Comparison of Linear Regression and Logistic Regression Algorithms for Ground Water Level Detection with Improved Accuracy

Abstract

Comparison of the innovative Linear Regression and Logistic Regression Algorithms for Ground Water Level Detection with Improved Accuracy is the goal of this study, which was designed to investigate that question. A total of 30 Specimens are split up into their respective groups. Every person received 15 different samples. The Novel Linear Regression Algorithm is used for Group 1, whereas the Logistic Regression Algorithm is used for Group 2. The accuracy of the model generated by the Novel Linear Regression Algorithm is (93.27%), which is higher than the accuracy generated by the Logistic Regression Algorithm, which is (86.5%). It is determined using an independent sample T-test, and the Significance Value is 0.439, which indicates that the hypothesis is not significant. This is shown by the fact that p>0.01 is returned. Therefore, the accuracy of the Novel Linear Regression Algorithm, which was found to be 93.23%, is discovered to be greater than the accuracy of the Logistic Regression Algorithm, which was found to be 86.5%.