This repository contains code and information on various regression methods, including shrinkage methods, unsupervised and supervised dimensionality reduction, tree-based methods, and other regression techniques.

• Introduction
• Methods Covered
• Getting Started
• Usage
• Contributing
• License

Regression models are widely used in various fields, including finance, healthcare, and social sciences. This repository aims to provide an overview of different regression methods, their advantages, and limitations, and how to implement them in Python.

The repository covers the following regression methods:

• Shrinkage methods - Regularization - Ridge, Lasso, and Elastic net
• Unsupervised dimensionality reduction - Principal Components Analysis (PCA)
• Logistic regression (binary)
• Supervised dimensionality reduction - Linear Discriminant Analysis (LDA)
• Tree-Based methods - Regression Trees
• Other Regression methods - Piecewise Linear Regression and Linear Quantile Regression
• Generalized linear model (GLM)
• Generalized Additive Models (GAM)

Each folder contains code and an explanation for the corresponding method.

To use the code in this repository, you will need to have Python 3 installed on your machine.

You can modify the code to fit your own dataset and experiment with different hyperparameters.

Contributions to this repository are welcome. If you have any suggestions, or bug reports, or would like to add a new method, please open an issue or submit a pull request.

This repository is licensed under the MIT License. Feel free to use the code for personal or commercial purposes. However, we are not responsible for any damages that may arise from the use of the code.