A Study on Machine Learning Techniques for Rainfall Prediction in the Indian Agricultural Landscape

India's economy relies heavily on its agricultural sector, making rainfall a critical parameter that significantly impacts agricultural productivity. Predicting rainfall not only aids Indian farmers directly but also paves the way for the integration of other precision agriculture techniques into the farming landscape. This study delves into the exploration of three distinct Machine Learning (ML) methods utilized for rainfall prediction and their evolution over time. Specifically, the ML methods investigated include the Artificial Neural Network, Random Forest Algorithm, and Linear Regression. After a comprehensive review of prior research, Linear Regression is selected for conducting predictive analysis on an extensive dataset encompassing rainfall patterns in India from 1901 to 2015. Furthermore, this study transcends mere predictive capabilities by leveraging these models to conduct a detailed regional analysis. This regional examination is exemplified by a nuanced investigation of East and West Madhya Pradesh. The primary objective of this regional comparison is to elucidate the diverse influences that shape rainfall patterns, thereby contributing to a more contextualized understanding of the complex Indian climate. By dissecting the distinct climatic nuances between these regions, this study aims to provide valuable insights for agricultural planning, water resource management, and disaster preparedness efforts tailored to the unique needs of different geographical areas within India.