Anomaly Detection

Using PyOD and Ensemble Methods you own this product

This project is part of the liveProject series Anomaly Detection with Python
basic Python • basic pandas • basic scikit-learn • basics of machine learning
skills learned
load MATLAB data in Python • run anomaly detection algorithms in PyOD • use the LSCP algorithm in PyOD in order to detect anomalies
Stylios Kampakis and Shreesha Jagadeesh
1 week · 5-8 hours per week · INTERMEDIATE

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Look inside

In this liveProject, you’ll explore a dataset with more variables and use scikit-learn and the PyOD library to build an unsupervised machine learning model for detecting cardiac arrhythmias. You’ll develop an algorithm which will detect arrhythmias from device data like EEG, using the Locally Selective Combination in Parallel Outlier Ensembles (LSCP) algorithm. A LSCP model accepts input as various other algorithms, and can be used to set up detectors with different settings.

This project is designed for learning purposes and is not a complete, production-ready application or solution.

book resources

When you start your liveProject, you get full access to the following books for 90 days.

project authors

Stylianos Kampakis
Dr. Stylianos (Stelios) Kampakis is a data scientist with more than 10 years of experience. He has worked with decision-makers from companies of all sizes from startups to organizations like the US Navy, Vodafone, and British Land. He has also helped many people follow a career in data science and technology. He is a member of the Royal Statistical Society, honorary research fellow at the UCL Centre for Blockchain Technologies, a data science advisor for London Business School and CEO of The Tesseract Academy. A natural polymath with a PhD in machine learning and degrees in artificial intelligence, statistics, psychology, and economics, he loves using his broad skillset to solve difficult problems and help companies improve their efficiency.
Shreesha Jagadeesh
Shreesha Jagadeesh is a product manager at Amazon creating data science-driven HR products for talent retention, career growth and internal mobility. He has previously worked as a manager at Ernst & Young where he led a large global team of 25+ data scientists and engineers to apply data science-driven digital transformation of their tax business units. Aside from his day job, he is a startup advisor helping young companies build out their data science functions. He has a master’s in electrical and computer engineering from the University of Toronto. He has been teaching for more than a decade and has written data science articles on Medium, reviewed other Manning courses and developed a popular Udemy course for Agile data science.


This liveProject is for Python programmers who are interested in exploring machine learning. To begin this liveProject, you will need to be familiar with the following:

  • Basic Python
  • Basic pandas
  • Basic scikit-learn
  • Basic Mat2Py
  • Basics of PyOD
  • Basics of machine learning

you will learn

In this liveProject, you’ll master the domain of anomaly detection through exploring various methods.

  • Load MATLAB data in Python
  • Use the LSCP algorithm in PyOD in order to detect anomalies


You choose the schedule and decide how much time to invest as you build your project.
Project roadmap
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book resources
Get full access to select books for 90 days. Permanent access to excerpts from Manning products are also included, as well as references to other resources.

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