🚀 Machine Learning

Implementations

This repository contains implementations of core machine learning regression algorithms using Python. Some models are implemented from scratch to understand the underlying mathematics, while others are implemented using scikit-learn for comparison.

I try to add new implementations regularly as part of my learning process.

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✅ Implemented Algorithms

• Gradient Descent
• Simple Linear Regression
• Multiple Linear Regression
• Polynomial Regression
• Ridge Regression
• Lasso Regression
• ElasticNet Regression
• Logistic Regression
• Perceptron trick
• Classification Metrics
• Softmax Regression
• Naive Bayes classifier
• KNN classifier
• SVM classifier

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🛠️ Modules Used

NumPy
Pandas
Matplotlib
Scikit-learn
Plotly

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🎯 Purpose

The goal of this repository is to:

• Understand ML algorithms mathematically and programmatically.
• Practice implementing models from scratch and using sklearn as well.
• Result Comparison.

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🛣️ Work in Progress

More algorithms will be added over time as I continue learning machine learning.

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