Student Performance Prediction Using Machine Learning Approach and Data Mining Techniques

Abstract

Nowadays Data Mining and Machine Learning Algorithms have made our work easier by developing prediction capability. We can implement Data Mining and Machine Learning Algorithms in many sectors. Among those, education is one of the most important sectors for the application of machine learning. If we identify the factors that are responsible for student academic performance and apply machine learning algorithms then it will be helpful for students. We can take extra care and necessary steps for students with bad results if we build a prediction model that can predict their performance earlier based on their different types of attributes. In that case those attributes must be correlated with their academic performance. That’s why for our research we have collected many instances of different types of students attributes using survey forms that are correlated with academic performance. After that, we have selected some important features using different feature extraction algorithms. Then we applied some machine learning algorithms to that preprocessed dataset. Comparison among different algorithms is also shown in our research. Among those algorithms, we have chosen the one algorithm for our model which gave the best accuracy. For building our model and visualizing data we used both Python, Jupyter Notebook and Weka. We wanted to deploy our model to the web so that anyone can check their academic performance by giving values of attributes as input. For this, we used Python Flask Framework and attached our model with it. Finally, we deployed our Flask App to the Heroku Cloud Application Platform. And by using this one students can check their academic performance. Also, the authority and teachers can take necessary steps considering relevant attributes for those students whose performance is very poor. And it will be possible for our prediction model at the beginning of their learning process.