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A Comparative Analysis of Machine Learning Algorithms to Predict Academic Performance using KDD methodology

Repository of Individual Project (SID:10002997)


This repository contains the dataset, files and code used for my Computer Science with Artificial Intelligence dissertation. The aim of this project was to answer the following questions:

  • How accurately can different machine learning algorithms predict academic performance in higher education?

  • To what extend does different student features influence academic performance predictions?



To run the .ipynb code file, please download the following software:

To set up the working environment on Jupyter notebook, please download the following libraries :

!pip install pandas numpy matplotlib seaborn plotly 
!pip install tensorflow keras livelossplot scikit-learn Pillow
!pip install openpyxl
!pip install xgboost



  • This project is licensed under the MIT License - see the MIT file for further details.

  • The dataset used in this research is licensed by the CC BY 4.0 licence - see the CC 4.0 for further details.