Isolation Forests you own this product

define functions and classes • use libraries • read documentation
skills learned
apply the Isolation Forest method • evaluate algorithms under adversarial distribution change

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Red alert! One of the energy production companies managed by Sigma Corp has suffered an outage. An investigation has led to the conclusion that the facility’s anomaly detection mechanism failed to detect early signals due to a sudden change in the distribution of the analyzed data. As a lead data scientist at Sigma, you’ll build an Isolation Forest algorithm, which is less likely than the Empirical Cumulative distribution-based Outlier Detection (ECOD) method to fail in such scenarios. To gauge how robust your method is, you’ll benchmark your algorithms against adversarial scenarios, synthetic anomalies, and standard datasets. When you’re done, you’ll have practical experience creating, using, and testing the Isolation Forest algorithm as an effective alternative to ECOD in circumstances where the data distribution changes.

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

project author

Sergio Solorzano

Sergio Solórzano holds a PhD in physics from ETH Zürich, where he specialized in computational physics and published various papers on numerical algorithms for physical simulation and analysis. Currently, he’s a senior researcher and developer at Exeon Analytics, developing systems for anomaly detection in cybersecurity.


This liveProject is for beginner data scientists who want to learn how to build an anomaly detection algorithm for multidimensional data that is effective even when the distribution of data changes. To begin these liveProjects you’ll need to be familiar with the following:

  • Basic Python
  • Basic NumPy
  • Basic Matplotlib (or Seaborn or Bokeh)
  • Basic data science
  • Basic testing


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