Learn how to find the unusual, interesting, extreme, or inaccurate parts of your data. Outliers can be the most informative parts of your data, revealing hidden insights, novel patterns, and potential problems. For a business, this can mean finding new products, expanding markets, and flagging fraud or other suspicious activity. Outlier Detection in Python introduces the tools and techniques you'll need to uncover the parts of a dataset that don't look like the rest, even when they're the more hidden or intertwined among the expected bits. In Outlier Detection in Python you'll learn how to: Use standard Python libraries to identify outliersPick the right detection methodsCombine multiple outlier detection methods for improved resultsInterpret your resultsWork with numeric, categorical, time series, and text data Outlier detection (OD) is a vital tool for everything from financial auditing to network security. OD techniques also work for testing datasets for quality, collection errors, and data drift. This unique guide introduces the core tools of outlier detection like scikit-learn and PyOD, the principal algorithms used in outlier detection, and common pitfalls you might encounter.
