Health cost calculator-checkpoint.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 58,
   "id": "ab07cf37",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Collecting scikit-learn\n",
      "  Downloading scikit_learn-0.24.2-cp38-cp38-win_amd64.whl (6.9 MB)\n",
      "Collecting threadpoolctl>=2.0.0\n",
      "  Using cached threadpoolctl-2.1.0-py3-none-any.whl (12 kB)\n",
      "Requirement already satisfied: scipy>=0.19.1 in c:\\programdata\\anaconda3\\lib\\site-packages (from scikit-learn) (1.6.2)\n",
      "Collecting joblib>=0.11\n",
      "  Using cached joblib-1.0.1-py3-none-any.whl (303 kB)\n",
      "Requirement already satisfied: numpy>=1.13.3 in c:\\programdata\\anaconda3\\lib\\site-packages (from scikit-learn) (1.20.1)\n",
      "Installing collected packages: threadpoolctl, joblib, scikit-learn\n",
      "Successfully installed joblib-1.0.1 scikit-learn-0.24.2 threadpoolctl-2.1.0\n"
     ]
    }
   ],
   "source": [
    "! pip install -U scikit-learn"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 59,
   "id": "8a0bf98a",
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "from sklearn.linear_model import LinearRegression\n",
    "import seaborn as sns\n",
    "%matplotlib inline"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "id": "ed6174ae",
   "metadata": {},
   "outputs": [],
   "source": [
    "df = pd.read_csv(\"Downloads\\insurance.csv\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "id": "d6c2f809",
   "metadata": {},
   "outputs": [
    {
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       "      <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",
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       "      <th>0</th>\n",
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      ],
      "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": 16,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "id": "f6476d42",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "<ipython-input-23-f8b0eef61894>:1: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  df['sex'][df['sex']=='female']=0\n",
      "<ipython-input-23-f8b0eef61894>:2: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  df['sex'][df['sex']=='male']=1\n",
      "<ipython-input-23-f8b0eef61894>:3: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  df['smoker'][df['smoker']=='yes']=1\n",
      "<ipython-input-23-f8b0eef61894>:4: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  df['smoker'][df['smoker']=='no']=0\n"
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      "text/plain": [
       "   age sex   bmi  children smoker     region  expenses\n",
       "0   19   0  27.9         0      1  southwest  16884.92\n",
       "1   18   1  33.8         1      0  southeast   1725.55\n",
       "2   28   1  33.0         3      0  southeast   4449.46\n",
       "3   33   1  22.7         0      0  northwest  21984.47\n",
       "4   32   1  28.9         0      0  northwest   3866.86"
      ]
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     "execution_count": 23,
     "metadata": {},
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   "source": [
    "df['sex'][df['sex']=='female']=0\n",
    "df['sex'][df['sex']=='male']=1\n",
    "df['smoker'][df['smoker']=='yes']=1\n",
    "df['smoker'][df['smoker']=='no']=0\n",
    "df.head()"
   ]
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   "cell_type": "code",
   "execution_count": 24,
   "id": "16004182",
   "metadata": {},
   "outputs": [
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       "      northeast  northwest  southeast  southwest\n",
       "0             0          0          0          1\n",
       "1             0          0          1          0\n",
       "2             0          0          1          0\n",
       "3             0          1          0          0\n",
       "4             0          1          0          0\n",
       "...         ...        ...        ...        ...\n",
       "1333          0          1          0          0\n",
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       "1335          0          0          1          0\n",
       "1336          0          0          0          1\n",
       "1337          0          1          0          0\n",
       "\n",
       "[1338 rows x 4 columns]"
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     "execution_count": 24,
     "metadata": {},
     "output_type": "execute_result"
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   "source": [
    "df1 = pd.get_dummies(df['region'])\n",
    "df1"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "id": "5790b2f1",
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       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>southwest</td>\n",
       "      <td>2007.95</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</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>northwest</td>\n",
       "      <td>29141.36</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>1338 rows × 11 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "      age sex   bmi  children smoker     region  expenses  northeast  \\\n",
       "0      19   0  27.9         0      1  southwest  16884.92          0   \n",
       "1      18   1  33.8         1      0  southeast   1725.55          0   \n",
       "2      28   1  33.0         3      0  southeast   4449.46          0   \n",
       "3      33   1  22.7         0      0  northwest  21984.47          0   \n",
       "4      32   1  28.9         0      0  northwest   3866.86          0   \n",
       "...   ...  ..   ...       ...    ...        ...       ...        ...   \n",
       "1333   50   1  31.0         3      0  northwest  10600.55          0   \n",
       "1334   18   0  31.9         0      0  northeast   2205.98          1   \n",
       "1335   18   0  36.9         0      0  southeast   1629.83          0   \n",
       "1336   21   0  25.8         0      0  southwest   2007.95          0   \n",
       "1337   61   0  29.1         0      1  northwest  29141.36          0   \n",
       "\n",
       "      northwest  southeast  southwest  \n",
       "0             0          0          1  \n",
       "1             0          1          0  \n",
       "2             0          1          0  \n",
       "3             1          0          0  \n",
       "4             1          0          0  \n",
       "...         ...        ...        ...  \n",
       "1333          1          0          0  \n",
       "1334          0          0          0  \n",
       "1335          0          1          0  \n",
       "1336          0          0          1  \n",
       "1337          1          0          0  \n",
       "\n",
       "[1338 rows x 11 columns]"
      ]
     },
     "execution_count": 28,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df = pd.concat([df,df1],axis=1)\n",
    "df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "id": "53ba99ca",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0       16884.92\n",
       "1        1725.55\n",
       "2        4449.46\n",
       "3       21984.47\n",
       "4        3866.86\n",
       "          ...   \n",
       "1333    10600.55\n",
       "1334     2205.98\n",
       "1335     1629.83\n",
       "1336     2007.95\n",
       "1337    29141.36\n",
       "Name: expenses, Length: 1338, dtype: float64"
      ]
     },
     "execution_count": 35,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "y = df.expenses\n",
    "y\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "id": "78e30680",
   "metadata": {},
   "outputs": [],
   "source": [
    "df.drop(['region','expenses'],axis=1,inplace=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "id": "b69ea758",
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "      <th>4</th>\n",
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       "      <th>1333</th>\n",
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       "      <td>0</td>\n",
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       "    <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>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
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       "    <tr>\n",
       "      <th>1335</th>\n",
       "      <td>18</td>\n",
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       "      <td>0</td>\n",
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       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>1338 rows × 9 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "      age sex   bmi  children smoker  northeast  northwest  southeast  \\\n",
       "0      19   0  27.9         0      1          0          0          0   \n",
       "1      18   1  33.8         1      0          0          0          1   \n",
       "2      28   1  33.0         3      0          0          0          1   \n",
       "3      33   1  22.7         0      0          0          1          0   \n",
       "4      32   1  28.9         0      0          0          1          0   \n",
       "...   ...  ..   ...       ...    ...        ...        ...        ...   \n",
       "1333   50   1  31.0         3      0          0          1          0   \n",
       "1334   18   0  31.9         0      0          1          0          0   \n",
       "1335   18   0  36.9         0      0          0          0          1   \n",
       "1336   21   0  25.8         0      0          0          0          0   \n",
       "1337   61   0  29.1         0      1          0          1          0   \n",
       "\n",
       "      southwest  \n",
       "0             1  \n",
       "1             0  \n",
       "2             0  \n",
       "3             0  \n",
       "4             0  \n",
       "...         ...  \n",
       "1333          0  \n",
       "1334          0  \n",
       "1335          0  \n",
       "1336          1  \n",
       "1337          0  \n",
       "\n",
       "[1338 rows x 9 columns]"
      ]
     },
     "execution_count": 38,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "id": "2476f0b4",
   "metadata": {},
   "outputs": [],
   "source": [
    "df['sex']=df['sex'].astype(int)\n",
    "df['smoker']=df['smoker'].astype(int)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "id": "ba0f1518",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "    .dataframe tbody tr th {\n",
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       "</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>northeast</th>\n",
       "      <th>northwest</th>\n",
       "      <th>southeast</th>\n",
       "      <th>southwest</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",
       "      <td>1338.000000</td>\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>0.505232</td>\n",
       "      <td>30.665471</td>\n",
       "      <td>1.094918</td>\n",
       "      <td>0.204783</td>\n",
       "      <td>0.242152</td>\n",
       "      <td>0.242900</td>\n",
       "      <td>0.272048</td>\n",
       "      <td>0.242900</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>14.049960</td>\n",
       "      <td>0.500160</td>\n",
       "      <td>6.098382</td>\n",
       "      <td>1.205493</td>\n",
       "      <td>0.403694</td>\n",
       "      <td>0.428546</td>\n",
       "      <td>0.428995</td>\n",
       "      <td>0.445181</td>\n",
       "      <td>0.428995</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>18.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>16.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>27.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>26.300000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>39.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>30.400000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>51.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>34.700000</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>64.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>53.100000</td>\n",
       "      <td>5.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "               age          sex          bmi     children       smoker  \\\n",
       "count  1338.000000  1338.000000  1338.000000  1338.000000  1338.000000   \n",
       "mean     39.207025     0.505232    30.665471     1.094918     0.204783   \n",
       "std      14.049960     0.500160     6.098382     1.205493     0.403694   \n",
       "min      18.000000     0.000000    16.000000     0.000000     0.000000   \n",
       "25%      27.000000     0.000000    26.300000     0.000000     0.000000   \n",
       "50%      39.000000     1.000000    30.400000     1.000000     0.000000   \n",
       "75%      51.000000     1.000000    34.700000     2.000000     0.000000   \n",
       "max      64.000000     1.000000    53.100000     5.000000     1.000000   \n",
       "\n",
       "         northeast    northwest    southeast    southwest  \n",
       "count  1338.000000  1338.000000  1338.000000  1338.000000  \n",
       "mean      0.242152     0.242900     0.272048     0.242900  \n",
       "std       0.428546     0.428995     0.445181     0.428995  \n",
       "min       0.000000     0.000000     0.000000     0.000000  \n",
       "25%       0.000000     0.000000     0.000000     0.000000  \n",
       "50%       0.000000     0.000000     0.000000     0.000000  \n",
       "75%       0.000000     0.000000     1.000000     0.000000  \n",
       "max       1.000000     1.000000     1.000000     1.000000  "
      ]
     },
     "execution_count": 44,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 60,
   "id": "82799ff2",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(1070, 9) (1070,)\n",
      "(268, 9) (268,)\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "0.7626681935307796"
      ]
     },
     "execution_count": 60,
     "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(df, y, test_size=0.2 )\n",
    "print(X_train.shape,y_train.shape)\n",
    "print(X_test.shape,y_test.shape)\n",
    "model = LinearRegression()\n",
    "model.fit(X_train,y_train)\n",
    "y_pred = pd.DataFrame(model.predict(X_test))\n",
    "y_pred\n",
    "model.score(X_test,y_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "ab46c025",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "14fe18be",
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
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