Classification Method to Predict Chances of Students’ Admission in a Particular College

Abstract

There are many Predictor/Prediction systems or applications on the internet, but most of these prediction systems do not use any Machine Learning algorithms in their systems, it’s just mere if and else conditions that run these predictions. We see predictor systems are gaining more popularity in predicting colleges a student can get placed into, most of these have if-else methods, but a good number of them use Machine Learning algorithms but they’d show the set of colleges a student can be placed in. This research involved creating a model that would accurately classify if a student can get admission in a particular college give a set of attribute values that would act as the feature set for the model. The two objectives are to determine the chances of a student getting accepted by a college using past data of college-wise allotment based on certain attributes or parameters which highly influence the class attribute or have high-value dependency. The second objective to give an insight of the number of students willing or showing interest to join the college, their contact number, gender, the department or branch they are interested in, to the respective college management/Admission panel which would help them understand and draw certain conclusions on the students’ interest, the college’s popularity, etc. This will help the management work on these students who are interested in getting accepted/enrolled in the college. This would benefit both the student and the college management, and all of this achieved by MACHINE LEARNING!

Appendix

Acronyms and Abbreviations

EAMCET:

Engineering Agricultural and Medical Common Entrance Test

AU:

Andhra University, (Students who are from Andhra Pradesh come under this region)

NL:

Non–Local

OU:

Osmania University

SVU:

Sri Venkateswara University

CSE:

Computer Science and Engineering

ECE:

Electronics and Communications Engineering

EEE:

Electrical and Electronics Engineering

ME:

Mechanical Engineering

CE:

Civil Engineering

EIE:

Electronics and Instrumentation Engineering

IT:

Information Technology

TP:

True Positives

FN:

False Negatives

FP:

False Positives

TN:

True Negatives