Build practical skills in predictive modeling by working through the complete model development lifecycle using a real-world banking use case. In this course, you will define a business problem, explore and interpret data through Exploratory Data Analysis (EDA), and prepare datasets using data imputation and variable selection techniques.

Next, you will develop predictive models using Information Value (IV) analysis and multicollinearity checks to identify meaningful variables. You will evaluate model performance with ranking techniques, decile analysis, KS statistics, AUC, and Lift, then improve model performance through monotonic binning and tree-based optimization methods. The course concludes by validating models on unseen datasets and deploying them to a simulated production environment, giving you practical experience with the end-to-end predictive modeling workflow. Whether you are a learner interested in predictive analytics, machine learning workflows, or data-driven decision-making, this course provides a structured, hands-on approach to building, evaluating, optimizing, validating, and deploying predictive models using established statistical techniques and real-world business scenarios.