Rainfall Prediction Using Regression and Classification Analysis
Abstract
The use of logistic regression modeling for the purposes of prediction and forecasting has seen meteoric growth over the last ten years, as was suggested by our study. The occurrence of rainfall is among the most significant aspects of the climate system. It is common knowledge that changes in the frequency and severity of rainfall may have an effect on natural systems, agricultural systems, human systems, and even whole biological systems. Therefore, it is of the utmost importance to be able to forecast rainfall by determining the factors that are reliable predictors. In this article, an effort is made to forecast rainfall by using logistic regression. The results of this endeavor are presented. Despite the fact that this issue often escapes the attention of the analysts, it is abundantly clear that the meteorological data are frequently affected by significant recording inaccuracies. In this research, we have utilized fairly modern screening techniques to verify and rectify the climatic data that we use in our study. This is something that we have done in order to ensure the accuracy of the data. The suggested approach has an accuracy of 88%. Some keywords that go together with accuracy include machine learning, regression, and rainfall.Downloads
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