Developing Accurate Prediction Model Using Machine Learning and Business Intelligence on University Students GPA

It has been challenging to determine students' grades and performances throughout college. The purpose of this study is to raise students' performance during each semester and guarantee satisfactory course completion with the use of accurate models for predicting student GPA. The process is using the dataset of student GPA in the first year of university as an input for the machine learning model. Then, using the dataset to develop prediction models such as Linear regression, Decision Tree, Fully Dense Neural Network and MLP. Lastly, results are evaluated using proper evaluation measures. This research provides an accurate prediction model with the usage of machine learning algorithms for predicting Students GPA. As it happens, the linear Regression Model has the best result with 0.1396 on the MSE and 0.3736 on the RMSE. By doing so, Students can then view their GPA to determine and strategize on how they might raise it in the upcoming semester as well as for academic advisor to give more accurate strategy and actions to the students to obtain a good study performance. Because graduates who have high GPAs can start their professions off well and have big opportunities in the foreseeable future.