CMPT 459 Project (SFU)

The purpose of this project is to build various classification models and evaluate the performance of the models using the provided COVID-19 case data.

COVID-19 case data

• https://drive.google.com/drive/folders/1D_rvFNM0EqnOq8_O3OH1ksjgk2pLxc_f

external libraries

• numpy
• pandas
• matplotlib
• seaborn
• sklearn
• imblearn

Run the Project

• Run each cell in project.ipynb to reimplement the result shown in the report

References

• All lectures of CMPT459, https://coursys.sfu.ca/2022fa-cmpt-459-d1/pages/Lecture_notes
• Classification accuracy is enough: more performance measures you can use, https://machinelearningmastery.com/classification-accuracy-is-not-enough-more-performance-measures-you-can-use/
• Confusion Matrix, Accuracy, Precision, Recall, F1 Score, https://medium.com/analytics-vidhya/confusion-matrix-accuracy-precision-recall-f1-score-ade299cf63cd

Contribution

• Zhuo Liu: task 1.1 – task 1.4 and the report (Data preparation, Conclusion, Lessons learnt and future work)

• Teliang Yu: task 1.5 – task 1.7 and the report (Classification models, Model predictions)

See the report for other project information