Waste Management is one of the biggest challenges for modern cities caused by urbanisation and increased population. Smart Waste Management tries to solve this challenge with the help of techniques such as Internet of Things, machine learning and cloud computing. By utilising smart algorithms the time when a recycling container is going to be full can be predicted. By continuously measuring the filling level of containers and then partitioning the filling level data between consecutive emptyings a regression model can be used for prediction. In order to do this an accurate emptying detection is a requirement. This thesis investigates different data-driven approaches to solve the problem of an accurate emptying detection in a setting where the majority of the data are non-emptyings, i.e. suspected emptyings which by manual examination have been concluded not to be actual emptyings. This is done by starting with the currently deployed legacy solution and step-by-step increasing the performance by optimisation and machine learning models. The final solution achieves the classification accuracy of 99.1 % and the recall of 98.2 % by using a random forest classifier on a set of features based on the filling level at different given time spans. To be compared with the recall of 50 % by the legacy solution. In the end, it is concluded that the final solution, with a few minor practical modifications, is feasible for deployment in the next release of the system.