Machine Learning Projects

This repository contains various machine learning projects, each focusing on a specific algorithm or technique. The goal is to explore, implement, and analyze different machine learning methods using Python.

Project Structure

Each folder represents a distinct topic or algorithm in machine learning, with relevant code and examples.

1. Area Under the ROC Curve

• Implements the computation and visualization of the Receiver Operating Characteristic (ROC) curve.
• Includes methods to calculate the Area Under the Curve (AUC) for model evaluation.
• Focuses on binary classification performance metrics.

2. Decision Tree Regression

• Implements decision trees for regression tasks.
• Includes visualization of decision boundaries and tree structures.
• Demonstrates parameter tuning for depth and splitting criteria.

3. Discrimination by Regression

• Explores regression models used for discrimination tasks.
• Demonstrates logistic regression and other discriminative models.
• Analyzes data separability and feature importance.

4. Expectation-Maximization Clustering

• Implements the Expectation-Maximization (EM) algorithm for clustering.
• Focuses on Gaussian Mixture Models (GMM) and probabilistic clustering methods.
• Includes visualization of cluster distributions.

5. Naive Bayes Classifier

• Implements the Naive Bayes algorithm for classification.
• Covers both Gaussian and Multinomial variants.
• Demonstrates its use in text classification and other domains.

6. Nonparametric Regression

• Explores nonparametric regression techniques like kernel regression and splines.
• Highlights their flexibility in capturing nonlinear patterns.
• Includes comparisons with parametric methods.