Anomaly Detection
Normal is easy to define. Abnormal is whatever doesn't fit. Language: Python Implement Z-score, IQR, and Isolation Forest anomaly detection methods from scratch. Distinguish between point, contextual, and collective anomalies and select the appropriate detection method for each. Explain why anomaly detection is framed as modeling normal data rather than classifying anomalies. Compare unsupervised anomaly detection with supervised classification and evaluate the tradeoff between novel anomaly coverage and precision. A credit card is used in New York at 2pm, then in Tokyo at 2:05pm. A factory sensor reads 150 degrees when the normal range is 80-120. A server sends 50,000 requests per second when the daily average is 200. These are anomalies. Finding them matters. Fraud costs billions. Equipment failures cost downtime. Network intrusions cost data. The challenge: you rarely have labeled examples of anomalies. Fraud makes up 0.1% of transactions. Equipment failures happen a few times per year. You cannot train a standard classifier because there is almost nothing in the "anomaly" class to learn from. Even if you have some labels, the anomalies you have seen are not the only types you will encounter. Tomorrow's fraud scheme looks different from today's. Anomaly detection flips the problem. Instead of learning what is abnormal, learn what is normal. Anything that deviates from normal is suspicious. This works without labels, adapts…
Anomaly Detection: Normal is easy to define. Abnormal is whatever doesn't fit. Language: Python Implement Z-score, IQR, and Isolation Forest anomaly detection…
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