> <td> 2.47e+04</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>x6</th>    <td> -859.1326</td> <td>  415.199</td> <td>   -2.069</td> <td> 0.039</td> <td>-1673.647</td> <td>  -44.618</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>x7</th>    <td> -782.0894</td> <td>  413.737</td> <td>   -1.890</td> <td> 0.059</td> <td>-1593.736</td> <td>   29.558</td>\n",
       "</tr>\n",
       "</table>\n",
       "<table class=\"simpletable\">\n",
       "<tr>\n",
       "  <th>Omnibus:</th>       <td>300.260</td> <th>  Durbin-Watson:     </th> <td>   2.092</td> \n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Prob(Omnibus):</th> <td> 0.000</td>  <th>  Jarque-Bera (JB):  </th> <td> 717.088</td> \n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Skew:</th>          <td> 1.212</td>  <th>  Prob(JB):          </th> <td>1.93e-156</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Kurtosis:</th>      <td> 5.644</td>  <th>  Cond. No.          </th> <td>1.23e+17</td> \n",
       "</tr>\n",
       "</table><br/><br/>Notes:<br/>[1] Standard Errors assume that the covariance matrix of the errors is correctly specified.<br/>[2] The smallest eigenvalue is 2.33e-28. This might indicate that there are<br/>strong multicollinearity problems or that the design matrix is singular."
      ],
      "text/plain": [
       "<class 'statsmodels.iolib.summary.Summary'>\n",
       "\"\"\"\n",
       "                            OLS Regression Results                            \n",
       "==============================================================================\n",
       "Dep. Variable:                      y   R-squared:                       0.751\n",
       "Model:                            OLS   Adj. R-squared:                  0.750\n",
       "Method:                 Least Squares   F-statistic:                     668.3\n",
       "Date:                Wed, 25 Jan 2023   Prob (F-statistic):               0.00\n",
       "Time:                        05:47:57   Log-Likelihood:                -13548.\n",
       "No. Observations:                1338   AIC:                         2.711e+04\n",
       "Df Residuals:                    1331   BIC:                         2.715e+04\n",
       "Df Model:                           6                                         \n",
       "Covariance Type:            nonrobust                                         \n",
       "==============================================================================\n",
       "                 coef    std err          t      P>|t|      [0.025      0.975]\n",
       "------------------------------------------------------------------------------\n",
       "const      -6084.1767    474.763    -12.815      0.000   -7015.542   -5152.811\n",
       "x1         -6084.1767    474.763    -12.815      0.000   -7015.542   -5152.811\n",
       "x2           256.9892     11.889     21.616      0.000     233.666     280.312\n",
       "x3           338.7383     28.553     11.864      0.000     282.725     394.752\n",
       "x4           471.7334    137.651      3.427      0.001     201.696     741.771\n",
       "x5          2.384e+04    411.645     57.921      0.000     2.3e+04    2.47e+04\n",
       "x6          -859.1326    415.199     -2.069      0.039   -1673.647     -44.618\n",
       "x7          -782.0894    413.737     -1.890      0.059   -1593.736      29.558\n",
       "==============================================================================\n",
       "Omnibus:                      300.260   Durbin-Watson:                   2.092\n",
       "Prob(Omnibus):                  0.000   Jarque-Bera (JB):              717.088\n",
       "Skew:                           1.212   Prob(JB):                    1.93e-156\n",
       "Kurtosis:                       5.644   Cond. No.                     1.23e+17\n",
       "==============================================================================\n",
       "\n",
       "Notes:\n",
       "[1] Standard Errors assume that the covariance matrix of the errors is correctly specified.\n",
       "[2] The smallest eigenvalue is 2.33e-28. This might indicate that there are\n",
       "strong multicollinearity problems or that the design matrix is singular.\n",
       "\"\"\""
      ]
     },
     "execution_count": 275,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#Apply the feature selection using backward elemination\n",
    "def reg_ols(X,y):\n",
    "    columns=list(range(X.shape[1]))#it will do it just one time\n",
    "    a={}\n",
    "    for i in range(X.shape[1]):\n",
    "        X_opt=np.array(X[:,columns],dtype=float) #every time X_opt will change depend on the columns\n",
    "        regressor_ols=sm.OLS(endog=y,exog=X_opt).fit()#regressor between x_opt and y\n",
    "        pvalues = list(regressor_ols.pvalues)#save pvalues column as a list\n",
    "        d=max(pvalues)#find the maximum value in the list\n",
    "        if (d>0.06):#if the maximum value bigger than 0.09 then we need to drop the column\n",
    "            for k in range(len(pvalues)):#for loop to check the values in pvalues to find where is the max\n",
    "                if(pvalues[k] == d):#if the value of in the index = max the delete it\n",
    "                    a[k]=d\n",
    "                    del(columns[k])  \n",
    "    \n",
    "    return(X_opt,regressor_ols,a)\n",
    "\n",
    "X_opt,regressor_ols,a=reg_ols(X, y)\n",
    "regressor_ols.summary()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "ffe02d26",
   "metadata": {},
   "source": [
    "### Step two: Splet the data to train and test:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 276,
   "id": "43ba5cdf",
   "metadata": {},
   "outputs": [],
   "source": [
    "#splitting the dataset\n",
    "from sklearn.model_selection import train_test_split\n",
    "X_opt_train,X_opt_test,y_opt_train,y_opt_test=train_test_split(X_opt,y,test_size=1/3,random_state=0)\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "b511abeb",
   "metadata": {},
   "source": [
    "### Step three: Train the mode:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 277,
   "id": "eed3358e",
   "metadata": {},
   "outputs": [],
   "source": [
    "linearRegression=LinearRegression()\n",
    "linearRegression.fit(X_opt_train,y_opt_train)\n",
    "\n",
    "y_opt_pred=linearRegression.predict(X_opt_test)\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "825c8a6b",
   "metadata": {},
   "source": [
    "### Step four: Test the accuracy:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 280,
   "id": "69989a16",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Explain variance score = 78.8259452489119\n"
     ]
    }
   ],
   "source": [
    "print(\"Explain variance score =\", (acc.r2_score(y_opt_test,y_opt_pred)*100))"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "38941300",
   "metadata": {},
   "source": [
    "### Step five: model enhancement using decision tree regression"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "71058a20",
   "metadata": {},
   "source": [
    "Through the enhancement model , the forward selection and decision tree were selected as feature selection and regression moddel respectively"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 285,
   "id": "fff584c5",
   "metadata": {},
   "outputs": [],
   "source": [
    "X=data.loc[:, data.columns != 'charges'].iloc[:,:] # Independent variables  \n",
    "y=data.iloc[:,5] #Dependent variables"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 286,
   "id": "47c228b9",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Greedy feuter selection  and model selection\n",
    "def forward_selection(data,target,significance_level=0.05):\n",
    "    initial_features=data.columns.tolist()\n",
    "    best_features2=[]\n",
    "    while (len(initial_features)>0):\n",
    "        remaining_features=list(set(initial_features)-set(best_features2))\n",
    "        new_pval=pd.Series(index=remaining_features)\n",
    "        for new_column in remaining_features:\n",
    "            model=sm.OLS(target,sm.add_constant(data[best_features2+[new_column]])).fit()\n",
    "            new_pval[new_column]=model.pvalues[new_column]\n",
    "        min_p_value=new_pval.min()\n",
    "        if (min_p_value<significance_level):\n",
    "            best_features2.append(new_pval.idxmin())\n",
    "        else:\n",
    "            break\n",
    "    return best_features2     \n",
    "\n",
    "best_features2=forward_selection(X, y)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 287,
   "id": "9797074a",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "['smoker', 'age', 'bmi', 'children']"
      ]
     },
     "execution_count": 287,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "best_features2"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 295,
   "id": "cef26898",
   "metadata": {},
   "outputs": [],
   "source": [
    "X2=data.loc[:,best_features2]\n",
    "y= data['charges']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 300,
   "id": "a378c270",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Best parameters: {'max_depth': 4, 'min_samples_leaf': 4, 'min_samples_split': 2}\n"
     ]
    }
   ],
   "source": [
    "from sklearn.tree import DecisionTreeRegressor\n",
    "from sklearn.model_selection import GridSearchCV\n",
    "## DTreeRegression  \n",
    "X_train,X_test,y_train,y_test=train_test_split(X ,y, test_size=0.2 ,random_state=0)\n",
    "\n",
    "DTR=(DecisionTreeRegressor(random_state=0))\n",
    "param_grid = {'max_depth': [2, 4, 6, 8, 10],\n",
    "              'min_samples_split': [2, 4, 6, 8, 10],\n",
    "              'min_samples_leaf': [1, 2, 4]}\n",
    "grid_search = GridSearchCV(estimator=DTR, param_grid=param_grid, cv=5)\n",
    "grid_search.fit(X_train, y_train)\n",
    "print(f'Best parameters: {grid_search.best_params_}')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 308,
   "id": "dc0f9c8c",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Explain variance score = 89.13953640004728\n"
     ]
    }
   ],
   "source": [
    "dt_optimal = DecisionTreeRegressor(**grid_search.best_params_,random_state=0)\n",
    "dt_optimal.fit(X_train, y_train)\n",
    "y_pred = dt_optimal.predict(X_test)\n",
    "print(\"Explain variance score =\", (acc.r2_score(y_test,y_pred)*100))"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "019678c9",
   "metadata": {},
   "source": [
    "## Conclusion "
   ]
  },
  {
   "cell_type": "markdown",
   "id": "8ead2eb9",
   "metadata": {},
   "source": [
    "###### To be concluded, the decision tree model provided better results of about 89.1% in comparision with the multiple linear regression, the feature selection said that the 'smoker', 'age', 'bmi', 'children' , are most likely to affect the cost of insurance while the region, and gender has no effect. For further recommendation , paitents with BMI higher than 30 and smoke are most likely to require further healthcare in the future and accordingly the cost of chareges will increase, however, it is still chalanging to provide accurate results without further nformation regarding the patient life stype and disease history. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "31a0f23e",
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3 (ipykernel)",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.9.12"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 5
}