Exploring Supervised Learning Techniques: A Comparative Study of Decision Trees, Naive Bayes, and Ensemble Models

Summary

I conducted a performance analysis of two classifiers and two ensemble classifiers: one utilizing bagging with Decision Trees and the other employing boosting with Decision Stumps. This analysis was carried out on the ”Bank Marketing Data Set”. The primary objective of this classification task was to predict whether clients would subscribe to a term deposit.

Please refer to my GitHub repo for full project here.