A Survey on Analysis and Prediction of Data Using Data Science

Abstract

Intelligent technology development is gaining traction in the sphere of education. The increasing rise of educational data suggests that standard processing methods may be limited and distorted. As a result, rebuilding data mining research technologies in the education industry has become necessary. Becoming more visible To avoid erroneous assessment findings and to anticipate students' future performance, this research analyses and predicts students' academic achievement using applicable clustering, discriminating, and convolution neural network theories. To begin, this work suggests that the clustering-number determination be optimized by employing a statistic that has never been employed in the K-means approach. The clustering impact of the K-means method is next assessed using discriminate analysis. The Convolutional neural network is presented for training and testing with labeled data. The produced model can be used to forecast future performance. Finally, the efficacy of the constructed model is tested using two metrics in two cross validation procedures in order to validate the prediction findings. The experimental findings show that the statistic not only addresses the objective and quantitative problem of determining the clustering number in the K-means method, but also enhances the predictability of the outcomes.