Introduction: COVID-19 is an acute respiratory illness that directly affects the lungs. It is much needed to predict the possibility of occurrence of COVID-19 based on their characteristics. Objective: This paper studies the different machine learning classification algorithms to predict the COVID-19 recovered and deceased cases. Methods: The k-fold cross-validation resampling technique is used to validate the prediction model. Aim and The prediction scores of each algorithm are evaluated with performance metrics such as prediction accuracy, precision, recall, mean square error, confusion matrix, and kappa score. For the preprocessed dataset, the k-nearest neighbour (KNN) classification algorithm produces 80.4 % of predication accuracy and 1.5 to 3.3 % of improved accuracy over other algorithms. Results: The KNN algorithm predicts 92 % (true positive rate) of the deceased cases correctly, with 0.077% of misclassification. Further, the KNN algorithm produces the lowest error rate as 0.19 on the prediction of accurate COVID-19 cases than the other algorithm. Also, it produces the receiver operator characteristic curve with an output value of 82 %. Conclusion: Based on the prediction results of various machine learning classification algorithms on the COVID-19 dataset, this paper shows that the KNN algorithm predicts COVID-19 possibilities well for the smaller (730 records) dataset than other algorithms.