{"product_id":"9781633436473","title":"Outlier Detection in Python","description":"\u003ctable\u003e\u003ctbody\u003e\n\u003ctr\u003e\n\u003ctd style=\"\"\u003e\u003cstrong\u003eAuthor\/Contributor(s):\u003c\/strong\u003e\u003c\/td\u003e\n\u003ctd style=\"\"\u003eKennedy, Brett\u003cbr\u003e\n\u003c\/td\u003e\n\u003c\/tr\u003e\n\u003ctr\u003e\n\u003ctd style=\"\"\u003e\u003cstrong\u003ePublisher:\u003c\/strong\u003e\u003c\/td\u003e\n\u003ctd\u003eManning\u003cbr\u003e\n\u003c\/td\u003e\n\u003c\/tr\u003e\n\u003ctr\u003e\n\u003ctd style=\"\"\u003e\u003cstrong\u003eDate:\u003c\/strong\u003e\u003c\/td\u003e\n\u003ctd\u003e1\/7\/2025\u003cbr\u003e\n\u003c\/td\u003e\n\u003c\/tr\u003e\n\u003ctr\u003e\n\u003ctd style=\"\"\u003e\u003cstrong\u003eBinding:\u003c\/strong\u003e\u003c\/td\u003e\n\u003ctd style=\"\"\u003ePaperback\u003c\/td\u003e\n\u003c\/tr\u003e\n\u003ctr\u003e\n\u003ctd style=\"\"\u003e\u003cstrong\u003eCondition:\u003c\/strong\u003e\u003c\/td\u003e\n\u003ctd style=\"\"\u003eNEW\u003cbr\u003e\n\u003c\/td\u003e\n\u003c\/tr\u003e\n\u003c\/tbody\u003e\u003c\/table\u003e\u003cb\u003eLearn how to identify the unusual, interesting, extreme, or inaccurate parts of your data.\u003c\/b\u003e\u003cbr\u003e\u003cbr\u003eData 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. \u003ci\u003eOutlier Detection in Python\u003c\/i\u003e 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.\u003cbr\u003e \u003cbr\u003eIn \u003ci\u003eOutlier Detection in Python\u003c\/i\u003e you'll learn how to:\u003cbr\u003e \u003cbr\u003e• Use standard Python libraries to identify outliers\u003cbr\u003e • Select the most appropriate detection methods\u003cbr\u003e • Combine multiple outlier detection methods for improved results\u003cbr\u003e • Interpret your results effectively\u003cbr\u003e • Work with numeric, categorical, time series, and text data\u003cbr\u003e \u003cbr\u003e 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. To get started, you'll only need a basic understanding of statistics and the Python data ecosystem.\u003cbr\u003e \u003cbr\u003e Purchase of the print book includes a free eBook in PDF and ePub formats from Manning Publications.\u003cbr\u003e \u003cbr\u003e \u003cb\u003eAbout the technology\u003c\/b\u003e\u003cbr\u003e \u003cbr\u003e Outliers—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.\u003cbr\u003e \u003cbr\u003e \u003cb\u003eAbout the book\u003c\/b\u003e\u003cbr\u003e \u003cbr\u003e \u003ci\u003eOutlier Detection in Python\u003c\/i\u003e 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.\u003cbr\u003e \u003cbr\u003e \u003cb\u003eWhat's inside\u003c\/b\u003e\u003cbr\u003e \u003cbr\u003e• Python libraries to identify outliers\u003cbr\u003e • Combine outlier detection methods\u003cbr\u003e • Interpret your results\u003cbr\u003e \u003cbr\u003e\u003cb\u003eAbout the reader\u003c\/b\u003e\u003cbr\u003e \u003cbr\u003e For Python programmers familiar with tools like pandas and NumPy, and the basics of statistics.\u003cbr\u003e \u003cbr\u003e \u003cb\u003eAbout the author\u003c\/b\u003e\u003cbr\u003e \u003cbr\u003e \u003cb\u003eBrett Kennedy\u003c\/b\u003e is a data scientist with over thirty years’ experience in software development and data science.\u003cbr\u003e \u003cbr\u003e \u003cb\u003eTable fo Contents\u003c\/b\u003e\u003cbr\u003e \u003cbr\u003e Part 1\u003cbr\u003e 1 Introducing outlier detection\u003cbr\u003e 2 Simple outlier detection\u003cbr\u003e 3 Machine learning-based outlier detection\u003cbr\u003e 4 The outlier detection process\u003cbr\u003e Part 2\u003cbr\u003e 5 Outlier detection using scikit-learn\u003cbr\u003e 6 The PyOD library\u003cbr\u003e 7 Additional libraries and algorithms for outlier detection\u003cbr\u003e Part 3\u003cbr\u003e 8 Evaluating detectors and parameters\u003cbr\u003e 9 Working with specific data types\u003cbr\u003e 10 Handling very large and very small datasets\u003cbr\u003e 11 Synthetic data for outlier detection\u003cbr\u003e 12 Collective outliers\u003cbr\u003e 13 Explainable outlier detection\u003cbr\u003e 14 Ensembles of outlier detectors\u003cbr\u003e 15 Working with outlier detection predictions\u003cbr\u003e Part 4\u003cbr\u003e 16 Deep learning-based outlier detection\u003cbr\u003e 17 Time-series data","brand":"Manning","offers":[{"title":"Default Title","offer_id":46454492791039,"sku":"9781633436473","price":69.99,"currency_code":"USD","in_stock":true}],"thumbnail_url":"\/\/cdn.shopify.com\/s\/files\/1\/0452\/0886\/2873\/files\/Jacket_dcd946a2-6467-41ff-afd3-4409f0712668.jpg?v=1771253727","url":"https:\/\/massivebookshop.com\/de\/products\/9781633436473","provider":"MASSIVE BOOKSHOP","version":"1.0","type":"link"}