One of the current challenges withholding personalised lane-level driving advice is the inaccuracy and error of GPS signal from commonly used navigation devices and mobile phones. These GPS signals have an uncertainty margin up to several meters, therefore potentially indicating the vehicle location on a different lane than the actual lane it would be in. This unreliability therefore currently makes it impossible to accurately recognise lane changes from solely this data. This study looks into the recognition of lane changes from only Floating Car Data by the use of a Random Forest algorithm. In order to find the ground truth, a trajectory reconstruction algorithm is implemented, which uses the matching of trajectories with loop detector passages in order to find the lane a vehicle is in at each loop detector location. This information is then used to know whether, for each vehicle, a lane change is made on the road section in-between two consecutive loop detector locations. By training the model on this data, it was found that when using solely Floating Car Data, lane changes can be recognised with an accuracy of up to 64%. Indicators for lane change were found to be the lateral distance of a vehicle to the middle of the road, as well as the heading of the vehicle. The study additionally looks into a rule based method of lane change recognition, which is compared with the Random Forest model.