Predictive analytics with factor variance association
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© Springer Nature Switzerland AG 2019.Organizations are turning to predictive analytics to help solve difficult problems and uncover new opportunities. Nowadays, the processes are saturated in data, which must be used properly to generate the necessary key information in the decision making process. Although there are several useful techniques to process and analyze data, the main value starts with the treatment of key factors. In this way, a Predictive Factor Variance Association (PFVA) is proposed to solve a multi-class classification problem. The methodology combines well-known machine learning techniques along with linear algebra and statistical models to provide the probability that a particular sample belongs to a class or not. It can also give predictions based on regression for quantitative dependent variables and carry-out clustering of samples. The main contribution of this research is its robustness to execute different processes simultaneously without fail as well as the accuracy of the results.