Rainfall prediction is difficult and indeterminate responsibility that leaves impact on the life of nature, animals and humans. Accurate and up-to-the-time forecasting can be beneficial for human society and economy. Heavy rainfall can cause many disasters like flood, land slides etc. and low rainfall also can cause disaster like drought, damage of corps etc. Climate changes had been become a major concern for last decade. It also affects the weather conditions by which a drastic change in pattern of rainfall had been noticed lately. The rainfall depends on the various factors that are: temperature, pressure, humidity, precipitation and wind speed. Various tools and method are available for predicting rainfall but the achievement of accuracy is still a concern. The other methods which exist becomes less effective whenever a huge and diverse dataset are used. The machine learning algorithm such as: linear regression, Decision Tree, Ridge and LASSO are used for prediction of rainfall. This model intends to provide the easy and accurate technique for the rainfall prediction. It also provides comparative study of papers among the different machine learning techniques for rainfall prediction. The experiment is done by designing the application of framework using real time dataset.

Machine Learning, Rainfall, Rainfall prediction, Region wise rainfall, Region wise rainfall prediction