README.md

# 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.