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