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Machine Learning Algorithms for Crime Prediction under Indian Penal Code

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Abstract

In this paper, the authors propose a data-driven approach to draw insightful knowledge from the Indian crime data. The proposed approach can be helpful for police and other law enforcement bodies in India for controlling and preventing crime region-wise. In the proposed approach different regression models are built based on different regression algorithms, viz., random forest regression (RFR), decision tree regression (DTR), multiple linear regression (MLR), simple linear regression (SLR), and support vector regression (SVR) after pre-processing the data using MySQL Workbench and R programming. These regression models can predict 28 different types of IPC cognizable crime counts and also a total number of Indian Penal Code (IPC) cognizable crime counts region-wise, state-wise, and year-wise (for all over the country) provided the desired inputs to the model. Data visualization techniques, namely, chord diagrams and map plots, are used to visualize pre-processed data (corresponding to the years 2014 to 2020) and predicted data by the relatively best regression model for the year 2022. For the chosen data, it is concluded that Random Forest Regression (RFR), which predicts total IPC cognizable crime, fits relatively the best, with a 0.96 adjusted r squared value and a MAPE value of 0.2, and among regression models predicting region-wise theft crime count, the random forest regression-based model relatively fits the best, with an adjusted R squared value of 0.96 and a MAPE value of 0.166. These regression models predict that Andhra Pradesh state will have the highest crime counts, with Adilabad district at the top, having 31,933 predicted crime counts.

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Data Availability

All used data are benchmark and are freely available in repositories.

Code Availability

All used code are freely available on net.

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The authors declare that no funds, grants, or other support were received during the preparation of this manuscript.

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Authors and Affiliations

  1. VIT Bhopal University, Bhopal-Indore Highway, Kothrikalan, Sehore, Bhopal, M.P., 466116, India

    Rabia Musheer Aziz, Prajwal Sharma & Aftab Hussain

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  1. Rabia Musheer Aziz
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  2. Prajwal Sharma
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  3. Aftab Hussain
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Contributions

Material preparation, data collection, and data analysis were performed by Prajwal Sharma and Aftab Hussian. Manuscript writing and all other work performed by Dr. Rabia Musheer Aziz.

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Correspondence to Rabia Musheer Aziz.

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Aziz, R.M., Sharma, P. & Hussain, A. Machine Learning Algorithms for Crime Prediction under Indian Penal Code. Ann. Data. Sci. 11, 379–410 (2024). https://doi.org/10.1007/s40745-022-00424-6

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