Comparative Analysis of Decision Three Methods For The Prediction Of Paddy Productivity

Abstract

Data mining applications has got rich focus due to its significance of classification algorithms. The agricultural data is difficult to study. The challenge from a research perspective is to identify the key attributes that determine paddy performance across different farming situations such as geographic location, soil types, and seasonal conditions. This study aims to survey on the two different decision tree algorithms with primary data set collected in Kanchanpur district and to implement as well as assist by comparing J48 and Simple Cart decision tree methods to predict the production of paddy. From the result analysis it was seen that Simple Cart was able to classify 80.198% of the data correctly which was better than J48 in comparison to results of evaluation metrics (Accuracy, Precision, Recall and F-Measure). In a nut shell, the experiment result showed that J48 has got smaller tree size than Simple Cart but Simple Cart has got 1.9802% better accuracy than J48 for the prediction of paddy productivity.