Comparison of machine learning techniques for weather prediction.

The direction of numerical projections has actively been developed in recent years by an intensive investigation of processed observational data to identify trends of change and generate numerical data of climatic parameters for the future. Weather forecasting offers knowledge that people and organizations may use to lessen weather-related losses and improve social benefits. This study compares different machine learning models in an effort to identify which one provides the most accurate weather prediction data. In our proposed approach, we employed the models such as GridSearch Cross Validation, Random Forest, Logistic Regression, and Gaussian Naïve Bayes. The Random Forest Tree method, which has a very high accuracy of 99%, is judged to be the best for predicting the weather after evaluation. [ABSTRACT FROM AUTHOR]