Abstract: “Weather forecasting is a most important application in meteorology and has been one of the most scientifically and technologically challenging problems around the world”. We investigate the use of data mining techniques in forecasting attributes like maximum temperature, minimum temperature. This was carried out using Decision Tree algorithms and meteorological data collected between 2012 and 2015 from the different cities. Weather prediction approaches are challenged by complex weather phenomena. Weather phenomena have many parameters like maximum temperature, minimum temperature, humidity and wind speed that are impossible to enumerate and measure. On available datasets we apply the Decision Tree Algorithm for deleting the inappropriate data. Generally maximum temperature and minimum temperature are mainly responsible for the weather prediction. On the percentage of these parameters we predict there is a full cold or full hot or snow fall. This paper develop a model using decision tree to predict weather phenomena like full cold, full hot and snow fall which can be a lifesaving information.

Keywords: Weather Prediction, Data Mining, Decision tree, Meteorological Data Sets.