Predictive Analytics: Data Mining, Machine Learning and Data Science for Practitioners, 2nd Edition

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Standardized Processes for Predictive Analytics

As has been the case in many other computational paradigms, extracting knowledge from large data repositories (e.g., data mining, predictive analytics, data science, business analytics) started as a trial-and-error experimental process. Many practitioners have approached the problem from the perspective of trying to characterize what works and what doesn’t. For quite some time, data mining (or, more recently, data analytics and data science) projects were carried out as rather artistic, ad hoc, experimental endeavors. However, in order to methodically conduct these analytics, a standardized process needed to be developed and followed. Based on best practices, in the early days, data mining researchers ...