Detecting Data Anomalies

Overview

Identify anomalies and outliers in datasets using statistical and machine learning algorithms including Isolation Forest, One-Class SVM, Local Outlier Factor, and autoencoders. This skill handles the full detection pipeline from data ingestion and feature scaling through algorithm selection, threshold tuning, and result interpretation with anomaly scoring.

Prerequisites

• Python 3.9+ with scikit-learn >= 1.3 (pip install scikit-learn)
• pandas and NumPy for data manipulation (pip install pandas numpy)
• matplotlib or seaborn for anomaly visualizations (pip install matplotlib seaborn)
• Dataset in CSV, JSON, Parquet, or database-queryable format
• Minimum 500 data points for statistical significance (1000+ recommended)
• Optional: PyTorch or TensorFlow for autoencoder-based detection on complex patterns

Instructions