In this module, learners gain a strong foundation in building a credit default prediction model using Python. The module introduces the project’s scope, outlines the workflow, and emphasizes the importance of structured data handling. Learners will explore data preprocessing techniques such as handling missing values, encoding categorical features, and scaling numerical variables. In addition, they will perform exploratory data analysis (EDA) to identify patterns, visualize distributions, and uncover key relationships within the dataset. Finally, learners will split the dataset into training and testing sets to ensure reliable evaluation of logistic regression models for predicting credit default risk.

In this module, learners advance beyond data preparation into the core of predictive modeling. The module introduces evaluation metrics such as the confusion matrix and ROC curve to assess classification performance in credit default prediction. Learners will then explore hyperparameter tuning methods like Grid Search and Randomized Search to optimize logistic regression models. The module further builds knowledge with decision tree theory, covering splitting criteria, visualization using Graphviz, and practical implementation in Python. Finally, learners will apply ensemble techniques with Random Forest to reduce overfitting and improve model accuracy for robust credit risk prediction.