Linear Regression from Scratch

This project demonstrates a simple implementation of linear regression using Python. It includes a custom-built linear regression model and provides sample datasets for testing and evaluation.

Features

• Custom Linear Regression Implementation: Understand the fundamentals by building a linear regression model from scratch without relying on external machine learning libraries.
• Sample Datasets: Includes CSV files (linTest.csv, r2test.csv, test2.csv) to test and validate the model.
• Project Files: Contains Python script (LinearReg.py) and project files (LinearReg.pyproj, LinearReg.sln) for structured development.

Getting Started

Prerequisites

• Python 3.x installed on your system.
• Basic understanding of Python programming and linear regression concepts.

Installation

1. Clone the repository:

   git clone https://github.com/BYT18/Linear-Regression-.git
   cd Linear-Regression-

2. Run the script:

   python LinearReg.py

   Ensure that the required CSV files are in the same directory as the script.

Project Structure

Linear-Regression-/
├── LinearReg.py           # Main Python script with linear regression implementation
├── LinearReg.pyproj       # Python project file
├── LinearReg.sln          # Solution file for IDEs like Visual Studio
├── linTest.csv            # Sample dataset 1
├── r2test.csv             # Sample dataset 2
└── test2.csv              # Sample dataset 3

Usage

The LinearReg.py script reads data from the provided CSV files, fits a linear regression model, and outputs the results, including coefficients and evaluation metrics.

Contributing

Contributions are welcome! If you have suggestions for improvements or additional features, feel free to fork the repository and submit a pull request.

License

This project is open-source and available under the MIT License.

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Feel free to customize this README further to match any specific requirements or to add more detailed instructions based on the functionalities within LinearReg.py.