Predicting Weather Forecasting State Based on Data Mining Classification Algorithms

Weather forecasting is the process of predicting status atmosphere for certain regions or locations by utilizing recent technology. Thousands years ago, humans tried to foretell weather state in some civilizations studying science stars and astronomy. Realizing conditions has a direct impact on many fields, such as commercial, agricultural, airlines, etc. With development technology, especially DM machine learning techniques, researchers proposed prediction systems based data mining classification techniques. In this paper, we utilized neural networks, Naïve Bayes, random forest, K-nearest neighbor algorithms build models. These models classify unseen instances multiple class rain, fog, partly-cloudy day, clear-day cloudy. model performance each algorithm been trained tested using synoptic from Kaggle website. This dataset contains (1796) (8) attributes our possession. Comparing with other algorithms, Random forest achieved best accuracy 89%. results indicate ability present optimal tools predict forecasting.