This thesis investigates patterns in members of the European Parliament (MEP) voting behaviour using data from the European Parliament Vote Monitor (EPVM) (https://epvm.iep.unibocconi.eu/). Clustering methods such as k-means, hierarchical clustering, and model-based clustering will be used to identify groups of MEPs with similar voting patterns. The resulting clusters will be compared with characteristics such as political group and country to assess whether voting behaviour follows existing political divisions or reveals alternative groupings.
Required skills: Statistical Inference, Statistical Modelling