Overview

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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.

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.