Performance Tuning Of J48 Algorithm For Prediction Of Soil Fertility

Data mining involves the systematic analysis of large data sets , and data mining in agricultural soil datasets is exciting and modern research area. The productive capacity of a soil depends on soil fertility. Achieving and maintaining appropriate levels of soil fertility, is of utmost importance if agricultural land is to remain capable of nourishing crop production. In this research, Steps for building a predictive model of soil fertility have been explained. This paper aims at predicting soil fertility class using decision tree algorithms in data mining. Further, it focuses on performance tuning of J48 decision tree algorithm with the help of meta-techniques such as attribute selection and boosting. 1. INTRODUCTION: Data mining is a relatively young and interdisciplinary field of computer science, is the process that attempts to discover patterns in large data sets. It utilizes methods at the intersection of artificial intelligence, machine learning, statistics, and database systems. The overall goal of the data mining process is to extract information from a data set and transform it into an understandable structure for further use(" data mining " ,Wikipedia). A soil test is the analysis of a soil sample to determine nutrient content, composition and other characteristics. Tests are usually performed to measure fertility and indicate deficiencies that need to be remedied (" Soil Test " , Wikipedia).. In this research , soil dataset containing soil test results has been used to apply various classification techniques in data mining. Soil fertility is a crucial attribute which is considered for land evaluation , also achieving and maintaining necessary levels of fertility is important for nurturing crop production, hence this paper includes steps for building an efficient and accurate predictive model of soil fertility with the help of J48 algorithm.