SAS Enterprise Miner streamlines the data mining process to create highly accurate predictive and descriptive models based on analysis of vast amounts of data from across an enterprise. Data mining is applicable in a variety of industries and provides methodologies for such diverse business problems as fraud detection, householding, customer retention and attrition, database marketing, market segmentation, risk analysis, affinity analysis, customer satisfaction, bankruptcy prediction, and portfolio analysis. In SAS Enterprise Miner, the data mining process has the following (SEMMA) steps: • Sample the data by creating one or more data sets. The sample should be large enough to contain significant information, yet small enough to process. This step includes the use of data preparation tools for data import, merge, append, and filter, as well as statistical sampling techniques. • Explore the data by searching for relationships, trends, and anomalies in order to gain understanding and ideas. This step includes the use of tools for statistical reporting and graphical exploration, variable selection methods, and variable clustering. • Modify the data by creating, selecting, and transforming the variables to focus the model selection process. This step includes the use of tools for defining transformations, missing value handling, value recoding, and interactive binning. • Model the data by using the analytical tools to train a statistical or machine learning model to reliably predict a desired outcome. This step includes the use of techniques such as linear and logistic regression, decision trees, neural networks, partial least squares, LARS and LASSO, nearest neighbor, and importing models defined by other users or even outside SAS Enterprise Miner. • Assess the data by evaluating the usefulness and reliability of the findings from the data mining process. This step includes the use of tools for comparing models and computing new fit statistics, cutoff analysis, decision support, report generation, and score code management. You might or might not include all of the SEMMA steps in an analysis, and it might be necessary to repeat one or more of the steps several times before you are satisfied with the results. After you have completed the SEMMA steps, you can apply a scoring formula from one or more champion models to new data that might or might not contain the target variable. Scoring new data that is not available at the time of model training is the goal of most data mining problems. Furthermore, advanced visualization tools enable you to quickly and easily examine large amounts of data in multidimensional histograms and to graphically compare modeling results.