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Crime Prediction in Swedish Municipalities with machine learning algorithms

Dominguez Berndtsson, Nils LU (2019) STAH11 20182
Department of Statistics
Abstract
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 factors for forecasting crime in Sweden seem to be divorce rates, male age, unemployment and unsuccessful high school education.
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author
Dominguez Berndtsson, Nils LU
supervisor
organization
course
STAH11 20182
year
type
M2 - Bachelor Degree
subject
keywords
Crime rates, machine learning algorithms, Random forest, K-NN
language
English
id
8973689
date added to LUP
2019-06-17 08:50:40
date last changed
2019-06-17 08:50:40
@misc{8973689,
  abstract     = {{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 factors for forecasting crime in Sweden seem to be divorce rates, male age, unemployment and unsuccessful high school education.}},
  author       = {{Dominguez Berndtsson, Nils}},
  language     = {{eng}},
  note         = {{Student Paper}},
  title        = {{Crime Prediction in Swedish Municipalities with machine learning algorithms}},
  year         = {{2019}},
}