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  • Data visualization
  • Descriptive statistics
  • Evaluation metrics for regression tasks
  • Regression tasks
  • Strategies to handle unknown variable values
  • Model selection/comparison through k-fold cross-validation
  • Multiple linear regression
  • Regression trees
  • Classification methods
  • Clustering methods
  • Imbalanced class distributions and methods for handling this type of problems
  • Naive Bayes classifiers
  • Precision/recall and precision/recall curves
  • Feature selection methods for problems with a very large number of predictors
  • K-Nearest neighbors
  • Random forests