Power DataMate Tool: Leveraging Logistic Regression Classification for Interactive Data Modeling - Abu Alrub, Mahmoud; Shaout, Adnan (Transactions on Engineering and Computing Sciences, 2024)
The demand for efficient predictive modeling techniques has become crucial due to the growing occurrence of binary classification problems in diverse fields. This paper presents a new and innovative software tool “Power DataMate” (PDM) which performs logistic regression (LR) classification as a potent technique for data modeling. PDM is a powerful tool in capturing and analyzing correlations across varied datasets. One objective of PDM is to focus on logistic regression for inquiry based on its capability to represent intricate interactions between predictors and the binary response variable. Another important goal of the new tool is to forecast the likelihood of discovering Primary Keys (PK) and Foreign Keys (FK) within datasets. The new tool PDM also allows users not only to automatically have their data for a project modeled, but also interactively review and confirm primary keys and features for further data analysis and modeling. While the research entails a comprehensive evaluation of model performance indicators, including accuracy, precision, and recall, results show that the accuracy of PK prediction is 89% and 82% for the FK. Hence, these results are the first of their kind and could be a starting point for further model enhancements and data analytics research, especially for projects which include large data files where PDM end user has the choice to interactively feed the learning algorithm for better outcomes.