Outlier Detection in Python

Learn how to identify the unusual, interesting, extreme, or inaccurate parts of your data.Data scientists have two main tasks: finding patterns in data and finding the exceptions. These outliers are often the most informative parts of data, revealing hidden insights, novel patterns, and potential problems. Outlier Detection in Python 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.In Outlier Detection in Python you'll learn how to:Use standard Python libraries to identify outliersSelect the most appropriate detection methodsCombine multiple outlier detection methods for improved resultsInterpret your results effectivelyWork with numeric, categorical, time series, and text dataOutlier 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. To get started, you'll only need a basic understanding of statistics and the Python data ecosystem.About the technologyOutliers—values that appear inconsistent with the rest of your data—can be the key to identifying fraud, performing a security audit, spotting bot activity, or just assessing the quality of a dataset. This unique guide introduces the outlier detection tools, techniques, and algorithms you’ll need to find, understand, and respond to the anomalies in your data.About the bookOutlier Detection in Python illustrates the principles and practices of outlier detection with diverse real-world examples including social media, finance, network logs, and other important domains. You’ll explore a comprehensive set of statistical methods and machine learning approaches to identify and interpret the unexpected values in tabular, text, time series, and image data. Along the way, you’ll explore scikit-learn and PyOD, apply key OD algorithms, and add some high value techniques for real world OD scenarios to your toolkit.What's insidePython libraries to identify outliersCombine outlier detection methodsInterpret your resultsAbout the readerFor Python programmers familiar with tools like pandas and NumPy, and the basics of statistics.About the authorBrett Kennedy is a data scientist with over thirty years’ experience in software development and data science.Table fo ContentsPart 11 Introducing outlier detection2 Simple outlier detection3 Machine learning-based outlier detection4 The outlier detection processPart 25 Outlier detection using scikit-learn6 The PyOD library7 Additional libraries and algorithms for outlier detectionPart 38 Evaluating detectors and parameters9 Working with specific data types10 Handling very large and very small datasets11 Synthetic data for outlier detection12 Collective outliers13 Explainable outlier detection14 Ensembles of outlier detectors15 Working with outlier detection predictionsPart 416 Deep learning-based outlier detection17 Time-series data

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