Assignment 3 includes unsupervised learning algorithm (Kmean clustering, Agglomerative clustering) and supervised learning algorithms (Logistic Regression, Support Vector Machine, Random Forest) implemented both from scratch and using sklean library. Data is divided 80-20 for training and testing. F1 score and confusion matrix are performed.
Final project includes running different variations data with Support Vector Machine, Random Forest and Naive Bayes algorithm from sklearn. Data is divided 80-20 for training and testing, using cross-validation, f1 and learning curves for performance metrics.