Scholars' Showcase 2025

This project analyzes the factors influencing customers' decisions to subscribe to a term deposit at a bank using machine learning models. The dataset, sourced from a public bank marketing campaign, includes client demographics, financial indicators, and past interactions. Data preprocessing involved handling missing values, creating dummy variables, and addressing class imbalances. Various classification models were tested, including Logistic Regression, Decision Trees, Naive Bayes, and Random Forest. Among these, the Random Forest model achieved the highest accuracy of 77.72%, effectively identifying potential subscribers. Key predictors included age, balance, and housing status, providing actionable insights for targeted marketing strategies.