College NIRF Rank Predictor using ML

Abstract

Abstract: The National Institutional Ranking Framework (NIRF) is an annual ranking system initiated by the Indian government to rank higher education institutions based on several parameters such as teaching, research, and outreach activities. In this project, we propose to develop a machine learning model that can predict the NIRF rank of an institution. Here we have used 2020 NIRF ranking dataset from Kaggle. Then based on the score of previous years, we predict the rank by giving the performance indicators to the model. The paper focuses on the use of Random Forest Regressor based Machine learning technique to predict NIRF rank. Factors considered are Teaching, Learning and Resources (TLR) score, Research and Professional Practice (RPC) score, Graduation Outcome (GO) score, Outreach and Inclusivity (OI) score and Perception Score for particular college. The model is evaluated using standard strategic indicator: Root Mean Square Error. The low value of this indicator show that the model is efficient in predicting NIRF rank. We got score of 93% and RMSE of 15.47. We have completed ML model save and load operations using Joblib. We have created a flask server for model deployment and deployed on Render as web service. We conducted comprehensive evaluations on frequently used machine learning models and conclude that our proposed solution outperforms due to the comprehensive feature engineering that we built. The system achieves overall high accuracy for College NIRF rank prediction.