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LASSO-based feature selection and naïve Bayes classifier for crime prediction and its type

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Abstract

For centuries, crime has been viewed as random because it is based on human behavior; even now, it incorporates an excessive number of factors for current machine learning models to forecast accurately. In this work, we tend to discuss the early crime prediction results from a model developed using the data from the Chicago crime dataset. In any case, with a superior execution future crime is to anticipated accurately, it is a testing assignment as a result of the increase in several crimes in present days. Therefore, the crime foreseeing method is foremost, and it identifies the future crimes and number of crimes are degraded. In this paper, we built up a model to anticipate future crime occurrences at a future time and also predict which type of crime may be happening in a given area. First, we analyze how certain crime features like given a date, time and some geologically important relevant features such as latitude and longitude. Second, we discuss several analytics techniques we used to find meaning in our data, such as LASSO feature selection analysis, classification models like naïve Bayes and SVM. Finally, we select the best model for foreseeing crime type and seriousness of the crime for giving different features.

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Authors and Affiliations

  1. Computer Science Department, Viganan University, Vadlamudi, India

    Gnaneswara Rao Nitta

  2. Vignan University, Vadlamudi, India

    B. Yogeshwara Rao

  3. Tata Consultancy Services, Mumbai, India

    T. Sravani

  4. JNTU Kakinada, Kakinada, India

    N. Ramakrishiah

  5. V.R.S. College of Engineering and Technology, Villupuram, India

    M. BalaAnand

Authors
  1. Gnaneswara Rao Nitta
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  2. B. Yogeshwara Rao
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  3. T. Sravani
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  4. N. Ramakrishiah
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  5. M. BalaAnand
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Correspondence to Gnaneswara Rao Nitta.

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Nitta, G.R., Rao, B.Y., Sravani, T. et al. LASSO-based feature selection and naïve Bayes classifier for crime prediction and its type. SOCA 13, 187–197 (2019). https://doi.org/10.1007/s11761-018-0251-3

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  • DOI: https://doi.org/10.1007/s11761-018-0251-3

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