Linear Regression

Introduction

This project is a simple implementation of linear regression using the least squares method. The project is written in Python and uses the numpy library for matrix operations.

Usage

The project is written in Python 3. To run the project, you need to have the numpy library installed. You can install it using pip3:

pip3 install numpy

To run the project, simply run the main.py file (feel free to modify it to your needs) using the following command:

python3 main.py

Architecture

The project is divided into the following files:

• main.py: The main file that runs the project.
• model.py: Contains the implementation of linear regression models using the least squares method and their corresponding evaluation methods. Contains also the solver using the least squares method.
• metrics.py: Contains the implementation of several metrics to evaluate the performance of the linear regression models.
• fit_optimizer.py: Contains a few fitting algorithms to optimize the model.
• plot.py: Contains the implementation of the plot method to visualize the datasets and their corresponding linear regression models.
• dataset.py: Contains examples of datasets according to their linear regression models and splitting dataset functions.

License

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