COGS118A_FinalProject

Final project for COGS118A Supervised Machine Learning course

This project examines multiple supervised machine learning algorithms used to solve classification problems. In this project, I perform an empirical analysis and comparison between the supervised learning methods: logistic regression, k-nearest neighbors, random forests, and decision trees. Performance is measured across multiple trials and datasets for each classifier.

Classification problems explored:

• Heart Disease
• Mushrooms
• Drug Consumption

Classification notebooks are comprised of:

• Exploratory Data Analysis
• GridSearchCV
• Three separate trials
• Optimal parameter tuning
• Trial results