medical_insurance_cost.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 82,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as py\n",
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "import seaborn as sns\n",
    "from sklearn.model_selection import train_test_split\n",
    "from sklearn.linear_model import LinearRegression\n",
    "from sklearn.metrics import accuracy_score, r2_score"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 83,
   "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": 83,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "datasts_dir = \"./datasets/\"\n",
    "insurance_data = py.read_csv(datasts_dir + \"insurance.csv\")\n",
    "insurance_data.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 84,
   "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   charges   1338 non-null   float64\n",
      "dtypes: float64(2), int64(2), object(3)\n",
      "memory usage: 73.3+ KB\n"
     ]
    }
   ],
   "source": [
    "insurance_data.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 85,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "age         0\n",
       "sex         0\n",
       "bmi         0\n",
       "children    0\n",
       "smoker      0\n",
       "region      0\n",
       "charges     0\n",
       "dtype: int64"
      ]
     },
     "execution_count": 85,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "insurance_data.isnull().sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 86,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "southeast    364\n",
       "southwest    325\n",
       "northwest    325\n",
       "northeast    324\n",
       "Name: region, dtype: int64"
      ]
     },
     "execution_count": 86,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "insurance_data['region'].value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 87,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "<ipython-input-87-cab0803ba1fc>:3: UserWarning: \n",
      "\n",
      "`distplot` is a deprecated function and will be removed in seaborn v0.14.0.\n",
      "\n",
      "Please adapt your code to use either `displot` (a figure-level function with\n",
      "similar flexibility) or `histplot` (an axes-level function for histograms).\n",
      "\n",
      "For a guide to updating your code to use the new functions, please see\n",
      "https://gist.github.com/mwaskom/de44147ed2974457ad6372750bbe5751\n",
      "\n",
      "  sns.distplot(insurance_data['age'])\n"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 432x432 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.set()\n",
    "plt.figure(figsize=(6, 6))\n",
    "sns.distplot(insurance_data['age'])\n",
    "plt.title('Age Distribution')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 88,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 432x432 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# gneder\n",
    "plt.figure(figsize=(6, 6))\n",
    "sns.countplot(x=\"sex\", data=insurance_data)\n",
    "plt.title('Sex distribution')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 89,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "<ipython-input-89-cc5ec870018c>:3: UserWarning: \n",
      "\n",
      "`distplot` is a deprecated function and will be removed in seaborn v0.14.0.\n",
      "\n",
      "Please adapt your code to use either `displot` (a figure-level function with\n",
      "similar flexibility) or `histplot` (an axes-level function for histograms).\n",
      "\n",
      "For a guide to updating your code to use the new functions, please see\n",
      "https://gist.github.com/mwaskom/de44147ed2974457ad6372750bbe5751\n",
      "\n",
      "  sns.distplot(insurance_data['bmi'])\n"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 432x432 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# bmi distribution\n",
    "plt.figure(figsize=(6, 6))\n",
    "sns.distplot(insurance_data['bmi'])\n",
    "plt.title('BMI Distribution')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 90,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 432x432 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# children distribution\n",
    "plt.figure(figsize=(6, 6))\n",
    "sns.countplot(x=\"children\", data=insurance_data)\n",
    "plt.title('Children Distribution')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 91,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0    574\n",
       "1    324\n",
       "2    240\n",
       "3    157\n",
       "4     25\n",
       "5     18\n",
       "Name: children, dtype: int64"
      ]
     },
     "execution_count": 91,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "insurance_data['children'].value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 92,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "<ipython-input-92-222b65602528>:3: UserWarning: \n",
      "\n",
      "`distplot` is a deprecated function and will be removed in seaborn v0.14.0.\n",
      "\n",
      "Please adapt your code to use either `displot` (a figure-level function with\n",
      "similar flexibility) or `histplot` (an axes-level function for histograms).\n",
      "\n",
      "For a guide to updating your code to use the new functions, please see\n",
      "https://gist.github.com/mwaskom/de44147ed2974457ad6372750bbe5751\n",
      "\n",
      "  sns.distplot(insurance_data['charges'])\n"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 432x432 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# charges distribution\n",
    "plt.figure(figsize=(6, 6))\n",
    "sns.distplot(insurance_data['charges'])\n",
    "plt.title('Charges Distribution')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 93,
   "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": 93,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "insurance_data.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 94,
   "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>0</td>\n",
       "      <td>27.900</td>\n",
       "      <td>0</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>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>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>3</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>3</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    0  27.900         0       1       1  16884.92400\n",
       "1   18    1  33.770         1       0       0   1725.55230\n",
       "2   28    1  33.000         3       0       0   4449.46200\n",
       "3   33    1  22.705         0       0       3  21984.47061\n",
       "4   32    1  28.880         0       0       3   3866.85520"
      ]
     },
     "execution_count": 94,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "insurance_data.replace({\"sex\": {\"male\": 1, \"female\": 0}, \"smoker\": {\"yes\": 1, \"no\": 0}, \"region\": {\"southeast\": 0, \"southwest\": 1, \"northeast\": 2, \"northwest\": 3}}, inplace=True)\n",
    "insurance_data.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 95,
   "metadata": {},
   "outputs": [],
   "source": [
    "X = insurance_data.drop(columns=\"charges\")\n",
    "Y = insurance_data[\"charges\"]\n",
    "X_train, X_test, Y_train, Y_test = train_test_split(X, Y, test_size=0.2, random_state=2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 96,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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      ],
      "text/plain": [
       "LinearRegression()"
      ]
     },
     "execution_count": 96,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "model = LinearRegression()\n",
    "model.fit(X_train, Y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 97,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Accuracy on test data :  0.7447273869684077\n"
     ]
    }
   ],
   "source": [
    "# accuracy on test data\n",
    "X_test_prediction = model.predict(X_test)\n",
    "test_data_accuracy = model.score(X_test, Y_test)\n",
    "print('Accuracy on test data : ', test_data_accuracy)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 98,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Accuracy on train data :  0.751505643411174\n"
     ]
    }
   ],
   "source": [
    "# accuracy on train data\n",
    "X_train_prediction = model.predict(X_train)\n",
    "train_data_accuracy = model.score(X_train, Y_train)\n",
    "print('Accuracy on train data : ', train_data_accuracy)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 99,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "R2 score on train data :  0.751505643411174\n",
      "R2 score on test data :  0.7447273869684077\n"
     ]
    }
   ],
   "source": [
    "r2_train = r2_score(Y_train, X_train_prediction)\n",
    "print('R2 score on train data : ', r2_train)\n",
    "\n",
    "r2_test = r2_score(Y_test, X_test_prediction)\n",
    "print('R2 score on test data : ', r2_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 100,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[25597.73941347]\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/opt/homebrew/Caskroom/miniconda/base/envs/ai/lib/python3.9/site-packages/sklearn/base.py:439: UserWarning: X does not have valid feature names, but LinearRegression was fitted with feature names\n",
      "  warnings.warn(\n"
     ]
    }
   ],
   "source": [
    "input_data = (19, 0, 27.900, 0, 1, 1)\n",
    "prediction = np.array(input_data)\n",
    "prediction_reshaped = prediction.reshape(1, -1)\n",
    "result = model.predict(prediction_reshaped)\n",
    "print(result)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
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