Insurance_prices_Prediction.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 32,
   "id": "51c6a6d9",
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "import seaborn as sns\n",
    "import warnings\n",
    "warnings.filterwarnings('ignore')\n",
    "plt.rcParams['figure.figsize']=(10,8)\n",
    "sns.set_style('whitegrid')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "id": "8d96021d",
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>age</th>\n",
       "      <th>sex</th>\n",
       "      <th>bmi</th>\n",
       "      <th>children</th>\n",
       "      <th>smoker</th>\n",
       "      <th>region</th>\n",
       "      <th>expenses</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>19</td>\n",
       "      <td>female</td>\n",
       "      <td>27.9</td>\n",
       "      <td>0</td>\n",
       "      <td>yes</td>\n",
       "      <td>southwest</td>\n",
       "      <td>16884.92</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>18</td>\n",
       "      <td>male</td>\n",
       "      <td>33.8</td>\n",
       "      <td>1</td>\n",
       "      <td>no</td>\n",
       "      <td>southeast</td>\n",
       "      <td>1725.55</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>28</td>\n",
       "      <td>male</td>\n",
       "      <td>33.0</td>\n",
       "      <td>3</td>\n",
       "      <td>no</td>\n",
       "      <td>southeast</td>\n",
       "      <td>4449.46</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>33</td>\n",
       "      <td>male</td>\n",
       "      <td>22.7</td>\n",
       "      <td>0</td>\n",
       "      <td>no</td>\n",
       "      <td>northwest</td>\n",
       "      <td>21984.47</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>32</td>\n",
       "      <td>male</td>\n",
       "      <td>28.9</td>\n",
       "      <td>0</td>\n",
       "      <td>no</td>\n",
       "      <td>northwest</td>\n",
       "      <td>3866.86</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   age     sex   bmi  children smoker     region  expenses\n",
       "0   19  female  27.9         0    yes  southwest  16884.92\n",
       "1   18    male  33.8         1     no  southeast   1725.55\n",
       "2   28    male  33.0         3     no  southeast   4449.46\n",
       "3   33    male  22.7         0     no  northwest  21984.47\n",
       "4   32    male  28.9         0     no  northwest   3866.86"
      ]
     },
     "execution_count": 33,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df=pd.read_csv('Insurance_prices.csv')\n",
    "df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "id": "ec7c44c2",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 1338 entries, 0 to 1337\n",
      "Data columns (total 7 columns):\n",
      " #   Column    Non-Null Count  Dtype  \n",
      "---  ------    --------------  -----  \n",
      " 0   age       1338 non-null   int64  \n",
      " 1   sex       1338 non-null   object \n",
      " 2   bmi       1338 non-null   float64\n",
      " 3   children  1338 non-null   int64  \n",
      " 4   smoker    1338 non-null   object \n",
      " 5   region    1338 non-null   object \n",
      " 6   expenses  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": 35,
   "id": "54146506",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array(['southwest', 'southeast', 'northwest', 'northeast'], dtype=object)"
      ]
     },
     "execution_count": 35,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.region.unique()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "id": "b626900d",
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>age</th>\n",
       "      <th>bmi</th>\n",
       "      <th>children</th>\n",
       "      <th>expenses</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>1338.000000</td>\n",
       "      <td>1338.000000</td>\n",
       "      <td>1338.000000</td>\n",
       "      <td>1338.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>39.207025</td>\n",
       "      <td>30.665471</td>\n",
       "      <td>1.094918</td>\n",
       "      <td>13270.422414</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>14.049960</td>\n",
       "      <td>6.098382</td>\n",
       "      <td>1.205493</td>\n",
       "      <td>12110.011240</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>18.000000</td>\n",
       "      <td>16.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>1121.870000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>27.000000</td>\n",
       "      <td>26.300000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>4740.287500</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>39.000000</td>\n",
       "      <td>30.400000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>9382.030000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>51.000000</td>\n",
       "      <td>34.700000</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>16639.915000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>64.000000</td>\n",
       "      <td>53.100000</td>\n",
       "      <td>5.000000</td>\n",
       "      <td>63770.430000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "               age          bmi     children      expenses\n",
       "count  1338.000000  1338.000000  1338.000000   1338.000000\n",
       "mean     39.207025    30.665471     1.094918  13270.422414\n",
       "std      14.049960     6.098382     1.205493  12110.011240\n",
       "min      18.000000    16.000000     0.000000   1121.870000\n",
       "25%      27.000000    26.300000     0.000000   4740.287500\n",
       "50%      39.000000    30.400000     1.000000   9382.030000\n",
       "75%      51.000000    34.700000     2.000000  16639.915000\n",
       "max      64.000000    53.100000     5.000000  63770.430000"
      ]
     },
     "execution_count": 36,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "id": "f551d46b",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "age         0\n",
       "sex         0\n",
       "bmi         0\n",
       "children    0\n",
       "smoker      0\n",
       "region      0\n",
       "expenses    0\n",
       "dtype: int64"
      ]
     },
     "execution_count": 37,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.isnull().sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "id": "adb44b5a",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "1"
      ]
     },
     "execution_count": 38,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.duplicated().sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "id": "4baec71c",
   "metadata": {},
   "outputs": [],
   "source": [
    "dff=df.drop_duplicates()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "id": "2792bb10",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0"
      ]
     },
     "execution_count": 40,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "dff.duplicated().sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "id": "6831504b",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Index(['age', 'sex', 'bmi', 'children', 'smoker', 'region', 'expenses'], dtype='object')"
      ]
     },
     "execution_count": 41,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "dff.columns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "id": "dc867f0a",
   "metadata": {},
   "outputs": [],
   "source": [
    "key_features= ['age', 'sex', 'bmi', 'children', 'expenses']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "id": "1feceec5",
   "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>expenses</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>sex</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>female</th>\n",
       "      <td>39.503021</td>\n",
       "      <td>30.379758</td>\n",
       "      <td>1.074018</td>\n",
       "      <td>12569.578897</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>male</th>\n",
       "      <td>38.946667</td>\n",
       "      <td>30.945778</td>\n",
       "      <td>1.117037</td>\n",
       "      <td>13974.999111</td>\n",
       "    </tr>\n",
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      ],
      "text/plain": [
       "              age        bmi  children      expenses\n",
       "sex                                                 \n",
       "female  39.503021  30.379758  1.074018  12569.578897\n",
       "male    38.946667  30.945778  1.117037  13974.999111"
      ]
     },
     "execution_count": 43,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "dff.pivot_table(values=['age','bmi', 'children', 'expenses'], index ='sex')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "id": "9f061fe3",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 720x576 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.countplot(dff['sex'])\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "id": "1815af60",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[<AxesSubplot:title={'center':'age'}>,\n",
       "        <AxesSubplot:title={'center':'bmi'}>],\n",
       "       [<AxesSubplot:title={'center':'children'}>,\n",
       "        <AxesSubplot:title={'center':'expenses'}>]], dtype=object)"
      ]
     },
     "execution_count": 46,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x576 with 4 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "dff.hist()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "id": "84a534a6",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x576 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.boxplot(data=dff)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 51,
   "id": "5545fe23",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x576 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.boxplot(x='region', y='expenses', data=dff)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "4482d2d8",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 52,
   "id": "c084643a",
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.preprocessing import LabelEncoder"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 53,
   "id": "1309a7b6",
   "metadata": {},
   "outputs": [],
   "source": [
    "lc=LabelEncoder()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 54,
   "id": "2dd3013e",
   "metadata": {},
   "outputs": [],
   "source": [
    "dff['sex']=lc.fit_transform(dff['sex'])\n",
    "dff['region']=lc.fit_transform(dff['region'])\n",
    "dff['smoker']=lc.fit_transform(dff['smoker'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 55,
   "id": "985d7b22",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>age</th>\n",
       "      <th>sex</th>\n",
       "      <th>bmi</th>\n",
       "      <th>children</th>\n",
       "      <th>smoker</th>\n",
       "      <th>region</th>\n",
       "      <th>expenses</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>19</td>\n",
       "      <td>0</td>\n",
       "      <td>27.9</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "      <td>16884.92</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>18</td>\n",
       "      <td>1</td>\n",
       "      <td>33.8</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>1725.55</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>28</td>\n",
       "      <td>1</td>\n",
       "      <td>33.0</td>\n",
       "      <td>3</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>4449.46</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>33</td>\n",
       "      <td>1</td>\n",
       "      <td>22.7</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>21984.47</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>32</td>\n",
       "      <td>1</td>\n",
       "      <td>28.9</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>3866.86</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</td>\n",
       "      <td>31.0</td>\n",
       "      <td>3</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>10600.55</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1334</th>\n",
       "      <td>18</td>\n",
       "      <td>0</td>\n",
       "      <td>31.9</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>2205.98</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1335</th>\n",
       "      <td>18</td>\n",
       "      <td>0</td>\n",
       "      <td>36.9</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>1629.83</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1336</th>\n",
       "      <td>21</td>\n",
       "      <td>0</td>\n",
       "      <td>25.8</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>2007.95</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1337</th>\n",
       "      <td>61</td>\n",
       "      <td>0</td>\n",
       "      <td>29.1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>29141.36</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>1337 rows × 7 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "      age  sex   bmi  children  smoker  region  expenses\n",
       "0      19    0  27.9         0       1       3  16884.92\n",
       "1      18    1  33.8         1       0       2   1725.55\n",
       "2      28    1  33.0         3       0       2   4449.46\n",
       "3      33    1  22.7         0       0       1  21984.47\n",
       "4      32    1  28.9         0       0       1   3866.86\n",
       "...   ...  ...   ...       ...     ...     ...       ...\n",
       "1333   50    1  31.0         3       0       1  10600.55\n",
       "1334   18    0  31.9         0       0       0   2205.98\n",
       "1335   18    0  36.9         0       0       2   1629.83\n",
       "1336   21    0  25.8         0       0       3   2007.95\n",
       "1337   61    0  29.1         0       1       1  29141.36\n",
       "\n",
       "[1337 rows x 7 columns]"
      ]
     },
     "execution_count": 55,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "dff"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "584bfa52",
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
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