WHAT’S NEXT? Prediction Model for Students Future Development
Neethu C Sekhar, Merlin Sebastian , Nandita Suresh , Leoyon Reji, Shahad C K
Artificial Intelligence, Deep Learning, future prediction, Convolution Neural Network, Recurrent Neural Net- work, Tensor flow, prediction modelling.
Categorizing and predicting the future of students based on their exam performance are typical challenges for edu- cators. Therefore, proper and accurate method is very important to give timely advice to students regarding choice of program and university. Previously, traditional data mining methods, such as decision trees and association rules, were used for classification. The rapid development of artificial intelligence and deep learning algorithms in recent years offers a different method for intelligent classification and prediction of results. Tensorflow’s artificial intelligence engine helps to classify students’ performance and predict their future university. In our system, the deep learning model analyzes subjects like physics, mathematics, English, chem, bio, history and Malayalam, as well as non academic achievements like driving, sports, and art. With a dataset of 2500 students, 80 per cent of that data is used as training data and 20 per cent is used as test data, accuracy ranges from 97 per cent to 99 per cent. The optimal configuration of the Tensorflow deep learning model to achieve the best prediction accuracy is determined.
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Unique Paper ID: 154164

Publication Volume & Issue: Volume 8, Issue 7

Page(s): 7 - 11
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