A Comparative Study on Various Data Mining Algorithms with Special Reference to Crop Yield Prediction

Abstract

To compare different data mining algorithms with the same parameters on the 10fold cross validation test to predict the crop yield. Methods/Analysis: Different data mining classification algorithms like K-nearest Neighbor, K-means, Neural Network, Support Vector Machine, Case-based Reasoning, Decision Tree algorithm, etc. are applied for various application of agriculture domain. A comparative study is done by using J48, Naïve Bayes and Simple Cart algorithms to determine which classification algorithm is best fitted for crop prediction. Findings: In this study, this work reveals the superior performance of J48 classification algorithm with accuracy 89.33% for crop prediction than the other two classification algorithms Simple Cart and Naïve Bayes. Novelty /Improvement: This study first time demonstrates the application of different data mining classification techniques (as discussed above) in the domain of agriculture for yield prediction.