Automated Machine Learning (AutoML) Model for Efficient Rain Classification and Prediction

Rainfall is a key aspect in agriculture, which is important to the Indian economy. Rainfall forecasting, however, has grown more challenging in recent years. Accurate forecasts can assist farmers in making better plans for their crops and in taking the appropriate measures. The effects of global warming are also seen in the changing climatic conditions, which have an impact on both humans and nature. Floods, droughts, and unpredictable and excessive rains are all being brought on by the warming of the atmosphere and rising ocean levels. For the different types of industries, comprising agriculture, research, and energy production, the ability to predict rainfall is crucial since it aids in understanding climate change and its associated variables, including temperature, humidity, precipitation, and wind speed. In order to create a more precise rainfall prediction system, the objective of this research piece is to employ multiple machine learning classification techniques such as Random Forest Classifier, Gaussian NB, K-Neighbors Classifier, and XGB Classifier, to a dataset obtained from the Kaggle repository that contains multiple features. These algorithms were ranked in order of efficiency, with Random Forest Classifier and XGB Classifier giving highest accuracy.