Machine Learning Linear Models

This repository contains practical examples and resources for building and training linear models in machine learning using Python. It focuses on applying linear regression techniques to real-world datasets, demonstrating the full workflow from data preparation to model evaluation.

---

Table of Contents

• Project Overview
• Dataset
• Contents

---

Project Overview

This project aims to provide hands-on experience with linear models in machine learning, particularly linear regression. Using Python and popular libraries such as scikit-learn and pandas, it covers:

• Loading and exploring datasets
• Preparing data for modeling
• Training linear regression models
• Saving and loading trained models
• Evaluating model performance
• Practical examples with real datasets

The project is ideal for beginners and intermediate learners who want to understand how linear models work and how to implement them effectively.

---

Dataset

The repository includes the Advertising.csv dataset, which contains advertising budgets across different media channels and corresponding sales figures. This dataset is commonly used for regression tasks and model training demonstrations.

---

Contents

• Advertising.csv — Dataset file
• adv.ipynb — Jupyter Notebook with step-by-step implementation of linear regression on the advertising dataset
• machinelearningfitmodel.ipynb — Notebook demonstrating fitting and saving a linear regression model
• final_sales_mode.joblib — A saved model file for reuse