ABSTRACT
Technologies such as big data analytics, data warehousing, data mining, and dredging software have automated the future-oriented analyses to make proactive, knowledge-driven decisions. Based on the observed happenings, the future prospects and measures are predicted with the aid of predictive knowledge analytics to make the recommendations called prescriptive analytics. This research artifact is the implementation and performance evaluation of data mining predictive analytics on different weather datasets to predict various climate conditions with accuracy levels. A climate change framework has been designed, which revolves around data resources and decision tree analytics to investigate its value creation for prediction and optimization. The association rule mining paradigms of market basket analysis and classification decision tree induction algorithms are explored for knowledge extraction. The mathematical models of interestingness parameters and decision tree postulates are investigated for future forecasting. The algorithms are implemented for a case study of predicting whether a player can practice for national and international tournaments depending on various weather conditions. The chapter concludes with the issues of those algorithms and futuristic approaches to these knowledge discovery paradigms.
