Abstract
In order to prevent a crime it is very important to analyze and understand the patterns of criminal activity of that place. Police Department can work effectively and efficiently if the crime pattern is known to them. In this work, we attempted an exploratory analysis of a standard dataset in order to predict the resolution that was given for the crimes that occurred from 2003 to 2015. The dataset is obtained from San Francisco Police Department Crime Incident Reporting System. We used Machine Learning Algorithms like CART, K-NN, Gaussian Naive Bayes, and Multilayer Perceptron (MLP). Validation and cross validation were used to test the results of each technique. The experiment shows that we can obtains higher accuracy by using CART algorithm.
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Yadav, N., Kumar, A., Bhatnagar, R., Verma, V.K. (2020). City Crime Mapping Using Machine Learning Techniques. In: Hassanien, A., Azar, A., Gaber, T., Bhatnagar, R., F. Tolba, M. (eds) The International Conference on Advanced Machine Learning Technologies and Applications (AMLTA2019). AMLTA 2019. Advances in Intelligent Systems and Computing, vol 921. Springer, Cham. https://doi.org/10.1007/978-3-030-14118-9_65
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DOI: https://doi.org/10.1007/978-3-030-14118-9_65
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