- Data Understanding (semantics, descriptive statistics, quality assessment, variable transformation, pairwise correlation and elimination of redundant variables)
- Clustering Analysis (K-means, density-based, hierarchical)
- Association Rule Mining
- Classification via Decision Trees (model interpretation and validation with different gain formulae) and via K-Nearest Neighbors
-
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Analysis of Human Resources Analytics Dataset using Clustering, Association Rules, and Decision Trees - Fall 2017
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