Data mining for business analytics : concepts techniques and applications in R Overview of the data mining process Data visualization Dimension reduction Evaluating predictive performance Multiple linear regression k Nearest Neighbors (kNN) The Naive Bayes classifier Classification and regression trees Logistic regression Neural nets Discriminant analysis Combining methods : ensembles and uplift modeling Association rules and collaborative filtering Cluster analysis Handling time series Regression based forecasting Smoothing methods Social network analytics Text mining Cases