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.
