Lwp - Portfolio - Medical Insurance Cost Prediction.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Medical Insurance Cost Prediction <br> Lucky Wijaya Pengestu**"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Import Library & Read Dataset"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "#Import Library\n",
    "import warnings\n",
    "warnings.filterwarnings('ignore')\n",
    "\n",
    "#DataFrame Library\n",
    "import pandas as pd\n",
    "import numpy as np\n",
    "\n",
    "#Visualization Library\n",
    "import matplotlib.pyplot as plt\n",
    "import seaborn as sns\n",
    "\n",
    "#Modelin Library\n",
    "from sklearn.preprocessing import MinMaxScaler\n",
    "from sklearn.model_selection import train_test_split\n",
    "from sklearn.metrics import mean_squared_error, mean_absolute_error, r2_score\n",
    "from sklearn.model_selection import RandomizedSearchCV\n",
    "from sklearn.linear_model import Ridge, Lasso, ElasticNet\n",
    "from sklearn.linear_model import LinearRegression\n",
    "from sklearn.tree import DecisionTreeRegressor\n",
    "from sklearn.ensemble import RandomForestRegressor\n",
    "from sklearn.svm import SVR"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "#Visualization Plot Settings\n",
    "sns.set(rc={'figure.figsize':(15,5)})\n",
    "sns.set_style('whitegrid')\n",
    "sns.color_palette('viridis')\n",
    "plt.style.use('seaborn-bright')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>age</th>\n",
       "      <th>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>27.900</td>\n",
       "      <td>0</td>\n",
       "      <td>yes</td>\n",
       "      <td>southwest</td>\n",
       "      <td>16884.92400</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>18</td>\n",
       "      <td>male</td>\n",
       "      <td>33.770</td>\n",
       "      <td>1</td>\n",
       "      <td>no</td>\n",
       "      <td>southeast</td>\n",
       "      <td>1725.55230</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>28</td>\n",
       "      <td>male</td>\n",
       "      <td>33.000</td>\n",
       "      <td>3</td>\n",
       "      <td>no</td>\n",
       "      <td>southeast</td>\n",
       "      <td>4449.46200</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>33</td>\n",
       "      <td>male</td>\n",
       "      <td>22.705</td>\n",
       "      <td>0</td>\n",
       "      <td>no</td>\n",
       "      <td>northwest</td>\n",
       "      <td>21984.47061</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>32</td>\n",
       "      <td>male</td>\n",
       "      <td>28.880</td>\n",
       "      <td>0</td>\n",
       "      <td>no</td>\n",
       "      <td>northwest</td>\n",
       "      <td>3866.85520</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",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1333</th>\n",
       "      <td>50</td>\n",
       "      <td>male</td>\n",
       "      <td>30.970</td>\n",
       "      <td>3</td>\n",
       "      <td>no</td>\n",
       "      <td>northwest</td>\n",
       "      <td>10600.54830</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1334</th>\n",
       "      <td>18</td>\n",
       "      <td>female</td>\n",
       "      <td>31.920</td>\n",
       "      <td>0</td>\n",
       "      <td>no</td>\n",
       "      <td>northeast</td>\n",
       "      <td>2205.98080</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1335</th>\n",
       "      <td>18</td>\n",
       "      <td>female</td>\n",
       "      <td>36.850</td>\n",
       "      <td>0</td>\n",
       "      <td>no</td>\n",
       "      <td>southeast</td>\n",
       "      <td>1629.83350</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1336</th>\n",
       "      <td>21</td>\n",
       "      <td>female</td>\n",
       "      <td>25.800</td>\n",
       "      <td>0</td>\n",
       "      <td>no</td>\n",
       "      <td>southwest</td>\n",
       "      <td>2007.94500</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1337</th>\n",
       "      <td>61</td>\n",
       "      <td>female</td>\n",
       "      <td>29.070</td>\n",
       "      <td>0</td>\n",
       "      <td>yes</td>\n",
       "      <td>northwest</td>\n",
       "      <td>29141.36030</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>1338 rows × 7 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "      age     sex     bmi  children smoker     region      charges\n",
       "0      19  female  27.900         0    yes  southwest  16884.92400\n",
       "1      18    male  33.770         1     no  southeast   1725.55230\n",
       "2      28    male  33.000         3     no  southeast   4449.46200\n",
       "3      33    male  22.705         0     no  northwest  21984.47061\n",
       "4      32    male  28.880         0     no  northwest   3866.85520\n",
       "...   ...     ...     ...       ...    ...        ...          ...\n",
       "1333   50    male  30.970         3     no  northwest  10600.54830\n",
       "1334   18  female  31.920         0     no  northeast   2205.98080\n",
       "1335   18  female  36.850         0     no  southeast   1629.83350\n",
       "1336   21  female  25.800         0     no  southwest   2007.94500\n",
       "1337   61  female  29.070         0    yes  northwest  29141.36030\n",
       "\n",
       "[1338 rows x 7 columns]"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#Read DataFrame\n",
    "df = pd.read_csv('insurance.csv')\n",
    "df"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Exploratory Data Analysis"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Feature Engineering"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "      <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",
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       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>19</td>\n",
       "      <td>female</td>\n",
       "      <td>27.900</td>\n",
       "      <td>0</td>\n",
       "      <td>yes</td>\n",
       "      <td>southwest</td>\n",
       "      <td>16884.92400</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>18</td>\n",
       "      <td>male</td>\n",
       "      <td>33.770</td>\n",
       "      <td>1</td>\n",
       "      <td>no</td>\n",
       "      <td>southeast</td>\n",
       "      <td>1725.55230</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>28</td>\n",
       "      <td>male</td>\n",
       "      <td>33.000</td>\n",
       "      <td>3</td>\n",
       "      <td>no</td>\n",
       "      <td>southeast</td>\n",
       "      <td>4449.46200</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>33</td>\n",
       "      <td>male</td>\n",
       "      <td>22.705</td>\n",
       "      <td>0</td>\n",
       "      <td>no</td>\n",
       "      <td>northwest</td>\n",
       "      <td>21984.47061</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>32</td>\n",
       "      <td>male</td>\n",
       "      <td>28.880</td>\n",
       "      <td>0</td>\n",
       "      <td>no</td>\n",
       "      <td>northwest</td>\n",
       "      <td>3866.85520</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
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      "text/plain": [
       "   age     sex     bmi  children smoker     region      charges\n",
       "0   19  female  27.900         0    yes  southwest  16884.92400\n",
       "1   18    male  33.770         1     no  southeast   1725.55230\n",
       "2   28    male  33.000         3     no  southeast   4449.46200\n",
       "3   33    male  22.705         0     no  northwest  21984.47061\n",
       "4   32    male  28.880         0     no  northwest   3866.85520"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "  <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",
       "      <th>weight_status</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>19</td>\n",
       "      <td>female</td>\n",
       "      <td>27.900</td>\n",
       "      <td>0</td>\n",
       "      <td>yes</td>\n",
       "      <td>southwest</td>\n",
       "      <td>16884.92400</td>\n",
       "      <td>overweight</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>18</td>\n",
       "      <td>male</td>\n",
       "      <td>33.770</td>\n",
       "      <td>1</td>\n",
       "      <td>no</td>\n",
       "      <td>southeast</td>\n",
       "      <td>1725.55230</td>\n",
       "      <td>obese</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>28</td>\n",
       "      <td>male</td>\n",
       "      <td>33.000</td>\n",
       "      <td>3</td>\n",
       "      <td>no</td>\n",
       "      <td>southeast</td>\n",
       "      <td>4449.46200</td>\n",
       "      <td>obese</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>33</td>\n",
       "      <td>male</td>\n",
       "      <td>22.705</td>\n",
       "      <td>0</td>\n",
       "      <td>no</td>\n",
       "      <td>northwest</td>\n",
       "      <td>21984.47061</td>\n",
       "      <td>normal</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>32</td>\n",
       "      <td>male</td>\n",
       "      <td>28.880</td>\n",
       "      <td>0</td>\n",
       "      <td>no</td>\n",
       "      <td>northwest</td>\n",
       "      <td>3866.85520</td>\n",
       "      <td>overweight</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",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1333</th>\n",
       "      <td>50</td>\n",
       "      <td>male</td>\n",
       "      <td>30.970</td>\n",
       "      <td>3</td>\n",
       "      <td>no</td>\n",
       "      <td>northwest</td>\n",
       "      <td>10600.54830</td>\n",
       "      <td>obese</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1334</th>\n",
       "      <td>18</td>\n",
       "      <td>female</td>\n",
       "      <td>31.920</td>\n",
       "      <td>0</td>\n",
       "      <td>no</td>\n",
       "      <td>northeast</td>\n",
       "      <td>2205.98080</td>\n",
       "      <td>obese</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1335</th>\n",
       "      <td>18</td>\n",
       "      <td>female</td>\n",
       "      <td>36.850</td>\n",
       "      <td>0</td>\n",
       "      <td>no</td>\n",
       "      <td>southeast</td>\n",
       "      <td>1629.83350</td>\n",
       "      <td>obese</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1336</th>\n",
       "      <td>21</td>\n",
       "      <td>female</td>\n",
       "      <td>25.800</td>\n",
       "      <td>0</td>\n",
       "      <td>no</td>\n",
       "      <td>southwest</td>\n",
       "      <td>2007.94500</td>\n",
       "      <td>overweight</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1337</th>\n",
       "      <td>61</td>\n",
       "      <td>female</td>\n",
       "      <td>29.070</td>\n",
       "      <td>0</td>\n",
       "      <td>yes</td>\n",
       "      <td>northwest</td>\n",
       "      <td>29141.36030</td>\n",
       "      <td>overweight</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>1338 rows × 8 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "      age     sex     bmi  children smoker     region      charges  \\\n",
       "0      19  female  27.900         0    yes  southwest  16884.92400   \n",
       "1      18    male  33.770         1     no  southeast   1725.55230   \n",
       "2      28    male  33.000         3     no  southeast   4449.46200   \n",
       "3      33    male  22.705         0     no  northwest  21984.47061   \n",
       "4      32    male  28.880         0     no  northwest   3866.85520   \n",
       "...   ...     ...     ...       ...    ...        ...          ...   \n",
       "1333   50    male  30.970         3     no  northwest  10600.54830   \n",
       "1334   18  female  31.920         0     no  northeast   2205.98080   \n",
       "1335   18  female  36.850         0     no  southeast   1629.83350   \n",
       "1336   21  female  25.800         0     no  southwest   2007.94500   \n",
       "1337   61  female  29.070         0    yes  northwest  29141.36030   \n",
       "\n",
       "     weight_status  \n",
       "0       overweight  \n",
       "1            obese  \n",
       "2            obese  \n",
       "3           normal  \n",
       "4       overweight  \n",
       "...            ...  \n",
       "1333         obese  \n",
       "1334         obese  \n",
       "1335         obese  \n",
       "1336    overweight  \n",
       "1337    overweight  \n",
       "\n",
       "[1338 rows x 8 columns]"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#Feature Engineering weight_status\n",
    "df['weight_status'] = np.where(df['bmi'] < 18.000, 'underweigth',\n",
    "                               np.where(df['bmi'] < 25.000, 'normal',\n",
    "                                       np.where(df['bmi'] < 30.000, 'overweight', 'obese')))\n",
    "df"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## df info"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 1338 entries, 0 to 1337\n",
      "Data columns (total 8 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",
      " 7   weight_status  1338 non-null   object \n",
      "dtypes: float64(2), int64(2), object(4)\n",
      "memory usage: 83.8+ KB\n"
     ]
    }
   ],
   "source": [
    "#Check df info\n",
    "df.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [],
   "source": [
    "#Separating Categorical & Numerical Values\n",
    "cats = ['sex', 'smoker', 'region', 'weight_status']\n",
    "nums = ['age', 'bmi', 'children', 'charges']"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Numeric Approach"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Describe Data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "\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",
       "      <th>weight_status</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>962</th>\n",
       "      <td>63</td>\n",
       "      <td>female</td>\n",
       "      <td>35.200</td>\n",
       "      <td>1</td>\n",
       "      <td>no</td>\n",
       "      <td>southeast</td>\n",
       "      <td>14474.67500</td>\n",
       "      <td>obese</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>21</th>\n",
       "      <td>30</td>\n",
       "      <td>female</td>\n",
       "      <td>32.400</td>\n",
       "      <td>1</td>\n",
       "      <td>no</td>\n",
       "      <td>southwest</td>\n",
       "      <td>4149.73600</td>\n",
       "      <td>obese</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>428</th>\n",
       "      <td>21</td>\n",
       "      <td>female</td>\n",
       "      <td>16.815</td>\n",
       "      <td>1</td>\n",
       "      <td>no</td>\n",
       "      <td>northeast</td>\n",
       "      <td>3167.45585</td>\n",
       "      <td>underweigth</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>207</th>\n",
       "      <td>35</td>\n",
       "      <td>male</td>\n",
       "      <td>27.740</td>\n",
       "      <td>2</td>\n",
       "      <td>yes</td>\n",
       "      <td>northeast</td>\n",
       "      <td>20984.09360</td>\n",
       "      <td>overweight</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>163</th>\n",
       "      <td>32</td>\n",
       "      <td>female</td>\n",
       "      <td>29.800</td>\n",
       "      <td>2</td>\n",
       "      <td>no</td>\n",
       "      <td>southwest</td>\n",
       "      <td>5152.13400</td>\n",
       "      <td>overweight</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>672</th>\n",
       "      <td>36</td>\n",
       "      <td>male</td>\n",
       "      <td>29.700</td>\n",
       "      <td>0</td>\n",
       "      <td>no</td>\n",
       "      <td>southeast</td>\n",
       "      <td>4399.73100</td>\n",
       "      <td>overweight</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>639</th>\n",
       "      <td>56</td>\n",
       "      <td>male</td>\n",
       "      <td>33.660</td>\n",
       "      <td>4</td>\n",
       "      <td>no</td>\n",
       "      <td>southeast</td>\n",
       "      <td>12949.15540</td>\n",
       "      <td>obese</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>274</th>\n",
       "      <td>25</td>\n",
       "      <td>male</td>\n",
       "      <td>27.550</td>\n",
       "      <td>0</td>\n",
       "      <td>no</td>\n",
       "      <td>northwest</td>\n",
       "      <td>2523.16950</td>\n",
       "      <td>overweight</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>695</th>\n",
       "      <td>26</td>\n",
       "      <td>female</td>\n",
       "      <td>40.185</td>\n",
       "      <td>0</td>\n",
       "      <td>no</td>\n",
       "      <td>northwest</td>\n",
       "      <td>3201.24515</td>\n",
       "      <td>obese</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1193</th>\n",
       "      <td>48</td>\n",
       "      <td>female</td>\n",
       "      <td>36.575</td>\n",
       "      <td>0</td>\n",
       "      <td>no</td>\n",
       "      <td>northwest</td>\n",
       "      <td>8671.19125</td>\n",
       "      <td>obese</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "      age     sex     bmi  children smoker     region      charges  \\\n",
       "962    63  female  35.200         1     no  southeast  14474.67500   \n",
       "21     30  female  32.400         1     no  southwest   4149.73600   \n",
       "428    21  female  16.815         1     no  northeast   3167.45585   \n",
       "207    35    male  27.740         2    yes  northeast  20984.09360   \n",
       "163    32  female  29.800         2     no  southwest   5152.13400   \n",
       "672    36    male  29.700         0     no  southeast   4399.73100   \n",
       "639    56    male  33.660         4     no  southeast  12949.15540   \n",
       "274    25    male  27.550         0     no  northwest   2523.16950   \n",
       "695    26  female  40.185         0     no  northwest   3201.24515   \n",
       "1193   48  female  36.575         0     no  northwest   8671.19125   \n",
       "\n",
       "     weight_status  \n",
       "962          obese  \n",
       "21           obese  \n",
       "428    underweigth  \n",
       "207     overweight  \n",
       "163     overweight  \n",
       "672     overweight  \n",
       "639          obese  \n",
       "274     overweight  \n",
       "695          obese  \n",
       "1193         obese  "
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#Sampling\n",
    "df.sample(10)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "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",
       "      <th>weight_status</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",
       "      <td>1338</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>unique</th>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>4</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",
       "      <td>obese</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>freq</th>\n",
       "      <td>676</td>\n",
       "      <td>1064</td>\n",
       "      <td>364</td>\n",
       "      <td>707</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "         sex smoker     region weight_status\n",
       "count   1338   1338       1338          1338\n",
       "unique     2      2          4             4\n",
       "top     male     no  southeast         obese\n",
       "freq     676   1064        364           707"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#Describe Categorical Values\n",
    "df[cats].describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "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>30.663397</td>\n",
       "      <td>1.094918</td>\n",
       "      <td>13270.422265</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>14.049960</td>\n",
       "      <td>6.098187</td>\n",
       "      <td>1.205493</td>\n",
       "      <td>12110.011237</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>18.000000</td>\n",
       "      <td>15.960000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>1121.873900</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>27.000000</td>\n",
       "      <td>26.296250</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>4740.287150</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>9382.033000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>51.000000</td>\n",
       "      <td>34.693750</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>16639.912515</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>64.000000</td>\n",
       "      <td>53.130000</td>\n",
       "      <td>5.000000</td>\n",
       "      <td>63770.428010</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    30.663397     1.094918  13270.422265\n",
       "std      14.049960     6.098187     1.205493  12110.011237\n",
       "min      18.000000    15.960000     0.000000   1121.873900\n",
       "25%      27.000000    26.296250     0.000000   4740.287150\n",
       "50%      39.000000    30.400000     1.000000   9382.033000\n",
       "75%      51.000000    34.693750     2.000000  16639.912515\n",
       "max      64.000000    53.130000     5.000000  63770.428010"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#Describe Numerical Values\n",
    "df[nums].describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "smoker\n",
       "no     1064\n",
       "yes     274\n",
       "Name: charges, dtype: int64"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#Check Smoker Feature\n",
    "df.groupby(['smoker'])['charges'].count()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "sex\n",
       "female    662\n",
       "male      676\n",
       "Name: charges, dtype: int64"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#Check Sex Feature\n",
    "df.groupby(['sex'])['charges'].count()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "region\n",
       "northeast    324\n",
       "northwest    325\n",
       "southeast    364\n",
       "southwest    325\n",
       "Name: charges, dtype: int64"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#Check Region Feature\n",
    "df.groupby(['region'])['charges'].count()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "children\n",
       "0    574\n",
       "1    324\n",
       "2    240\n",
       "3    157\n",
       "4     25\n",
       "5     18\n",
       "Name: charges, dtype: int64"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#Check Children Feature\n",
    "df.groupby(['children'])['charges'].count()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "weight_status\n",
       "normal         230\n",
       "obese          707\n",
       "overweight     386\n",
       "underweigth     15\n",
       "Name: charges, dtype: int64"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#Check Weight_Status Feature\n",
    "df.groupby(['weight_status'])['charges'].count()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Visual Approach"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Univariate Analysis"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Boxplot"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<Figure size 1080x360 with 4 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "#Boxplot Visual\n",
    "for i in range(0, len(nums)):\n",
    "    plt.subplot(1, len(nums), i + 1)\n",
    "    sns.boxplot(y = df[nums[i]])\n",
    "    plt.tight_layout()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### KDEplot"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1080x216 with 4 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "#KDEplot Visual\n",
    "plt.figure(figsize=(15, 3))\n",
    "for i in range(0, len(nums)):\n",
    "    plt.subplot(1, len(nums), i + 1)\n",
    "    sns.kdeplot(x = df[nums[i]])\n",
    "    plt.tight_layout()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Histplot"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1080x360 with 4 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "#Histplot Visual\n",
    "for i in range(0, len(nums)):\n",
    "    plt.subplot(1, len(nums), i +1)\n",
    "    sns.histplot(data = df[nums[i]])\n",
    "    plt.tight_layout()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Countplot"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1080x360 with 4 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "#Countplot Visual\n",
    "for i in range(0, len(cats)):\n",
    "    plt.subplot(1, len(cats), i + 1)\n",
    "    sns.countplot(df[cats[i]])\n",
    "    plt.tight_layout()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Multivariate Analysis"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Heatmap"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:>"
      ]
     },
     "execution_count": 20,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 576x576 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "#Heatmap Correlation\n",
    "plt.figure(figsize=(8, 8))\n",
    "sns.heatmap(df.corr(), annot=True, fmt='.2f')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Pairplot"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<seaborn.axisgrid.PairGrid at 0x205d0f250>"
      ]
     },
     "execution_count": 21,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x720 with 20 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "#Pairplot Visualization\n",
    "sns.pairplot(df, diag_kind='kde')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Scatterplot"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:xlabel='bmi', ylabel='charges'>"
      ]
     },
     "execution_count": 22,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1080x360 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "#Scatterplor Visualization\n",
    "sns.scatterplot(x = 'bmi', y = 'charges', hue = 'smoker', data = df)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:xlabel='age', ylabel='charges'>"
      ]
     },
     "execution_count": 23,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1080x360 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "#Scatterplor Visualization\n",
    "sns.scatterplot(x = 'age', y = 'charges', hue = 'smoker', data = df)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Barplot"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "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>region</th>\n",
       "      <th>charges</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>southeast</td>\n",
       "      <td>5.363690e+06</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>northeast</td>\n",
       "      <td>4.343669e+06</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>northwest</td>\n",
       "      <td>4.035712e+06</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>southwest</td>\n",
       "      <td>4.012755e+06</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "      region       charges\n",
       "2  southeast  5.363690e+06\n",
       "0  northeast  4.343669e+06\n",
       "1  northwest  4.035712e+06\n",
       "3  southwest  4.012755e+06"
      ]
     },
     "execution_count": 24,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#Charges per Region\n",
    "region_charges = df.groupby(['region']).agg({'charges' : sum}).reset_index()\n",
    "region_charges.sort_values(['charges'], ascending = False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:xlabel='region', ylabel='charges'>"
      ]
     },
     "execution_count": 25,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1080x360 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "#Barplot Charges per Region\n",
    "sns.barplot(x = 'region', y ='charges', data = region_charges)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:xlabel='region', ylabel='charges'>"
      ]
     },
     "execution_count": 26,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1080x360 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "#Barplot with Hue Smoker\n",
    "sns.barplot(x = df['region'], y = df['charges'], hue = df['smoker'], data = df)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:xlabel='region', ylabel='charges'>"
      ]
     },
     "execution_count": 27,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1080x360 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "#Barplot with Hue Sex\n",
    "sns.barplot(x = df['region'], y = df['charges'], hue = 'sex', data = df)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "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>smoker</th>\n",
       "      <th>sex</th>\n",
       "      <th>count</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>yes</td>\n",
       "      <td>male</td>\n",
       "      <td>159</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>yes</td>\n",
       "      <td>female</td>\n",
       "      <td>115</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>no</td>\n",
       "      <td>female</td>\n",
       "      <td>547</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>no</td>\n",
       "      <td>male</td>\n",
       "      <td>517</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "  smoker     sex  count\n",
       "3    yes    male    159\n",
       "2    yes  female    115\n",
       "0     no  female    547\n",
       "1     no    male    517"
      ]
     },
     "execution_count": 28,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#Count Smoker with Sex Hue\n",
    "smoker_sex = df.groupby(['smoker', 'sex']).agg({'charges' : 'count'}).reset_index()\n",
    "smoker_sex.columns = ['smoker', 'sex', 'count']\n",
    "smoker_sex.sort_values(['smoker', 'count'], ascending = False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:xlabel='sex', ylabel='count'>"
      ]
     },
     "execution_count": 29,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1080x360 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "#Barplot Visualization\n",
    "sns.barplot(x = smoker_sex['sex'], y = smoker_sex['count'], hue = smoker_sex['smoker'], data = smoker_sex)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:xlabel='children', ylabel='charges'>"
      ]
     },
     "execution_count": 30,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1080x360 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "#Barplot with Hue Sex\n",
    "sns.barplot(x = df['children'], y = df['charges'], hue = df['sex'], data = df)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:xlabel='children', ylabel='charges'>"
      ]
     },
     "execution_count": 31,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1080x360 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "#Barplot with Hue Smoker\n",
    "sns.barplot(x = df['children'], y = df['charges'], hue = df['smoker'], data = df)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "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>weight_status</th>\n",
       "      <th>smoker</th>\n",
       "      <th>count</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>normal</td>\n",
       "      <td>no</td>\n",
       "      <td>179</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>normal</td>\n",
       "      <td>yes</td>\n",
       "      <td>51</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>obese</td>\n",
       "      <td>no</td>\n",
       "      <td>562</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>obese</td>\n",
       "      <td>yes</td>\n",
       "      <td>145</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>overweight</td>\n",
       "      <td>no</td>\n",
       "      <td>312</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>overweight</td>\n",
       "      <td>yes</td>\n",
       "      <td>74</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>underweigth</td>\n",
       "      <td>no</td>\n",
       "      <td>11</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>underweigth</td>\n",
       "      <td>yes</td>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "  weight_status smoker  count\n",
       "0        normal     no    179\n",
       "1        normal    yes     51\n",
       "2         obese     no    562\n",
       "3         obese    yes    145\n",
       "4    overweight     no    312\n",
       "5    overweight    yes     74\n",
       "6   underweigth     no     11\n",
       "7   underweigth    yes      4"
      ]
     },
     "execution_count": 32,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#Count Weight_Status with Hue Smoker\n",
    "smoker_status = df.groupby(['weight_status', 'smoker']).agg({'charges' : 'count'}).reset_index()\n",
    "smoker_status.columns = ('weight_status', 'smoker', 'count')\n",
    "smoker_status"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:xlabel='weight_status', ylabel='count'>"
      ]
     },
     "execution_count": 33,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1080x360 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "#Barplot Smoker_Status\n",
    "sns.barplot(x = 'weight_status', y = 'count', hue = 'smoker', data = smoker_status)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "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>weight_status</th>\n",
       "      <th>smoker</th>\n",
       "      <th>charges</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>normal</td>\n",
       "      <td>no</td>\n",
       "      <td>1.389364e+06</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>normal</td>\n",
       "      <td>yes</td>\n",
       "      <td>1.016134e+06</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>obese</td>\n",
       "      <td>no</td>\n",
       "      <td>4.969593e+06</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>obese</td>\n",
       "      <td>yes</td>\n",
       "      <td>6.025909e+06</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>overweight</td>\n",
       "      <td>no</td>\n",
       "      <td>2.576484e+06</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>overweight</td>\n",
       "      <td>yes</td>\n",
       "      <td>1.664695e+06</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>underweigth</td>\n",
       "      <td>no</td>\n",
       "      <td>3.862044e+04</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>underweigth</td>\n",
       "      <td>yes</td>\n",
       "      <td>7.502586e+04</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "  weight_status smoker       charges\n",
       "0        normal     no  1.389364e+06\n",
       "1        normal    yes  1.016134e+06\n",
       "2         obese     no  4.969593e+06\n",
       "3         obese    yes  6.025909e+06\n",
       "4    overweight     no  2.576484e+06\n",
       "5    overweight    yes  1.664695e+06\n",
       "6   underweigth     no  3.862044e+04\n",
       "7   underweigth    yes  7.502586e+04"
      ]
     },
     "execution_count": 34,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#Count Weight_Status with Hue Smoker\n",
    "smoker_status = df.groupby(['weight_status', 'smoker']).agg({'charges' : 'sum'}).reset_index()\n",
    "smoker_status.columns = ('weight_status', 'smoker', 'charges')\n",
    "smoker_status "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "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>weight_status</th>\n",
       "      <th>smoker</th>\n",
       "      <th>count</th>\n",
       "      <th>total</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>normal</td>\n",
       "      <td>no</td>\n",
       "      <td>179</td>\n",
       "      <td>230</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>normal</td>\n",
       "      <td>yes</td>\n",
       "      <td>51</td>\n",
       "      <td>230</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>obese</td>\n",
       "      <td>no</td>\n",
       "      <td>562</td>\n",
       "      <td>707</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>obese</td>\n",
       "      <td>yes</td>\n",
       "      <td>145</td>\n",
       "      <td>707</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>overweight</td>\n",
       "      <td>no</td>\n",
       "      <td>312</td>\n",
       "      <td>386</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>overweight</td>\n",
       "      <td>yes</td>\n",
       "      <td>74</td>\n",
       "      <td>386</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>underweigth</td>\n",
       "      <td>no</td>\n",
       "      <td>11</td>\n",
       "      <td>15</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>underweigth</td>\n",
       "      <td>yes</td>\n",
       "      <td>4</td>\n",
       "      <td>15</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "  weight_status smoker  count  total\n",
       "0        normal     no    179    230\n",
       "1        normal    yes     51    230\n",
       "2         obese     no    562    707\n",
       "3         obese    yes    145    707\n",
       "4    overweight     no    312    386\n",
       "5    overweight    yes     74    386\n",
       "6   underweigth     no     11     15\n",
       "7   underweigth    yes      4     15"
      ]
     },
     "execution_count": 35,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#Insurance Cost based on Weight_Status and smoker\n",
    "smoker_status_charges = df.groupby(['weight_status', 'smoker']).agg({'charges' : 'count'}).reset_index()\n",
    "smoker_status_charges.columns = ['weight_status', 'smoker', 'count']\n",
    "smoker_status_charges['total'] = smoker_status_charges.groupby(['weight_status'])['count'].transform('sum')\n",
    "smoker_status_charges"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:xlabel='weight_status', ylabel='count'>"
      ]
     },
     "execution_count": 36,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1080x360 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "#Barplot Smoker_Status_Charges\n",
    "sns.barplot(x = 'weight_status', y = 'count', hue = 'smoker', data = smoker_status_charges)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "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>weight_status</th>\n",
       "      <th>smoker</th>\n",
       "      <th>charges</th>\n",
       "      <th>count</th>\n",
       "      <th>total</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>normal</td>\n",
       "      <td>no</td>\n",
       "      <td>1.389364e+06</td>\n",
       "      <td>179</td>\n",
       "      <td>230</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>normal</td>\n",
       "      <td>yes</td>\n",
       "      <td>1.016134e+06</td>\n",
       "      <td>51</td>\n",
       "      <td>230</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>obese</td>\n",
       "      <td>no</td>\n",
       "      <td>4.969593e+06</td>\n",
       "      <td>562</td>\n",
       "      <td>707</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>obese</td>\n",
       "      <td>yes</td>\n",
       "      <td>6.025909e+06</td>\n",
       "      <td>145</td>\n",
       "      <td>707</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>overweight</td>\n",
       "      <td>no</td>\n",
       "      <td>2.576484e+06</td>\n",
       "      <td>312</td>\n",
       "      <td>386</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>overweight</td>\n",
       "      <td>yes</td>\n",
       "      <td>1.664695e+06</td>\n",
       "      <td>74</td>\n",
       "      <td>386</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>underweigth</td>\n",
       "      <td>no</td>\n",
       "      <td>3.862044e+04</td>\n",
       "      <td>11</td>\n",
       "      <td>15</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>underweigth</td>\n",
       "      <td>yes</td>\n",
       "      <td>7.502586e+04</td>\n",
       "      <td>4</td>\n",
       "      <td>15</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "  weight_status smoker       charges  count  total\n",
       "0        normal     no  1.389364e+06    179    230\n",
       "1        normal    yes  1.016134e+06     51    230\n",
       "2         obese     no  4.969593e+06    562    707\n",
       "3         obese    yes  6.025909e+06    145    707\n",
       "4    overweight     no  2.576484e+06    312    386\n",
       "5    overweight    yes  1.664695e+06     74    386\n",
       "6   underweigth     no  3.862044e+04     11     15\n",
       "7   underweigth    yes  7.502586e+04      4     15"
      ]
     },
     "execution_count": 37,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#Merge Columns\n",
    "condition_cost = smoker_status.merge(smoker_status_charges, \n",
    "                   left_on = ['weight_status', 'smoker'],\n",
    "                    right_on = ['weight_status', 'smoker'],\n",
    "                    how = 'inner')\n",
    "condition_cost"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "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>weight_status</th>\n",
       "      <th>smoker</th>\n",
       "      <th>charges</th>\n",
       "      <th>count</th>\n",
       "      <th>total</th>\n",
       "      <th>avg_charges</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>normal</td>\n",
       "      <td>no</td>\n",
       "      <td>1.389364e+06</td>\n",
       "      <td>179</td>\n",
       "      <td>230</td>\n",
       "      <td>7761.811458</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>normal</td>\n",
       "      <td>yes</td>\n",
       "      <td>1.016134e+06</td>\n",
       "      <td>51</td>\n",
       "      <td>230</td>\n",
       "      <td>19924.204746</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>obese</td>\n",
       "      <td>no</td>\n",
       "      <td>4.969593e+06</td>\n",
       "      <td>562</td>\n",
       "      <td>707</td>\n",
       "      <td>8842.691548</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>obese</td>\n",
       "      <td>yes</td>\n",
       "      <td>6.025909e+06</td>\n",
       "      <td>145</td>\n",
       "      <td>707</td>\n",
       "      <td>41557.989840</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>overweight</td>\n",
       "      <td>no</td>\n",
       "      <td>2.576484e+06</td>\n",
       "      <td>312</td>\n",
       "      <td>386</td>\n",
       "      <td>8257.961955</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>overweight</td>\n",
       "      <td>yes</td>\n",
       "      <td>1.664695e+06</td>\n",
       "      <td>74</td>\n",
       "      <td>386</td>\n",
       "      <td>22495.874163</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>underweigth</td>\n",
       "      <td>no</td>\n",
       "      <td>3.862044e+04</td>\n",
       "      <td>11</td>\n",
       "      <td>15</td>\n",
       "      <td>3510.948941</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>underweigth</td>\n",
       "      <td>yes</td>\n",
       "      <td>7.502586e+04</td>\n",
       "      <td>4</td>\n",
       "      <td>15</td>\n",
       "      <td>18756.466225</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "  weight_status smoker       charges  count  total   avg_charges\n",
       "0        normal     no  1.389364e+06    179    230   7761.811458\n",
       "1        normal    yes  1.016134e+06     51    230  19924.204746\n",
       "2         obese     no  4.969593e+06    562    707   8842.691548\n",
       "3         obese    yes  6.025909e+06    145    707  41557.989840\n",
       "4    overweight     no  2.576484e+06    312    386   8257.961955\n",
       "5    overweight    yes  1.664695e+06     74    386  22495.874163\n",
       "6   underweigth     no  3.862044e+04     11     15   3510.948941\n",
       "7   underweigth    yes  7.502586e+04      4     15  18756.466225"
      ]
     },
     "execution_count": 38,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#Average Charges\n",
    "condition_cost['avg_charges'] = condition_cost['charges'] / condition_cost['count']\n",
    "condition_cost"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:xlabel='weight_status', ylabel='avg_charges'>"
      ]
     },
     "execution_count": 39,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1080x360 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "#Average Charges Barplot\n",
    "sns.barplot(x = 'weight_status', y = 'avg_charges', hue = 'smoker', data = condition_cost)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Person who smoke have to pay more cost for insurance, obese person who smoke have the highest medical cost."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Data Processing"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Missing Value"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "age              0\n",
       "sex              0\n",
       "bmi              0\n",
       "children         0\n",
       "smoker           0\n",
       "region           0\n",
       "charges          0\n",
       "weight_status    0\n",
       "dtype: int64"
      ]
     },
     "execution_count": 40,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#Check Missing Value\n",
    "df.isna().sum()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "There are no missing value"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Duplicate Value"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "1"
      ]
     },
     "execution_count": 41,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#Check Duplicate Value\n",
    "df.duplicated().sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0"
      ]
     },
     "execution_count": 42,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#Remove Duplicate Value\n",
    "df = df.drop_duplicates()\n",
    "\n",
    "#Check Duplicate Value\n",
    "df.duplicated().sum()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## dfori"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "metadata": {},
   "outputs": [],
   "source": [
    "#Copy DataFrame\n",
    "dfori = df.copy()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Feature Transformation"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "metadata": {},
   "outputs": [],
   "source": [
    "#Ordinal Encoding\n",
    "dfori['smoker'] = dfori['smoker'].replace({'yes' : 0, 'no' : 1})\n",
    "\n",
    "#One Hot Encoding\n",
    "sex_ori = pd.get_dummies(dfori['sex'], prefix = 'sex')\n",
    "region_ori = pd.get_dummies(dfori['region'], prefix = 'reg')\n",
    "status_ori = pd.get_dummies(dfori['weight_status'], prefix = 'status')\n",
    "\n",
    "#Concat Feature Encoding\n",
    "dfori = pd.concat([dfori, sex_ori], axis=1)\n",
    "dfori = pd.concat([dfori, region_ori], axis=1)\n",
    "dfori = pd.concat([dfori, status_ori], axis=1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "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>smoker</th>\n",
       "      <th>charges</th>\n",
       "      <th>sex_female</th>\n",
       "      <th>sex_male</th>\n",
       "      <th>reg_northeast</th>\n",
       "      <th>reg_northwest</th>\n",
       "      <th>reg_southeast</th>\n",
       "      <th>reg_southwest</th>\n",
       "      <th>status_normal</th>\n",
       "      <th>status_obese</th>\n",
       "      <th>status_overweight</th>\n",
       "      <th>status_underweigth</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>19</td>\n",
       "      <td>27.900</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>16884.92400</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>18</td>\n",
       "      <td>33.770</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1725.55230</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>28</td>\n",
       "      <td>33.000</td>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "      <td>4449.46200</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>33</td>\n",
       "      <td>22.705</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>21984.47061</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>32</td>\n",
       "      <td>28.880</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>3866.85520</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</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",
       "      <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>30.970</td>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "      <td>10600.54830</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</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>31.920</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>2205.98080</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1335</th>\n",
       "      <td>18</td>\n",
       "      <td>36.850</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1629.83350</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1336</th>\n",
       "      <td>21</td>\n",
       "      <td>25.800</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>2007.94500</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1337</th>\n",
       "      <td>61</td>\n",
       "      <td>29.070</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>29141.36030</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>1337 rows × 15 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "      age     bmi  children  smoker      charges  sex_female  sex_male  \\\n",
       "0      19  27.900         0       0  16884.92400           1         0   \n",
       "1      18  33.770         1       1   1725.55230           0         1   \n",
       "2      28  33.000         3       1   4449.46200           0         1   \n",
       "3      33  22.705         0       1  21984.47061           0         1   \n",
       "4      32  28.880         0       1   3866.85520           0         1   \n",
       "...   ...     ...       ...     ...          ...         ...       ...   \n",
       "1333   50  30.970         3       1  10600.54830           0         1   \n",
       "1334   18  31.920         0       1   2205.98080           1         0   \n",
       "1335   18  36.850         0       1   1629.83350           1         0   \n",
       "1336   21  25.800         0       1   2007.94500           1         0   \n",
       "1337   61  29.070         0       0  29141.36030           1         0   \n",
       "\n",
       "      reg_northeast  reg_northwest  reg_southeast  reg_southwest  \\\n",
       "0                 0              0              0              1   \n",
       "1                 0              0              1              0   \n",
       "2                 0              0              1              0   \n",
       "3                 0              1              0              0   \n",
       "4                 0              1              0              0   \n",
       "...             ...            ...            ...            ...   \n",
       "1333              0              1              0              0   \n",
       "1334              1              0              0              0   \n",
       "1335              0              0              1              0   \n",
       "1336              0              0              0              1   \n",
       "1337              0              1              0              0   \n",
       "\n",
       "      status_normal  status_obese  status_overweight  status_underweigth  \n",
       "0                 0             0                  1                   0  \n",
       "1                 0             1                  0                   0  \n",
       "2                 0             1                  0                   0  \n",
       "3                 1             0                  0                   0  \n",
       "4                 0             0                  1                   0  \n",
       "...             ...           ...                ...                 ...  \n",
       "1333              0             1                  0                   0  \n",
       "1334              0             1                  0                   0  \n",
       "1335              0             1                  0                   0  \n",
       "1336              0             0                  1                   0  \n",
       "1337              0             0                  1                   0  \n",
       "\n",
       "[1337 rows x 15 columns]"
      ]
     },
     "execution_count": 45,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#Drop Encoded Feature\n",
    "dfori = dfori.drop(columns = ['sex', 'region', 'weight_status'])\n",
    "\n",
    "#Check df\n",
    "dfori"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "Int64Index: 1337 entries, 0 to 1337\n",
      "Data columns (total 15 columns):\n",
      " #   Column              Non-Null Count  Dtype  \n",
      "---  ------              --------------  -----  \n",
      " 0   age                 1337 non-null   int64  \n",
      " 1   bmi                 1337 non-null   float64\n",
      " 2   children            1337 non-null   int64  \n",
      " 3   smoker              1337 non-null   int64  \n",
      " 4   charges             1337 non-null   float64\n",
      " 5   sex_female          1337 non-null   uint8  \n",
      " 6   sex_male            1337 non-null   uint8  \n",
      " 7   reg_northeast       1337 non-null   uint8  \n",
      " 8   reg_northwest       1337 non-null   uint8  \n",
      " 9   reg_southeast       1337 non-null   uint8  \n",
      " 10  reg_southwest       1337 non-null   uint8  \n",
      " 11  status_normal       1337 non-null   uint8  \n",
      " 12  status_obese        1337 non-null   uint8  \n",
      " 13  status_overweight   1337 non-null   uint8  \n",
      " 14  status_underweigth  1337 non-null   uint8  \n",
      "dtypes: float64(2), int64(3), uint8(10)\n",
      "memory usage: 75.7 KB\n"
     ]
    }
   ],
   "source": [
    "dfori.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Index(['age', 'bmi', 'children', 'smoker', 'sex_female', 'sex_male',\n",
      "       'reg_northeast', 'reg_northwest', 'reg_southeast', 'reg_southwest',\n",
      "       'status_normal', 'status_obese', 'status_overweight',\n",
      "       'status_underweigth'],\n",
      "      dtype='object')\n"
     ]
    }
   ],
   "source": [
    "#Grouping Features for Normalization\n",
    "norm_ori = dfori.drop(columns = ['charges']).columns\n",
    "print(norm_ori)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "metadata": {},
   "outputs": [],
   "source": [
    "#Normalization Features\n",
    "for i in range(len(norm_ori)):\n",
    "    dfori[norm_ori[i]] = MinMaxScaler().fit_transform(dfori[norm_ori[i]].values.reshape(len(dfori), 1))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>age</th>\n",
       "      <th>bmi</th>\n",
       "      <th>children</th>\n",
       "      <th>smoker</th>\n",
       "      <th>charges</th>\n",
       "      <th>sex_female</th>\n",
       "      <th>sex_male</th>\n",
       "      <th>reg_northeast</th>\n",
       "      <th>reg_northwest</th>\n",
       "      <th>reg_southeast</th>\n",
       "      <th>reg_southwest</th>\n",
       "      <th>status_normal</th>\n",
       "      <th>status_obese</th>\n",
       "      <th>status_overweight</th>\n",
       "      <th>status_underweigth</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
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       "      <th>1</th>\n",
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       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>0.217391</td>\n",
       "      <td>0.458434</td>\n",
       "      <td>0.6</td>\n",
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       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>0.326087</td>\n",
       "      <td>0.181464</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>21984.47061</td>\n",
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       "      <td>1.0</td>\n",
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       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>0.304348</td>\n",
       "      <td>0.347592</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>3866.85520</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
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       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
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       "    <tr>\n",
       "      <th>1333</th>\n",
       "      <td>0.695652</td>\n",
       "      <td>0.403820</td>\n",
       "      <td>0.6</td>\n",
       "      <td>1.0</td>\n",
       "      <td>10600.54830</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
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       "    <tr>\n",
       "      <th>1334</th>\n",
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       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
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       "    <tr>\n",
       "      <th>1335</th>\n",
       "      <td>0.000000</td>\n",
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       "      <td>0.0</td>\n",
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       "      <td>1629.83350</td>\n",
       "      <td>1.0</td>\n",
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       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
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       "    <tr>\n",
       "      <th>1336</th>\n",
       "      <td>0.065217</td>\n",
       "      <td>0.264730</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>2007.94500</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1337</th>\n",
       "      <td>0.934783</td>\n",
       "      <td>0.352704</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>29141.36030</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>1337 rows × 15 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "           age       bmi  children  smoker      charges  sex_female  sex_male  \\\n",
       "0     0.021739  0.321227       0.0     0.0  16884.92400         1.0       0.0   \n",
       "1     0.000000  0.479150       0.2     1.0   1725.55230         0.0       1.0   \n",
       "2     0.217391  0.458434       0.6     1.0   4449.46200         0.0       1.0   \n",
       "3     0.326087  0.181464       0.0     1.0  21984.47061         0.0       1.0   \n",
       "4     0.304348  0.347592       0.0     1.0   3866.85520         0.0       1.0   \n",
       "...        ...       ...       ...     ...          ...         ...       ...   \n",
       "1333  0.695652  0.403820       0.6     1.0  10600.54830         0.0       1.0   \n",
       "1334  0.000000  0.429379       0.0     1.0   2205.98080         1.0       0.0   \n",
       "1335  0.000000  0.562012       0.0     1.0   1629.83350         1.0       0.0   \n",
       "1336  0.065217  0.264730       0.0     1.0   2007.94500         1.0       0.0   \n",
       "1337  0.934783  0.352704       0.0     0.0  29141.36030         1.0       0.0   \n",
       "\n",
       "      reg_northeast  reg_northwest  reg_southeast  reg_southwest  \\\n",
       "0               0.0            0.0            0.0            1.0   \n",
       "1               0.0            0.0            1.0            0.0   \n",
       "2               0.0            0.0            1.0            0.0   \n",
       "3               0.0            1.0            0.0            0.0   \n",
       "4               0.0            1.0            0.0            0.0   \n",
       "...             ...            ...            ...            ...   \n",
       "1333            0.0            1.0            0.0            0.0   \n",
       "1334            1.0            0.0            0.0            0.0   \n",
       "1335            0.0            0.0            1.0            0.0   \n",
       "1336            0.0            0.0            0.0            1.0   \n",
       "1337            0.0            1.0            0.0            0.0   \n",
       "\n",
       "      status_normal  status_obese  status_overweight  status_underweigth  \n",
       "0               0.0           0.0                1.0                 0.0  \n",
       "1               0.0           1.0                0.0                 0.0  \n",
       "2               0.0           1.0                0.0                 0.0  \n",
       "3               1.0           0.0                0.0                 0.0  \n",
       "4               0.0           0.0                1.0                 0.0  \n",
       "...             ...           ...                ...                 ...  \n",
       "1333            0.0           1.0                0.0                 0.0  \n",
       "1334            0.0           1.0                0.0                 0.0  \n",
       "1335            0.0           1.0                0.0                 0.0  \n",
       "1336            0.0           0.0                1.0                 0.0  \n",
       "1337            0.0           0.0                1.0                 0.0  \n",
       "\n",
       "[1337 rows x 15 columns]"
      ]
     },
     "execution_count": 49,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#Check dfori\n",
    "dfori"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Modeling"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 50,
   "metadata": {},
   "outputs": [],
   "source": [
    "#Model Evaluation\n",
    "def evaluation(model, prediction, xtrain, ytrain, xtest, ytest):\n",
    "    print('MAE : %.2f' % mean_absolute_error(ytest, prediction)) # MAE\n",
    "    print('RMSE : %.2f' % mean_squared_error(ytest, prediction, squared=False)) # RMSE\n",
    "    print('R2 score : %.2f' % r2_score(ytest, prediction)) # R2 score\n",
    "    print('Train Accuracy :', model.score(xtrain, ytrain)) # Train Accuracy\n",
    "    print('Test Accuracy :', model.score(xtest, ytest)) # Test Accuracy"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 51,
   "metadata": {},
   "outputs": [],
   "source": [
    "#Best Hyperparameter Evaluation\n",
    "def best_hyperparameter(model, parameters):\n",
    "    for key, value in parameters.items() :\n",
    "        print('Best '+ key +':', model.get_params()[key])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 52,
   "metadata": {},
   "outputs": [],
   "source": [
    "#Best Feature Importance Evaluation\n",
    "def feature_importance(model, x):\n",
    "    feat_importances = pd.Series(model.best_estimator_.feature_importances_, index = x.columns)\n",
    "    ax = feat_importances.nlargest(10).plot(kind = 'barh', figsize = (10, 8))\n",
    "    ax.invert_yaxis()\n",
    "    \n",
    "    plt.xlabel('Score')\n",
    "    plt.ylabel('Feature')\n",
    "    plt.title('Feature Importance Score')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Model dfori"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 53,
   "metadata": {},
   "outputs": [],
   "source": [
    "#Separating Target & Features\n",
    "x1 = dfori.drop(columns = ['charges']) #Features\n",
    "y1 = dfori['charges'] #Target"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 54,
   "metadata": {},
   "outputs": [],
   "source": [
    "#Splitting Train & Test \n",
    "xtrain1, xtest1, ytrain1, ytest1 = train_test_split(x1, y1, test_size = 0.3, random_state = 42)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 55,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0       16884.92400\n",
      "1        1725.55230\n",
      "2        4449.46200\n",
      "3       21984.47061\n",
      "4        3866.85520\n",
      "           ...     \n",
      "1333    10600.54830\n",
      "1334     2205.98080\n",
      "1335     1629.83350\n",
      "1336     2007.94500\n",
      "1337    29141.36030\n",
      "Name: charges, Length: 1337, dtype: float64\n"
     ]
    }
   ],
   "source": [
    "print(dfori['charges'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 56,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Model: LinearRegression\n",
      "MAE : 4305.20\n",
      "RMSE : 6209.88\n",
      "R2 score : 0.77\n",
      "Train Accuracy : 0.7426419147048781\n",
      "Test Accuracy : 0.7746428937659737\n",
      "None \n",
      "\n",
      "Model: DecisionTree\n",
      "MAE : 2798.83\n",
      "RMSE : 6067.50\n",
      "R2 score : 0.78\n",
      "Train Accuracy : 1.0\n",
      "Test Accuracy : 0.7848585651488205\n",
      "None \n",
      "\n",
      "Model: RandomForest\n",
      "MAE : 2608.55\n",
      "RMSE : 4841.88\n",
      "R2 score : 0.86\n",
      "Train Accuracy : 0.9757555448222377\n",
      "Test Accuracy : 0.8629961375778817\n",
      "None \n",
      "\n",
      "Model: Ridge\n",
      "MAE : 4311.10\n",
      "RMSE : 6238.13\n",
      "R2 score : 0.77\n",
      "Train Accuracy : 0.7431554038170928\n",
      "Test Accuracy : 0.7725878147456858\n",
      "None \n",
      "\n",
      "Model: Lasso\n",
      "MAE : 4304.22\n",
      "RMSE : 6222.44\n",
      "R2 score : 0.77\n",
      "Train Accuracy : 0.7432010617510405\n",
      "Test Accuracy : 0.7737299357175588\n",
      "None \n",
      "\n"
     ]
    }
   ],
   "source": [
    "#Modeling\n",
    "models = {\n",
    "    'LinearRegression' : LinearRegression(),\n",
    "    'DecisionTree'     : DecisionTreeRegressor(),\n",
    "    'RandomForest'     : RandomForestRegressor(),\n",
    "    'Ridge'            : Ridge(),\n",
    "    'Lasso'            : Lasso()\n",
    "}\n",
    "\n",
    "for model_name, model in models.items():\n",
    "    model.fit(xtrain1, ytrain1)\n",
    "    prediction1 = model.predict(xtest1)\n",
    "    print('Model: {}'.format(model_name))\n",
    "    print(evaluation(model, prediction1, xtrain1, ytrain1, xtest1, ytest1), '\\n')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 57,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
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       "         111.57792885, 31455.6160834 ,  7411.61196355, 31883.90224251,\n",
       "       13697.20634061,  4590.38588801,  1073.84905676,  3837.6345372 ,\n",
       "       33731.42950865,  3854.00010539, 12211.27082347, 17398.97074383,\n",
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       "        1925.19287862, 33981.27549896, 30410.6513696 ,  8925.79311401,\n",
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       "       10110.51328094,  9537.92001785,  7774.27614054, 13801.75493793,\n",
       "       29481.76890601, 38367.61717835, 15696.28339633, 16194.43844839,\n",
       "       15676.52415584,  7124.9668045 , 38413.66317   ,  7797.36858005,\n",
       "        1739.79521502,  2723.17537546, 14306.65625136, 16030.3014967 ,\n",
       "        9369.2172574 , 11355.80565938, 11572.59442967, 11650.32247498,\n",
       "        5343.63044389,  9591.9104265 ,  1203.17363303, 16962.3830874 ,\n",
       "        4621.07504299,  8662.963595  , 13255.82304326,  2719.7996153 ,\n",
       "       13328.72610388, 11012.21740686,  4609.52788174,  7175.74038522,\n",
       "       10005.79588637, 10002.74609301, 37243.96803753, 14745.28356391,\n",
       "        4029.14168767, -1665.39700599, 30765.38839532, 12911.85043791,\n",
       "        1444.30816313,  8651.82842603,  9939.35768217, 11429.3216159 ,\n",
       "         909.38753926,  4335.09746608, 38411.53968326, 30178.84243581,\n",
       "        5241.78587841,  2315.32283542,  9899.01518419,  3328.3871845 ,\n",
       "       17358.24137709,  5685.15021092,  4295.53738712, 11073.47704346,\n",
       "       32382.17484837, 14441.76929471, 24938.57063932,  9034.56116685,\n",
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       "       14349.61572184, 32614.53127102, 13243.62976098, 11467.57460005,\n",
       "        5756.85601598, 14991.65901375,  5314.1181295 ,  6603.64155246,\n",
       "        7652.54378817,  4626.04357034, 15390.82098098,  3202.03991809,\n",
       "       16920.62477592,  8461.66869759, 11733.42695604,  6219.25913329,\n",
       "        4578.49248364,  3475.69570419, 10557.35986367,  7099.1390396 ,\n",
       "        1578.29094018, 10420.36982665, 34171.71630114,  7626.21298128,\n",
       "        5215.8231384 ,  7982.21184568,  5483.52274027, 30543.76350612,\n",
       "       12984.41183339, 25982.72048708, 35722.50125871,  6621.72881253,\n",
       "        7734.08491261,  9737.95213041, 33473.51947439,  3320.63430091,\n",
       "       32134.63172072, 28203.46871437, 35266.90083999,  5615.93696486,\n",
       "       27636.43045442,  9803.06550457, 13679.71680149, 13749.61050729,\n",
       "        6961.01522327,  8521.73707756, 10556.58610394,  4307.7306694 ,\n",
       "        -867.79705262, 10759.38447919,  4032.63129034,  3207.81349871,\n",
       "       17724.45472933,  5469.98975085, 10578.65155275,  4261.38150802,\n",
       "       14930.5694223 , 12447.43687488,  4348.87240264, 39037.41986447,\n",
       "        5162.66815557,  7119.45847524, 15491.50495021, 14669.57365931,\n",
       "       10176.94357785, 11693.43266654,  9752.41303732,   231.85261787,\n",
       "       11499.2867872 ,  3745.12220748, 10093.5137832 , 28980.0547258 ,\n",
       "        -358.8606813 ,  8835.69800677, 14320.2242411 , 13674.02705148,\n",
       "       14961.39737538,  7217.5316416 ])"
      ]
     },
     "execution_count": 57,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "prediction1"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Tuning Hyperparameter"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 58,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "dict_keys(['copy_X', 'fit_intercept', 'n_jobs', 'normalize', 'positive'])"
      ]
     },
     "execution_count": 58,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "LinearRegression().get_params().keys()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 59,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "MAE : 4305.31\n",
      "RMSE : 6221.51\n",
      "R2 score : 0.77\n",
      "Train Accuracy : 0.7432025636860958\n",
      "Test Accuracy : 0.7737981066498897\n"
     ]
    }
   ],
   "source": [
    "#List Hyperparameters\n",
    "hyperparameters = dict(\n",
    "                       fit_intercept = [True, False],\n",
    "                       n_jobs = [-1, 0, 1, 2],\n",
    "                       normalize = [True, False],\n",
    "                       copy_X = [True, False]\n",
    "                      )\n",
    "\n",
    "#Initiation\n",
    "LR1 = LinearRegression()\n",
    "LR_tuned1 = RandomizedSearchCV(LR1, hyperparameters, cv=5, random_state=42, scoring='r2')\n",
    "LR_tuned1.fit(xtrain1, ytrain1)\n",
    "\n",
    "# Predict & Evaluation\n",
    "prediction1 = LR_tuned1.predict(xtest1)\n",
    "evaluation(LR_tuned1, prediction1, xtrain1, ytrain1, xtest1, ytest1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 60,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Best fit_intercept: False\n",
      "Best n_jobs: 1\n",
      "Best normalize: False\n",
      "Best copy_X: False\n"
     ]
    }
   ],
   "source": [
    "#Check Best Hyperparameter\n",
    "best_hyperparameter(LR_tuned1.best_estimator_, hyperparameters)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 61,
   "metadata": {},
   "outputs": [],
   "source": [
    "alpha = [0.01, 0.1, 1, 2, 5, 10, 100]\n",
    "solver = ['lsqr', 'auto', 'sag', 'saga', 'cholesky']\n",
    "fit_intercept = [True, False]\n",
    "hyperparameters = dict(alpha=alpha, solver=solver, fit_intercept=fit_intercept)\n",
    "\n",
    "Ridge1 = Ridge()\n",
    "Ridge_tuned = RandomizedSearchCV(Ridge1, hyperparameters, cv=5, random_state=42, scoring='r2')\n",
    "\n",
    "#Fitting Model\n",
    "hyper_ridge = Ridge_tuned.fit(xtrain1, ytrain1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 62,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "o3QwtSmOKnoB",
    "outputId": "48656d4a-33ed-4e99-a9dd-458c945c7a06"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "MAE : 4312.37\n",
      "RMSE : 6229.02\n",
      "R2 score : 0.77\n",
      "Train Accuracy : 0.7431790989612017\n",
      "Test Accuracy : 0.7732512319942585\n"
     ]
    }
   ],
   "source": [
    "prediction1 = hyper_ridge.predict(xtest1)\n",
    "evaluation(hyper_ridge, prediction1, xtrain1, ytrain1, xtest1, ytest1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 63,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Best alpha: 0.1\n",
      "Best solver: lsqr\n",
      "Best fit_intercept: True\n"
     ]
    }
   ],
   "source": [
    "#Check Best Hyperparameter\n",
    "best_hyperparameter(hyper_ridge.best_estimator_, hyperparameters)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Conclusion"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Exploratory Data Analysis**\n",
    "- most people are a non smokers & obese\n",
    "- feature sex, region has an almost balanced amount\n",
    "- person who smoke & have a higher BMI, has higher medical charges\n",
    "- older people who smoke have more expensive charges\n",
    "- an obese person who smokes have higher charges\n",
    "\n",
    "**Modeling** <br>\n",
    "Based on the predictive modeling, Linear Regression algorithm has the best score compared to the others, with MAE Score 4305.20, RMSE Score 6209.88, & R2 Score 0.77. Linear Regression algorithm is fit based on the train & test accuracy."
   ]
  }
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