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SAS Global Forum 2007 Data Mining and Predictive Modeling

Due to the large amount of data typically involved, data mining analyses can exacerbate some common modeling problems and create a number of new ones. These problems can greatly increase the time that it takes to develop useful models and can hamper the development of potentially superior models. This paper discusses how to identify and overcome several common modeling mistakes. The presentation begins by providing insights into common mistakes in data preparation; it then follows the data flow of a typical predictive modeling analysis through setting variable roles, creating and using data partitions, performing variable selection, replacing missing values, building different types of models, comparing resulting models, and scoring those models using SAS Enterprise MinerTM. The paper concludes with a discussion of common issues with cluster analysis and association/sequence analysis. Applying these techniques can greatly decrease the time it takes to build useful models and improve the quality of the models that are created. INTRODUCTION We seek to identify several common data mining mistakes and provide suggestions for how to overcome the problems associated with these mistakes. The collection of mistakes and corrective measures discussed here should not be considered complete, because a full discussion could fill several volumes. However, the collection does include those mistakes that have been frequently observed and can almost always be overcome. Please note that the choice of the best approach is highly subjective, and it is possible that certain suggestions recommended in this paper are not well suited for a particular situation. In the end, it is the responsibility of the analyst to choose the most appropriate method for a given analysis. The discussion that follows seeks to raise awareness of certain situations that can lead to undesirable results and describes ways in which those situations can be addressed. PREPARING THE DATA It often requires more time to prepare the data than to analyze the data. Unfortunately, deadlines can force shortcuts to be made since a good answer today is often more desirable than a better answer tomorrow. These shortcuts often minimize the time spent in data preparation, but failing to prepare the data adequately can greatly increase the analysis time, thereby minimizing the value of the shortcut. Additionally, the resulting models often perform more poorly than a model developed with adequate data preparation. The mistakes involved with data preparation often appear in the form of failing to consider enough variables, improperly preparing (or failing to prepare) categorical predictors, and improperly preparing (or failing to prepare) continuous predictors. FAILING TO CONSIDER ENOUGH VARIABLES Faced with the potentially daunting task of investigating all of their data, users often want to know which variables to use for a given model. This type of thinking has at least one inherent problem: it relies on the existence of some common subset of traits that can be used to satisfactorily model the problem in question. Consider the following points: • Not every company has the same “variables.” Every company has variables that are similar, but they often have a reasonably large number of variables that they collect which their competitors do not capture. The variables that are collected by multiple companies can differ in how they are defined or in how often they are measured. • Reducing a modeling exercise to the subset of common variables ignores the richness and the uniqueness of a company’s data. If all companies are using the same subset of variables, then each company loses a great opportunity to identify patterns that might be unique to that company. Customizing your model based on your own data enables you to choose the approach that works best in your situation. • Being satisfied with doing what everyone else is doing leaves you at a disadvantage compared to those who take advantage of the richness of their data. This isn’t merely a question of growth in many markets; it is a question of survival. If your competitor is able to increase retention or cross-sell/up-sell more than your company, your competitor is likely to gain market share over time. Adopting this one-size-fits-all approach doesn’t make sense in most business situations, and it certainly doesn’t make sense when doing modeling. Having modeled similar problems in many largeand medium-sized companies, I have found information is often gleaned from unexpected sources. The process of analyzing the data provides great SAS Global Forum 2007 Data Mining and Predictive Modeling