Data-Science

This repository showcases a curated collection of Machine Learning algorithms, categorized into Supervised and Unsupervised learning techniques, with direct links to their respective GitHub repositories for easy access and exploration.

Supervised Learning

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

1. Classification

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

2. Regression

• Linear Regression – Linear Regression Repo
• Support Vector Regressor - SVR Repo

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Unsupervised Learning

Unsupervised learning uses unlabeled data to find patterns and relationships.

1. Clustering

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

2. Dimensionality Reduction

• PCA (Principal Component Analysis) – PCA Repo