Comparative Analysis of Decision Three Methods For The Prediction Of Paddy Productivity
Date
2019-11
Authors
Journal Title
Journal ISSN
Volume Title
Publisher
Department of Computer Science
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.
Description
Keywords
Classification, Paddy Productivity, Cart, Data Mining