Bafna, A., Parkhe, A., Iyer, A., & Halbe, A. (2019). A novel approach to data visualization by supporting ad-hoc query and predictive analysis : (An Intelligent Data Analyzer and visualizer). 2019 International Conference on Intelligent Computing and Control Systems (ICCS), Intelligent Computing and Control Systems (ICCS), 113–119. This resource highlights an improvement over the existing business intelligence (BI) tools as it supports predictive analytics along with the existing functionalities offered by any BI Tool.
Sinharay, S. (2016). An NCME instructional module on data mining methods for classification and regression. Educational Measurement: Issues & Practice, 35(3), 38–54. This resource demonstrates using three real-data examples that the prescribed methods may lead to an improvement over traditionally used methods such as linear and logistic regression in educational measurement.
Ramona Marge, Stefan Iovan, & Alina-Anabela Iovan. (2018). Predictive analysis in the big data era. Analele Universităţii “Constantin Brâncuşi” Din Târgu Jiu: Seria Inginerie, 2018(4), 124–129. This resource highlights insights that are the basis of operational excellence, providing a significant competitive advantage and leading to business success.
Ghorpade, J., & Sonkamble, B. (2020). Predictive Analysis of Heterogeneous Data – Techniques & Tools. 2020 5th International Conference on Computer and Communication Systems (ICCCS), 40–44. This resource highlights why processing heterogeneous data is necessary to extract useful information, making it a decisive factor for the better survival of the future with automation.
Lingjun He, Levine, R. A., Juanjuan Fan, Beemer, J., & Stronach, J. (2018). Random forest as a predictive analytics alternative to regression in institutional research. Practical Assessment, Research & Evaluation, 23(1), 1–16. This resource highlights the advantages of tree-based machine learning algorithms over classic (logistic) regression methods for data-informed decision making and stresses the success of random forest in circumstances where the regression assumptions are often violated in big data applications.