Rainfall Accuracy Prediction using Machine Learning Technique based on Linear Regression over Logistic Regression

Abstract

Aim: The main aim of the research is to predict rainfall using Linear Regression over Logistic Regression. Materials and Methods: Linear Regression and Logistic Regression are implemented in this research work. Sample size is calculated using G power software and determined as 10 per group with pretest power 80%, threshold 0.05% and CI 95%. Result: Linear Regression provides a higher of 91.18% compared to Logistic Regression algorithm with 87.05% in predicting rainfall. Two groups differ significantly from one another, as shown by a significance value of 0.003 (p<0.05). Conclusion: Linear Regression algorithm predicts the rainfall better than Logistic Regression algorithm.