health (1).ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "\n",
    "import pandas as pd\n",
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "import seaborn as sns\n",
    "%matplotlib inline"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "df = pd.read_csv(r\"C:\\Users\\cse\\Desktop\\insurance.csv\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>age</th>\n",
       "      <th>sex</th>\n",
       "      <th>bmi</th>\n",
       "      <th>children</th>\n",
       "      <th>smoker</th>\n",
       "      <th>region</th>\n",
       "      <th>charges</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>19</td>\n",
       "      <td>female</td>\n",
       "      <td>27.900</td>\n",
       "      <td>0</td>\n",
       "      <td>yes</td>\n",
       "      <td>southwest</td>\n",
       "      <td>16884.92400</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>18</td>\n",
       "      <td>male</td>\n",
       "      <td>33.770</td>\n",
       "      <td>1</td>\n",
       "      <td>no</td>\n",
       "      <td>southeast</td>\n",
       "      <td>1725.55230</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>28</td>\n",
       "      <td>male</td>\n",
       "      <td>33.000</td>\n",
       "      <td>3</td>\n",
       "      <td>no</td>\n",
       "      <td>southeast</td>\n",
       "      <td>4449.46200</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>33</td>\n",
       "      <td>male</td>\n",
       "      <td>22.705</td>\n",
       "      <td>0</td>\n",
       "      <td>no</td>\n",
       "      <td>northwest</td>\n",
       "      <td>21984.47061</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>32</td>\n",
       "      <td>male</td>\n",
       "      <td>28.880</td>\n",
       "      <td>0</td>\n",
       "      <td>no</td>\n",
       "      <td>northwest</td>\n",
       "      <td>3866.85520</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   age     sex     bmi  children smoker     region      charges\n",
       "0   19  female  27.900         0    yes  southwest  16884.92400\n",
       "1   18    male  33.770         1     no  southeast   1725.55230\n",
       "2   28    male  33.000         3     no  southeast   4449.46200\n",
       "3   33    male  22.705         0     no  northwest  21984.47061\n",
       "4   32    male  28.880         0     no  northwest   3866.85520"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "def map_smoking (column):\n",
    "    mapped = []\n",
    "    for i in column:\n",
    "        if i == \"yes\":\n",
    "            mapped.append(1)\n",
    "        else:\n",
    "            mapped.append(0)\n",
    "    return mapped"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [],
   "source": [
    "new_col = map_smoking(df[\"smoker\"])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
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     "execution_count": 6,
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   ],
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    "new_col"
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  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "data": {
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      "text/plain": [
       "   age     sex     bmi  children  smoker     region      charges\n",
       "0   19  female  27.900         0       1  southwest  16884.92400\n",
       "1   18    male  33.770         1       0  southeast   1725.55230\n",
       "2   28    male  33.000         3       0  southeast   4449.46200\n",
       "3   33    male  22.705         0       0  northwest  21984.47061\n",
       "4   32    male  28.880         0       0  northwest   3866.85520"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df[\"smoker\"] = new_col\n",
    "df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
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      "text/plain": [
       "   age  sex     bmi  children  smoker     region      charges\n",
       "0   19    0  27.900         0       1  southwest  16884.92400\n",
       "1   18    1  33.770         1       0  southeast   1725.55230\n",
       "2   28    1  33.000         3       0  southeast   4449.46200\n",
       "3   33    1  22.705         0       0  northwest  21984.47061\n",
       "4   32    1  28.880         0       0  northwest   3866.85520"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "def map_sex(column):\n",
    "    mapped = []\n",
    "    for i in column:\n",
    "        if i == \"male\":\n",
    "            mapped.append(1)\n",
    "        else:\n",
    "            mapped.append(0)\n",
    "    return mapped\n",
    "\n",
    "df[\"sex\"] = map_sex(df[\"sex\"])\n",
    "df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Index(['age', 'sex', 'bmi', 'children', 'smoker', 'region', 'charges'], dtype='object')"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "\n",
    "df.columns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x2a639f34f28>"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
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\n",
      "text/plain": [
       "<Figure size 864x288 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig,axes = plt.subplots(nrows = 1 , ncols = 2, figsize = (12,4))\n",
    "sns.countplot(x=\"sex\", data = df, hue=\"smoker\" ,ax=axes[0] )\n",
    "sns.barplot(x=\"sex\",y = \"charges\",data = df,hue = \"smoker\", ax = axes[1])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x2a63a0c05f8>"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.countplot(x = \"children\",data=df)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x2a63a0ff320>"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "\n",
    "sns.heatmap(df.corr(),annot = True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [],
   "source": [
    "x = df[['age', 'bmi', 'children', 'smoker', 'sex']]\n",
    "y = df[[\"charges\"]]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.model_selection import train_test_split"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [],
   "source": [
    "x_train,x_test,y_train,y_test = train_test_split(x,y, test_size = 0.2, random_state = 0)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>age</th>\n",
       "      <th>bmi</th>\n",
       "      <th>children</th>\n",
       "      <th>smoker</th>\n",
       "      <th>sex</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>621</th>\n",
       "      <td>37</td>\n",
       "      <td>34.100</td>\n",
       "      <td>4</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>194</th>\n",
       "      <td>18</td>\n",
       "      <td>34.430</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>240</th>\n",
       "      <td>23</td>\n",
       "      <td>36.670</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1168</th>\n",
       "      <td>32</td>\n",
       "      <td>35.200</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1192</th>\n",
       "      <td>58</td>\n",
       "      <td>32.395</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "      age     bmi  children  smoker  sex\n",
       "621    37  34.100         4       1    1\n",
       "194    18  34.430         0       0    1\n",
       "240    23  36.670         2       1    0\n",
       "1168   32  35.200         2       0    1\n",
       "1192   58  32.395         1       0    0"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "x_train.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [],
   "source": [
    "\n",
    "from sklearn import linear_model"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [],
   "source": [
    "lm = linear_model.LinearRegression()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "LinearRegression(copy_X=True, fit_intercept=True, n_jobs=None,\n",
       "         normalize=False)"
      ]
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "lm.fit(x_train,y_train)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([-11901.10455877])"
      ]
     },
     "execution_count": 20,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "lm.intercept_\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[ 2.54952280e+02,  3.20693337e+02,  4.30809389e+02,\n",
       "         2.35890247e+04, -2.34557931e+01]])"
      ]
     },
     "execution_count": 21,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "\n",
    "lm.coef_"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [],
   "source": [
    "\n",
    "pred = lm.predict(x_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.collections.PathCollection at 0x2a63b9fae48>"
      ]
     },
     "execution_count": 23,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.scatter(y_test,pred)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x2a63b77dda0>"
      ]
     },
     "execution_count": 24,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.distplot(y_test - pred)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>age</th>\n",
       "      <th>sex</th>\n",
       "      <th>bmi</th>\n",
       "      <th>children</th>\n",
       "      <th>smoker</th>\n",
       "      <th>charges</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>age</th>\n",
       "      <td>1.000000</td>\n",
       "      <td>-0.020856</td>\n",
       "      <td>0.109272</td>\n",
       "      <td>0.042469</td>\n",
       "      <td>-0.025019</td>\n",
       "      <td>0.299008</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>sex</th>\n",
       "      <td>-0.020856</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.046371</td>\n",
       "      <td>0.017163</td>\n",
       "      <td>0.076185</td>\n",
       "      <td>0.057292</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>bmi</th>\n",
       "      <td>0.109272</td>\n",
       "      <td>0.046371</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.012759</td>\n",
       "      <td>0.003750</td>\n",
       "      <td>0.198341</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>children</th>\n",
       "      <td>0.042469</td>\n",
       "      <td>0.017163</td>\n",
       "      <td>0.012759</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.007673</td>\n",
       "      <td>0.067998</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>smoker</th>\n",
       "      <td>-0.025019</td>\n",
       "      <td>0.076185</td>\n",
       "      <td>0.003750</td>\n",
       "      <td>0.007673</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.787251</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>charges</th>\n",
       "      <td>0.299008</td>\n",
       "      <td>0.057292</td>\n",
       "      <td>0.198341</td>\n",
       "      <td>0.067998</td>\n",
       "      <td>0.787251</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "               age       sex       bmi  children    smoker   charges\n",
       "age       1.000000 -0.020856  0.109272  0.042469 -0.025019  0.299008\n",
       "sex      -0.020856  1.000000  0.046371  0.017163  0.076185  0.057292\n",
       "bmi       0.109272  0.046371  1.000000  0.012759  0.003750  0.198341\n",
       "children  0.042469  0.017163  0.012759  1.000000  0.007673  0.067998\n",
       "smoker   -0.025019  0.076185  0.003750  0.007673  1.000000  0.787251\n",
       "charges   0.299008  0.057292  0.198341  0.067998  0.787251  1.000000"
      ]
     },
     "execution_count": 25,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.corr()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.metrics import r2_score"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.7978644236809905"
      ]
     },
     "execution_count": 27,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "r2_score(y_test,pred)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
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