Yuhang Yuan (301357634):

• Built Random Forest Model + hyperparameter tuning of own model
• Drafted outline and contents of project reports in milestone one and final project
• Data visualization for overfitting section

Sunny Yang (301351209):

• Built + Hyperparameter tuning KNN model
• Dataset preprocessing, class balancing
• Finalised Code formatting and bug fixes

Zhe Liu (301449316):

• SVM model construction and hyperparameter tuning
• Finalised projects reports (grammar)
• Feature selection and hyperparameter research

In December 2019, the first cases of Coronavirus disease 2019 (Covid-19) were reported in Wuhan, China. The high prevalence of Covid-19 has made it a new pandemic in most countries. Hence for the longevity of the world, it is imperative for researchers to understand and predict the transmission of the virus. Operating upon two provided COVID-19 case data sets, the goal of this project is to evaluate various models on their performance in predicting Covid-19 outcome cases. Through tuning hyperparameters, cross-validations, and comparing various model predictions against each other, the aim is to find the best-performing model based on macro-F1 values for Covid-19