| Summary: | This thesis considers the problem of churn within the electricity distribution sector. More specifically, this study evaluates how supervised machine learning can be used by a Swedish electricity distributor in order to identify customer churn. The data was by provided by the electricity distributor and covered personal, geographical and contract specific information regarding the company’s customers. The provided data was complemented with external data covering the customers’ financial positions. Based on this information the possibility to predict customer churn over a three-month period with a gradient boosted decision tree was evaluated. The results from the proposed models suggests that the possibility to identify customer churn is rather poor and could not be used in a practice. This is believed to be a result of unbalanced class distributions and that the data provided simply is not informative enough to accurately predict customer churn. If more information about the customers is collected, with predictive analyses in mind, the performance of the model is likely to increase.
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