Outlier Detection in Python (MEAP V01)

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.   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