ML Algorithms from Scratch 🧠✨

This repository contains implementations of core Machine Learning algorithms written from scratch using only Python, NumPy, and pandas. The goal is to build a deeper understanding of how these algorithms work under the hood without relying on external ML libraries like scikit-learn.

📌 What’s Inside

📈 Regression

• Univariate Linear Regression
• Multiple variable Linear Regression
• Ridge & Lasso Regression

🔍 Classification

• Logistic Regression
• Support Vector Machine (SVM)
• Naive Bayes (Multinomial for text classification)
• K-Nearest Neighbours

⚙️ Preprocessing Utilities

• Feature Scaling
• Custom Train-Test Split
• Loss Visualization

📚 Datasets Used

• Ames Housing Dataset
• SMS Spam Collection Dataset
• Banknote Authentication Dataset
• Iris Dataset

🚀 Purpose

This project is part of my journey into Machine Learning, aimed at solidifying foundational concepts by building each algorithm from the ground up. Ideal for beginners looking to move beyond theory and understand the math and code that power modern ML systems.

💡 Getting Started

1. Clone the repository:

   git clone https://github.com/piyusha2001/ml-algorithms-from-scratch.git
   cd ml-algorithms-from-scratch