Predicting Eligible Educator Category for Disability Student Welfare using Decision Tree Method

Abstract

private, public and aided enroll thousands of students into various standards. At the time of admission schools collect the information from the students and store into the computer. Understanding the use of data in student point of view is very important. This work is proposed to analyze the student enrolment data and classify the nature of disability students for admissibility of special welfare scheme using data mining decision tree technique's ID3 algorithm. Decision tree is a method that helps to make good choice, particularly decisions that involve values and risk highly. Decision trees use an explicit methodology to compare challenging alternatives and allocate values to those replacements by combining uncertainties, costs into particular numerical values. ID3 is a popular and most used decision tree algorithm popular for the intrinsic worth of high classifying speed, easy and strong understanding ability and easy creation. Using this method, create the suggestion to the disability student for selecting suitable educator for their studies.