Rainfall Prediction Using Logistic Regression and Support Vector Regression Algorithms

Abstract

Rainfall Prediction is the use of science and innovation to anticipate the condition of the climate for a given area. Expectation of rainfall is perhaps the most fundamental and requesting undertakings for the climate forecasters. Rainfall expectation assumes a significant part in the field of cultivating and businesses. Exact precipitation forecast is indispensable for recognizing the substantial rainfall and to give the data of alerts with respect to the characteristic disasters. Rainfall expectation includes recording the different boundaries of climate like humidity, wind speed, stickiness, and temperature so on. In this Paper, we distinguish the issues of rainfall prediction and fixing them using the machine learning algorithms like Logistic Regression method and Support Vector Regression. Experimental results show that Logistic Regression algorithm is best suitable for prediction of rainfall with accuracy 96% when compare to the support vector regression algorithm. This prediction results helps in the agriculture work.