Taxi Stockholm is a Swedish taxi company which would like to improve their mobile phone application with a destination prediction feature. This thesis has created an algo- rithm which predicts a destination to which a taxi customer would like to go. The problem is approached using the KDD process and data mining methods. A dataset consisting of previous taxi rides is cleaned, transformed, and then used to evaluate the performance of three machine learning models. More specifically a neural network model paired with K- Means clustering, a random forest model, and a k-nearest neighbour model. The results show that the models that were developed in this thesis could be used as a first step in a destination prediction system. The results also show that personal data increase the accu- racy of the neural network model and that there exists a threshold for how much personal information is needed to increase the performance.