Answer: Supervised learning

## Explanation This is a **supervised learning** problem because: - **Labeled Data**: The bank has historical data where customers are explicitly labeled as "default" or "non-default" - **Classification Task**: The goal is to predict categorical outcomes (default vs non-default) based on input features - **Training Process**: The model learns from labeled examples to make predictions on new, unseen data ### Why Other Options Are Incorrect: - **A) Unsupervised learning**: Used when data has no labels and the goal is to find patterns or groupings - **C) Transfer learning**: Involves using knowledge from one domain to improve learning in another domain - **D) Reinforcement learning**: Focuses on learning through trial-and-error interactions with an environment to maximize rewards ### Key Characteristics of Supervised Learning: - Requires labeled training data - Used for classification (categorical outcomes) and regression (continuous outcomes) - Common algorithms: Logistic Regression, Decision Trees, Random Forests, Neural Networks - Perfect for credit risk assessment where historical outcomes are known

Author: Ritesh Yadav