This repository contains a collection of common methods and metrics used in the field of machine learning, along with their implementation in Python.

The main goal of this repository is to provide a centralized reference for various machine learning methods and associated metrics for evaluating model performance.

• /methods: Contains implementations of different machine learning algorithms such as linear regression, decision tree classification, etc.
• /metrics: Includes code for calculating model evaluation metrics such as accuracy, recall, F1-score, etc.
• README.md: This file provides an overview of the repository and how to use it.

In each folder (/methods and /metrics), you'll find additional README files that explain each method and metric in detail, along with usage examples and additional references.

The source code is primarily written in Python and is well-documented to facilitate understanding and usage.

To run the code in this repository, you'll need to have the following Python libraries installed:

• NumPy
• scikit-learn
• Pandas

You can install these dependencies by running:

pip install numpy scikit-learn pandas


Additional Resources
Link to Machine Learning Book
Public Dataset for Experimentation