Rainfall Prediction for Agriculture Optimization Using Machine Learning

Abstract- Accurate downfall vaticination plays a pivotal part in agrarian planning, especially in areas affected by climate oscillations. This study presents a machine literacy- grounded approach aimed at perfecting the delicacy of downfall vaticinations. colorful models including Random Forest, Linear Retrogression, and Decision Tree Regressor were trained and tested using literal rainfall data. Among these, the Random Forest model delivered the most accurate results. The proposed system supports perfection husbandry by enabling informed opinions related to irrigation scheduling and crop operation. also, the study highlights how data- driven ways can be integrated into scalable decision- support tools to enhance resource effectiveness and agrarian adaptability. Indicator Terms: Rainfall Prediction, Machine Learning, Agriculture Optimization, Random Forest, Decision Tree, Linear Retrogression, Meteorological Data.