Anomaly Detection

Using scikit-learn you own this product

This free project is part of the liveProject series Anomaly Detection with Python
basic Python • basic pandas • basic scikit-learn • basics of machine learning
skills learned
run robust covariance with scikit-learn • run isolation forest with scikit-learn • run the LOF algorithm with scikit-learn
Stylios Kampakis and Shreesha Jagadeesh
1 week · 5-8 hours per week · INTERMEDIATE
filed under

placing your order...

Don't refresh or navigate away from the page.
This free project is part of the liveProject series Anomaly Detection with Python explore series
Check your email for instructions on accessing Using scikit-learn (liveProject)
continue shopping
adding to cart

choose your plan


only $41.67 per month
  • five seats for your team
  • access to all Manning books, MEAPs, liveVideos, liveProjects, and audiobooks!
  • choose another free eBook every time you renew
  • choose twelve free eBooks per year
  • exclusive 50% discount on all purchases
  • Using scikit-learn eBook for free
Look inside

In this liveProject, you’ll explore the basics of anomaly detection by analyzing a medical dataset using unsupervised learning. You’ll create a model that can determine whether patients referred to a clinic have abnormal thyroid function. To accomplish this, you’ll download and prepare your dataset, and then utilize scikit-learn to compare different anomaly detection algorithms to find the most effective. You are going to use Isolation Forest, the Local Outlier Factor (LOF), One-Class SVM and Robust Covariance.

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 machine learning

you will learn

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

  • Load and preprocess MATLAB data in Python
  • Run the One-Class SVM with scikit-learn
  • Run robust covariance with scikit-learn
  • Run Isolation Forest with scikit-learn
  • Run the LOF algorithm with scikit-learn


You choose the schedule and decide how much time to invest as you build your project.
Project roadmap
Each project is divided into several achievable steps.
Get Help
While within the liveProject platform, get help from other participants.
Compare with others
For each step, compare your deliverable to the solutions by the author and other participants.