A neural network model can be used effectively in predicting training accuracy using machine learning. Based on the comparison of forward and backward neural networks, coded to communicate their output in the requisite manner using machine language is the basis of the present study. With the help of students' background information, to predict the Grade Point Average (GPA) of 580 engineering students based on various parameters, including mental health. The study is based on the Boruta algorithm and the random forest methods for data preparation in the matrices (12 * 2 = 24) of single-layered, multiple-layers, and forward and reverse algorithms adopted to test the prediction and accuracy of the grade point average by analyzing histograms, confusion matrices, and regression analysis. This study suggests the best model for predictions with the help of artificial neuron network that has roughly half the number of single layers and with three hidden layers.
Neural Network Model for Performance Evaluation of Academic Staff of Tertiary Institutions
