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# A Comparative Analysis of Machine Learning Algorithms to Predict Academic Performance using KDD methodology | |
Repository of Individual Project (SID:10002997) | |
![image](https://github.coventry.ac.uk/storage/user/5162/files/0090b3cb-9cdf-4e14-b3e9-ee93318a6edb) | |
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? | |
## Authors | |
- [Philbert Baby](https://github.coventry.ac.uk/babyp)- Project Manager | |
- Aram Saeed - Supervisor | |
## Installation | |
To run the .ipynb code file, please download the following software: | |
- [Jupyter Notebook](https://jupyter.org/) | |
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 | |
``` | |
## Acknowledgements | |
- [Coventry University](https://www.coventry.ac.uk/) - Dissertation submitted on behalf of this Univeristy. | |
- [Universidad Tecnologica de Bolivar](https://www.utb.edu.co/) - Obtained the Dataset from this University. | |
- [Enrique De La Hoz](https://data.mendeley.com/datasets/83tcx8psxv/1) - Developed the dataset used in this research. | |
## License | |
- This project is licensed under the MIT License - see the [MIT](https://choosealicense.com/licenses/mit/) file for further details. | |
- The [dataset](https://data.mendeley.com/datasets/83tcx8psxv/1) used in this research is licensed by the CC BY 4.0 licence - see the [CC 4.0](https://joinup.ec.europa.eu/licence/creative-commons-attribution-40-international-cc-40) for further details. | |