Rain forecasting is a fairly difficult task. Rainfall forecasting is an application of science and technology for predicting atmospheric conditions at specific locations and times. Rain forecasting is done by collecting quantitative data about the current state of the atmosphere at a particular location and using meteorology to predict how the atmosphere will change. Monitored machine learning technology for collecting a variety of information such as variable identification, univariate analysis, bivariate and multivariate analysis, missing value handling and data validation analysis, data cleaning / preparation, and data visualization. Analysis of the dataset by (SMLT) is performed across the given dataset. Our analysis provides a comprehensive guide to the sensitivity analysis of model parameters in terms of the performance of rainfall prediction by accuracy calculation. Proposal of a machine learning-based method for accurately predicting rainfall index values with the highest accuracy prediction results obtained from comparisons of monitored classification machine learning algorithms. In addition, in order to discuss the performance of various machine learning algorithms on a particular dataset in comparison to the evaluation classification report, we identify the confusion matrix and classify the data by priority. The results compare the effectiveness of the proposed machine learning algorithm method with the highest accuracy in precision, recall, and F1 score.

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