Prediction of Student Dropout by Feature Selection Algorithm
Pertinent feature distinguishing proof has turned into a fundamental assignment to apply data mining calculations successfully in genuine situations. Thusly, many feature selection methods have been proposed to acquire the applicable feature subsets in the writing to accomplish their destinations of grouping and bunching. This paper presents the ideas of feature pertinence, general methodology, assessment rules, and the qualities of feature selection lastly feature selection calculation (utilizing the chi square test )will be utilized on expectation of school dropouts. The objective of this paper is to discover comparable examples of utilization in the data accumulated from datasets and ultimately have the option to make expectations for every student dependent on different segment, scholarly and viewpoint ascribes. In conclusion data from the review could reveal insight into how to all the more likely help in danger students. We will close this work with genuine application (like early forecast of student dropouts), difficulties, and future examination headings of feature selection utilizing filter method.
Keywords - Feature Selection, Filter Method, Student Dropout, Data Mining, Machine Learning, Chi Square Test.
Abbreviations - FS, Feature Selection; FM, Filter method; SD, Student Dropout