Many public bus services have their timetable which provide time information of arrival and/or departure at waypoints along the route. On the other hand, there are some bus services that do not have fixed time schedule, such as in Ulaanbaatar, the city of Mongolia. In this case, a lot of confusion occurs for the passengers. Prediction of bus travel time can help to provide services such as efficient scheduling for the passengers of their trips by avoiding to wait a long time. For this purpose, we investigate some machine learning methods to predict bus travel time. Concretely, for bus travel data, we employ three regression methods: linear regression (LR), support vector regression (SVR) and artificial neural network (ANN) to predict travel time. The performances of these machine learning methods are estimated and compared using conventional measures such as mean absolute error and root mean squared error. In a quantitative study, the artificial neural network is the best model having errors less than 1 minute in most cases. We also performed a qualitative study to investigate the details of our prediction results by using heatmap visualizations. Our visualization results offer easily grasping the tendency of travel time and error values.