Outlier Detection in Python, Video Edition

In Video Editions the narrator reads the book while the content, figures, code listings, diagrams, and text appear on the screen. Like an audiobook that you can also watch as a video.

In Outlier Detection in Python you’ll learn how to:

• 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

About the Technology
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.

About the Book
Outlier 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 Inside

• Python libraries to identify outliers
• Combine outlier detection methods
• Interpret your results

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

More Info