Data Preparation for Analytics Using SAS by Gerhard Svolba

By Gerhard Svolba

Written for somebody all for the knowledge training approach for analytics, this elementary textual content deals functional recommendation within the type of SAS coding assistance and methods, in addition to supplying the reader with a conceptual history on information constructions and issues from the enterprise standpoint. subject matters addressed contain viewing analytic information coaching within the mild of its enterprise setting, deciding on the specifics of predictive modeling for information mart production, knowing the strategies and issues for information guidance for time sequence research, utilizing a variety of SAS techniques and SAS firm Miner for scoring, developing significant derived variables for all facts mart kinds, utilizing strong SAS macros to make alterations one of the a variety of info mart constructions, and extra!

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Example Assume we perform predictive modeling in order to predict the purchase event for a certain product. This analysis is performed based on historic purchase data, and the purchase event is explained by various customer attributes. The result of this analysis is a probability for the purchase event for each customer in the analysis table. Additionally, the calculation rule for the purchase probability can be output as a scoring rule. In logistic regression, this scoring rule is based on the regression coefficients.

The creative and exploratory part of a data mining project should not be ended prematurely because we might leave out potentially useful data and miss important findings. The fact that we select input characteristics from a business point of view does not mean that there is no place for clever data preparation and meaningful derived variables. On the other hand, not every technically possible derived variable needs to be built into the analysis paradigm. We also need to bear in mind the necessary resources, such as data allocation and extraction in the sources systems, data loading times, disk space for data storage, analysis time, business coordination, and selection time to separate useful information from non-useful information.

5 “Old Data” and Many Attributes From an IT point of view Daniele will need data that are in many cases hard to provide. Historic snapshots are needed, not only for the last period, but for a series of prior periods. If in sales analysis the influence of price on the sold quantity will be analyzed, the sale for each historic period has to be compared with the historic price in the same time period. We will discuss in Chapter 12 – Considerations for Predictive Modeling, the case of latency windows.

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