• JEYADEVAN SUGADEVAN
• SUBHA V
• Dr. Ganganagunta Srinivas

Abstract: Predicting the amount of rain is crucial since excessive downpours can trigger numerous disasters. In addition to assisting people in taking preventative action, the prediction should also be accurate. Short-term and long-term rainfall predictions are the two different categories of predictions. Most of the time, short-term predictions can provide us with accurate outcomes. Developing a model for long-term rainfall prediction is the primary obstacle. Because heavy precipitation is intimately related to the economy and human lifespan, it could be a significant disadvantage for the Earth Science Department. It is the reason behind the annual natural calamities like drought and flood that affect people all over the planet. It is the reason behind the annual natural calamities like drought and flood that affect people all over the planet. Since agriculture is the main driver of the economies of nations like India, the accuracy of the rainfall statement is very important. Because of the dynamic nature of the environment, precipitation statements cannot be reasonably accurately predicted using applied mathematics techniques. Regression may be used in machine learning approaches for precipitation prediction. The goal of this project is to provide a comparative analysis of the different machine learning algorithms and to make the techniques and approaches used in the field of precipitation prediction easily accessible to non-experts.

Key terms: Weather prediction, Rainfall prediction, Naïve bayes classifier.

"A Hybrid Machine Learning Model for Rain fall Prediction", International Journal of Emerging Technologies and Innovative Research (www.jetir.org), ISSN:2349-5162, Vol.10, Issue 12, page no.g692-g697, December-2023, Available :http://www.jetir.org/papers/JETIR2312682.pdf

"A Hybrid Machine Learning Model for Rain fall Prediction", International Journal of Emerging Technologies and Innovative Research (www.jetir.org | UGC and issn Approved), ISSN:2349-5162, Vol.10, Issue 12, page no. ppg692-g697, December-2023, Available at : http://www.jetir.org/papers/JETIR2312682.pdf