INS-PRE-LIN-V1.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "ceb4bd25",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Note: you may need to restart the kernel to use updated packages.Requirement already satisfied: matplotlib in c:\\users\\sathy\\appdata\\local\\programs\\python\\python311\\lib\\site-packages (3.9.0)\n",
      "Requirement already satisfied: contourpy>=1.0.1 in c:\\users\\sathy\\appdata\\local\\programs\\python\\python311\\lib\\site-packages (from matplotlib) (1.2.1)\n",
      "Requirement already satisfied: cycler>=0.10 in c:\\users\\sathy\\appdata\\local\\programs\\python\\python311\\lib\\site-packages (from matplotlib) (0.12.1)\n",
      "Requirement already satisfied: fonttools>=4.22.0 in c:\\users\\sathy\\appdata\\local\\programs\\python\\python311\\lib\\site-packages (from matplotlib) (4.52.4)\n",
      "Requirement already satisfied: kiwisolver>=1.3.1 in c:\\users\\sathy\\appdata\\local\\programs\\python\\python311\\lib\\site-packages (from matplotlib) (1.4.5)\n",
      "Requirement already satisfied: numpy>=1.23 in c:\\users\\sathy\\appdata\\roaming\\python\\python311\\site-packages (from matplotlib) (1.25.2)\n",
      "Requirement already satisfied: packaging>=20.0 in c:\\users\\sathy\\appdata\\roaming\\python\\python311\\site-packages (from matplotlib) (24.0)\n",
      "Requirement already satisfied: pillow>=8 in c:\\users\\sathy\\appdata\\local\\programs\\python\\python311\\lib\\site-packages (from matplotlib) (10.0.1)\n",
      "Requirement already satisfied: pyparsing>=2.3.1 in c:\\users\\sathy\\appdata\\local\\programs\\python\\python311\\lib\\site-packages (from matplotlib) (3.1.2)\n",
      "Requirement already satisfied: python-dateutil>=2.7 in c:\\users\\sathy\\appdata\\roaming\\python\\python311\\site-packages (from matplotlib) (2.9.0.post0)\n",
      "Requirement already satisfied: six>=1.5 in c:\\users\\sathy\\appdata\\local\\programs\\python\\python311\\lib\\site-packages (from python-dateutil>=2.7->matplotlib) (1.16.0)\n",
      "\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "\n",
      "[notice] A new release of pip is available: 23.2.1 -> 24.0\n",
      "[notice] To update, run: python.exe -m pip install --upgrade pip\n"
     ]
    }
   ],
   "source": [
    "pip install matplotlib"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "4cc4fbb0",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Requirement already satisfied: scipy in c:\\users\\sathy\\appdata\\local\\programs\\python\\python311\\lib\\site-packages (1.13.1)\n",
      "Requirement already satisfied: joblib in c:\\users\\sathy\\appdata\\local\\programs\\python\\python311\\lib\\site-packages (1.4.2)\n",
      "Requirement already satisfied: threadpoolctl in c:\\users\\sathy\\appdata\\local\\programs\\python\\python311\\lib\\site-packages (3.5.0)\n",
      "Requirement already satisfied: numpy<2.3,>=1.22.4 in c:\\users\\sathy\\appdata\\roaming\\python\\python311\\site-packages (from scipy) (1.25.2)\n",
      "Note: you may need to restart the kernel to use updated packages.\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "\n",
      "[notice] A new release of pip is available: 23.2.1 -> 24.0\n",
      "[notice] To update, run: python.exe -m pip install --upgrade pip\n"
     ]
    }
   ],
   "source": [
    "pip install scipy joblib threadpoolctl"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "91c30dd3",
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "import pandas as pd\n",
    "import matplotlib.pyplot as plt\n",
    "import seaborn as sns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "9abe745a",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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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>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>18</td>\n",
       "      <td>male</td>\n",
       "      <td>16.0</td>\n",
       "      <td>0</td>\n",
       "      <td>no</td>\n",
       "      <td>northeast</td>\n",
       "      <td>1694.80</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>18</td>\n",
       "      <td>male</td>\n",
       "      <td>17.3</td>\n",
       "      <td>2</td>\n",
       "      <td>yes</td>\n",
       "      <td>northeast</td>\n",
       "      <td>12829.46</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>18</td>\n",
       "      <td>female</td>\n",
       "      <td>20.8</td>\n",
       "      <td>0</td>\n",
       "      <td>no</td>\n",
       "      <td>southeast</td>\n",
       "      <td>1607.51</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>18</td>\n",
       "      <td>male</td>\n",
       "      <td>21.5</td>\n",
       "      <td>0</td>\n",
       "      <td>no</td>\n",
       "      <td>northeast</td>\n",
       "      <td>1702.46</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>18</td>\n",
       "      <td>male</td>\n",
       "      <td>21.6</td>\n",
       "      <td>0</td>\n",
       "      <td>yes</td>\n",
       "      <td>northeast</td>\n",
       "      <td>13747.87</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   age     sex   bmi  children smoker     region  expenses\n",
       "0   18    male  16.0         0     no  northeast   1694.80\n",
       "1   18    male  17.3         2    yes  northeast  12829.46\n",
       "2   18  female  20.8         0     no  southeast   1607.51\n",
       "3   18    male  21.5         0     no  northeast   1702.46\n",
       "4   18    male  21.6         0    yes  northeast  13747.87"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df = pd.read_csv('insurance.csv')\n",
    "df.head(5)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "a0bed65b",
   "metadata": {},
   "outputs": [],
   "source": [
    "df['children'] = df['children'].astype('object')\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "b4d18442",
   "metadata": {},
   "outputs": [],
   "source": [
    "#plt.figure(figsize = (12,8))\n",
    "#sns.histplot(x= 'expenses',data = df,kde = True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "9a556fa9",
   "metadata": {},
   "outputs": [],
   "source": [
    "#df['expenses'] = np.sqrt(df['expenses'])\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "1db969a2",
   "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>18</td>\n",
       "      <td>male</td>\n",
       "      <td>16.0</td>\n",
       "      <td>0</td>\n",
       "      <td>no</td>\n",
       "      <td>northeast</td>\n",
       "      <td>1694.80</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>18</td>\n",
       "      <td>male</td>\n",
       "      <td>17.3</td>\n",
       "      <td>2</td>\n",
       "      <td>yes</td>\n",
       "      <td>northeast</td>\n",
       "      <td>12829.46</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>18</td>\n",
       "      <td>female</td>\n",
       "      <td>20.8</td>\n",
       "      <td>0</td>\n",
       "      <td>no</td>\n",
       "      <td>southeast</td>\n",
       "      <td>1607.51</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>18</td>\n",
       "      <td>male</td>\n",
       "      <td>21.5</td>\n",
       "      <td>0</td>\n",
       "      <td>no</td>\n",
       "      <td>northeast</td>\n",
       "      <td>1702.46</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>18</td>\n",
       "      <td>male</td>\n",
       "      <td>21.6</td>\n",
       "      <td>0</td>\n",
       "      <td>yes</td>\n",
       "      <td>northeast</td>\n",
       "      <td>13747.87</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>95</th>\n",
       "      <td>19</td>\n",
       "      <td>male</td>\n",
       "      <td>27.7</td>\n",
       "      <td>0</td>\n",
       "      <td>yes</td>\n",
       "      <td>southwest</td>\n",
       "      <td>16297.85</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>96</th>\n",
       "      <td>19</td>\n",
       "      <td>male</td>\n",
       "      <td>27.8</td>\n",
       "      <td>0</td>\n",
       "      <td>no</td>\n",
       "      <td>northwest</td>\n",
       "      <td>1635.73</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>97</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>98</th>\n",
       "      <td>19</td>\n",
       "      <td>female</td>\n",
       "      <td>27.9</td>\n",
       "      <td>3</td>\n",
       "      <td>no</td>\n",
       "      <td>northwest</td>\n",
       "      <td>18838.70</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>99</th>\n",
       "      <td>19</td>\n",
       "      <td>female</td>\n",
       "      <td>28.3</td>\n",
       "      <td>0</td>\n",
       "      <td>yes</td>\n",
       "      <td>southwest</td>\n",
       "      <td>17081.08</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>100 rows × 7 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "    age     sex   bmi children smoker     region  expenses\n",
       "0    18    male  16.0        0     no  northeast   1694.80\n",
       "1    18    male  17.3        2    yes  northeast  12829.46\n",
       "2    18  female  20.8        0     no  southeast   1607.51\n",
       "3    18    male  21.5        0     no  northeast   1702.46\n",
       "4    18    male  21.6        0    yes  northeast  13747.87\n",
       "..  ...     ...   ...      ...    ...        ...       ...\n",
       "95   19    male  27.7        0    yes  southwest  16297.85\n",
       "96   19    male  27.8        0     no  northwest   1635.73\n",
       "97   19  female  27.9        0    yes  southwest  16884.92\n",
       "98   19  female  27.9        3     no  northwest  18838.70\n",
       "99   19  female  28.3        0    yes  southwest  17081.08\n",
       "\n",
       "[100 rows x 7 columns]"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.head(100)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "061322f4",
   "metadata": {},
   "outputs": [],
   "source": [
    "#plt.figure(figsize = (12,8))\n",
    "#sns.histplot(x= 'expenses',data = df,kde = True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "c4370a40",
   "metadata": {},
   "outputs": [],
   "source": [
    "#df['expenses'] = np.log(df['expenses'])\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "f590c6f2",
   "metadata": {},
   "outputs": [],
   "source": [
    "#plt.figure(figsize = (12,8))\n",
    "#sns.histplot(x= 'expenses',data = df,kde = True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "eaf15bbb",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Index(['sex', 'children', 'smoker', 'region'], dtype='object')"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "cat_columns = df.select_dtypes(include = 'object').columns\n",
    "cat_columns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "id": "8455e103",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\sathy\\AppData\\Local\\Temp\\ipykernel_8692\\3569672404.py:1: FutureWarning: In a future version, the Index constructor will not infer numeric dtypes when passed object-dtype sequences (matching Series behavior)\n",
      "  df  = pd.get_dummies(df,cat_columns,drop_first = True)\n"
     ]
    }
   ],
   "source": [
    "df  = pd.get_dummies(df,cat_columns,drop_first = True)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "id": "e219c99f",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Index(['age', 'bmi', 'expenses', 'sex_male', 'children_1', 'children_2',\n",
       "       'children_3', 'children_4', 'children_5', 'smoker_yes',\n",
       "       'region_northwest', 'region_southeast', 'region_southwest'],\n",
       "      dtype='object')"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.columns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "id": "f15bdae8",
   "metadata": {},
   "outputs": [],
   "source": [
    "y = df['expenses']\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "id": "aa67442b",
   "metadata": {},
   "outputs": [],
   "source": [
    "X = df[['age', 'bmi', 'sex_male', 'children_1', 'children_2',\n",
    "       'children_3', 'children_4', 'children_5', 'smoker_yes',\n",
    "       'region_northwest', 'region_southeast', 'region_southwest']]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "id": "64a0cebb",
   "metadata": {},
   "outputs": [],
   "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=0.3, random_state=410)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "id": "edc5e3e5",
   "metadata": {},
   "outputs": [
    {
     "data": {
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      ],
      "text/plain": [
       "LinearRegression()"
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from sklearn.linear_model import LinearRegression\n",
    "\n",
    "lm = LinearRegression()\n",
    "lm.fit(X_train,y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "id": "7fa4c308",
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "       -1.11216367e+01,  3.86876816e+04,  8.55511280e+03,  1.53441727e+04,\n",
       "        6.78483034e+03,  3.64918494e+04,  9.64824459e+03,  1.22808016e+02,\n",
       "        1.13201586e+04, -5.34457643e+02,  1.56840152e+04,  1.74059918e+04,\n",
       "        2.85830488e+04,  5.67165107e+03,  3.36264249e+04,  2.40005997e+03,\n",
       "        1.42116514e+04,  3.05461182e+03])"
      ]
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "predictions = lm.predict(X_test)\n",
    "predictions"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "id": "15882de4",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[<matplotlib.lines.Line2D at 0x1b2925eaa50>]"
      ]
     },
     "execution_count": 20,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.scatter(y_test,predictions)\n",
    "plt.xlabel('Y Test')\n",
    "plt.ylabel('Predicted Y')\n",
    "plt.plot([y_test.min(), y_test.max()], [y_test.min(), y_test.max()], 'k--', lw=2) "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "id": "5e1e92ab",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "MAE: 4215.726336166608\n",
      "MSE: 37267420.1821046\n",
      "RMSE: 6104.704757980078\n"
     ]
    }
   ],
   "source": [
    "from sklearn import metrics\n",
    "\n",
    "print('MAE:', metrics.mean_absolute_error(y_test, predictions))\n",
    "print('MSE:', metrics.mean_squared_error(y_test, predictions))\n",
    "print('RMSE:', np.sqrt(metrics.mean_squared_error(y_test, predictions)))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "id": "42f4acb5",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "74.79294576853582"
      ]
     },
     "execution_count": 22,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from sklearn.metrics import r2_score\n",
    "r2_score(y_test, predictions)*100"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "id": "0cd6d0fd",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Predicted and Actual Values:\n",
      "        Actual     Predicted\n",
      "1005   9877.61  13421.306184\n",
      "842    9500.57   9024.284906\n",
      "1007   9880.07  14441.359477\n",
      "40     2207.70   3992.789189\n",
      "969   10118.42  12337.295195\n",
      "...        ...           ...\n",
      "480    4562.84   5671.651069\n",
      "930   23306.55  33626.424873\n",
      "204    2156.75   2400.059974\n",
      "863    8733.23  14211.651426\n",
      "291    2523.17   3054.611823\n",
      "\n",
      "[402 rows x 2 columns]\n"
     ]
    }
   ],
   "source": [
    "results_df = pd.DataFrame({'Actual': y_test, 'Predicted': predictions})\n",
    "print(\"Predicted and Actual Values:\")\n",
    "print(results_df)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "id": "f0c4f419",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Predicted expenses: 6424.958578640477\n"
     ]
    }
   ],
   "source": [
    "new_data = pd.DataFrame({\n",
    "    'age': [29],\n",
    "    'bmi': [31.1],\n",
    "    'sex_male': [1],\n",
    "    'children_1': [1],\n",
    "    'children_2': [0],\n",
    "    'children_3': [1],\n",
    "    'children_4': [0],\n",
    "    'children_5': [0],\n",
    "    'smoker_yes': [0],\n",
    "    #'region_northeast':[0],\n",
    "    'region_northwest': [0],\n",
    "    'region_southeast': [0],\n",
    "    'region_southwest': [1]\n",
    "})\n",
    "\n",
    "# Predicting the expenses for the new data\n",
    "predicted_expenses = lm.predict(new_data)\n",
    "print(f\"Predicted expenses: {predicted_expenses[0]}\")"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3 (ipykernel)",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.11.5"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 5
}