ML Algorithms from Scratch

Welcome to the ml-algos-from-scratch repository! This repository contains implementations of widely used machine learning algorithms from scratch using Python. The main goal is to understand the inner workings of these algorithms.

About

Machine learning algorithms are often used as black boxes, with the inner workings hidden behind libraries and frameworks. This work aims to better understand these algorithms by implementing them from scratch.

Implemented Algorithms

• Linear Regression
• Logistic Regression
• k-Nearest Neighbors (kNN)
• Decision Tree
• Random Forest
• Support Vector Machine (SVM)
• AdaBoost
• k-Means Clustering
• Principal Component Analysis (PCA)
• Linear Discriminant Analysis (LDA)
• Naive Bayes
• Perceptron

Installation

To get started, you can clone the repository and install the necessary dependencies.

git clone https://github.com/mahesh973/ml-algos-from-scratch.git

cd ml-algos-from-scratch

pip install -r requirements.txt

Contributing

Contributions are welcome! If you have an algorithm you'd like to add or improvements to existing ones, please follow these steps:

1. Fork the repository
2. Create a new branch (git checkout -b feature/YourFeature)
3. Commit your changes (git commit -m 'Add some feature')
4. Push to the branch (git push origin feature/YourFeature)
5. Open a pull request

Happy coding!