Abstract
Substantial increase in criminal activities has been directly affecting socio-economical development and quality of life. Predictive crime data analysis is one of the challenging tasks in the field of data analysis, where machine learning algorithm plays a significant role. In this paper, machine learning techniques are applied to a crime dataset for predicting features that affect the pattern of crime occurrence. Decomposition method has used to split the large dataset into sub-dataset in order to increase the accuracy rate. In this work, we used different supervised classification machine learning techniques to predict crime incident by resolution, depending on its feature attributes. Different classification techniques are used like Naïve Bayes, Generalized linear model, Binary Logistic Regression, Decision Tree, Random Forest, Gradient Boost. Results of different algorithms have been compared and effective approach has been discussed. The average prediction accuracy of these machine learning approaches is approximately 86%.
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Mukherjee, A., Ghosh, A. (2020). Heterogeneous Decomposition of Predictive Modeling Approach on Crime Dataset Using Machine Learning. In: Dawn, S., Balas, V., Esposito, A., Gope, S. (eds) Intelligent Techniques and Applications in Science and Technology. ICIMSAT 2019. Learning and Analytics in Intelligent Systems, vol 12. Springer, Cham. https://doi.org/10.1007/978-3-030-42363-6_116
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DOI: https://doi.org/10.1007/978-3-030-42363-6_116
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