Decision Tree for the Weather Forecasting

Abstract

Predicting the classification of data in a suitable class is a challenging task. It depends on various factors to predict the dependent variables. Since decision tree evaluation can be quantified and it is simple to use, so a model using decision tree has been proposed by the author to predict the event like fog, rain and thunder by inputting average temperature, humidity and pressure. Which can be used by farmers or by peoples of all walk of life in taking the intelligent decisions. This model can be used in machine learning and further the proposed model has scope for improvement as more and more relevant attributes can be used in predicting the dependent variable. Decision tree(Decision stump) has been implemented in Weka to facilitate the forecasting of weather..