Crime Prediction in Swedish Municipalities with machine learning algorithms
In this thesis we use a number of common machine learning algorithms to predict crime rates in Swedish municipalities. As predictors we use a mix of municipal socioeconomic variables. For some years we are able to correctly classify up to 85% of the municipalities that have a high crime rate. The highest prediction accuracy rates are obtained from tree and clustering based models. Important factor