{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "e2d9e6fa",
   "metadata": {},
   "outputs": [],
   "source": [
    "import seaborn as sns\n",
    "import pandas as pd\n",
    "from sklearn.model_selection import train_test_split\n",
    "from sklearn.linear_model import LinearRegression\n",
    "from sklearn.metrics import mean_squared_error, r2_score\n",
    "\n",
    "# Load dataset\n",
    "df = sns.load_dataset('mpg')\n",
    "df.dropna(inplace=True)  # Dropping missing values"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "6eb9757f",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Selecting relevant features for simplicity\n",
    "features = df[['cylinders', 'displacement', 'horsepower', 'weight', 'acceleration', 'model_year']]\n",
    "target = df['mpg']\n",
    "\n",
    "# Splitting the dataset into training and testing sets\n",
    "X_train, X_test, y_train, y_test = train_test_split(features, target, test_size=0.2, random_state=42)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "72821417",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Create and train the model\n",
    "model = LinearRegression()\n",
    "model.fit(X_train, y_train)\n",
    "\n",
    "# Predictions and Evaluation\n",
    "y_pred = model.predict(X_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "5dc111db",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Requirement already satisfied: joblib in c:\\users\\user\\anaconda3\\lib\\site-packages (1.2.0)\n"
     ]
    }
   ],
   "source": [
    "#!pip install joblib"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "c41776ae",
   "metadata": {},
   "outputs": [],
   "source": [
    "import joblib"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "318d866d",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "['mpg_model.pkl']"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Save the model\n",
    "joblib.dump(model, 'mpg_model.pkl')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "7636f0d3",
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
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