Data Mining Classification and analytical model of prediction for Job Placements using Fuzzy Logic

Abstract

One of the most common issues that new graduates experience is the insufficient understanding of basic concepts. Major skill gaps in this area include a lack of deep comprehension on technical information, lack of customer management abilities, and insufficient knowledge of numerous disciplines. The study has attributed a lack of English communication skills, which they found in 73.63% of applicants, and poor analytical and quantitative skills, which they found in 57.96% of applicants, as a major cause of unemployment. Aptitude tests are conducted to analyze the problem-solving skills of the candidate; this evaluation helps to solve a problem at a given point in time. The proposed study has collected data on students, who had different information about their previous and current academic records, and then different classification algorithms along with the Data Mining Tool (VEKA) are used to analyze academic performance in training and accommodation. This study presents a proposed model based on a classification approach to find a better evaluation method in order to predict the student accommodation. There are many basic classification algorithms and statistical methods that can be used as good resources for classifying student datasets in education. In this article, a fuzzy inference system was used to predict the student performance and improve academic performance. This model can determine the relationship between student achievement and campus placement.