Machine Learning Algorithms from Scratch

This repository contains implementations of essential machine learning algorithms built entirely from scratch using NumPy, Pandas, and Matplotlib. It covers both supervised and unsupervised learning techniques, with each algorithm trained and tested on datasets to evaluate its accuracy and performance.

Implemented Algorithms

• Linear Regression – Predicts continuous target values from input features.
• Polynomial Regression – Extends linear regression to model non-linear relationships.
• Logistic Regression – Used for binary classification tasks.
• Artificial Neural Network (ANN) – A simple feedforward neural network for recognizing complex patterns.
• K-Nearest Neighbors (KNN) – A non-parametric classification and regression algorithm.
• K-Means Clustering – An unsupervised method for grouping data into clusters.

Key Features

• Model Training & Evaluation – Each algorithm is tested on multiple datasets to measure effectiveness.
• Minimal Dependencies – Built using only NumPy, Pandas, and Matplotlib for data handling and visualization.
• No Pre-built Libraries – Every algorithm is implemented from the ground up, offering a deeper insight into its workings.