Finding the weird stuff in your data with Python
Original: Anomaly Detection in Machine Learning Using Python | The PyCharm Blog
Cheuk Ting Ho
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May 29, 2025
12 min read
Tutorial
Intermediate
Summary
Learn how to detect anomalies in machine learning using Python. Explore key techniques with code examples and visualizations in PyCharm for data science tasks.
Who This Is For
Data Scientists
Analytics Engineers
Business Analysts
Key Takeaways
- Learn the difference between outlier detection and novelty detection approaches
- Implement OneClassSVM and Isolation Forest algorithms for anomaly detection
- Build visualizations to spot anomalies in datasets using PyCharm
- Apply machine learning techniques to detect fraud, security threats, and system issues
- Work with the Beehives dataset to practice real anomaly detection scenarios
Tools & Technologies
Python PyCharm IDE OneClassSVM Isolation Forest Local Outlier Factor Elliptic Envelope FastAPI
Topics Covered
anomaly-detection machine-learning python data-science visualization
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