Machine Learning Refreshers

This repository consists implementations of various Machine Learning Algorithms serving as a quick refresher to their nuances.

Data pre-processing

• Filling missing data
• Encoding categorical variables
• Feature Scaling
• Train/test split

Regression

• Quick Linear Regression Overview
• Simple/Multiple Linear Regression
• Polynomial Linear Regression
• Support Vector Regression
• Decision Tree Regression
• Random Forest Regression
• Model Evaluation
• Ridge (L2) Regression
• Lasso (L1) Regression
• Elastic Net Regression
• Pros and Cons of Regression Models
• Assumptions for Linear Regression
• Goodness of fit ($R^2$)

Classification

• Logistic Regression
• KNN Classification
• Decision Tree Classification
• Random Forest Classification
• Support Vector Machine with Grid Search
• Naive Bayes Classification
• Confusion Matrix
• Classification Report

Clustering

• K Means Clustering (With Elbow Method)
• Hierarchical Clustering (Agglomerative)