Outlier Detection in Python (MEAP V01)

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 outliers
Pick the right detection methods
Combine multiple outlier detection methods for improved results
Interpret your results
Work 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.

about the book
Outlier Detection in Python is a comprehensive guide to the statistical methods, machine learning, and deep learning approaches you can use to detect outliers in different types of data. Throughout the book, you’ll find real-world examples taken from author Brett Kennedy’s extensive experience developing outlier detection tools for financial auditors and social media analysis. Plus, the book’s emphasis on interpretability ensures you can identify why your outliers are unusual and make informed decisions from your detection results. Each key concept and technique is illustrated with clear Python examples. All you’ll need to get started is a basic understanding of statistics and the Python data ecosystem.

about the reader
For Python programmers familiar with tools like pandas and NumPy, and the basics of statistics.

about the author
Brett Kennedy is a data scientist with over thirty years’ experience in software development and data science. He has worked in outlier detection related to financial auditing, fraud detection, and social media analysis. He previously led a research team focusing on outlier detection.