Thunderstorms can have significant impacts on society, including causing damage to infrastructure, disrupting transportation, and posing risks to human safety. Accurate prediction of thunderstorm occurrences and intensities is therefore important for mitigating these impacts. In this project, we propose a machine learning approach for predicting thunderstorms using meteorological data. Our method involves collecting meteorological data from various sources, including Government of Jharkhand. We then preprocess and clean the data, and apply various machine learning algorithms to build prediction models. We evaluate the performance of our model’s using evaluation of many metrics and compare the results to baseline models. Our results show that our machine learning approach is able to accurately predict thunderstorms improvement over the baseline models. We also identify important factors that contribute to the prediction accuracy and discuss potential applications of our approach. Overall, this project demonstrates the potential of machine learning for improving thunderstorm prediction and highlights the importance of carefully selecting and processing data for effective model training.
