Machine Learning Methods from Scratch in Python

This repository contains simple implementations of some of the most common machine learning algorithms in python. Each algorithm is implemented from scratch to provide a deeper understanding of how they work.

Algorithms included

• Decision Tree (DecisionTree.py)
• K-Nearest Neighbors (KNN) (KNN.py)
• Linear Regression (LinearRegression.py)
• Logistic Regression (LogisticRegression.py)

Running the Models

test_models.ipynb demonstrates how to load data, train each model, evaluate the results, ensuring the implemented algorithms work as expected.

Work in progress (WIP)

The following models are currently under development and will be added soon:

• Naive Bayes
• Principal Component Analysis (PCA)
• Perceptron (simple neural network)
• Support Vector Machine
• K-means Clustering