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Machine Learning from Scratch

This repository contains Python implementations of various foundational machine learning algorithms, built entirely from scratch without using external machine learning libraries such as scikit-learn. The focus is on understanding the underlying mathematics and logic behind each algorithm.

Implemented Algorithms

1. K-Nearest Neighbors (KNN)

  • A simple, non-parametric algorithm used for classification and regression.
  • Predicts the class of a data point based on the majority vote of its k nearest neighbors.

2. K-Means Clustering

  • A popular unsupervised learning algorithm for clustering data points into k clusters.
  • Assigns data points to clusters by minimizing the variance within each cluster.

3. K-Means++ Initialization

  • An improved version of K-Means clustering.
  • Optimizes the initial selection of centroids to improve convergence speed and accuracy.

4. Stochastic Gradient Descent (SGD)

  • An optimization technique used for minimizing a cost function.
  • Efficient for large datasets by updating weights incrementally rather than in bulk.

Requirements

  • Python 3.x
  • NumPy (for matrix and vector operations)
  • Matplotlib (optional, for visualizations)

You can install the dependencies using:

pip install numpy matplotlib