Cost Prediction finall.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "0714a69b",
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "5e0b11af",
   "metadata": {},
   "outputs": [],
   "source": [
    "data = pd.read_csv('insurance final1.csv')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "57a44de1",
   "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>No.of.children</th>\n",
       "      <th>smoker</th>\n",
       "      <th>high blood pressure</th>\n",
       "      <th>diabetes</th>\n",
       "      <th>cancer</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>yes</td>\n",
       "      <td>yes</td>\n",
       "      <td>yes</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>no</td>\n",
       "      <td>no</td>\n",
       "      <td>no</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>no</td>\n",
       "      <td>yes</td>\n",
       "      <td>yes</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>no</td>\n",
       "      <td>no</td>\n",
       "      <td>yes</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>no</td>\n",
       "      <td>no</td>\n",
       "      <td>yes</td>\n",
       "      <td>3866.85520</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   age     sex     bmi  No.of.children smoker high blood pressure diabetes  \\\n",
       "0   19  female  27.900               0    yes                 yes      yes   \n",
       "1   18    male  33.770               1     no                  no       no   \n",
       "2   28    male  33.000               3     no                  no      yes   \n",
       "3   33    male  22.705               0     no                  no       no   \n",
       "4   32    male  28.880               0     no                  no       no   \n",
       "\n",
       "  cancer      charges  \n",
       "0    yes  16884.92400  \n",
       "1     no   1725.55230  \n",
       "2    yes   4449.46200  \n",
       "3    yes  21984.47061  \n",
       "4    yes   3866.85520  "
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "d174444e",
   "metadata": {},
   "outputs": [
    {
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       "  <thead>\n",
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       "      <th></th>\n",
       "      <th>age</th>\n",
       "      <th>sex</th>\n",
       "      <th>bmi</th>\n",
       "      <th>No.of.children</th>\n",
       "      <th>smoker</th>\n",
       "      <th>high blood pressure</th>\n",
       "      <th>diabetes</th>\n",
       "      <th>cancer</th>\n",
       "      <th>charges</th>\n",
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       "  </thead>\n",
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       "      <th>0</th>\n",
       "      <td>19</td>\n",
       "      <td>female</td>\n",
       "      <td>27.900</td>\n",
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       "      <td>yes</td>\n",
       "      <td>yes</td>\n",
       "      <td>yes</td>\n",
       "      <td>yes</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>no</td>\n",
       "      <td>no</td>\n",
       "      <td>no</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>no</td>\n",
       "      <td>yes</td>\n",
       "      <td>yes</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>no</td>\n",
       "      <td>no</td>\n",
       "      <td>yes</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>no</td>\n",
       "      <td>no</td>\n",
       "      <td>yes</td>\n",
       "      <td>3866.85520</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
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      ],
      "text/plain": [
       "   age     sex     bmi  No.of.children smoker high blood pressure diabetes  \\\n",
       "0   19  female  27.900               0    yes                 yes      yes   \n",
       "1   18    male  33.770               1     no                  no       no   \n",
       "2   28    male  33.000               3     no                  no      yes   \n",
       "3   33    male  22.705               0     no                  no       no   \n",
       "4   32    male  28.880               0     no                  no       no   \n",
       "\n",
       "  cancer      charges  \n",
       "0    yes  16884.92400  \n",
       "1     no   1725.55230  \n",
       "2    yes   4449.46200  \n",
       "3    yes  21984.47061  \n",
       "4    yes   3866.85520  "
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "89207272",
   "metadata": {},
   "outputs": [
    {
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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>No.of.children</th>\n",
       "      <th>smoker</th>\n",
       "      <th>high blood pressure</th>\n",
       "      <th>diabetes</th>\n",
       "      <th>cancer</th>\n",
       "      <th>charges</th>\n",
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       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <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>no</td>\n",
       "      <td>yes</td>\n",
       "      <td>no</td>\n",
       "      <td>10600.5483</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <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>no</td>\n",
       "      <td>no</td>\n",
       "      <td>no</td>\n",
       "      <td>2205.9808</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <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>no</td>\n",
       "      <td>no</td>\n",
       "      <td>yes</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>no</td>\n",
       "      <td>no</td>\n",
       "      <td>no</td>\n",
       "      <td>2007.9450</td>\n",
       "    </tr>\n",
       "    <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>yes</td>\n",
       "      <td>yes</td>\n",
       "      <td>yes</td>\n",
       "      <td>29141.3603</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
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      ],
      "text/plain": [
       "      age     sex    bmi  No.of.children smoker high blood pressure diabetes  \\\n",
       "1333   50    male  30.97               3     no                  no      yes   \n",
       "1334   18  female  31.92               0     no                  no       no   \n",
       "1335   18  female  36.85               0     no                  no       no   \n",
       "1336   21  female  25.80               0     no                  no       no   \n",
       "1337   61  female  29.07               0    yes                 yes      yes   \n",
       "\n",
       "     cancer     charges  \n",
       "1333     no  10600.5483  \n",
       "1334     no   2205.9808  \n",
       "1335    yes   1629.8335  \n",
       "1336     no   2007.9450  \n",
       "1337    yes  29141.3603  "
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data.tail()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "be04b8eb",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(1338, 9)"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "20b38206",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Number of Rows 1338\n",
      "Number of Columns 9\n"
     ]
    }
   ],
   "source": [
    "print(\"Number of Rows\",data.shape[0])\n",
    "print(\"Number of Columns\",data.shape[1])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "96939437",
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 1338 entries, 0 to 1337\n",
      "Data columns (total 9 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   No.of.children       1338 non-null   int64  \n",
      " 4   smoker               1338 non-null   object \n",
      " 5   high blood pressure  1338 non-null   object \n",
      " 6   diabetes             1338 non-null   object \n",
      " 7   cancer               1338 non-null   object \n",
      " 8   charges              1338 non-null   float64\n",
      "dtypes: float64(2), int64(2), object(5)\n",
      "memory usage: 94.2+ KB\n"
     ]
    }
   ],
   "source": [
    "data.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "f65333f2",
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "age                    0\n",
       "sex                    0\n",
       "bmi                    0\n",
       "No.of.children         0\n",
       "smoker                 0\n",
       "high blood pressure    0\n",
       "diabetes               0\n",
       "cancer                 0\n",
       "charges                0\n",
       "dtype: int64"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data.isnull().sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "76ffe21b",
   "metadata": {
    "scrolled": true
   },
   "outputs": [
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       "      <th>age</th>\n",
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       "      <th>bmi</th>\n",
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       "      <th>high blood pressure</th>\n",
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       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>1338.000000</td>\n",
       "      <td>1338</td>\n",
       "      <td>1338.000000</td>\n",
       "      <td>1338.000000</td>\n",
       "      <td>1338</td>\n",
       "      <td>1338</td>\n",
       "      <td>1338</td>\n",
       "      <td>1338</td>\n",
       "      <td>1338.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>unique</th>\n",
       "      <td>NaN</td>\n",
       "      <td>2</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>top</th>\n",
       "      <td>NaN</td>\n",
       "      <td>male</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>no</td>\n",
       "      <td>no</td>\n",
       "      <td>no</td>\n",
       "      <td>no</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>freq</th>\n",
       "      <td>NaN</td>\n",
       "      <td>676</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1064</td>\n",
       "      <td>722</td>\n",
       "      <td>728</td>\n",
       "      <td>964</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>39.207025</td>\n",
       "      <td>NaN</td>\n",
       "      <td>30.663397</td>\n",
       "      <td>1.094918</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>13270.422265</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>14.049960</td>\n",
       "      <td>NaN</td>\n",
       "      <td>6.098187</td>\n",
       "      <td>1.205493</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>12110.011237</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>18.000000</td>\n",
       "      <td>NaN</td>\n",
       "      <td>15.960000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1121.873900</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>27.000000</td>\n",
       "      <td>NaN</td>\n",
       "      <td>26.296250</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>4740.287150</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>39.000000</td>\n",
       "      <td>NaN</td>\n",
       "      <td>30.400000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>9382.033000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>51.000000</td>\n",
       "      <td>NaN</td>\n",
       "      <td>34.693750</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>16639.912515</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>64.000000</td>\n",
       "      <td>NaN</td>\n",
       "      <td>53.130000</td>\n",
       "      <td>5.000000</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>63770.428010</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                age   sex          bmi  No.of.children smoker  \\\n",
       "count   1338.000000  1338  1338.000000     1338.000000   1338   \n",
       "unique          NaN     2          NaN             NaN      2   \n",
       "top             NaN  male          NaN             NaN     no   \n",
       "freq            NaN   676          NaN             NaN   1064   \n",
       "mean      39.207025   NaN    30.663397        1.094918    NaN   \n",
       "std       14.049960   NaN     6.098187        1.205493    NaN   \n",
       "min       18.000000   NaN    15.960000        0.000000    NaN   \n",
       "25%       27.000000   NaN    26.296250        0.000000    NaN   \n",
       "50%       39.000000   NaN    30.400000        1.000000    NaN   \n",
       "75%       51.000000   NaN    34.693750        2.000000    NaN   \n",
       "max       64.000000   NaN    53.130000        5.000000    NaN   \n",
       "\n",
       "       high blood pressure diabetes cancer       charges  \n",
       "count                 1338     1338   1338   1338.000000  \n",
       "unique                   2        2      2           NaN  \n",
       "top                     no       no     no           NaN  \n",
       "freq                   722      728    964           NaN  \n",
       "mean                   NaN      NaN    NaN  13270.422265  \n",
       "std                    NaN      NaN    NaN  12110.011237  \n",
       "min                    NaN      NaN    NaN   1121.873900  \n",
       "25%                    NaN      NaN    NaN   4740.287150  \n",
       "50%                    NaN      NaN    NaN   9382.033000  \n",
       "75%                    NaN      NaN    NaN  16639.912515  \n",
       "max                    NaN      NaN    NaN  63770.428010  "
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data.describe(include='all')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "be71f70a",
   "metadata": {},
   "outputs": [],
   "source": [
    "data['sex'].unique()\n",
    "data['sex']=data['sex'].map({'female':0,'male':1})\n",
    "data['smoker']=data['smoker'].map({'yes':1,'no':0})\n",
    "data['high blood pressure']=data['high blood pressure'].map({'yes':1,'no':0})\n",
    "data['diabetes']=data['diabetes'].map({'yes':1,'no':0})\n",
    "data['cancer']=data['cancer'].map({'yes':1,'no':0})"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "39c5b879",
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>age</th>\n",
       "      <th>sex</th>\n",
       "      <th>bmi</th>\n",
       "      <th>No.of.children</th>\n",
       "      <th>smoker</th>\n",
       "      <th>high blood pressure</th>\n",
       "      <th>diabetes</th>\n",
       "      <th>cancer</th>\n",
       "      <th>charges</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>19</td>\n",
       "      <td>0</td>\n",
       "      <td>27.900</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>16884.92400</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>18</td>\n",
       "      <td>1</td>\n",
       "      <td>33.770</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1725.55230</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>28</td>\n",
       "      <td>1</td>\n",
       "      <td>33.000</td>\n",
       "      <td>3</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>4449.46200</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>33</td>\n",
       "      <td>1</td>\n",
       "      <td>22.705</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>21984.47061</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>32</td>\n",
       "      <td>1</td>\n",
       "      <td>28.880</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>3866.85520</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   age  sex     bmi  No.of.children  smoker  high blood pressure  diabetes  \\\n",
       "0   19    0  27.900               0       1                    1         1   \n",
       "1   18    1  33.770               1       0                    0         0   \n",
       "2   28    1  33.000               3       0                    0         1   \n",
       "3   33    1  22.705               0       0                    0         0   \n",
       "4   32    1  28.880               0       0                    0         0   \n",
       "\n",
       "   cancer      charges  \n",
       "0       1  16884.92400  \n",
       "1       0   1725.55230  \n",
       "2       1   4449.46200  \n",
       "3       1  21984.47061  \n",
       "4       1   3866.85520  "
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "id": "629b423f",
   "metadata": {},
   "outputs": [],
   "source": [
    "X = data.drop(['charges'],axis=1)\n",
    "y = data['charges']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "id": "36f4cab3",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "560      9193.83850\n",
       "1285     8534.67180\n",
       "1142    27117.99378\n",
       "969      8596.82780\n",
       "486     12475.35130\n",
       "           ...     \n",
       "1095     4561.18850\n",
       "1130     8582.30230\n",
       "1294    11931.12525\n",
       "860     46113.51100\n",
       "1126    10214.63600\n",
       "Name: charges, Length: 1070, dtype: float64"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "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=0.2,random_state=42)\n",
    "y_train"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "id": "11a84c76",
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.linear_model import LinearRegression\n",
    "from sklearn.svm import SVR\n",
    "from sklearn.ensemble import RandomForestRegressor\n",
    "from sklearn.ensemble import GradientBoostingRegressor"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "id": "74ebb369",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "GradientBoostingRegressor()"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "lr = LinearRegression()\n",
    "lr.fit(X_train,y_train)\n",
    "svm = SVR()\n",
    "svm.fit(X_train,y_train)\n",
    "rf = RandomForestRegressor()\n",
    "rf.fit(X_train,y_train)\n",
    "gr = GradientBoostingRegressor()\n",
    "gr.fit(X_train,y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "id": "51b1276f",
   "metadata": {},
   "outputs": [],
   "source": [
    "y_pred1 = lr.predict(X_test)\n",
    "y_pred2 = svm.predict(X_test)\n",
    "y_pred3 = rf.predict(X_test)\n",
    "y_pred4 = gr.predict(X_test)\n",
    "\n",
    "df1 = pd.DataFrame({'Actual':y_test,'Lr':y_pred1,\n",
    "                  'svm':y_pred2,'rf':y_pred3,'gr':y_pred4})"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "id": "70f7890f",
   "metadata": {
    "scrolled": true
   },
   "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>rf</th>\n",
       "      <th>gr</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>764</th>\n",
       "      <td>9095.06825</td>\n",
       "      <td>8566.703121</td>\n",
       "      <td>9550.796572</td>\n",
       "      <td>12650.096169</td>\n",
       "      <td>10647.548841</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>887</th>\n",
       "      <td>5272.17580</td>\n",
       "      <td>6989.214392</td>\n",
       "      <td>9500.631572</td>\n",
       "      <td>5344.549028</td>\n",
       "      <td>6005.926158</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>890</th>\n",
       "      <td>29330.98315</td>\n",
       "      <td>36844.859084</td>\n",
       "      <td>9643.651895</td>\n",
       "      <td>28064.352756</td>\n",
       "      <td>28175.712497</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1293</th>\n",
       "      <td>9301.89355</td>\n",
       "      <td>9441.751871</td>\n",
       "      <td>9556.949102</td>\n",
       "      <td>10144.036639</td>\n",
       "      <td>9471.617014</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>259</th>\n",
       "      <td>33750.29180</td>\n",
       "      <td>26888.960118</td>\n",
       "      <td>9432.481252</td>\n",
       "      <td>34389.838113</td>\n",
       "      <td>33950.224747</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>109</th>\n",
       "      <td>47055.53210</td>\n",
       "      <td>39164.828398</td>\n",
       "      <td>9643.548845</td>\n",
       "      <td>47662.924574</td>\n",
       "      <td>46342.976895</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>575</th>\n",
       "      <td>12222.89830</td>\n",
       "      <td>12237.835091</td>\n",
       "      <td>9621.245855</td>\n",
       "      <td>12723.691088</td>\n",
       "      <td>13191.993254</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>535</th>\n",
       "      <td>6067.12675</td>\n",
       "      <td>7154.515244</td>\n",
       "      <td>9510.869622</td>\n",
       "      <td>5955.968018</td>\n",
       "      <td>6419.527825</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>543</th>\n",
       "      <td>63770.42801</td>\n",
       "      <td>40999.466108</td>\n",
       "      <td>9603.434514</td>\n",
       "      <td>46582.503819</td>\n",
       "      <td>48431.163250</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>846</th>\n",
       "      <td>9872.70100</td>\n",
       "      <td>12531.928986</td>\n",
       "      <td>9589.464486</td>\n",
       "      <td>10212.440871</td>\n",
       "      <td>11008.003603</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>268 rows × 5 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "           Actual            Lr          svm            rf            gr\n",
       "764    9095.06825   8566.703121  9550.796572  12650.096169  10647.548841\n",
       "887    5272.17580   6989.214392  9500.631572   5344.549028   6005.926158\n",
       "890   29330.98315  36844.859084  9643.651895  28064.352756  28175.712497\n",
       "1293   9301.89355   9441.751871  9556.949102  10144.036639   9471.617014\n",
       "259   33750.29180  26888.960118  9432.481252  34389.838113  33950.224747\n",
       "...           ...           ...          ...           ...           ...\n",
       "109   47055.53210  39164.828398  9643.548845  47662.924574  46342.976895\n",
       "575   12222.89830  12237.835091  9621.245855  12723.691088  13191.993254\n",
       "535    6067.12675   7154.515244  9510.869622   5955.968018   6419.527825\n",
       "543   63770.42801  40999.466108  9603.434514  46582.503819  48431.163250\n",
       "846    9872.70100  12531.928986  9589.464486  10212.440871  11008.003603\n",
       "\n",
       "[268 rows x 5 columns]"
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df1"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "id": "1b299b37",
   "metadata": {},
   "outputs": [],
   "source": [
    "import matplotlib.pyplot as plt"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "id": "ed7f70cc",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.legend.Legend at 0x1939e74bdc0>"
      ]
     },
     "execution_count": 20,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 640x480 with 4 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.subplot(221)\n",
    "plt.plot(df1['Actual'].iloc[0:11],label='Actual')\n",
    "plt.plot(df1['Lr'].iloc[0:11],label=\"Lr\")\n",
    "plt.legend()\n",
    "\n",
    "plt.subplot(222)\n",
    "plt.plot(df1['Actual'].iloc[0:11],label='Actual')\n",
    "plt.plot(df1['svm'].iloc[0:11],label=\"svr\")\n",
    "plt.legend()\n",
    "\n",
    "plt.subplot(223)\n",
    "plt.plot(df1['Actual'].iloc[0:11],label='Actual')\n",
    "plt.plot(df1['rf'].iloc[0:11],label=\"rf\")\n",
    "plt.legend()\n",
    "\n",
    "plt.subplot(224)\n",
    "plt.plot(df1['Actual'].iloc[0:11],label='Actual')\n",
    "plt.plot(df1['gr'].iloc[0:11],label=\"gr\")\n",
    "\n",
    "plt.tight_layout()\n",
    "\n",
    "plt.legend()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "id": "ce7ea8d1",
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn import metrics"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "id": "99414e09",
   "metadata": {},
   "outputs": [],
   "source": [
    "score1 = metrics.r2_score(y_test,y_pred1)\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)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "id": "902f9d35",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0.7813529683979135 -0.07240709981512938 0.8571772066958336 0.877936606994107\n"
     ]
    }
   ],
   "source": [
    "print(score1,score2,score3,score4)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "id": "233b34ff",
   "metadata": {},
   "outputs": [],
   "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)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "id": "4d74955a",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "4205.0267964363875 8596.453981264038 2608.3754863171644 2435.094883637563\n"
     ]
    }
   ],
   "source": [
    "print(s1,s2,s3,s4)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "id": "5abc3ed0",
   "metadata": {},
   "outputs": [],
   "source": [
    "data = {'age' : 35,\n",
    "        'sex' : 1,\n",
    "        'bmi' : 40.30,\n",
    "        'No.of.children' : 2,\n",
    "        'smoker' : 1,\n",
    "        'high blood pressure' : 0,\n",
    "        'diabetes' : 1,\n",
    "        'cancer' : 1}"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "id": "d651bf97",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>age</th>\n",
       "      <th>sex</th>\n",
       "      <th>bmi</th>\n",
       "      <th>No.of.children</th>\n",
       "      <th>smoker</th>\n",
       "      <th>high blood pressure</th>\n",
       "      <th>diabetes</th>\n",
       "      <th>cancer</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>35</td>\n",
       "      <td>1</td>\n",
       "      <td>40.3</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   age  sex   bmi  No.of.children  smoker  high blood pressure  diabetes  \\\n",
       "0   35    1  40.3               2       1                    0         1   \n",
       "\n",
       "   cancer  \n",
       "0       1  "
      ]
     },
     "execution_count": 27,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df = pd.DataFrame(data,index=[0])\n",
    "df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "id": "32d83b6c",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Medical Insurance cost for New Customer is :  43837.52742161664\n"
     ]
    }
   ],
   "source": [
    "new_pred = gr.predict(df)\n",
    "print(\"Medical Insurance cost for New Customer is : \",new_pred[0])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "id": "df118a89",
   "metadata": {},
   "outputs": [],
   "source": [
    "data = {'age' : 25,\n",
    "        'sex' : 0,\n",
    "        'bmi' : 32.66,\n",
    "        'No.of.children' : 0,\n",
    "        'smoker' : 0,\n",
    "        'high blood pressure' : 1,\n",
    "        'diabetes' : 1,\n",
    "        'cancer' : 1}"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "id": "1db98063",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
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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>No.of.children</th>\n",
       "      <th>smoker</th>\n",
       "      <th>high blood pressure</th>\n",
       "      <th>diabetes</th>\n",
       "      <th>cancer</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
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       "      <th>0</th>\n",
       "      <td>25</td>\n",
       "      <td>0</td>\n",
       "      <td>32.66</td>\n",
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       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   age  sex    bmi  No.of.children  smoker  high blood pressure  diabetes  \\\n",
       "0   25    0  32.66               0       0                    1         1   \n",
       "\n",
       "   cancer  \n",
       "0       1  "
      ]
     },
     "execution_count": 32,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df = pd.DataFrame(data,index=[0])\n",
    "df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "id": "85009522",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Medical Insurance cost for New Customer is :  5243.34975132534\n"
     ]
    }
   ],
   "source": [
    "new_pred = gr.predict(df)\n",
    "print(\"Medical Insurance cost for New Customer is : \",new_pred[0])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "id": "196a6d44",
   "metadata": {},
   "outputs": [],
   "source": [
    "data = {'age' : 60,\n",
    "        'sex' : 1,\n",
    "        'bmi' : 55.32,\n",
    "        'No.of.children' : 2,\n",
    "        'smoker' : 1,\n",
    "        'high blood pressure' : 1,\n",
    "        'diabetes' : 1,\n",
    "        'cancer' : 1}"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "id": "03d48801",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
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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>No.of.children</th>\n",
       "      <th>smoker</th>\n",
       "      <th>high blood pressure</th>\n",
       "      <th>diabetes</th>\n",
       "      <th>cancer</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>60</td>\n",
       "      <td>1</td>\n",
       "      <td>55.32</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   age  sex    bmi  No.of.children  smoker  high blood pressure  diabetes  \\\n",
       "0   60    1  55.32               2       1                    1         1   \n",
       "\n",
       "   cancer  \n",
       "0       1  "
      ]
     },
     "execution_count": 35,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df = pd.DataFrame(data,index=[0])\n",
    "df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "id": "023d3cb5",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Medical Insurance cost for New Customer is :  48882.15638382923\n"
     ]
    }
   ],
   "source": [
    "new_pred = gr.predict(df)\n",
    "print(\"Medical Insurance cost for New Customer is : \",new_pred[0])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "id": "7a608c70",
   "metadata": {},
   "outputs": [],
   "source": [
    "data = {'age' : 88,\n",
    "        'sex' : 1,\n",
    "        'bmi' : 63.88,\n",
    "        'No.of.children' : 2,\n",
    "        'smoker' : 0,\n",
    "        'high blood pressure' : 1,\n",
    "        'diabetes' : 1,\n",
    "        'cancer' : 0}"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "id": "9a2fb94d",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>age</th>\n",
       "      <th>sex</th>\n",
       "      <th>bmi</th>\n",
       "      <th>No.of.children</th>\n",
       "      <th>smoker</th>\n",
       "      <th>high blood pressure</th>\n",
       "      <th>diabetes</th>\n",
       "      <th>cancer</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>88</td>\n",
       "      <td>1</td>\n",
       "      <td>63.88</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   age  sex    bmi  No.of.children  smoker  high blood pressure  diabetes  \\\n",
       "0   88    1  63.88               2       0                    1         1   \n",
       "\n",
       "   cancer  \n",
       "0       0  "
      ]
     },
     "execution_count": 38,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df = pd.DataFrame(data,index=[0])\n",
    "df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "id": "4f6a58dc",
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Medical Insurance cost for New Customer is :  15613.49825892001\n"
     ]
    }
   ],
   "source": [
    "new_pred = gr.predict(df)\n",
    "print(\"Medical Insurance cost for New Customer is : \",new_pred[0])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "id": "52ab87fc",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "GradientBoostingRegressor()"
      ]
     },
     "execution_count": 40,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "gr = GradientBoostingRegressor()\n",
    "gr.fit(X,y)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "id": "f5903fa7",
   "metadata": {},
   "outputs": [],
   "source": [
    "import joblib"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "id": "8cdffd03",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "['model_joblib_gr']"
      ]
     },
     "execution_count": 42,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "joblib.dump(gr,'model_joblib_gr')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "id": "15e1c53e",
   "metadata": {},
   "outputs": [],
   "source": [
    "model = joblib.load('model_joblib_gr')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "id": "e18c3673",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([15600.72117454])"
      ]
     },
     "execution_count": 44,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "model.predict(df)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "id": "2607892e",
   "metadata": {},
   "outputs": [],
   "source": [
    "from tkinter import *"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "id": "1f3ef0b7",
   "metadata": {},
   "outputs": [],
   "source": [
    "import joblib"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "id": "01a98b44",
   "metadata": {},
   "outputs": [],
   "source": [
    "master =Tk()\n",
    "master.title(\"Medical Charges Prediction\")\n",
    "label = Label(master,text = \"Medical charges Prediction\",bg = \"black\",fg = \"white\").grid(row=0,columnspan=2)\n",
    "\n",
    "Label(master,text = \"Enter YourAge\").grid(row=1)\n",
    "Label(master,text = \"Male Or Female [1/0]\").grid(row=2)\n",
    "Label(master,text = \"Enter Your BMI Value\").grid(row=3)\n",
    "Label(master,text = \"Enter Number Of Children\").grid(row=4)\n",
    "Label(master,text = \"Smoker Yes/No [1/0]\").grid(row=5)\n",
    "Label(master,text = \"High Blood Pressure Yes/No [1/0]\").grid(row=6)\n",
    "Label(master,text = \"Diabetes Yes/No [1/0]\").grid(row=7)\n",
    "Label(master,text = \"Cancer Yes/No [1/0]\").grid(row=8)\n",
    "\n",
    "mainloop()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "d1865f69",
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3 (ipykernel)",
   "language": "python",
   "name": "python3"
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  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
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