Unsupervised Learning for Financial Insights

Explain the primary goals of unsupervised learning: clustering, anomaly detection, and dimensionality reduction.

Describe the use of clustering algorithms (e.g., K-means) for customer segmentation.

Explain how anomaly detection techniques can identify fraudulent transactions.

Describe dimensionality reduction (e.g., PCA) for simplifying complex financial datasets.

Explain the role of the Python ecosystem (e.g., scikit-learn, pandas) for implementing ML models in finance.