ModelBuilding

A collection of machine learning models implemented from scratch using Python and NumPy, without relying on high-level libraries like scikit-learn.
This repository is intended for educational purposes and aims to deepen understanding of how models work internally.

📂 Structure

Each folder contains a different model implementation with code and explanations.

🔧 Models (to be added)

• Linear Regression
• Multiple Linear Regression
• Logistic Regression
• Support Vector Machine (SVM)
• Lasso Regression
• k-Nearest Neighbors (KNN)
• Decision Tree
• Naive Bayes
• K-Means Clustering
• Principal Component Analysis (PCA)
• Neural Networks (Basic)

🧠 Goal

To understand the working principles of machine learning algorithms by:

• Implementing each from scratch.
• Avoiding black-box libraries.
• Focusing on core math and code.

📌 Requirements

• Python
• NumPy
• Pandas
• Seaborn
• Scikit-learn
• Matplotlib

🚀 How to Use

git clone https://github.com/SwarupD21/ModelBuilding.git
cd ModelBuilding/LinearRegression
python linear_regression.py