Abstract
The goal of Knowledge Discovery in Data (KDD) is to extract information that is not obvious by using careful and detailed analysis and interpretation. To drive decisions and actions, analytics employs KDD, data mining, text mining, statistical and quantitative analysis, explanatory and predictive models, and advanced and interactive visualisation techniques. Cloud computing is a versatile technology that can be used for a variety of purposes. The following criteria distinguish these data: few predictor variables, many predictor variables, extremely collinear variables, very redundant variables, and the presence of outliers. In this paper, we describe the various predictive data-mining techniques that were used to achieve the goals, as well as the methods for comparing the performance of each technique.
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