Answer: Supervised learning

## Explanation This is a **supervised learning** problem because: 1. **Labeled Data**: The bank has historical data where customers are already labeled as "default" or "non-default." This means the target variable (the outcome we want to predict) is known for the training data. 2. **Classification Task**: The problem involves predicting a categorical outcome (default vs. non-default), which is a classic classification problem in supervised learning. 3. **Why not other options**: - **Unsupervised learning (A)**: Used when there are no labels in the data (e.g., clustering, anomaly detection). - **Transfer learning (C)**: Involves using knowledge gained from solving one problem to help solve a different but related problem. - **Reinforcement learning (D)**: Involves an agent learning through trial-and-error interactions with an environment to maximize cumulative reward. In supervised learning, the model learns the relationship between input features (customer characteristics) and output labels (default/non-default) from the labeled training data, then uses this learned pattern to make predictions on new, unseen data.

Author: Ritesh Yadav