Predictive Modeling with Python: Apply & Evaluate

Overview

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.

Syllabus

Foundations of Predictive Modeling

This module introduces learners to predictive modeling with Python, covering essential installations, preprocessing techniques, and fundamental regression concepts. Learners build a strong foundation in data preparation, feature scaling, and understanding regression basics.

Mastering Linear Regression

This module explores simple and multiple linear regression models, focusing on fitting techniques, dummy variables, and model refinement using backward elimination and adjusted R². Learners gain the ability to build and optimize regression models for accurate predictions.

Enhancing Regression Models

This module deepens regression knowledge with correlation analysis, multicollinearity detection, and performance evaluation using RMSE and VIF. Learners also transition into logistic regression and confusion matrix interpretation.

Logistic Regression in Depth

This module provides advanced insights into logistic regression, including model building with Sklearn and Statsmodels, optimization through backward elimination, and performance evaluation using ROC curves and threshold analysis.

Credit Risk Case Study

This capstone module applies predictive modeling techniques to credit risk analysis. Learners preprocess categorical variables, handle missing values and outliers, and build models to assess borrower default probability using ROC and AUC.