This book is a practical guide to spotting the parts of a dataset that deviate from the norm, even when they're hidden or intertwined among the expected data points. Brett Kennedy explains how outlier detection is a vital tool for modern business, whether it's discovering new products, expanding markets, or flagging fraud and other suspicious activities. This guide presents the core tools for outlier detection, as well as techniques utilizing the Python data stack familiar to data scientists.

<ASIN:1633436470 >

Author: Brett Kennedy
Publisher: Manning
Date: January 2025
Pages: 560
ISBN: 978-1633436473
Print: 1633436470
Kindle: B0DPLGQDJK
Audience: Data scientists
Level: Intermediate
Category: Data Science and Python

Topics include:

• Use standard Python libraries to identify outliers
• Select the most appropriate detection methods
• Combine multiple outlier detection methods for improved results
• Interpret your results effectively
• Work with numeric, categorical, time series, and text data

For recommendations of books on data science see Reading Your Way Into Big Data in our Programmer's Bookshelf section.

For recommendations of Python books see Books for Pythonistas and Python Books For Beginners in our Programmer's Bookshelf section.

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