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ISSN 2063-5346
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Enhancing Prediction Accuracy of Rainfall Using Machine Learning And Forecasting Methods

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Niharika Jain, Nikita Jain, Shivam Sharma, Rakshit Mishra, Vaishali Deshwal
» doi: 10.31838/ecb/2023.12.si4.261

Abstract

Agriculture plays a major part within the Indian economy. Rain is crucial for agriculture, but of late, predicting rain has become a really tough subject. An honest rain forecast provides farmers the data to set up ahead, take precautions, and have higher agricultural ways. Both nature and humanity are being severely impacted by global warming, that conjointly hastens the amendment in weather conditions. Flooding and therefore the transformation of the cultivated field into aridity are caused by the warming of its air and therefore the increasing ocean level. Unreasonable and unseasonal rain may be a result of unfavorable environmental conditions. One of the best ways for learning regarding rainfall and climate is to anticipate rain. This research’s major objective is to accurately describe to clients about precipitation from a variety of perspectives, including agriculture, research, the production of electricity, etc. In order to achieve accurate prediction, variables such as temperature, humidity, precipitation, and wind speed, are used which ultimately help with generating results in rainfall prediction. The ever-evolving portion of computer science that aids with rain prediction is named Machine Learning. For the aim of predicting the rain during this analysis study, we’ll use a dataset that has several properties. The most important goal of this work is to spot the best algorithmic rule for predicting rain and to try to do so accurately, we are using Machine Learning regression algorithms

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