By the end of this course, learners will be able to identify, apply, analyze, and evaluate predictive analytics techniques using Python. They will gain hands-on skills in data preprocessing, regression modeling, logistic regression, and credit risk analysis, equipping them to solve real-world data challenges with confidence.

This comprehensive program begins with the foundations of predictive modeling, guiding learners through data preparation, dummy variables, feature scaling, and basic regression concepts. It progresses to mastering linear regression, covering model fitting, handling multicollinearity, and optimizing models through backward elimination and adjusted R². Learners then enhance their models by analyzing correlations, calculating RMSE, and applying advanced validation techniques. The course deepens into logistic regression, focusing on classification, confusion matrices, ROC curves, and threshold adjustments to evaluate performance effectively. Finally, learners apply their knowledge in a credit risk case study, gaining practical experience with encoding, missing value treatment, outlier handling, and AUC-based evaluation. What makes this course unique is its step-by-step blend of theory and hands-on practice using real datasets, ensuring learners not only understand the concepts but can apply and evaluate predictive models in professional contexts.