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Using Machine Learning to Identify Top Antecedents Affecting Crime in US Communities

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Advances in Information and Communication (FICC 2023)

Part of the book series: Lecture Notes in Networks and Systems ((LNNS,volume 652))

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Abstract

One of the main concerns for countries has been always crime activities. In recent years, with the development of data collection and analysis techniques, a massive number of data-related studies have been performed to analyze crime data. Studying indirect features is important yet challenging task. In this work we are using machine learning (ML) techniques to try to identify the top variables affecting crime rates in different US communities. The data used in this work was collected from the Bureau of the Census and Bureau of Justice Statistics. Out of the 125 variables collected in this data we will try to identify the top factors that correlate with higher crime rates either in a positive or a negative way. The analysis in this paper was done using the Lasso Regression technique provided in the Python library Scikit-learn

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Author information

Authors and Affiliations

  1. University of Wisconsin-Parkside, Kenosha, USA

    Kamil Samara

Authors
  1. Kamil Samara
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Correspondence to Kamil Samara .

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Editors and Affiliations

  1. Faculty of Science and Engineering, Saga University, Saga, Japan

    Kohei Arai

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Samara, K. (2023). Using Machine Learning to Identify Top Antecedents Affecting Crime in US Communities. In: Arai, K. (eds) Advances in Information and Communication. FICC 2023. Lecture Notes in Networks and Systems, vol 652. Springer, Cham. https://doi.org/10.1007/978-3-031-28073-3_7

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