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Prediction of students\u2019 performance provides support in selecting courses and designing appropriate future study plans for students. In addition to predicting the performance of students, it helps teachers and managers to monitor students in order to provide support to them and to integrate the training programs to obtain the best results. One of the benefits of student\u2019s prediction is that it reduces the official warning signs as well as expelling students because of their inefficiency. Prediction provides support to the students themselves through their choice of courses and study plans appropriate to their abilities. The proposed method used deep neural network in prediction by extracting informative data as a feature with corresponding weights. Multiple updated hidden layers are used to design neural network automatically; number of nodes and hidden layers controlled by feed forwarding and backpropagation data are produced by previous cases. The training mode is used to train the system with labeled data from dataset and the testing mode is used for evaluating the system. Mean absolute error (MAE) and root mean squared error (RMSE) with accuracy used for evolution of the proposed method. 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