_____________________________________________________________________________________________________ *Corresponding author: E-mail: kajeen.ismael@gmail.com; Asian Journal of Research in Computer Science 8(2): 17-28, 2021; Article no.AJRCOS.68035 ISSN: 2581-8260 Data Mining Classification Algorithms for Analyzing Soil Data Kazheen Ismael Taher 1* , Adnan Mohsin Abdulazeez 2 and Dilovan Asaad Zebari 3 1 Akre Technical College of Informatics, Duhok Polytechnic University, Duhok, Kurdistan Region, Iraq. 2 Duhok Polytechnic University, Duhok, Kurdistan Region, Iraq. 3 Research Center of Duhok Polytechnic University, Duhok, Kurdistan Region, Iraq. Authors’ contributions This work was carried out in collaboration among all authors. Author KIT prepared a detailed review of previous works related to analyzing soil data based on data mining classification algorithms. More so, analysis and discussion of the study have been managed by all authors. All authors read and approved the final manuscript. Article Information DOI: 10.9734/AJRCOS/2021/v8i230196 Editor(s): (1) Dr. G. Sudheer, GVP College of Engineering for Women, India. Reviewers: (1) Vikram Bali, Jss Academy of Technical Education, India. (2) H K Shreedhar, Global Academy of Technology, India. Complete Peer review History: http://www.sdiarticle4.com/review-history/68035 Received 22 February 2021 Accepted 28 April 2021 Published 04 May 2021 ABSTRACT Rapid changes are occurring in our global ecosystem, and stresses on human well-being, such as climate regulation and food production, are increasing, soil is a critical component of agriculture. The project aims to use Data Mining (DM) classification techniques to predict soil data. Analysis DM classification strategies such as k-Nearest-Neighbors (k-NN), Random-Forest (RF), Decision- Tree (DT) and Naïve-Bayes (NB) are used to predict soil type. These classifier algorithms are used to extract information from soil data. The main purpose of using these classifiers is to find the optimal machine learning classifier in the soil classification. in this paper we are applying some algorithms of DM and machine learning on the data set that we collected by using Weka program, then we compare the experimental result with other papers that worked like our work. According to the experimental results, the highest accuracy is k-NN has of 84 % when compared to the NB (69.23%), DT and RF (53.85 %). As a result, it outperforms the other classifiers. The findings imply that k-NN could be useful for accurate soil type classification in the agricultural domain. Keywords: Data mining; soil dataset; classification; Weka. Original Research Article