Machine Learning from Scratch in R

ML algorithms implemented from scratch using R

This repository contains machine learning algorithm implementations built from the ground up in R, focusing on understanding the mathematical foundations and core concepts behind each algorithm.

Project Goals

• Learn by Building: Understand ML algorithms by implementing them from scratch
• Mathematical Foundation: Focus on the math and logic behind each algorithm
• Clean R Code: Well-documented, readable R implementations
• Educational Resource: Serve as a learning tool for ML enthusiasts

Algorithms Implemented

Completed

• Linear Regression (Simple & Multiple)
• Logistic Regression
• K-Nearest Neighbors (KNN)
• Naive Bayes
• Binary Decision Tree

Planned Implementations

• Full Recursive Decision Tree
• Random Forest
• Support Vector Machine (SVM)
• K-Means Clustering
• Principal Component Analysis (PCA)
• Linear Discriminant Analysis (LDA)
• Perceptron
• AdaBoost
• Neural Network

Installation and Dependencies

This project uses the following R packages:

Core Dependencies

• tidyverse - For data manipulation and visualization
• dplyr - For data wrangling
• ggplot2 - For plotting and visualization
• data - Default Built in datsets of R

Install Dependencies

# Install required packages
install.packages(c("tidyverse", "dplyr", "ggplot2", "datasets", "caret"))

Learning Resources

This repository is designed for:

• Students learning machine learning fundamentals
• Practitioners wanting to understand algorithms deeply
• R Enthusiasts interested in ML implementation
• Anyone curious about the math behind ML

Contributing

This is a personal learning project, but suggestions and improvements are welcome! Feel free to:

• Open issues for bugs or suggestions
• Submit pull requests for improvements
• Share feedback on implementations

Notes

Important: These implementations are for educational purposes.

Built with R for learning Machine Learning from scratch