Predictive Modeling for Bank Term Deposit Subscriptions

This project focuses on building a predictive analytics pipeline to estimate the likelihood of clients subscribing to a bank’s term-deposit product. The work blends exploratory data analysis, feature engineering, and supervised machine learning to extract actionable insights from campaign data. The primary objective was to understand customer profiles, identify high-propensity segments, and provide recommendations to optimize targeting and reduce campaign costs.

The methodology included comprehensive EDA to discover relationships between demographic, behavioral, and campaign-related variables. Data cleaning and preprocessing steps such as handling missing values, categorical encoding, and scaling were applied. Predictive models (logistic regression, decision-tree–based classifiers and ensemble methods) were evaluated using cross-validation. Model selection prioritized interpretability for business stakeholders and predictive performance for operational use.

The final deliverables included: a validated predictive model to score prospects, feature-importance analysis to inform marketing strategy, and a set of operational recommendations to improve campaign ROI. Insights from the model were used to re-prioritize leads, reduce outreach cost per conversion, and inform personalized outreach strategies. This project demonstrates how data-driven modeling can directly improve campaign effectiveness and business outcomes for financial institutions.