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| { | |
| "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": [] | |
| } | |
| ], | |
| "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 | |
| } | |