Build practical predictive modeling skills with Python and learn to prepare, build, refine, and evaluate models for real-world data analysis. You’ll begin by setting up the required tools and preparing datasets through dummy-variable creation, dataset splitting, feature scaling, missing-value treatment, and outlier handling.

You’ll construct simple and multiple linear regression models, visualize relationships, analyze correlations, and address multicollinearity. Using Scikit-learn and Statsmodels, you’ll refine models through backward elimination and adjusted R², then assess their reliability with RMSE and VIF. The course then explores logistic regression for classification. You’ll build and optimize logistic models, interpret confusion matrices, visualize decision boundaries, and evaluate performance using ROC curves, threshold analysis, and AUC. In the final credit risk case study, you’ll apply these techniques to prepare borrower data and assess default probability. Designed for learners who want hands-on experience with predictive analytics in Python, this course combines structured theory, practical modeling, and a focused case study. Its step-by-step approach helps you move from data preparation to model evaluation with confidence. Enroll to develop practical skills for building reliable regression and classification models in professional contexts.