Machine Learning in Weather Forecasting: A Comparative Approach with Emphasis on Neural Networks and Optimizer

Our initiative addresses the complex issues of fore- casting and analysis in a world where weather patterns remain a mystery. Defying convention, we utilise eight different machine learning technologies that are rigorously examined utilising measures such as Confusion Matrix, F1Score, Accuracy, Precision, and Recall. Our ability to analyse weather is strengthened by this method, which helps those who make decisions about transportation, agriculture, and disaster relief. We provide three neural network players-LSTM, Feed Forward Neural Network, and Recurrent Neural Network-to increase forecast precision. We compare various models based on criteria such as Activation Functions, Optimizers, Loss, and Accuracy in an effort to identify the optimal neural architecture. Combining machine learning with neural networks creates a comprehensive approach that addresses long-term climate trend analysis, provides a dependable tool for short-term forecasts, and closes knowledge gaps in complicated weather patterns. The project's potential to improve decision-making in the face of weather uncertainty makes it significant. By combining the flexibility of machine learning with the temporal awareness of neural networks, this method advances weather forecasting. It guarantees a more precise and knowledgeable reaction to the difficulties brought up by dynamic atmospheric circumstances.