Data Scientist – Machine Learning Algorithms

This repository contains a collection of Machine Learning algorithms organized by Supervised and Unsupervised Learning methods, along with links to respective GitHub repositories for each algorithm.

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Supervised Learning

Supervised learning uses labeled data to train models for predictions. It is divided into two types:

1. Classification

• Decision Trees – Decision Tree Repo
• Random Forest – Random Forest Repo
• SVM (Support Vector Machines) – SVM Repo
• KNN (K-Nearest Neighbors) – KNN Repo
• Logistic Regression – Logistic Regression Repo
• Naive Bayes – Naive Bayes Repo
• Gradient Boosting – Gradient Boosting Repo

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2. Regression

• Linear Regression – Linear Regression Repo
• Ridge Regression – Ridge Regression Repo
• Lasso Regression – Lasso Regression Repo
• Elastic Net Regression – Elastic Net Repo

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Unsupervised Learning

Unsupervised learning uses unlabeled data to find patterns and relationships.

1. Clustering

• K-Means – K-Means Repo
• Hierarchical Clustering – Hierarchical Clustering Repo
• DB_SCAN – DB_SCAN Repo

2. Dimensionality Reduction

• PCA (Principal Component Analysis) – PCA Repo

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