Dealing with missing data
  Eliminating samples or features with missing values
  Imputing missing values
  Understanding the scikit-learn estimator API
Handling categorical data
  Mapping ordinal features
  Encoding class labels
  Performing one-hot encoding on nominal features
Partitioning a dataset in training and test sets
Bringing features onto the same scale
Selecting meaningful features
  Sparse solutions with L1 regularization
  Sequential feature selection algorithms
Assessing feature importance with random forests
Summary
