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An Efficient Decision Tree Classifier to Predict Precipitation Using Gain Ratio  
Narasimha Prasad L V1,Prudhvi Kumar Reddy K2,M M Naidu3
*1, Vardhaman College of Engineering, Email :
2, Vardhaman College of Engineering, Email :
3, Vignan University, Email :
Abstract .The population of the world has been increasing substantially. The populous countries like India, seriously lagging behind to provide the basic needs to the people. Food is one of the basic needs that any country has to fulfill. Agriculture is one of the major sectors on which one third of Indian population depends on. The irrigation based countries like India where the water has been the basic resource that forges the plants’ growth. The main resource for the irrigation is rainfall which is scientifically a liquid form of precipitation. The atmospheric nimbus clouds are responsible for this precipitation. Prediction of the precipitation is necessary, as it has to be considered during the financial planning of a country. The meteorological departments of every nation are very keen in recording the datasets of precipitation which are huge in content. Hence, data mining is found to be an apt tool which would extract the relation between the datasets and their attributes. A Supervised Learning in Quest is one such data mining algorithm which is eventually a decision tree used to predict the precipitation based on the historical data. The Supervised Learning in Quest decision tree using gain ratio is a statistical analysis for establishing the relation between attribute set and precipitation which furnishes the prediction with an accuracy of 77.78%.
Keywords : Data Mining ; Decision Tree ; Meteorology ; Precipitation ; Prediction ; Rainfall ; SLIQ

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