The process of predicting the weather, which is essential and fundamental to people’s daily lives, evaluates the change that the atmosphere is currently undergoing. Analyzing large amounts of data to find hidden patterns and important information that could lead to better results is known as big data analytics. The meteorological institute shares the current obsession with big data shared by many parts of society. As a result, big data analytics will improve weather forecasting outcomes and help forecasters make more precise weather predictions. Several big data approaches and technologies have been proposed to organize and evaluate the vast volume of weather data from various resources in order to accomplish this goal and provide helpful solutions. A smart city is a project that uses computers to process vast amounts of data gathered from sensors, cameras, and other devices in order to manage resources, provide services, and address problems that arise in daily life, such as the weather. Forecasting the weather is a crucial process in daily life because it assesses changes in the atmosphere’s current state. In this study, a model for forecasting the weather based on machine learning is proposed. It is then put into practice using 5 classifier algorithms: the Random Forest classifier, the Decision Tree Algorithm, the Gaussian Naive Bayes model, the Gradient Boosting Classifier, and Artificial Neural Networks. A publicly accessible dataset was used to train these classification systems. Gradient Boosting Classifier algorithm, which had a plus 98% projected accuracy, won when the model’s performance was evaluated.
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