Predicting when and how much rain will fall is one of the most essential and difficult tasks of our day. Furthermore, in order to accurately anticipate weather patterns such as climate change and rainfall, complex computer simulations and models must be used in conjunction with modern computer modelling techniques. It is critical for all nations to determine the accuracy of their rainfall forecasts. For predicting rainfall, statistical methods are ineffective because of the nature of the atmosphere. Artificial Neural Network is a superior approach because of the nonlinearity of rainfall data. In the realm of meteorological science, predicting precipitation is a critical step. A combination of factual processes and machine learning approaches is used to anticipate and estimate meteorological factors in order to predict precipitation. Daily observations were taken into account while conducting the experiment. The accuracy of forecasting model experiments is evaluated by comparing the outcomes to the real world. In order to estimate the future condition of rainfall, it is necessary to take into account the variability of previous years. The results of the research suggest that it is possible to predict weather factors. For predicting precipitation for the upcoming season, ARIMA and Neural Network demonstrate classification accuracy that is superior to other machine learning techniques.

Rain fall, Forecasting, Accuracy, Machine Learning Algorithms, Regression, Random forest