Authors - Ritika Upadhyay, Eshita Dey, Munmun Patra, Roji Khatun, Chinmoy Kar, Somenath Chaterjee Abstract - Predicting accurate rainfall is crucial for a country like India, which has a diverse economy. Agriculture is a vital aspect of life for many rural communities in India, making timely rainfall a significant concern for improving agricultural yields. However, predicting rainfall has become increasingly challenging due to drastic climate changes, resulting in more frequent natural calamities like floods and soil erosion. To address this issue, extensive research is underway to enhance rainfall prediction, allowing people to take appropriate precautions to protect their crops. Currently, predictive models tend to be complex statistical frameworks that can be expensive in terms of both computation and budget. As a more effective solution, using historical data combined with machine learning algorithms is being proposed. This research aims to improve rainfall prediction through algorithms such as Gradient Boosting and Random Forest. Model evaluation will utilize metrics like Mean Squared Error (MSE) and Root Mean Squared Error (RMSE). This study has considered approximately 150 years of historical rainfall data (from 1813 to 2006) for different regions of India.
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