Linear Regression from Scratch

A simple and educational implementation of a Linear Regression model in Python from scratch. This project provides a clear and concise example of how to build, train, and evaluate a linear regression model without relying on high-level machine learning libraries like Scikit-learn.

🚀 Features

• Pure Python Implementation: The core linear regression model is built using only NumPy, making it easy to understand the underlying mechanics.
• Customizable Dataset: Generate your own datasets with varying levels of noise and complexity using the dataset_generator.py module.
• Train-Test Split: A custom implementation of train-test split is provided in data_preprocessing.py.
• Model Evaluation: The model's performance is evaluated using the R-squared metric.
• Jupyter Notebook Demo: A comprehensive demo.ipynb is included to provide a step-by-step guide on how to use the different modules.

🏁 Getting Started

These instructions will get you a copy of the project up and running on your local machine for development and testing purposes.

Prerequisites

• Python 3.x
• pip

Installation

1. Clone the repository:

   git clone https://github.com/your-username/LinearRegression.git
   cd LinearRegression

2. Install the required packages:

   pip install -r requirements.txt

🎈 Usage

The main.py file provides a quick way to see the model in action. To run it, simply execute:

python main.py

This will:

1. Generate a synthetic dataset.
2. Train the linear regression model.
3. Evaluate the model's performance and print the R-squared score.
4. Generate a plot of the regression fit and save it as output.png.

For a more detailed and interactive guide, check out the demo.ipynb Jupyter Notebook.

📂 Files in the Repository

• main.py: The main script to run the linear regression model.
• linear_regression.py: Contains the LinearRegression class.
• dataset_generator.py: A module to generate synthetic datasets for regression tasks.
• data_preprocessing.py: Includes functions for data splitting and evaluation metrics.
• demo.ipynb: A Jupyter Notebook that provides a detailed walkthrough of the project.
• requirements.txt: A list of required Python packages.
• output.png: An example of the model's output plot.

🤝 Contributing

Contributions, issues, and feature requests are welcome!

📝 License

This project is licensed under the MIT License - see the LICENSE.md file for details.