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Engineering Admission Analysis Using Data Mining

Chaynika Kapoor, Latika Singh


With the on growing increase in the institutions, increase in the data is taking place. The vast amount of institutional data being generated every year is required to be mined and analysed for knowledge extraction for intake and simplification of admission process. With the purpose of analysing the trends, various attributes affecting the institution’s admission scenario and retrieving auxiliary information to increase the admissions, the institutional data have been used for this purpose. The main purpose of this work is to analyse the admission data to increase the number of admission, along with maintaining the quality of admissions. The work uses various machine learning models like Decision Tree, Naïve Baye’s, K-NN and random forest to identify admission queries which are likely to turn into actual admissions. The analysis would be beneficial to the institution in planning and marketing during the admissions for forthcoming years by focusing their efforts on the students likely to get admitted based on the analysed data.


Data Mining, Knowledge Discovery, Admission Process, Classification Algorithm, Decision Tree, Naïve Bayes, K-Nn, Random Forest

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