Agriculture Data Analysis Using Random Forest And Linear Regression

Abstract

India is a horticultural nation and its economy is to a great extent founded on harvests and precipitation. To keep an eye on crop yield, all farmers need to know how much rain will fall. Guaging is the use of science and innovation to anticipate the condition of the air. Planning for water quality, crop productivity, and efficient use of water resources all depend on accurate rainfall measurements. Utilizing different learning strategies, the machine can foresee precipitation. AI strategies are utilized to assess precipitation. This article centers around some famous AI calculations for precipitation estimating. This article compares algorithms such as random forest, polynomial regression, and simple linear regression. From this examination, it is feasible to dissect which strategy gives the best precipitation exactness.