In this project the task is to predict bicycle theft and bicycle traffic in a city using machine learning methods. The project proposal was given in collaboration with BikeFinder AS, a Petter Stordalen"s #Strawberry Million” award winning company established in 2015. Bicycle theft is a problem in many places around the world and one of the objectives in this thesis is to help preventing it, based on data science analysis and machine learning methods applied on existing data. Predicting bicycle traffic as well as analyzing the factors that might affect traffic is another important goal for this thesis. However, throughout the project it is expected to work on various other steps such as gathering the relevant data, pre-processing, evaluating and comparing methods and results. It is also important to optimize and improve the performance of the methods to achieve as accurate results as possible. Lastly, interpreting the results, and solving the questions asked in the thesis. The project has been solved by first, gathering BikeFinder theft and traffic data, Stavanger weather conditions data, Rogaland Police District bike theft reports data and data from the bike counting sensors in the city of Stavanger. Secondly, various steps of preprocessing has been done on the data according to the use cases. Afterwards, machine learning method evaluations and comparisons, using a neutral and larger dataset, Chicago crime dataset was accomplished. Thereafter, applying the best performing methods on the theft and traffic datasets, as well as forecasting bike theft and traffic has been achieved. Finally, results interpretation and discussion on the findings of the project. The findings in this project reflects that bike theft and bike traffic can be predicted using machine learning methods on BikeFinder data. Furthermore, other factors such as weather conditions do affect bike traffic as well as improves the performances of bike traffic predictions. The results of the project provide useful insight to multiple parties and can be used to help preventing bike theft as well as providing suggestions for city planning improvements.