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Anomaly Detection for Data Quality in Python
Anomaly detection is a critical technique in modern data science and machine learning. By identifying patterns in data that deviate significantly from expected behaviour, anomaly detection can help detect fraud, prevent equipment failures, and more. Anomaly detection is used in a wide range of applications, from finance and cybersecurity to databases and manufacturing. In this article, we will explore the basics of anomaly detection, including the types of anomalies, the methods used for detecting them, and the different machine learning models that can be employed in Python. The following is the outline for the article.
Table of Contents
- What is Anomaly Detection?
- Importance of Anomaly Detection
- Types of Anomalies
- Anomaly Detection using Machine Learning
- Requirements
- Data Generation
- Isolation Forest
- 1-Class SVC
- Caveats of Anomaly Detection
- Conclusion
- Resources
