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Anomaly Detection Overview
What is Anomaly Detection?
Anomaly detection (also called outlier detection) is the process of identifying data points, events, or observations that deviate significantly from the expected pattern in a dataset.
Key Concepts
Types of Anomalies

Point Anomalies: Individual data points that are anomalous compared to the rest of the data
Contextual Anomalies: Data points that are anomalous in a specific context (e.g., temperature of 30°C is normal in summer, but anomalous in winter)
Collective Anomalies: A collection of data points that together indicate anomalous behavior

Common Approaches
Statistical Methods

Z-Score: Measures how many standard deviations a point is from the mean
IQR (Interquartile Range): Identifies outliers beyond Q1 - 1.5×IQR or Q3 + 1.5×IQR
MAD (Median Absolute Deviation): More robust to outliers than standard deviation

Machine Learning Methods

Isolation Forest: Isolates anomalies by randomly partitioning data
Local Outlier Factor (LOF): Measures local density deviation
One-Class SVM: Learns a boundary around normal data
Autoencoders: Neural networks that learn to reconstruct normal patterns

Use Cases

Fraud Detection: Identifying fraudulent transactions in financial data
Network Security: Detecting intrusions or unusual network traffic
Manufacturing: Identifying defective products or equipment failures
Healthcare: Detecting unusual patient vitals or disease outbreaks
IoT: Monitoring sensor data for abnormal readings

Demo Preview
The following Python block demonstrates anomaly detection using:

Statistical approach: Z-score method for quick detection
ML approach: Isolation Forest algorithm for more sophisticated detection
Visualization: Clear plots showing detected anomalies
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