Data Mining Classification Algorithms for Analyzing Soil Data

Rapid changes are occurring in our global ecosystem, and stresses on human well-being, such as climate regulation food production, increasing, soil is a critical component of agriculture. The project aims to use Data Mining (DM) classification techniques predict data. Analysis DM strategies k-Nearest-Neighbors (k-NN), Random-Forest (RF), Decision-Tree (DT) Naïve-Bayes (NB) used type. These classifier algorithms extract information from main purpose using these classifiers find the optimal machine learning classification. this paper we applying some data set that collected by Weka program, then compare experimental result with other papers worked like work. According results, highest accuracy k-NN has 84 % when compared NB (69.23%), DT RF (53.85 %). As result, it outperforms classifiers. findings imply could be useful for accurate type agricultural domain.