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Preventing operation failures and interruptions is mission-critical at Sigma Corp. The large conglomerate of energy production companies has recently implemented a z-score anomaly detection algorithm that focuses on a single feature. Now that the algorithm has proved its value, members of Sigma have requested additional algorithms that are just as simple to use, but that can handle multidimensional data. As a lead data scientist at Sigma, you’ll implement the Mahalanobis distance (MD) method and the principal component analysis (PCA) method as you build anomaly detection algorithms for multidimensional data. To gauge the performance of your algorithms, you’ll test them against a benchmark dataset as well as synthetic anomalies generated by your own algorithms. When you’re done, you’ll have firsthand experience building anomaly detection algorithms for multidimensional datasets as well as testing anomaly detection algorithms against both benchmark datasets and synthetic anomalies.
This liveProject is for beginner data scientists interested in learning to build multidimensional anomaly detection algorithms. To begin these liveProjects you’ll need to be familiar with the following:TOOLS
In this liveProject, you’ll learn to apply the MD and PCA methods to build algorithms for multidimensional anomaly detection.
geekle is based on a wordle clone.