Forecasting the Prediction for Atmosphere Using Machine Learning

This study includes machine learning techniques that are applied to historical meteorological data to propose an enhanced method of weather prediction. We systematically assess the performance of four algorithms: Decision Tree, Random Forest, Support Vector Machine and Logistic Regression, using a dataset that includes important meteorological variables. According to our experimental findings, the Random Forest algorithm attains the greatest accuracy at 88%, with Decision Tree model coming in second at 85%. While Logistic Regression exhibits a lower accuracy of 71%, the SVM model exhibits a noteworthy 76% accuracy. In order to increase predicted accuracy, this study emphasizes the importance of efficient feature selection, data preprocessing, and model optimization. We then go over how these discoveries may be used practically in various fields, like environmental observation, agriculture and disaster relief. The study highlights the potential for improving accuracy and dependability in actual weather prediction systems while offering useful details regarding the benefits and drawbacks of different machine learning approaches for atmospheric prediction.