Health insurance_Gradient Boosting Algorithm .ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "c56f8545",
   "metadata": {},
   "outputs": [],
   "source": [
    "#import library\n",
    "import numpy as np\n",
    "import pandas as pd\n",
    "import matplotlib.pyplot as plt\n",
    "import seaborn as sns\n",
    "import warnings\n",
    "warnings.filterwarnings(\"ignore\")\n",
    "from sklearn.linear_model import LinearRegression \n",
    "from sklearn.svm import SVR\n",
    "from sklearn.ensemble import GradientBoostingRegressor\n",
    "from sklearn.ensemble import RandomForestRegressor"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "407ceda4",
   "metadata": {},
   "outputs": [],
   "source": [
    "# read dataset\n",
    "df = pd.read_csv(\"health_insurance.csv\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "50b65288",
   "metadata": {},
   "outputs": [
    {
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       "      <th></th>\n",
       "      <th>age</th>\n",
       "      <th>gender</th>\n",
       "      <th>BMI</th>\n",
       "      <th>children</th>\n",
       "      <th>smoker_status</th>\n",
       "      <th>location</th>\n",
       "      <th>health_insurance_price</th>\n",
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       "  <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",
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       "      <th>...</th>\n",
       "      <td>...</td>\n",
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       "      <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  gender     BMI  children smoker_status   location  \\\n",
       "0      19  female  27.900         0           yes  southwest   \n",
       "1      18    male  33.770         1            no  southeast   \n",
       "2      28    male  33.000         3            no  southeast   \n",
       "3      33    male  22.705         0            no  northwest   \n",
       "4      32    male  28.880         0            no  northwest   \n",
       "...   ...     ...     ...       ...           ...        ...   \n",
       "1333   50    male  30.970         3            no  northwest   \n",
       "1334   18  female  31.920         0            no  northeast   \n",
       "1335   18  female  36.850         0            no  southeast   \n",
       "1336   21  female  25.800         0            no  southwest   \n",
       "1337   61  female  29.070         0           yes  northwest   \n",
       "\n",
       "      health_insurance_price  \n",
       "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",
       "\n",
       "[1338 rows x 7 columns]"
      ]
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    "df"
   ]
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  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "63bb1ab9",
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   "outputs": [
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       "      <td>southeast</td>\n",
       "      <td>4449.46200</td>\n",
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       "      <th>3</th>\n",
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       "      <td>22.705</td>\n",
       "      <td>0</td>\n",
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       "      <th>4</th>\n",
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       "      <td>28.880</td>\n",
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       "      <td>no</td>\n",
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      "text/plain": [
       "   age  gender     BMI  children smoker_status   location  \\\n",
       "0   19  female  27.900         0           yes  southwest   \n",
       "1   18    male  33.770         1            no  southeast   \n",
       "2   28    male  33.000         3            no  southeast   \n",
       "3   33    male  22.705         0            no  northwest   \n",
       "4   32    male  28.880         0            no  northwest   \n",
       "\n",
       "   health_insurance_price  \n",
       "0             16884.92400  \n",
       "1              1725.55230  \n",
       "2              4449.46200  \n",
       "3             21984.47061  \n",
       "4              3866.85520  "
      ]
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     "execution_count": 6,
     "metadata": {},
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   "source": [
    "df.head()"
   ]
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   "cell_type": "code",
   "execution_count": 7,
   "id": "5afc66ad",
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   "outputs": [
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       "      <th>1333</th>\n",
       "      <td>50</td>\n",
       "      <td>male</td>\n",
       "      <td>30.97</td>\n",
       "      <td>3</td>\n",
       "      <td>no</td>\n",
       "      <td>northwest</td>\n",
       "      <td>10600.5483</td>\n",
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       "      <th>1334</th>\n",
       "      <td>18</td>\n",
       "      <td>female</td>\n",
       "      <td>31.92</td>\n",
       "      <td>0</td>\n",
       "      <td>no</td>\n",
       "      <td>northeast</td>\n",
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       "      <th>1335</th>\n",
       "      <td>18</td>\n",
       "      <td>female</td>\n",
       "      <td>36.85</td>\n",
       "      <td>0</td>\n",
       "      <td>no</td>\n",
       "      <td>southeast</td>\n",
       "      <td>1629.8335</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1336</th>\n",
       "      <td>21</td>\n",
       "      <td>female</td>\n",
       "      <td>25.80</td>\n",
       "      <td>0</td>\n",
       "      <td>no</td>\n",
       "      <td>southwest</td>\n",
       "      <td>2007.9450</td>\n",
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       "    <tr>\n",
       "      <th>1337</th>\n",
       "      <td>61</td>\n",
       "      <td>female</td>\n",
       "      <td>29.07</td>\n",
       "      <td>0</td>\n",
       "      <td>yes</td>\n",
       "      <td>northwest</td>\n",
       "      <td>29141.3603</td>\n",
       "    </tr>\n",
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      "text/plain": [
       "      age  gender    BMI  children smoker_status   location  \\\n",
       "1333   50    male  30.97         3            no  northwest   \n",
       "1334   18  female  31.92         0            no  northeast   \n",
       "1335   18  female  36.85         0            no  southeast   \n",
       "1336   21  female  25.80         0            no  southwest   \n",
       "1337   61  female  29.07         0           yes  northwest   \n",
       "\n",
       "      health_insurance_price  \n",
       "1333              10600.5483  \n",
       "1334               2205.9808  \n",
       "1335               1629.8335  \n",
       "1336               2007.9450  \n",
       "1337              29141.3603  "
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
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   ],
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    "df.tail()"
   ]
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   "id": "6ccd1ca9",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(1338, 7)"
      ]
     },
     "execution_count": 8,
     "metadata": {},
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   ],
   "source": [
    "df.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "cc6b6c53",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "age                       0\n",
       "gender                    0\n",
       "BMI                       0\n",
       "children                  0\n",
       "smoker_status             0\n",
       "location                  0\n",
       "health_insurance_price    0\n",
       "dtype: int64"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.isnull().sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "3fc8eed7",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "age                       0.0\n",
       "gender                    0.0\n",
       "BMI                       0.0\n",
       "children                  0.0\n",
       "smoker_status             0.0\n",
       "location                  0.0\n",
       "health_insurance_price    0.0\n",
       "dtype: float64"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.isnull().sum() / df.shape[0]*100"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "a8117387",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 1338 entries, 0 to 1337\n",
      "Data columns (total 7 columns):\n",
      " #   Column                  Non-Null Count  Dtype  \n",
      "---  ------                  --------------  -----  \n",
      " 0   age                     1338 non-null   int64  \n",
      " 1   gender                  1338 non-null   object \n",
      " 2   BMI                     1338 non-null   float64\n",
      " 3   children                1338 non-null   int64  \n",
      " 4   smoker_status           1338 non-null   object \n",
      " 5   location                1338 non-null   object \n",
      " 6   health_insurance_price  1338 non-null   float64\n",
      "dtypes: float64(2), int64(2), object(3)\n",
      "memory usage: 73.3+ KB\n"
     ]
    }
   ],
   "source": [
    "df.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "bd6117de",
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>age</th>\n",
       "      <th>BMI</th>\n",
       "      <th>children</th>\n",
       "      <th>health_insurance_price</th>\n",
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       "  </thead>\n",
       "  <tbody>\n",
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       "      <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  health_insurance_price\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": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "id": "002ac597",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([27.9  , 33.77 , 33.   , 22.705, 28.88 , 25.74 , 33.44 , 27.74 ,\n",
       "       29.83 , 25.84 , 26.22 , 26.29 , 34.4  , 39.82 , 42.13 , 24.6  ,\n",
       "       30.78 , 23.845, 40.3  , 35.3  , 36.005, 32.4  , 34.1  , 31.92 ,\n",
       "       28.025, 27.72 , 23.085, 32.775, 17.385, 36.3  , 35.6  , 26.315,\n",
       "       28.6  , 28.31 , 36.4  , 20.425, 32.965, 20.8  , 36.67 , 39.9  ,\n",
       "       26.6  , 36.63 , 21.78 , 30.8  , 37.05 , 37.3  , 38.665, 34.77 ,\n",
       "       24.53 , 35.2  , 35.625, 33.63 , 28.   , 34.43 , 28.69 , 36.955,\n",
       "       31.825, 31.68 , 22.88 , 37.335, 27.36 , 33.66 , 24.7  , 25.935,\n",
       "       22.42 , 28.9  , 39.1  , 36.19 , 23.98 , 24.75 , 28.5  , 28.1  ,\n",
       "       32.01 , 27.4  , 34.01 , 29.59 , 35.53 , 39.805, 26.885, 38.285,\n",
       "       37.62 , 41.23 , 34.8  , 22.895, 31.16 , 27.2  , 26.98 , 39.49 ,\n",
       "       24.795, 31.3  , 38.28 , 19.95 , 19.3  , 31.6  , 25.46 , 30.115,\n",
       "       29.92 , 27.5  , 28.4  , 30.875, 27.94 , 35.09 , 29.7  , 35.72 ,\n",
       "       32.205, 28.595, 49.06 , 27.17 , 23.37 , 37.1  , 23.75 , 28.975,\n",
       "       31.35 , 33.915, 28.785, 28.3  , 37.4  , 17.765, 34.7  , 26.505,\n",
       "       22.04 , 35.9  , 25.555, 28.05 , 25.175, 31.9  , 36.   , 32.49 ,\n",
       "       25.3  , 29.735, 38.83 , 30.495, 37.73 , 37.43 , 24.13 , 37.145,\n",
       "       39.52 , 24.42 , 27.83 , 36.85 , 39.6  , 29.8  , 29.64 , 28.215,\n",
       "       37.   , 33.155, 18.905, 41.47 , 30.3  , 15.96 , 33.345, 37.7  ,\n",
       "       27.835, 29.2  , 26.41 , 30.69 , 41.895, 30.9  , 32.2  , 32.11 ,\n",
       "       31.57 , 26.2  , 30.59 , 32.8  , 18.05 , 39.33 , 32.23 , 24.035,\n",
       "       36.08 , 22.3  , 26.4  , 31.8  , 26.73 , 23.1  , 23.21 , 33.7  ,\n",
       "       33.25 , 24.64 , 33.88 , 38.06 , 41.91 , 31.635, 36.195, 17.8  ,\n",
       "       24.51 , 22.22 , 38.39 , 29.07 , 22.135, 26.8  , 30.02 , 35.86 ,\n",
       "       20.9  , 17.29 , 34.21 , 25.365, 40.15 , 24.415, 25.2  , 26.84 ,\n",
       "       24.32 , 42.35 , 19.8  , 32.395, 30.2  , 29.37 , 34.2  , 27.455,\n",
       "       27.55 , 20.615, 24.3  , 31.79 , 21.56 , 28.12 , 40.565, 27.645,\n",
       "       31.2  , 26.62 , 48.07 , 36.765, 33.4  , 45.54 , 28.82 , 22.99 ,\n",
       "       27.7  , 25.41 , 34.39 , 22.61 , 37.51 , 38.   , 33.33 , 34.865,\n",
       "       33.06 , 35.97 , 31.4  , 25.27 , 40.945, 34.105, 36.48 , 33.8  ,\n",
       "       36.7  , 36.385, 34.5  , 32.3  , 27.6  , 29.26 , 35.75 , 23.18 ,\n",
       "       25.6  , 35.245, 43.89 , 20.79 , 30.5  , 21.7  , 21.89 , 24.985,\n",
       "       32.015, 30.4  , 21.09 , 22.23 , 32.9  , 24.89 , 31.46 , 17.955,\n",
       "       30.685, 43.34 , 39.05 , 30.21 , 31.445, 19.855, 31.02 , 38.17 ,\n",
       "       20.6  , 47.52 , 20.4  , 38.38 , 24.31 , 23.6  , 21.12 , 30.03 ,\n",
       "       17.48 , 20.235, 17.195, 23.9  , 35.15 , 35.64 , 22.6  , 39.16 ,\n",
       "       27.265, 29.165, 16.815, 33.1  , 26.9  , 33.11 , 31.73 , 46.75 ,\n",
       "       29.45 , 32.68 , 33.5  , 43.01 , 36.52 , 26.695, 25.65 , 29.6  ,\n",
       "       38.6  , 23.4  , 46.53 , 30.14 , 30.   , 38.095, 28.38 , 28.7  ,\n",
       "       33.82 , 24.09 , 32.67 , 25.1  , 32.56 , 41.325, 39.5  , 34.3  ,\n",
       "       31.065, 21.47 , 25.08 , 43.4  , 25.7  , 27.93 , 39.2  , 26.03 ,\n",
       "       30.25 , 28.93 , 35.7  , 35.31 , 31.   , 44.22 , 26.07 , 25.8  ,\n",
       "       39.425, 40.48 , 38.9  , 47.41 , 35.435, 46.7  , 46.2  , 21.4  ,\n",
       "       23.8  , 44.77 , 32.12 , 29.1  , 37.29 , 43.12 , 36.86 , 34.295,\n",
       "       23.465, 45.43 , 23.65 , 20.7  , 28.27 , 35.91 , 29.   , 19.57 ,\n",
       "       31.13 , 21.85 , 40.26 , 33.725, 29.48 , 32.6  , 37.525, 23.655,\n",
       "       37.8  , 19.   , 21.3  , 33.535, 42.46 , 38.95 , 36.1  , 29.3  ,\n",
       "       39.7  , 38.19 , 42.4  , 34.96 , 42.68 , 31.54 , 29.81 , 21.375,\n",
       "       40.81 , 17.4  , 20.3  , 18.5  , 26.125, 41.69 , 24.1  , 36.2  ,\n",
       "       40.185, 39.27 , 34.87 , 44.745, 29.545, 23.54 , 40.47 , 40.66 ,\n",
       "       36.6  , 35.4  , 27.075, 28.405, 21.755, 40.28 , 30.1  , 32.1  ,\n",
       "       23.7  , 35.5  , 29.15 , 27.   , 37.905, 22.77 , 22.8  , 34.58 ,\n",
       "       27.1  , 19.475, 26.7  , 34.32 , 24.4  , 41.14 , 22.515, 41.8  ,\n",
       "       26.18 , 42.24 , 26.51 , 35.815, 41.42 , 36.575, 42.94 , 21.01 ,\n",
       "       24.225, 17.67 , 31.5  , 31.1  , 32.78 , 32.45 , 50.38 , 47.6  ,\n",
       "       25.4  , 29.9  , 43.7  , 24.86 , 28.8  , 29.5  , 29.04 , 38.94 ,\n",
       "       44.   , 20.045, 40.92 , 35.1  , 29.355, 32.585, 32.34 , 39.8  ,\n",
       "       24.605, 33.99 , 28.2  , 25.   , 33.2  , 23.2  , 20.1  , 32.5  ,\n",
       "       37.18 , 46.09 , 39.93 , 35.8  , 31.255, 18.335, 42.9  , 26.79 ,\n",
       "       39.615, 25.9  , 25.745, 28.16 , 23.56 , 40.5  , 35.42 , 39.995,\n",
       "       34.675, 20.52 , 23.275, 36.29 , 32.7  , 19.19 , 20.13 , 23.32 ,\n",
       "       45.32 , 34.6  , 18.715, 21.565, 23.   , 37.07 , 52.58 , 42.655,\n",
       "       21.66 , 32.   , 18.3  , 47.74 , 22.1  , 19.095, 31.24 , 29.925,\n",
       "       20.35 , 25.85 , 42.75 , 18.6  , 23.87 , 45.9  , 21.5  , 30.305,\n",
       "       44.88 , 41.1  , 40.37 , 28.49 , 33.55 , 40.375, 27.28 , 17.86 ,\n",
       "       33.3  , 39.14 , 21.945, 24.97 , 23.94 , 34.485, 21.8  , 23.3  ,\n",
       "       36.96 , 21.28 , 29.4  , 27.3  , 37.9  , 37.715, 23.76 , 25.52 ,\n",
       "       27.61 , 27.06 , 39.4  , 34.9  , 22.   , 30.36 , 27.8  , 53.13 ,\n",
       "       39.71 , 32.87 , 44.7  , 30.97 ])"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df['BMI'].unique()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "1f887b94",
   "metadata": {},
   "source": [
    "## Handling with missing value"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "id": "077877ff",
   "metadata": {},
   "outputs": [],
   "source": [
    "newdf = df.fillna(df.mean())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "id": "86a75466",
   "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>gender</th>\n",
       "      <th>BMI</th>\n",
       "      <th>children</th>\n",
       "      <th>smoker_status</th>\n",
       "      <th>location</th>\n",
       "      <th>health_insurance_price</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  gender     BMI  children smoker_status   location  \\\n",
       "0      19  female  27.900         0           yes  southwest   \n",
       "1      18    male  33.770         1            no  southeast   \n",
       "2      28    male  33.000         3            no  southeast   \n",
       "3      33    male  22.705         0            no  northwest   \n",
       "4      32    male  28.880         0            no  northwest   \n",
       "...   ...     ...     ...       ...           ...        ...   \n",
       "1333   50    male  30.970         3            no  northwest   \n",
       "1334   18  female  31.920         0            no  northeast   \n",
       "1335   18  female  36.850         0            no  southeast   \n",
       "1336   21  female  25.800         0            no  southwest   \n",
       "1337   61  female  29.070         0           yes  northwest   \n",
       "\n",
       "      health_insurance_price  \n",
       "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",
       "\n",
       "[1338 rows x 7 columns]"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "newdf"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "id": "714a336c",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "age                       0\n",
       "gender                    0\n",
       "BMI                       0\n",
       "children                  0\n",
       "smoker_status             0\n",
       "location                  0\n",
       "health_insurance_price    0\n",
       "dtype: int64"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "newdf.isnull().sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "id": "86868f62",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(1338, 7)"
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "newdf.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "id": "bf247d54",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<Axes: xlabel='age', ylabel='Density'>"
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 360x360 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(5,5))\n",
    "sns.distplot(df['age'],bins=20,kde_kws={'linewidth':3, 'color':'red'})\n",
    "sns.distplot(newdf['age'],bins=20,kde_kws={'linewidth':3, 'color':'green'})"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "id": "654a723f",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<Axes: xlabel='BMI', ylabel='Density'>"
      ]
     },
     "execution_count": 20,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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Xfrdocw1bKlU9LaCqWqvee55C5wiXnnlRtG4ffnugE66+h7hSp709pYR9W1baDaRCjhZQVa1F62Z77cujzhrLOiwkpLRm4unAQNzvLtTHOlX9aAFV1Vqcv8lrX37B1RaT+OvKDpd47fkHllhMokKRFlB1ltxjn7E2+TQAYmDi1O9YTuSfKy8LfLYlCcf0diZVL1pA1VlWLvo7Fe5PxtDceFq2D53ZN+ur17DJ3u1MBTGwfP4zlhOpUKIFVJ1l6bZ3vPbE+P4WkzSNq2ICI0y9u/51i0lUqNECqs6yrGC7157Q70qLSZrGVcO+4rXnl+h88arutICqL8g5up/1yc7MaxEVMH7KnZYT+e+Sq75DQonT/iS5lE836MUkVTdaQNUXrHjvee/857C8BFLbdrUbqAnEJaYyqaCN1393kZ4HVXWjBVR9wbLt73rtCQn9atgyvFzZaaLXnn/oA4tJVCjRAqq+YFnhNq89od9VFpM0rSsv/67XXtoii8K8kxbTqFChBVR5sg/vZX2KM31xczn/eUb3wZfQLzcGgKJoWPafv1hOpEKBFlDlWbH4eYw47eF5CaSE8PTFDXFl3CCvPX/j7Bq2VMqhBVR5Vux4z2tf2ozOf55x1YU3ee35Zdt1sjlVq/AZIVedt5UFO8A5imV83yl2w/hs9euPnbUstriExGLIj4U9SWW8/eTdTLtfB1pW56Z7oAqAgtzjZCble/2xE2+3mMaO6Ng4xmcHZh3N3Leqhq2V0gKqXBlLX6Es0mn3y42hdee+dgNZMjaut9f+sGyvxSQqFOghfDN35lD2P8v+Bu695BcWpFR7iNscDO85Fk6sA2BlWgGns4/RomWbWl6lmivdA1UArDMHvfaQxPAbfb6uWrXtSv+Tzn5FSRQseefPlhOpYOZrARWRqSKyU0R2i8jD1ayPFZFZ7vrVItKtyvouIpIvIg/6mbO5KysrISO1wOv36xI+0xc3xISS9l773S1zLCZRwc63AioikcDTwJVAf+CrIlJ1bLQ7gGxjTC/gCeD3VdY/DryL8tXB/VvJj3XaHfOFtLbhO/5nXYxsO9Rrzy/fobczqXPycw90FLDbGLPHGFMCzASmV9lmOvCS254NTBYRARCR64C9wFYfMypg66GNXnvkqRQkonmf2enTZwxJxU57f1I5O9bMtxtIBS0/f1M6Ap9X6h9wl1W7jTGmDMgF0kQkEfgx8Csf8ynXxpLAP9PQ2Ob19FF1IqNjmJCd7PXnL/mbxTQqmAXrrsYvgSeMMfk1bSQid4lIpohkZmVlNU2yMGMqKshIyvP6AzoMsZgmeFwcH5iJdP7RDy0mUcHMzwJ6EOhcqd/JXVbtNiISBaQAJ4DRwB9EZB/wQ+CnInJv1TcwxjxnjBlhjBmRnp5edbWqg+yszzmcaABILIYOXQfU8ormYVivcV57RXI2p04csphGBSs/C+gaoLeIdBeRGOBmYF6VbeYBZx55uQFYYhzjjTHdjDHdgCeB3xhjdHgcH+z+PDB98bDcOCIj9dZggNT0Tgw64fxdlEbCioX/ZzmRCka+FVD3nOa9wEJgO/CaMWariMwQkWnuZi/gnPPcDdwPnHWrk/LXjrw9XntQhe7FV3ZxaVuvvWTr2xaTqGDl6+6GMWY+ML/KskcqtYuAG2v5Hr/0JZwCYHNE4Nxxv5TmewN9dYa16s+Zs05LCrbVvLFqloL1IpJqAuWlJaxPKfL6vToPtpgm+PTtNZpI9xbQDSmFnDiwy24gFXTqVEBF5E0RuVpEtOCGkR1r3vVuoG+fL6S27mQ3UJCJT0xlZF4iAEbgg0V6HlR9UV0L4l+BW4BdIvI7EWmeQ/WEmYy1c7320PzEZn8DfXUmJQX2ypfsXGAxiQpGdfqNMcYsNsbcCgwH9gGLRWSViHxDRKL9DKj8k3FgtdceFNm+hi2br0lDrvPaS0p22guiglKddzlEJA34OnAnsB54CqegLvIlmfLd6pLAeJd9WvWymCR4XXz5HcSUOe3tKSUc/nSD1TwquNT1HOgcYAWQAFxrjJlmjJlljPkekOhnQOWPwryTbEou9Po9ug21FyaIxSe34uJTqV5/qZ4HVZXUdQ/0/4wx/Y0xvzXGHAZnKDoAY8wI39Ip36xf+Trl7r9+n5xIEpJa2Q0UxCa2DAzv9/6nesClAupaQP+3mmUfNWYQ1bQyNgcuiAwpTLGYJPhNHP4lr728Yp+9ICro1FhARaSdiFwIxIvIMBEZ7n5NwDmcVyEq49h6rz0wtnMNW6qRE2/zzoPuTi7lyJ5NNb9ANRu1PYl0Bc6Fo044gxufcQr4qU+ZVBPIqDSuS582F1hMEvziElMZdSqZlS2dUatWLn2JG3o0zzmj1BfVWECNMS8BL4nIl40xbzRRJuWzEwd28Wmys0sVUwadeg6ynCh4nZlcb2hJS1biFNAFma/SObnDF7YbfeMDTZ5N2VdjARWR24wxrwDdROT+quuNMY9X8zIV5NasnOW1B+fEEB0bZzFNaBjUqi+wH4DM6ON8y24cFSRqu4jUwv0zEUiq5kuFoIydS7z24LI0i0lCR5+eIxFn2FQ2tyylMD/Hah4VHGo7hP+b+6dOrRFGVudsgVSn3a9FV6tZQkV8YksGnYxiU1oZFRGw69M1DB5yue1YyrK63kj/BxFJFpFoEXlfRLJE5Da/w6nGZyoqyIg97vV7d9AR6OtqZElgvNTNJ/WxTlX3+0CnGGPygGtwnoXvBfzIr1DKP/u2rOR4vHMsmlIEbTr0tpwodAxODvxdrZXDFpOoYFHXAnrmUP9q4HVjTK5PeZTPMlYHbqYYVdiKCJ3Co84u6BF46G5tq2JKi4tq2Fo1B3UtoO+IyA7gQuB9EUkH9KcnBGXsDcwwOSq5n8UkoSclrQM9c51fmeIo2LtnreVEyra6Dmf3MHAxMMIYUwqcBqb7GUz5I6MgMKr6qN6XWkwSmkYWBMYM2HJsq8UkKhjUZwTdC4CbRORrODNoTvEnkvJLWUkRaxMDc8CPvLjG6ahUNYYmdPfa6yqqztKtmpu6XoV/GfgjMA4Y6X7pKEwhZuvH8yh0h7/unB9J+55DreYJRf26DPfaGamnKS8vs5hG2VbXKwgjgP7GGONnGOWv1evmee1R5e0sJgldbdr3pN0h4UgLQ14sHNy/mS49htX+QhWW6noIvwXQ37gQl3FwjdcelaYzcDaEREQwOi/Z6289pCMzNWd13QNtDWwTkQyg+MxCY8w0X1IpX2SUf+a1Rw3QU9gNNSSmM3Nx7uTbUPwZV1rOo+ypawH9pZ8hlP/yTx5ha5Jz55kYuHD8VywnCl0DOgyB4i0AZCTlYSoqLCdSttT1NqYPcJ5Ainbba4B1PuZSjWzdytepcP+1B+TFkpTWoeYXqHPq1G0QSe5x2OFEw/Eje+wGUtbUaQ9URL4F3AW0AnoCHYFngcn+RVPn48w4lmcsXP6CcyIGGFSQdNZ6VXeRkVGMykng/bYFAGz/bB3XWM6k7KjrRaR7gLHgjChrjNkFtPErlGp8W0sPee0BcTqFx/kaHtHRa288vbeGLVU4q2sBLTbGlJzpiEgUoLc0hZD1LU557d7t+ltMEh4Gpgf+DtfEn7SYRNlU1wL6gYj8FGdyucuB14G3/YulGlPuicN8luxc6IgrhU5ddAi789Wtx4VElzvtT1LLObZPH+tsjupaQB8GsoDNwLeB+cB/+xVKNa5PP9vgtYfkxBIZHWMvTJiIjUtg+MlYr//hsn9aTKNsqetV+ArgLeC7xpgbjDH/V5enkkRkqojsFJHdIvJwNetjRWSWu361iHRzl48SkQ3u10YRub5+H0tVtjMncJV4cLlO4dFYRlS09dorPllsMYmypbZ54UVEfikix4GdwE53NPpHavvGIhIJPA1cCfQHvioiVU++3QFkG2N6AU8Av3eXb8EZ+WkoMBX4m3veVTXAZo567X6J3WvYUtXHwNQ+XntloY5Q3xzVtgd6H87V95HGmFbGmFbAaGCsiNxXy2tHAbuNMXvcC1AzOXsIvOnAS257NjBZRMQYU2CMOTNKQxx6warBTEUF61IKvH7vTjqFcWPp03Ok116XfJr8k0csplE21FZA/wv4qjHGu0/DGLMHuA34Wi2v7Qh8Xql/wF1W7TZuwcwF0gBEZLSIbMU573p3pYKq6uHooV3kuLMWpxVC63Y97AYKIy2S0+h/0jkwKo+A1UtftpxINbXaCmi0MeZ41YXGmCwg2p9I3nusNsYMwBk67ycictbk5SJyl4hkikhmVlaWn3FC1u6DW7z2sLwWSER9hoBVtRlZEjinvGLzfywmUTbU9ttU0sB1AAeByndsd3KXVbuNe44zBThReQNjzHYgHxhY9Q2MMc8ZY0YYY0akp6dXXa2A7af3ee1B0vbcG6oGGZLYy2uvyNGRmZqb2groEBHJq+brFFDbybQ1QG8R6S4iMcDNwLwq28wDbnfbNwBLjDHGfU0UgIh0xRkNf189PpdybYoM/H/UN7VXDVuqhriga2CA5Y9bZFNaVFDD1irc1FhAjTGRxpjkar6SjDE1HsK75yzvBRYC24HXjDFbRWSGiJwZBu8FIE1EdgP349xvCs7I9xtFZAMwB+f2qbNOJaialZUWsall4EChZ9chFtOEp1Ztu9Ilz/k1KoiB9ctnWU6kmpKvtwYZY+bj3HRfedkjldpFwFkT8xhjXgb0jPx5OrBvC8Xuv3C33AiS+ushvB9Gn07ls2Tncc4PMmczaso3LCdSTUWvKISxnce2e+2hBck1bKnOx/CEwJ0NS7PW1LClCjdaQMPYtqIDXntgTNU7yFRjGdg1ML/i8oQsPQ/ajGgBDWMb43K8dp/Wfe0FCXOt2/WgS34kAKdjIHPpK5YTqaaiBTRMFeZnszPVGS4osgK6ddNJ5PwiERFMNN28/tLM2fbCqCalBTRM7d23ESNOe0B2FDHxiXYDhblJ3Sd57aUn1lpMopqSFtAwtfPELq89pKSVxSTNw8RJd3jtDxNPUnw6z2Ia1VS0gIapzeWBKTz6J3SxmKR56NxvND3znHvGCqNh9fsv1fIKFQ60gIapDYn5Xlun8GgaEyN6eu2l69+0mEQ1FS2gYejgJ5kcSnRGAGxRAu27aAFtChN7BiapXZq93mIS1VS0gIahjFWve+2hOXFERupY1E1h4uXf8tqrknN1fNBmQAtoGFq9+wOvPaiitcUkzUv7nkMZmOvMk1QaCcvm/9VyIuU3LaBh6OP8HV57QIqOwNSUpsYHBilbuOUte0FUk9ACGmbKSopY0yLX6/fuMtRemGboimGBsXEWlu6oYUsVDrSAhpktH82lwJ21uOMpITW9k91Azcy4qXcRX+q0dyWXsnfTcruBlK+0gIaZj9fO9doXntYRmJpaXGIqE04HZkdYuPhZi2mU37SAhpmPD2V47UFRHSwmab6uaD/Oay/4fKnFJMpvWkDDzMcmMBFq3/R+FpM0X1dMuNNrL4k/osPbhTEtoGEk+/BediY7U3hElUPX7kPtBmqm+o6Y6g1vdyoWPnrvBcuJlF+0gIaRjOX/9tqDs6OJjUuwmKb5kogIpkpvrz8/418W0yg/aQENIx/vWOy1h5bpDfQ2XTXweq/9dsEGe0GUr7SAhpGPc7Z47f4tutkLorjs2h8SW+a0t6UUs2fjMqt5lD+0gIaJivIyVscF5oDv00lHoLepRcs2XJbfxuu/vfBPFtMov2gBDRO71i0iO84Zgal1oZDevmctr1B+u7bLZV777UPL7AVRvtECGiY+zgiMPzm6OB2J0H9a2665+j6v/UFyNrnHPrOYRvlBf8vCxMefrfLaF7UcVMOWqql07DOC4TnxAJRFwsJ5T1hOpBqbFtAwsbpkr9e+qP8Ui0lUZdemjPLab2+fW8OWKhRpAQ0Dp7OPsSm5EAAxMPKSmy0nUmdcOz7wVNL86H2UlRRZTKMamxbQMJC5/FXK3X/JfnmxpLTRSeSCxfCJt9Ax3/nHORlvWPEfHWQ5nOhcD2Fg5aZ3vPbYqO4WkzRfq19/7Jzrrshvy98TDwPw8nuPMvH6+5sqlvKZ7oGGgRUnAxOYjes63mISVZ3xbUZ47YWJR6koL7OYRjUmLaAhrry0hFXxgRvox4+/zWIaVZ3eF4yhtTsg06FEQ8aiF63mUY3H1wIqIlNFZKeI7BaRh6tZHysis9z1q0Wkm7v8chFZKyKb3T8n+ZkzlG1eNYdTzjxmdDgdQbeB42p+gWpyUVExTMkNDLL85ornLKZRjcm3AioikcDTwJVAf+CrIlJ1gvI7gGxjTC/gCeD37vLjwLXGmEHA7cDLfuUMdSsyAlMYjyvrqDfQB6lL0oZ57TeL1mMqKiymUY3Fz9+2UcBuY8weY0wJMBOYXmWb6cBLbns2MFlExBiz3hhzyF2+FYgXkVgfs4aslYcDI9CPbz/aYhJVkwH9LiGp2Gl/mlzGppWz7QZSjcLPAtoR+LxS/4C7rNptjDFlQC6QVmWbLwPrjDHFVd9ARO4SkUwRyczKymq04KHCVFSwMuqg1x836gaLaVRNomPjuPxkS6//5pKnLaZRjSWoj/dEZADOYf23q1tvjHnOGDPCGDMiPT29uk3C2t7NyznUwjkUTCqGQRdfX8srlE0TUgIjZL2Zt9piEtVY/CygB4HOlfqd3GXVbiMiUUAKcMLtdwLmAF8zxnzqY86QtXJlYAT6iwvTiIyOsZhG1WZI/wnEuVMeb0kp5pPMhXYDqfPmZwFdA/QWke4iEgPcDMyrss08nItEADcAS4wxRkRSgf8ADxtjPvQxY0hbuX+F1x7falgNW6pgEJuQxKQTSV5/zsIn7YVRjcK3Auqe07wXWAhsB14zxmwVkRkiMs3d7AUgTUR2A/cDZ251uhfoBTwiIhvcrzaoL/igPLBjPnbQ1RaTqLqa2GKA1559fLnFJKoxiDHGdoZGMWLECJOZmWk7hu/OPDKYk3WAqVlPAhBbBov6zCBGJ5ELegWnTnLFvt9Q6kzayZ7rP6D74EvshlI1EpG1xpgR1a0L6otI6ty27AlchBh1Mk6LZ4hISGrFlFOBg6nX3/6dxTTqfGkBDVFr83d57VF0sphE1ddNvQN3S8w6vsxeEHXetICGqI8STnrtIW11ArlQMu2GnxLjjieyLrWQ3evftxtINZgW0BB0/PAe9qU4938mlECPXsMtJ1L1kdKmC1Pz23n91//zB4tp1PnQAhqCtuxb47XHZCcQFR1nMY1qiK/0DRzGv3ZiRQ1bqmCmBTQErS0I3L40MqJzDVuqYHXtl39KrHsYvyG1UG+qD1FaQEOMqahgVVK21x/cfojFNKqhktM7cVV+e6//2vxHLaZRDaVTeoSYo4d2cTDRuXc3uRi69BlqN5CqtzP38o6P6sEcnKk+Zp74gMurTAsy+sYHmjybqh/dAw0xm/ZXPv+ZSFSUPv8eqoYPnOw9G7+1VRmH9m+zG0jVmxbQEPNRceD850UxOoFcKItLSObyE8le/8O9H1hMoxpCC2gIKSnMZ3mrU15/WLeLLKZRjWFyUuAc9vzoffaCqAbRAhpCPlzwHKfdI/buuRG07dDbbiB13oYNmERCidPe3rKcg/u22A2k6kULaAhZsHaW1760oI3OfxQGYhOSuPxEitdfsU9HaAol+hsYQhac3uS1R7UaUMOWKpRMThnqtefH7NcJ50KIFtAQcWjXOjalFgEQXQ79+461nEg1lqEDJpLozvj1SWo5+/estxtI1ZkW0BCxcMFfvPaYE/HEJSTXsLUKJTHxiVx1spXXf/8zvRofKrSAhogFexd77bERXS0mUX6Y0iZwR8XbSYcpKyuxmEbVlRbQEFBSmM/C2MAM0cM7j7SYRvmhX/9LaHdaADjSwrBjmw4wEgq0gIaAD975C7nugEtd8iLo1HWQ3UCq0UVGRjH9VAevvzBLpz0OBVpAQ8CczFe89pSCdnr7Upia2OVSrz0/7SSns49ZTKPqQn8Tg1xFeRlvme1ef1w7PXwPV117DOWCbGe2udMxMHfWrywnUrXRAhrkMha9yOEWzn2BaYVCrz6jLSdSfpGICKaV9fT6r+x83WIaVRdaQIPcWyuf99rTynvp6Eth7pI+l3nt95KzOLZvq8U0qjZaQIOYqahgTuE6r3/94JssplFNoXX7Hlx8LBaA8giY9fovLCdSNdECGsS2Z7zDJ8nOgJEtSuCy6fdZTqSawlUxgcd0Xzw032ISVRstoEHs9YVPeO2phR2JT25Vw9YqXIwZeIU3X9K61EI2fDCr5hcoa7SABilTUcG/8j70+jf2+7LFNKoptUhO45qsll7/hXd/bTGNqokW0CC1ZvFL7HIP35OKYdpNei6sObm6bWCwmFdkC4V5Jy2mUeeiBTRI/WvpU177yyW99PC9mbmg3zh6nHLmfMyJM8z5988tJ1LV0QIahMpKiphZERj787Yxd1lMo2yIiIzimykTvP7zO/5tL4w6J18LqIhMFZGdIrJbRB6uZn2siMxy168WkW7u8jQRWSoi+SLyl7O+cZhb/OYfOZbgTF3c4XQEE6b9wHIiZcPXb/otEe7Yyktb5rB7/ft2A6mz+FZARSQSeBq4EugPfFVE+lfZ7A4g2xjTC3gC+L27vAj4OfCgX/mC2StrXvDaX40eRmS03jzfHHXsM4Ir89p4/Wde/7HFNKo6fu6BjgJ2G2P2GGNKgJnA9CrbTAdectuzgckiIsaY08aYlTiFtFnJPfYZc+L2ef3bpvzIXhhl3XdHftdr/92s0wFGgoyfBbQj8Hml/gF3WbXbGGPKgFwgzcdMQe+Vfz5IgbvDOTgnjiHjb7QbSFk19aaf0SsvGnAuJr3yoj5MEUxC+iKSiNwlIpkikpmVlWU7znkzFRU8e2ie17+703U6dF0zFxEZxT1trvb6f/78DZ10Loj4+dt5EOhcqd/JXVbtNiISBaQAJ+r6BsaY54wxI4wxI9LT088zrn2r3v0bW1Kc2cValMCtX3vUciIVDL7xjado4c7wsTWlmGVzn7SaRwX4WUDXAL1FpLuIxAA3A/OqbDMPuN1t3wAsMcYYHzMFtWeXBArmLaUXkJzeyWIaFSxS2nTh9rKBXv+p5fofa7DwrYC65zTvBRYC24HXjDFbRWSGiExzN3sBSBOR3cD9gHerk4jsAx4Hvi4iB6q5gh9Wjn++k9cT9nr9b1/xM4tpVLC590u/9dpzU4+wddVci2nUGVF+fnNjzHxgfpVlj1RqFwHVXiUxxnTzM1uw+cfL91Ps/muMzGnBhZNvsxtIBZV+o69h+sx2zE09AsBv3vgB/7q46k0tqqnpFYogUFKYz1PZC7z+3d31yrs628+u+p3Xnpm4n11rF1lMo0ALaFB47cUfcTDRubLatiCCW775RC2vUM3RyMtv54oc5y6/igj43cx7LCdSWkAtMxUV/PGTF73+9xInEZeYai2PCm7/PXmG1/5n/C72bVlpMY3y9Ryoqt2SOY+xMdV54Cq+FO6++6+WE6lgsfr1x85aFg2MOxbHyjZFlEXC/X+5ljefzW76cArQPVDr/rjyD177m2WDSOvU22IaFQruanul157TPoeV7+h/urZoAbVo3ZJ/sSD1OABi4L6bn7QbSIWEPv3Gct3hVK//g/cfpKK8zF6gZkwLqEUz3g4MNnVDXid6Dp1kMY0KJd8aeBtxzoQFrEst5MWnv2U3UDOlBdSSDR/M8u7pA/j5l560F0aFnLS23fj2ye5e/8eHXtI55C3Qi0iWzHjrPkh12tceSabg8L5qLxoodS5fGv013tg8gwNJhuPxhrufupw3HjugA9A0If2btmDTitnMST3s9b/WXZ8oUfUXm5DEL+Ku8PpzUg/z7BO3WkzU/GgBtWDGnB967etz2tO5+xB7YVRIGzTkMu4uCAwT8cOcmXz07nMWEzUvWkCb2KYVs3kjJTCq3yPX6VNH6vw8/rMVDM2JB6AkCqZ9cLc+5tlEtIA2sf+ptPc5PacdQy+9yV4YFRbik1sx+/Z3aF0oAByPN0yaeaVOQtcEtIA2oTWLXmJ2pb3PX0x/3GIaFU56Dp3E2xOeI969telAYjkXz7ycZW89aTVXuNOr8D6qfFXdVFTw0PJHwJ1k8ZojyZRkHdIr76rRXDT1Tt4+nc216x+iMBqyEgyXrb+PR3cs44cPvalX532gf6NNZMvmJaxo4zzzHlkBd/bWIetU45v85R+xaMxfaVPgHM6XR8D9xXO57P7W7FzzruV04Uf3QJtARXkZf8pb7M03euvRtrQb2M9uKBU2qh7FRAEvdf0+P9/+DJmtncmUlrTMZvC8q3jw7Yv58fdm6XQxjUT3QJvAh2vmsDnNeVY5oQRuGXKL5UQq3LVM78wTox/h20c6EelO4lkSBb+JXEWvP3bhr4/dTGlRgd2QYUALqM8K83P4Y2SG1/9WdndSW3e0mEg1F9GxcXxj0g95NeFWRhyP8ZZnJRjuyZ/FoJ+1ZO4/f6rTJJ8HPYT32cyMFznazplotGO+cP3o/7KcSDU3XXoM409dB/Fh5ls8xWoOJDk/jzuTS7hu728Z+70nuKfDNHr0GeW9ZvSND9iKG1J0D9RHh/Zv44X0A17/AS4mLiHZYiLVXEVERjF+9A38a8iv+HFWX5KLA+s+bFPELWWv8cySRynMz7GWMRRpAfWJqajgyb2vUhbp9Mcdi2PMCH3mXdkVG5fA9Zd+ize6/Yg7Drcnqjyw7qV2R7lt26/ZsGGhvYAhRguoT5594laWtykEnNuWftD1Rr0PTwWNpJZt+dbkB5jd8ltccSTRW34w0XB3zCLu/FFf8rIO1PAdFGgB9cWutYt48ORMr39XVmcdMEQFpXad+vLLCY/wxOlxpFe6KP9C4icM+X13ls/7s71wIUALaCMrKyni9pe/RIF70bP/ySi+OkZHC1fBSyIiGDPyOl7t+RDTD6d4y/cllTFh3fd58GcjKNJzo9XSAtrIZvx6Ch+1zAcgqhx+0f4mYuISLKdSqnaJLdvw8MSf8Xj+WFKLnCeZjMBjMWu58JF2rFvyL8sJg48W0Eb05j8e4n8iVnj9H57oTdeewywmUqp+JCKCi0ddz5ZvZDAlO81bvi2lmNHLbuN/Z1xGWUmRxYTBRQtoI1nx9l+49dNHvf6U7DSuH3+HxURKNVzHPiNY8Pgx/pp4MwnO06CURcLPzfuM/XFrtq6aazdgkBBjjO0MjWLEiBEmMzPTynsvn/dnrvn4+5yKdfo986LIeGAHuz58y0oepRrT0YO7+PXuF8lID9w8GlEBNx5N43f3vkW3geMspvOfiKw1xoyobp3ugZ6nV575DldkBIpn24II3vvaIlp16Gk3mFKNpG3H3jwxzrkBP9q9b7QiAma1P0Hv18bz5fs78t5rv22Wc9PrHmgDHf98J/c/dRUvJ+3xlrU/HcF702czcOz1wNmj5CgV6g7s3cQz+2bzftuzByJJKxQuL+3MpM6XMqT/RPqPuJLEVu0spGxcNe2B+lpARWQq8BQQCTxvjPldlfWxwD+BC4ETwE3GmH3uup8AdwDlwPeNMTU+HtFUBfTz7at5buaDPFW60tvrBOiXG8PbX3uXnkMnecu0gKpwtWPbCl44ttAb4/ZcWhUKbUqiSauIJZYoYiSSGPfPWIkmPiKWhKg4EqLiiY+KJzE2iTYpHWjfpgftO/alQ/chpKR3tvoQSk0F1LfBREQkEngauBw4AKwRkXnGmG2VNrsDyDbG9BKRm4HfAzeJSH/gZmAA0AFYLCJ9jDHlNKGC3OPs2bKCHTtWkrn3Qxac3sjG1CLnxEel4nlLXlee/dkqktI6NGU8pay5oP94Hu0/nuQufXnurUd4tWIjRxPOHtXpZLzhZHwJUFK3b1wMHHO/tjiLEkugc2EMnSuS6BydRufEDnRu2Y3O7fvSuetg2ncbSFJaByIim35sJD/fcRSw2xizB0BEZgLTgcoFdDrwS7c9G/iLiIi7fKYxphjYKyK73e/3UWMEe+WZ7/DqJ29Qasopwf2SCu+rIKKcEzHl3s3wAEQDqV/8PhfkxvDo8B9zzW0zGiOWUiEn77Od3Dz8Vr5SfhMH9m9m/YG1bCs9zCdx+exKLvfGgjgf+TGwPaaE7ZzAOVD9BLJxvipVk8QSSCqNIKk8khYVUUQRQZQRooggkgjnTxGiiOSZu+bRud/o887mZwHtCHxeqX8AqJrY28YYUyYiuTjjtncEPq7y2kYbRHP30e3MT81q0Gujy+HSvFZ8e9DXue4nvyYqJq6xYikVsiIio+jSYxhdegzjzJA55eVl5OdkcSovi9OFuZSWl1JWXkZ5eSmlFWWUVpRSXF5CcXkxxRWlFFeUkm+KOBUfxeGKXA5HFnIwvpTC6LplyI+B/JgKDlMBlNa4beHpnPP5uJ6QHg9URO4C7nK7+SKys9Lq1sDxxn7PUmAxJ1nM43CntVk1fflsQSScP59+tiDQ95dT67N513Ot8LOAHgQ6V+p3cpdVt80BEYkCUnD20evyWowxzwHPVffmIpJ5rhO/oS6cPxuE9+fTzxZe/Ly0tQboLSLdRSQG56LQvCrbzANud9s3AEuMc1vAPOBmEYkVke5AbyADpZQKIr7tgbrnNO8FFuLcxvR3Y8xWEZkBZBpj5gEvAC+7F4lO4hRZ3O1ewzlFXAbc09RX4JVSqjZhcyN9VSJyl3uIH3bC+bNBeH8+/WzhJWwLqFJK+U2fhVdKqQYKiwIqIn8XkWMisqXSslYiskhEdrl/trSZsaFEpLOILBWRbSKyVUR+4C4P+c8nInEikiEiG93P9it3eXcRWS0iu0VklnsRMiSJSKSIrBeRd9x+OH22fSKyWUQ2iEimuyzkfy7rIywKKPAiUPXGroeB940xvYH33X4oKgMeMMb0By4C7nEfdQ2Hz1cMTDLGDAGGAlNF5CKcR3qfMMb0wnneJJQHVv0BsL1SP5w+G8BEY8zQSrcvhcPPZZ2FRQE1xizHuYpf2XTgJbf9EnBdU2ZqLMaYw8aYdW77FM4vY0fC4PMZR77bjXa/DDAJ59FeCNHPBiAinYCrgefdvhAmn60GIf9zWR9hUUDPoa0x5rDbPgK0tRmmMYhIN2AYsJow+XzuIe4GnOEjFgGfAjnGmDODSzbqY7xN7EngIeDMKBtphM9nA+c/u/dEZK37VCCEyc9lXYX0o5x1ZYwxIhLStxuISCLwBvBDY0yeszPjCOXP597fO1REUoE5wAV2EzUOEbkGOGaMWSsiEyzH8cs4Y8xBEWkDLBKRHZVXhvLPZV2F8x7oURFpD+D+ecxyngYTkWic4vkvY8yb7uKw+XwAxpgcYCkwBkh1H+2FczzGGwLGAtNEZB8wE+fQ/SnC47MBYIw56P55DOc/v1GE2c9lbcK5gFZ+TPR2ICRnwXLPm70AbDfGVB69JOQ/n4iku3ueiEg8ztix23EK6Q3uZiH52YwxPzHGdDLGdMN5wm6JMeZWwuCzAYhICxFJOtMGpuCM4BnyP5f1ERY30ovIq8AEnNFgjgK/AN4CXgO6APuBrxhjql5oCnoiMg5YAWwmcC7tpzjnQUP684nIYJwLDZE4/5m/ZoyZISI9cPbaWgHrgdvcsWFDknsI/6Ax5ppw+Wzu55jjdqOAfxtjfi0iaYT4z2V9hEUBVUopG8L5EF4ppXylBVQppRpIC6hSSjWQFlCllGogLaBKKdVAWkBVWBKRcneUoI0isk5ELnaXdxMRIyL/W2nb1iJSKiJ/cfu/FJEHbWVXoUMLqApXhe4oQUOAnwC/rbRuL84gH2fcCGxtynAqPGgBVc1BMs7QcWcUANtF5MwQbDfh3PytVL00i8FEVLMU747yFAe0x3kWvbKZODO/HgXKgUNAhyZNqEKeFlAVrgqNMUMBRGQM8E8RGVhp/QLgf3Ae/Z3V9PFUONBDeBX2jDEf4YyTkF5pWQmwFniAwADHStWL7oGqsCciF+AMWHICSKi06jHgA2PMycrjqypVV1pAVbg6cw4UQIDbjTHlVQai3opefVfnQUdjUkqpBtJzoEop1UBaQJVSqoG0gCqlVANpAVVKqQbSAqqUUg2kBVQppRpIC6hSSjWQFlCllGqg/wcsmHozG6XYxQAAAABJRU5ErkJggg==",
      "text/plain": [
       "<Figure size 360x360 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(5,5))\n",
    "sns.distplot(df['BMI'],bins=20,kde_kws={'linewidth':3, 'color':'red'})\n",
    "sns.distplot(newdf['BMI'],bins=20,kde_kws={'linewidth':3, 'color':'green'})"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "id": "9367576b",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<Axes: xlabel='age'>"
      ]
     },
     "execution_count": 21,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAWAAAAEGCAYAAABbzE8LAAAAOXRFWHRTb2Z0d2FyZQBNYXRwbG90bGliIHZlcnNpb24zLjcuMSwgaHR0cHM6Ly9tYXRwbG90bGliLm9yZy/bCgiHAAAACXBIWXMAAAsTAAALEwEAmpwYAAAKK0lEQVR4nO3db4xld13H8c+3OxK2RSjLNptmCi5k1NoHUMhG20BQEf+GmBCNMVFDDAkxwWFNNEZ9pCYafSJuxmBSUeMDiSJYNIQghH9BEwu7UqR0i16xxE4sXVz+6Zaalp8P7lm7WSDb2ezc771zX69ksnPPvbv3O7/Mec+Zs3PP1BgjACzedd0DAKwrAQZoIsAATQQYoIkAAzTZ2MuDjx49Oo4fP75PowAcTGfOnPncGOOmy7fvKcDHjx/P6dOnr91UAGugqj7z9bY7BQHQRIABmggwQBMBBmgiwABNBBigiQADNBFggCYCDNBEgAGaCDBAEwEGaCLAAE0EGKCJAAM0EWCAJgIM0ESAAZoIMECTPf1OOK5sZ2cns9mse4yVtbu7myTZ3NxsnmQ1bW1tZXt7u3sMniIBvsZms1nuve9snrj+SPcoK+nQhS8mSR5+zKfmXh26cL57BPbIZ/k+eOL6I3n01h/pHmMlHX7gXUli/a7CxbVjdTgHDNBEgAGaCDBAEwEGaCLAAE0EGKCJAAM0EWCAJgIM0ESAAZoIMEATAQZoIsAATQQYoIkAAzQRYIAmAgzQRIABmggwQBMBBmgiwABNBBigiQADNBFggCYCDNBEgAGaCDBAEwEGaCLAAE0EGKCJAAM0EWCAJgIM0ESAAZoIMEATAQZoIsAATQQYoIkAAzQRYIAmAgzQZCEB3tnZyc7OziKeCuCa2s9+bezLv3qZ2Wy2iKcBuOb2s19OQQA0EWCAJgIM0ESAAZoIMEATAQZoIsAATQQYoIkAAzQRYIAmAgzQRIABmggwQBMBBmgiwABNBBigiQADNBFggCYCDNBEgAGaCDBAEwEGaCLAAE0EGKCJAAM0EWCAJgIM0ESAAZoIMEATAQZoIsAATQQYoIkAAzQRYIAmAgzQRIABmggwQBMBBmgiwABNBBigiQADNNlYxJPs7u7m0UcfzcmTJxfxdK1ms1mu+9/RPQZr6LqvfCmz2ZfXYj9bpNlslsOHD+/Lv33FI+Cqel1Vna6q0+fOnduXIQDW0RWPgMcYdyW5K0lOnDhxVYd2m5ubSZJTp05dzV9fKSdPnsyZT3+2ewzW0Fef/sxsveDYWuxni7Sf31E4BwzQRIABmggwQBMBBmgiwABNBBigiQADNBFggCYCDNBEgAGaCDBAEwEGaCLAAE0EGKCJAAM0EWCAJgIM0ESAAZoIMEATAQZoIsAATQQYoIkAAzQRYIAmAgzQRIABmggwQBMBBmgiwABNBBigiQADNBFggCYCDNBEgAGaCDBAEwEGaCLAAE0EGKCJAAM0EWCAJgIM0ESAAZpsLOJJtra2FvE0ANfcfvZrIQHe3t5exNMAXHP72S+nIACaCDBAEwEGaCLAAE0EGKCJAAM0EWCAJgIM0ESAAZoIMEATAQZoIsAATQQYoIkAAzQRYIAmAgzQRIABmggwQBMBBmgiwABNBBigiQADNBFggCYCDNBEgAGaCDBAEwEGaCLAAE0EGKCJAAM0EWCAJgIM0ESAAZoIMEATAQZoIsAATQQYoIkAAzQRYIAmAgzQZKN7gIPo0IXzOfzAu7rHWEmHLvxXkli/q3Dowvkkx7rHYA8E+Brb2trqHmGl7e4+niTZ3BSSvTvm82/FCPA1tr293T0CsCKcAwZoIsAATQQYoIkAAzQRYIAmAgzQRIABmggwQBMBBmgiwABNBBigiQADNBFggCYCDNBEgAGaCDBAEwEGaCLAAE0EGKCJAAM0qTHGU39w1bkkn9m/cb7G0SSfW+DzLSvrMGcdnmQt5lZlHb5ljHHT5Rv3FOBFq6rTY4wT3XN0sw5z1uFJ1mJu1dfBKQiAJgIM0GTZA3xX9wBLwjrMWYcnWYu5lV6HpT4HDHCQLfsRMMCBJcAATZYmwFX13Kr6QFXdX1WfrKqT0/YjVfXeqvrX6c9nd8+6n6rq6VX1kar6+LQOvzFtf35V3VNVs6r6y6p6Wvesi1BVh6rqY1X1zun22q1DVT1YVZ+oqnur6vS0ba32iySpqhur6m1V9UBVna2qO1d9HZYmwEkeT/KLY4zbktyR5PVVdVuSX0nyvjHGtyZ533T7IHssySvGGC9KcnuSH6qqO5L8bpI3jjG2knw+yWv7Rlyok0nOXnJ7Xdfhe8cYt1/yM6/rtl8kyakk7x5j3JrkRZl/Xqz2OowxlvItyd8k+f4kn0py87Tt5iSf6p5tgWtwfZJ/SvJdmb/aZ2PafmeSv+uebwEf/y2Z71SvSPLOJLWm6/BgkqOXbVur/SLJs5L8e6YfHDgo67BMR8D/r6qOJ3lxknuSHBtj/Od018NJjnXNtSjTt933JnkkyXuT/FuSL4wxHp8e8lCSzabxFun3k/xykq9Ot5+T9VyHkeQ9VXWmql43bVu3/eL5Sc4l+dPplNSbq+qGrPg6LF2Aq+oZSd6e5BfGGF+69L4x/zJ34H9ubozxxBjj9syPAL8zya29Ey1eVb0qySNjjDPdsyyBl40xXpLkhzM/NffyS+9ck/1iI8lLkvzhGOPFSf4nl51uWMV1WKoAV9U3ZR7fPx9j/PW0+bNVdfN0/82ZHxWuhTHGF5J8IPNvtW+sqo3prluS7HbNtSAvTfKjVfVgkr/I/DTEqazfOmSMsTv9+UiSuzP/orxu+8VDSR4aY9wz3X5b5kFe6XVYmgBXVSX54yRnxxi/d8ldf5vkNdP7r8n83PCBVVU3VdWN0/uHMz8PfjbzEP/49LADvw5jjF8dY9wyxjie5CeTvH+M8VNZs3Woqhuq6psvvp/kB5LclzXbL8YYDyf5j6r69mnT9yW5Pyu+DkvzSriqelmSDyf5RJ485/drmZ8HfmuS52V+KcyfGGOcbxlyAarqhUn+LMmhzL9AvnWM8ZtV9YLMjwSPJPlYkp8eYzzWN+niVNX3JPmlMcar1m0dpo/37unmRpK3jDF+q6qekzXaL5Kkqm5P8uYkT0vy6SQ/m2kfyYquw9IEGGDdLM0pCIB1I8AATQQYoIkAAzQRYIAmAgzQRIABmggwK6Gq3jFdjOaTFy9IU1Wvrap/ma6f/EdV9QfT9puq6u1V9dHp7aW908PX54UYrISqOjLGOD+9PPujSX4wyT9kfj2ALyd5f5KPjzF+vqrekuRNY4y/r6rnZX7Jyu9oGx6+gY0rPwSWwhuq6tXT+89N8jNJPnTxZadV9VdJvm26/5VJbptfXiRJ8syqesYY478XOTBciQCz9KZrQbwyyZ1jjAtV9cEkDyT5Rke11yW5Y4zxlYUMCFfJOWBWwbOSfH6K762Z/8qqG5J8d1U9e7o85Y9d8vj3JNm+eGO6iAssHQFmFbw7yUZVnU3yO0n+MfPrAP92ko9kfi74wSRfnB7/hiQnquqfq+r+JD+38InhKfCfcKysi+d1pyPgu5P8yRjj7iv9PVgWjoBZZb8+/e68+zL/hY3vaJ0G9sgRMEATR8AATQQYoIkAAzQRYIAmAgzQ5P8Abk/dafWcvE0AAAAASUVORK5CYII=",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.boxplot(x='age',data=df)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "id": "1bfc1652",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<Axes: xlabel='age'>"
      ]
     },
     "execution_count": 22,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.boxplot(x='age',data=newdf)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "id": "e996114d",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<Axes: xlabel='BMI'>"
      ]
     },
     "execution_count": 23,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.boxplot(x='BMI',data=df)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "id": "8fa71b59",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<Axes: xlabel='BMI'>"
      ]
     },
     "execution_count": 24,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.boxplot(x='BMI',data=newdf)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "301d823b",
   "metadata": {},
   "source": [
    "## Removing outliers"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "id": "1d9e753a",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "IQR: 8.3975\n",
      "Quartile Q1: 26.29625\n",
      "Quartile Q3: 34.69375\n"
     ]
    }
   ],
   "source": [
    "Q1 = newdf.BMI.quantile(0.25)\n",
    "Q3 = newdf.BMI.quantile(0.75)\n",
    "IQR = Q3-Q1\n",
    "print(f\"IQR: {IQR}\")\n",
    "print(f\"Quartile Q1: {Q1}\")\n",
    "print(f\"Quartile Q3: {Q3}\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "id": "ce5d9b97",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Define outlier boundaries\n",
    "lower_bound = Q1 - 1.5 * IQR\n",
    "upper_bound = Q3 + 1.5 * IQR"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "id": "1b16c1d0",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Identify and remove outliers\n",
    "filtered_data = newdf[(newdf['BMI'] >= lower_bound) & (newdf['BMI'] <= upper_bound)]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "id": "f474e398",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<Axes: xlabel='BMI'>"
      ]
     },
     "execution_count": 29,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.boxplot(x='BMI',data=filtered_data)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "ffbbfa9c",
   "metadata": {},
   "source": [
    "## Exploratory data analysis(EDA)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "id": "e6b62139",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<function matplotlib.pyplot.show(close=None, block=None)>"
      ]
     },
     "execution_count": 30,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.countplot(x='gender',data=newdf)\n",
    "plt.title(\"distribution of gender\")\n",
    "plt.show"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "id": "304dc088",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<function matplotlib.pyplot.show(close=None, block=None)>"
      ]
     },
     "execution_count": 32,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.countplot(x='smoker_status',data=newdf)\n",
    "plt.title(\"distribution of smoker\")\n",
    "plt.show"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "id": "814439f8",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<function matplotlib.pyplot.show(close=None, block=None)>"
      ]
     },
     "execution_count": 33,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.countplot(x='location',data=newdf)\n",
    "plt.title(\"distribution of location\")\n",
    "plt.show"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "id": "14038232",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<Axes: xlabel='age', ylabel='Density'>"
      ]
     },
     "execution_count": 34,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.distplot(newdf['age'],color='g',kde=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "id": "d71bd9a9",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<Axes: xlabel='BMI', ylabel='Density'>"
      ]
     },
     "execution_count": 35,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.distplot(newdf['BMI'],color='g',kde=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "id": "1662e692",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<Axes: xlabel='health_insurance_price', ylabel='Density'>"
      ]
     },
     "execution_count": 36,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.distplot(newdf['health_insurance_price'],color='g',kde=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "id": "da04a814",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "543    54\n",
       "Name: age, dtype: int64"
      ]
     },
     "execution_count": 37,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "newdf.loc[newdf['health_insurance_price']==newdf['health_insurance_price'].max(), 'age']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "id": "4b7c8547",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<seaborn.axisgrid.FacetGrid at 0x2284c725970>"
      ]
     },
     "execution_count": 39,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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",
      "text/plain": [
       "<Figure size 430.5x360 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.catplot(x='smoker_status',kind='count',hue='gender',data=newdf[newdf.age==18])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "id": "ee44fe0f",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<Axes: xlabel='gender', ylabel='health_insurance_price'>"
      ]
     },
     "execution_count": 41,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.barplot(x='gender',y='health_insurance_price',hue='smoker_status',data=newdf[(newdf.age==18)])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "id": "1ff615cf",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "location\n",
       "southwest    52590.82939\n",
       "northeast    58571.07448\n",
       "northwest    60021.39897\n",
       "southeast    63770.42801\n",
       "Name: health_insurance_price, dtype: float64"
      ]
     },
     "execution_count": 42,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "y = newdf.groupby('location')['health_insurance_price'].max().sort_values()\n",
    "y"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "id": "e3bf2026",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<Axes: xlabel='location', ylabel='health_insurance_price'>"
      ]
     },
     "execution_count": 43,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.barplot(x='location', y='health_insurance_price',data=newdf) "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "id": "9f0db58e",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<Axes: xlabel='health_insurance_price', ylabel='Density'>"
      ]
     },
     "execution_count": 44,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.distplot(newdf[(newdf.BMI>=30)]['health_insurance_price'],color='r')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "id": "6c485711",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<Axes: xlabel='health_insurance_price', ylabel='Density'>"
      ]
     },
     "execution_count": 45,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.distplot(newdf[(newdf.BMI<=30)]['health_insurance_price'],color='r')"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "f898b7bd",
   "metadata": {},
   "source": [
    "## Encoding"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "id": "2f34026d",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "['gender', 'smoker_status', 'location']"
      ]
     },
     "execution_count": 46,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "categoricalfeature = [features for features in newdf.columns if newdf[features].dtypes=='object']\n",
    "categoricalfeature"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "id": "e5a20cc0",
   "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>gender</th>\n",
       "      <th>BMI</th>\n",
       "      <th>children</th>\n",
       "      <th>smoker_status</th>\n",
       "      <th>location</th>\n",
       "      <th>health_insurance_price</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>19</td>\n",
       "      <td>0.0</td>\n",
       "      <td>27.900</td>\n",
       "      <td>0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>16884.92400</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>18</td>\n",
       "      <td>1.0</td>\n",
       "      <td>33.770</td>\n",
       "      <td>1</td>\n",
       "      <td>0.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>1725.55230</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>28</td>\n",
       "      <td>1.0</td>\n",
       "      <td>33.000</td>\n",
       "      <td>3</td>\n",
       "      <td>0.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>4449.46200</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>33</td>\n",
       "      <td>1.0</td>\n",
       "      <td>22.705</td>\n",
       "      <td>0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>21984.47061</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>32</td>\n",
       "      <td>1.0</td>\n",
       "      <td>28.880</td>\n",
       "      <td>0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</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>1.0</td>\n",
       "      <td>30.970</td>\n",
       "      <td>3</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>10600.54830</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1334</th>\n",
       "      <td>18</td>\n",
       "      <td>0.0</td>\n",
       "      <td>31.920</td>\n",
       "      <td>0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>2205.98080</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1335</th>\n",
       "      <td>18</td>\n",
       "      <td>0.0</td>\n",
       "      <td>36.850</td>\n",
       "      <td>0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>1629.83350</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1336</th>\n",
       "      <td>21</td>\n",
       "      <td>0.0</td>\n",
       "      <td>25.800</td>\n",
       "      <td>0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>2007.94500</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1337</th>\n",
       "      <td>61</td>\n",
       "      <td>0.0</td>\n",
       "      <td>29.070</td>\n",
       "      <td>0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>29141.36030</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>1338 rows × 7 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "      age  gender     BMI  children  smoker_status  location  \\\n",
       "0      19     0.0  27.900         0            1.0       3.0   \n",
       "1      18     1.0  33.770         1            0.0       2.0   \n",
       "2      28     1.0  33.000         3            0.0       2.0   \n",
       "3      33     1.0  22.705         0            0.0       1.0   \n",
       "4      32     1.0  28.880         0            0.0       1.0   \n",
       "...   ...     ...     ...       ...            ...       ...   \n",
       "1333   50     1.0  30.970         3            0.0       1.0   \n",
       "1334   18     0.0  31.920         0            0.0       0.0   \n",
       "1335   18     0.0  36.850         0            0.0       2.0   \n",
       "1336   21     0.0  25.800         0            0.0       3.0   \n",
       "1337   61     0.0  29.070         0            1.0       1.0   \n",
       "\n",
       "      health_insurance_price  \n",
       "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",
       "\n",
       "[1338 rows x 7 columns]"
      ]
     },
     "execution_count": 47,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from sklearn.preprocessing import OrdinalEncoder\n",
    "orderen=OrdinalEncoder()\n",
    "newdf[['gender', 'smoker_status', 'location']]=orderen.fit_transform(newdf[['gender', 'smoker_status', 'location']])\n",
    "newdf"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "id": "bd54ea59",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "2.0    364\n",
       "3.0    325\n",
       "1.0    325\n",
       "0.0    324\n",
       "Name: location, dtype: int64"
      ]
     },
     "execution_count": 48,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "newdf['location'].value_counts()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "9c4ca95a",
   "metadata": {},
   "source": [
    "## feature selection"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "id": "a1701d8c",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "location                 -0.006208\n",
       "gender                    0.057292\n",
       "children                  0.067998\n",
       "BMI                       0.198341\n",
       "age                       0.299008\n",
       "smoker_status             0.787251\n",
       "health_insurance_price    1.000000\n",
       "Name: health_insurance_price, dtype: float64"
      ]
     },
     "execution_count": 49,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "newdf.corr()['health_insurance_price'].sort_values()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 51,
   "id": "f4c9c1cd",
   "metadata": {},
   "outputs": [],
   "source": [
    "newdff = newdf.loc[:,['children','BMI','age','smoker_status','health_insurance_price']]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 52,
   "id": "741cb1c3",
   "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>children</th>\n",
       "      <th>BMI</th>\n",
       "      <th>age</th>\n",
       "      <th>smoker_status</th>\n",
       "      <th>health_insurance_price</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0</td>\n",
       "      <td>27.900</td>\n",
       "      <td>19</td>\n",
       "      <td>1.0</td>\n",
       "      <td>16884.92400</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1</td>\n",
       "      <td>33.770</td>\n",
       "      <td>18</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1725.55230</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>3</td>\n",
       "      <td>33.000</td>\n",
       "      <td>28</td>\n",
       "      <td>0.0</td>\n",
       "      <td>4449.46200</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>0</td>\n",
       "      <td>22.705</td>\n",
       "      <td>33</td>\n",
       "      <td>0.0</td>\n",
       "      <td>21984.47061</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>0</td>\n",
       "      <td>28.880</td>\n",
       "      <td>32</td>\n",
       "      <td>0.0</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",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1333</th>\n",
       "      <td>3</td>\n",
       "      <td>30.970</td>\n",
       "      <td>50</td>\n",
       "      <td>0.0</td>\n",
       "      <td>10600.54830</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1334</th>\n",
       "      <td>0</td>\n",
       "      <td>31.920</td>\n",
       "      <td>18</td>\n",
       "      <td>0.0</td>\n",
       "      <td>2205.98080</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1335</th>\n",
       "      <td>0</td>\n",
       "      <td>36.850</td>\n",
       "      <td>18</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1629.83350</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1336</th>\n",
       "      <td>0</td>\n",
       "      <td>25.800</td>\n",
       "      <td>21</td>\n",
       "      <td>0.0</td>\n",
       "      <td>2007.94500</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1337</th>\n",
       "      <td>0</td>\n",
       "      <td>29.070</td>\n",
       "      <td>61</td>\n",
       "      <td>1.0</td>\n",
       "      <td>29141.36030</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>1338 rows × 5 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "      children     BMI  age  smoker_status  health_insurance_price\n",
       "0            0  27.900   19            1.0             16884.92400\n",
       "1            1  33.770   18            0.0              1725.55230\n",
       "2            3  33.000   28            0.0              4449.46200\n",
       "3            0  22.705   33            0.0             21984.47061\n",
       "4            0  28.880   32            0.0              3866.85520\n",
       "...        ...     ...  ...            ...                     ...\n",
       "1333         3  30.970   50            0.0             10600.54830\n",
       "1334         0  31.920   18            0.0              2205.98080\n",
       "1335         0  36.850   18            0.0              1629.83350\n",
       "1336         0  25.800   21            0.0              2007.94500\n",
       "1337         0  29.070   61            1.0             29141.36030\n",
       "\n",
       "[1338 rows x 5 columns]"
      ]
     },
     "execution_count": 52,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "newdff"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "e8707e9d",
   "metadata": {},
   "source": [
    "## split dataset"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 53,
   "id": "ed049a6a",
   "metadata": {},
   "outputs": [],
   "source": [
    "x = newdff.drop(['health_insurance_price'],axis=1)\n",
    "y = newdff.health_insurance_price"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 54,
   "id": "a94825d9",
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.model_selection import train_test_split\n",
    "x_train,x_test,y_train,y_test=train_test_split(x,y,test_size=0.20,random_state=42)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 55,
   "id": "e5952a5d",
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.preprocessing import StandardScaler\n",
    "sc=StandardScaler()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 56,
   "id": "6b0eaddb",
   "metadata": {},
   "outputs": [],
   "source": [
    "x_train=sc.fit_transform(x_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 57,
   "id": "ae0bf6ae",
   "metadata": {},
   "outputs": [],
   "source": [
    "x_test=sc.transform(x_test)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "5e3fcf8d",
   "metadata": {},
   "source": [
    "## Apply Algorithm"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 58,
   "id": "bae4ae68",
   "metadata": {},
   "outputs": [],
   "source": [
    "lr=LinearRegression()\n",
    "lr.fit(x_train,y_train)\n",
    "y_pred1=lr.predict(x_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 59,
   "id": "60b66f48",
   "metadata": {},
   "outputs": [],
   "source": [
    "svm=SVR()\n",
    "svm.fit(x_train,y_train)\n",
    "y_pred2=svm.predict(x_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 60,
   "id": "1b42489f",
   "metadata": {},
   "outputs": [],
   "source": [
    "gr=GradientBoostingRegressor()\n",
    "gr.fit(x_train,y_train)\n",
    "y_pred3=gr.predict(x_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 61,
   "id": "04e8e863",
   "metadata": {},
   "outputs": [],
   "source": [
    "rf=RandomForestRegressor()\n",
    "rf.fit(x_train,y_train)\n",
    "y_pred4=rf.predict(x_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 62,
   "id": "921778b0",
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.tree import DecisionTreeRegressor\n",
    "d=DecisionTreeRegressor()\n",
    "d.fit(x_train,y_train)\n",
    "y_pred5=d.predict(x_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 63,
   "id": "07d95c0a",
   "metadata": {},
   "outputs": [],
   "source": [
    "df1=pd.DataFrame({'Actual':y_test,'lr':y_pred1,'svm':y_pred2,'gr':y_pred3,'rf':y_pred4,'d':y_pred5,})"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 64,
   "id": "7d03ec8c",
   "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>Actual</th>\n",
       "      <th>lr</th>\n",
       "      <th>svm</th>\n",
       "      <th>gr</th>\n",
       "      <th>rf</th>\n",
       "      <th>d</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>764</th>\n",
       "      <td>9095.06825</td>\n",
       "      <td>8550.909213</td>\n",
       "      <td>9539.353787</td>\n",
       "      <td>10281.551568</td>\n",
       "      <td>9537.712756</td>\n",
       "      <td>8615.30000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>887</th>\n",
       "      <td>5272.17580</td>\n",
       "      <td>6969.715998</td>\n",
       "      <td>9472.370785</td>\n",
       "      <td>5815.008029</td>\n",
       "      <td>5017.927963</td>\n",
       "      <td>4399.73100</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>890</th>\n",
       "      <td>29330.98315</td>\n",
       "      <td>36794.136618</td>\n",
       "      <td>9678.595761</td>\n",
       "      <td>27881.255682</td>\n",
       "      <td>27919.280569</td>\n",
       "      <td>28950.46920</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1293</th>\n",
       "      <td>9301.89355</td>\n",
       "      <td>9421.868242</td>\n",
       "      <td>9567.745537</td>\n",
       "      <td>9657.255577</td>\n",
       "      <td>9274.097233</td>\n",
       "      <td>9500.57305</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>259</th>\n",
       "      <td>33750.29180</td>\n",
       "      <td>26875.010958</td>\n",
       "      <td>9655.777981</td>\n",
       "      <td>34157.524977</td>\n",
       "      <td>34593.727692</td>\n",
       "      <td>33732.68670</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",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>109</th>\n",
       "      <td>47055.53210</td>\n",
       "      <td>39224.388648</td>\n",
       "      <td>9681.770102</td>\n",
       "      <td>46167.072852</td>\n",
       "      <td>47467.010194</td>\n",
       "      <td>46889.26120</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>575</th>\n",
       "      <td>12222.89830</td>\n",
       "      <td>11691.841974</td>\n",
       "      <td>9641.596921</td>\n",
       "      <td>12283.836875</td>\n",
       "      <td>12536.078118</td>\n",
       "      <td>11073.17600</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>535</th>\n",
       "      <td>6067.12675</td>\n",
       "      <td>7257.650635</td>\n",
       "      <td>9469.565579</td>\n",
       "      <td>6519.693883</td>\n",
       "      <td>5786.007004</td>\n",
       "      <td>5484.46730</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>543</th>\n",
       "      <td>63770.42801</td>\n",
       "      <td>40945.827042</td>\n",
       "      <td>9639.671044</td>\n",
       "      <td>48113.541603</td>\n",
       "      <td>46737.671149</td>\n",
       "      <td>47269.85400</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>846</th>\n",
       "      <td>9872.70100</td>\n",
       "      <td>12622.027936</td>\n",
       "      <td>9599.430445</td>\n",
       "      <td>10569.113575</td>\n",
       "      <td>9830.840646</td>\n",
       "      <td>9583.89330</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>268 rows × 6 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "           Actual            lr          svm            gr            rf  \\\n",
       "764    9095.06825   8550.909213  9539.353787  10281.551568   9537.712756   \n",
       "887    5272.17580   6969.715998  9472.370785   5815.008029   5017.927963   \n",
       "890   29330.98315  36794.136618  9678.595761  27881.255682  27919.280569   \n",
       "1293   9301.89355   9421.868242  9567.745537   9657.255577   9274.097233   \n",
       "259   33750.29180  26875.010958  9655.777981  34157.524977  34593.727692   \n",
       "...           ...           ...          ...           ...           ...   \n",
       "109   47055.53210  39224.388648  9681.770102  46167.072852  47467.010194   \n",
       "575   12222.89830  11691.841974  9641.596921  12283.836875  12536.078118   \n",
       "535    6067.12675   7257.650635  9469.565579   6519.693883   5786.007004   \n",
       "543   63770.42801  40945.827042  9639.671044  48113.541603  46737.671149   \n",
       "846    9872.70100  12622.027936  9599.430445  10569.113575   9830.840646   \n",
       "\n",
       "                d  \n",
       "764    8615.30000  \n",
       "887    4399.73100  \n",
       "890   28950.46920  \n",
       "1293   9500.57305  \n",
       "259   33732.68670  \n",
       "...           ...  \n",
       "109   46889.26120  \n",
       "575   11073.17600  \n",
       "535    5484.46730  \n",
       "543   47269.85400  \n",
       "846    9583.89330  \n",
       "\n",
       "[268 rows x 6 columns]"
      ]
     },
     "execution_count": 64,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df1"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 65,
   "id": "5951bc12",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[<matplotlib.lines.Line2D at 0x22857b9ad00>]"
      ]
     },
     "execution_count": 65,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.subplot(221)\n",
    "plt.plot(df1['Actual'].iloc[0:11],label='Actual')\n",
    "plt.plot(df1['lr'],label='lr')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 66,
   "id": "07641bfb",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[<matplotlib.lines.Line2D at 0x22857be19a0>]"
      ]
     },
     "execution_count": 66,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.subplot(222)\n",
    "plt.plot(df1['Actual'].iloc[0:11],label='Actual')\n",
    "plt.plot(df1['svm'],label='svm')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 67,
   "id": "b98e9177",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[<matplotlib.lines.Line2D at 0x22857c43610>]"
      ]
     },
     "execution_count": 67,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.subplot(223)\n",
    "plt.plot(df1['Actual'].iloc[0:11],label='Actual')\n",
    "plt.plot(df1['gr'],label='gr')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 68,
   "id": "58c3620c",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[<matplotlib.lines.Line2D at 0x22857ca1400>]"
      ]
     },
     "execution_count": 68,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.subplot(224)\n",
    "plt.plot(df1['Actual'].iloc[0:11],label='Actual')\n",
    "plt.plot(df1['rf'],label='rf')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 69,
   "id": "495af494",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[<matplotlib.lines.Line2D at 0x22858cd11c0>]"
      ]
     },
     "execution_count": 69,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.subplot(221)\n",
    "plt.plot(df1['Actual'].iloc[0:11],label='Actual')\n",
    "plt.plot(df1['d'],label='d')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 70,
   "id": "00781856",
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn import metrics"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 71,
   "id": "460e80f3",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0.78\n",
      "None -0.06467991675329166 0.8777815965352825 0.8560878055897237 0.7423301147223973\n"
     ]
    }
   ],
   "source": [
    "score1=print(round(metrics.r2_score(y_test,y_pred1),2))\n",
    "score2=metrics.r2_score(y_test,y_pred2)\n",
    "score3=metrics.r2_score(y_test,y_pred3)\n",
    "score4=metrics.r2_score(y_test,y_pred4)\n",
    "score5=metrics.r2_score(y_test,y_pred5)\n",
    "\n",
    "print(score1,score2,score3,score4,score5)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 72,
   "id": "b98ead42",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "4213.798594527236 8570.301892512447 2403.1238566997627 2510.888816050996 2992.009782477612\n"
     ]
    }
   ],
   "source": [
    "s1=metrics.mean_absolute_error(y_test,y_pred1)\n",
    "s2=metrics.mean_absolute_error(y_test,y_pred2)\n",
    "s3=metrics.mean_absolute_error(y_test,y_pred3)\n",
    "s4=metrics.mean_absolute_error(y_test,y_pred4)\n",
    "s5=metrics.mean_absolute_error(y_test,y_pred5)\n",
    "\n",
    "\n",
    "print(s1,s2,s3,s4,s5)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "d7b6f155",
   "metadata": {},
   "source": [
    "### After data analysis Gradientboosting Regressor gives a best predication."
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3 (ipykernel)",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.9.12"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 5
}