| Autor: |
M., Stalin, S., Kalyani |
| Předmět: |
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| Zdroj: |
Automatika: Journal for Control, Measurement, Electronics, Computing & Communications; Aug2023, Vol. 64 Issue 3, p550-564, 15p |
| Abstrakt: |
In a survey conducted in the year 2020, we came to know that India's around 50% of population includes young people of the age group of 25 and students. Guiding this young mass in the right way and strengthening their future is a huge responsibility put over the head of the elder citizens of India such as their parents teachers and professors. This paper aims to build a model that can predict the students' competency level and the risk factors or the fields where he needs to put their effort to improve themselves, and this model also helps the parents, professors and Educational institutes to know about their children's and students in which zone they stand, are they ready to compete with others. This analysis is done by using different ML bifurcation algorithms. Also we aim to find the best classifier which can emerge with the highest predicting accuracy among all other classifiers to the above-said problem. The accuracy of 88.5% is achieved through the proposed machine learning algorithm for particular education datasets which have been taken into consideration. [ABSTRACT FROM AUTHOR] |
| Databáze: |
Supplemental Index |
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