An Effective College Prediction System using Time Series Analysis

Authors

  • Ravi Parmar  Department of Computer Engineering, Sinhgad Institute of Technology and Science, Narhe, Pune, Maharashtra, India
  • Dhaval Tandale  Department of Computer Engineering, Sinhgad Institute of Technology and Science, Narhe, Pune, Maharashtra, India
  • Avdhut Kanago  Department of Computer Engineering, Sinhgad Institute of Technology and Science, Narhe, Pune, Maharashtra, India
  • Rahul Repal  Department of Computer Engineering, Sinhgad Institute of Technology and Science, Narhe, Pune, Maharashtra, India
  • Prof. Geeta S. Navale  Head of Department of Computer Engineering, Sinhgad Institute of Technology and Science, Narhe, Pune, Maharashtra, India

DOI:

https://doi.org//10.32628/CSEIT1952164

Keywords:

Data Analytics, Time-Series, Data Prediction, ARIMA Model, Time Series Forecasting

Abstract

In recent times time series analysis has gained more importance with increasing applications. The time series data is related with a time stamp for each data. One of the possible applications is the prediction of cut-off of a college using time series analysis over is previous cut-offs. It is very important for a student to secure the best possible college for his graduation degree. For his further undergraduate studies, the student needs to apply for a list of colleges. It is very crucial which colleges the student applies for and what are the chances of him getting admission into that college. The future cut-off of a college can be predicted using methods such as time-series analysis which will aid the students to decide which colleges to apply to.

References

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Published

2019-04-30

Issue

Section

Research Articles

How to Cite

[1]
Ravi Parmar, Dhaval Tandale, Avdhut Kanago, Rahul Repal, Prof. Geeta S. Navale, " An Effective College Prediction System using Time Series Analysis, IInternational Journal of Scientific Research in Computer Science, Engineering and Information Technology(IJSRCSEIT), ISSN : 2456-3307, Volume 5, Issue 2, pp.1186-1188, March-April-2019. Available at doi : https://doi.org/10.32628/CSEIT1952164