Predicting Term Subscription Using ML Models

This project focuses on analyzing the factors influencing customers' decisions to sign up for a term deposit at
a bank, using various predictive models to identify patterns and trends. The dataset includes information
on client demographics, financial indicators, and previous marketing interactions. The primary goal is to
develop a model that can accurately predict whether a client is likely to subscribe to a term deposit, allowing
the bank to optimize its marketing efforts and reduce unnecessary costs.
Several classification models were employed, including Logistic Regression, Decision Trees, Naive Bayes, and
Random Forest. Data preprocessing involved transforming variables such as age, balance, and housing into
factors, and creating dummy variables to enable accurate analysis. We also addressed data imbalances by
focusing on variables that significantly influenced the likelihood of clients signing up for a term deposit.
Among the models tested, the Random Forest model proved to be the most effective, achieving an accuracy of
77.72% with a 95% confidence interval of (76.59%, 78.81%). This model's performance, as assessed through
the confusion matrix, highlighted its strength in predicting clients less likely to sign up, thus enabling the
bank to better target potential subscribers. The analysis demonstrated that key variables like age, balance,
and housing status were pivotal in influencing a client's decision to sign the term deposit.
The findings of this project provide actionable insights for the bank, enabling it to focus resources on high
potential clients and improve the efficiency of marketing strategies. Future iterations could further enhance
model accuracy by incorporating additional data and addressing class imbalances more comprehensively.

Machine Learning

Decision Tree Model

Random Forest 

Practice and Applications

Symposium on Data Science and Statistics (SDSS) 2025