In this thesis, we develop a decision support system for the amusement park Skara Sommarland. The aim is to predict how many visitors will come to the park in order to help the management allocate the correct amount of personnel on any given day. In order to achieve this, the widely used CRoss-Industry Standard for Data Mining framework was applied to finally build a multiple linear regression (MLR) function and an artificial neural network (ANN) model. The data used to develop the models were Skara Sommarland’s historical business data and historical weather data for the surrounding area. Additionally, a fully functional web application was built which allowed the management at Skara Sommarland to use the predictions in their daily operations. The ANN outperformed the MLR and managed to achieve about 80% accuracy in predicting the number of visitors, reaching the initial data mining goal set by the project group. The conclusion formed by this thesis is that an ANN can be used to predict the number of visitors to an amusement park similar to Skara Sommarland. The final IT artifact produced can realistically help improve an amusement park’s operations by avoiding over- and under-staffing.