IDEAS home Printed from https://ideas.repec.org/a/spr/aodasc/v11y2024i1d10.1007_s40745-022-00424-6.html
   My bibliography  Save this article

Machine Learning Algorithms for Crime Prediction under Indian Penal Code

Author

Listed:
  • Rabia Musheer Aziz

    (VIT Bhopal University)

  • Prajwal Sharma

    (VIT Bhopal University)

  • Aftab Hussain

    (VIT Bhopal University)

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.

Suggested Citation

  • Rabia Musheer Aziz & Prajwal Sharma & Aftab Hussain, 2024. "Machine Learning Algorithms for Crime Prediction under Indian Penal Code," Annals of Data Science, Springer, vol. 11(1), pages 379-410, February.
    • Handle: RePEc:spr:aodasc:v:11:y:2024:i:1:d:10.1007_s40745-022-00424-6
    DOI: 10.1007/s40745-022-00424-6
    as

    Download full text from publisher

    File URL: http://link.springer.com/10.1007/s40745-022-00424-6
    File Function: Abstract
    Download Restriction: Access to the full text of the articles in this series is restricted.
    File URL: https://libkey.io/10.1007/s40745-022-00424-6?utm_source=ideas
    LibKey link: if access is restricted and if your library uses this service, LibKey will redirect you to where you can use your library subscription to access this item
    ---><---

    As the access to this document is restricted, you may want to search for a different version of it.

    References listed on IDEAS

    as
    1. Rabia Aziz & C. K. Verma & Namita Srivastava, 2018. "Artificial Neural Network Classification of High Dimensional Data with Novel Optimization Approach of Dimension Reduction," Annals of Data Science, Springer, vol. 5(4), pages 615-635, December.
    2. Mamta Mittal & Lalit Mohan Goyal & Jasleen Kaur Sethi & D. Jude Hemanth, 2019. "Monitoring the Impact of Economic Crisis on Crime in India Using Machine Learning," Computational Economics, Springer;Society for Computational Economics, vol. 53(4), pages 1467-1485, April.
    3. Javad Hosseinkhani & Hamed Taherdoost & Solmaz Keikhaee, 2021. "ANTON Framework Based on Semantic Focused Crawler to Support Web Crime Mining Using SVM," Annals of Data Science, Springer, vol. 8(2), pages 227-240, June.
    4. Vojo Lakovic, 2020. "Modeling of Entrepreneurship Activity Crisis Management by Support Vector Machine," Annals of Data Science, Springer, vol. 7(4), pages 629-638, December.
    5. Suellen Teixeira Zavadzki de Pauli & Mariana Kleina & Wagner Hugo Bonat, 2020. "Comparing Artificial Neural Network Architectures for Brazilian Stock Market Prediction," Annals of Data Science, Springer, vol. 7(4), pages 613-628, December.
    6. Mohamad Kassem & Amjad Ali & Marc Audi, 2019. "Unemployment Rate, Population Density and Crime Rate in Punjab (Pakistan): An Empirical Analysis," Bulletin of Business and Economics (BBE), Research Foundation for Humanity (RFH), vol. 8(2), pages 92-104, June.
    7. James M. Tien, 2017. "Internet of Things, Real-Time Decision Making, and Artificial Intelligence," Annals of Data Science, Springer, vol. 4(2), pages 149-178, June.
    Full references (including those not matched with items on IDEAS)

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. Manoj Verma & Harish Kumar Ghritlahre & Surendra Bajpai, 2023. "A Case Study of Optimization of a Solar Power Plant Sizing and Placement in Madhya Pradesh, India Using Multi-Objective Genetic Algorithm," Annals of Data Science, Springer, vol. 10(4), pages 933-966, August.
    2. Huanyu Ma & Yan Xu & Yulong Liu, 2022. "Prediction of Listed Company Growth in Non-public Economy," Annals of Data Science, Springer, vol. 9(4), pages 847-861, August.
    3. Manoj Verma & Harish Kumar Ghritlahre, 2023. "Forecasting of Wind Speed by Using Three Different Techniques of Prediction Models," Annals of Data Science, Springer, vol. 10(3), pages 679-711, June.
    4. Asif Pervez & Irfan Ali, 2024. "Robust Regression Analysis in Analyzing Financial Performance of Public Sector Banks: A Case Study of India," Annals of Data Science, Springer, vol. 11(2), pages 677-691, April.
    5. Aidin Zehtab-Salmasi & Ali-Reza Feizi-Derakhshi & Narjes Nikzad-Khasmakhi & Meysam Asgari-Chenaghlu & Saeideh Nabipour, 2023. "Multimodal Price Prediction," Annals of Data Science, Springer, vol. 10(3), pages 619-635, June.
    6. Terence D. Agbeyegbe, 2023. "The Link Between Output Growth and Output Growth Volatility: Barbados," Annals of Data Science, Springer, vol. 10(3), pages 787-804, June.
    7. Deeksha Chandola & Akshit Mehta & Shikha Singh & Vinay Anand Tikkiwal & Himanshu Agrawal, 2023. "Forecasting Directional Movement of Stock Prices using Deep Learning," Annals of Data Science, Springer, vol. 10(5), pages 1361-1378, October.
    8. Muhammad Ali Faisal & Murat Donduran, 2025. "A Two-Stage Analysis of Interaction Between Stock and Exchange Rate Markets: Evidence from Turkey," Annals of Data Science, Springer, vol. 12(1), pages 171-198, February.
    9. Akshit Kurani & Pavan Doshi & Aarya Vakharia & Manan Shah, 2023. "A Comprehensive Comparative Study of Artificial Neural Network (ANN) and Support Vector Machines (SVM) on Stock Forecasting," Annals of Data Science, Springer, vol. 10(1), pages 183-208, February.
    10. Ankan Dash & Junyi Ye & Guiling Wang & Huiran Jin, 2024. "High Resolution Solar Image Generation Using Generative Adversarial Networks," Annals of Data Science, Springer, vol. 11(5), pages 1545-1561, October.
    11. Pritam, Kocherlakota Satya & Sugandha, & Mathur, Trilok & Agarwal, Shivi, 2021. "Underlying dynamics of crime transmission with memory," Chaos, Solitons & Fractals, Elsevier, vol. 146(C).
    12. Heba Soltan Mohamed & M. Masoom Ali & Haitham M. Yousof, 2023. "The Lindley Gompertz Model for Estimating the Survival Rates: Properties and Applications in Insurance," Annals of Data Science, Springer, vol. 10(5), pages 1199-1216, October.
    13. Amjad Ali & Marc Audi & Chan Bibi & Yannick Roussel, 2021. "The Impact of Gender Inequality and Environmental Degradation on Human Well-being in the Case of Pakistan: A Time Series Analysis," International Journal of Economics and Financial Issues, Econjournals, vol. 11(2), pages 92-99.
    14. Roberto Moro-Visconti & Salvador Cruz Rambaud & Joaquín López Pascual, 2023. "Artificial intelligence-driven scalability and its impact on the sustainability and valuation of traditional firms," Palgrave Communications, Palgrave Macmillan, vol. 10(1), pages 1-14, December.
    15. Mansoureh Beheshti Nejad & Seyed Mahmoud Zanjirchi & Seyed Mojtaba Hosseini Bamakan & Negar Jalilian, 2024. "Blockchain Adoption in Operations Management: A Systematic Literature Review of 14 Years of Research," Annals of Data Science, Springer, vol. 11(4), pages 1361-1389, August.
    16. M. Sridharan, 2023. "Generalized Regression Neural Network Model Based Estimation of Global Solar Energy Using Meteorological Parameters," Annals of Data Science, Springer, vol. 10(4), pages 1107-1125, August.
    17. Satti R. G. Reddy & G. P. Saradhi Varma & Rajya Lakshmi Davuluri, 2024. "Deep Neural Network (DNN) Mechanism for Identification of Diseased and Healthy Plant Leaf Images Using Computer Vision," Annals of Data Science, Springer, vol. 11(1), pages 243-272, February.
    18. Aftab Ahmad, 2020. "Poverty Terrorism Nexus: A Case Study Of Pakistan," Bulletin of Business and Economics (BBE), Research Foundation for Humanity (RFH), vol. 9(4), pages 162-172, December.
    19. Astha Modi & Khelan Shah & Shrey Shah & Samir Patel & Manan Shah, 2024. "Sentiment Analysis of Twitter Feeds Using Flask Environment: A Superior Application of Data Analysis," Annals of Data Science, Springer, vol. 11(1), pages 159-180, February.
    20. Muhammad Shahid & Khalil Ahmad & Muhammad Amir Inayat & Muhammad Kashif Bhatti, 2024. "Socio-Economic Determinants of Property Crime Across the Districts of Punjab: Highlighting the Role of Law Enforcement Agencies of Pakistan," Bulletin of Business and Economics (BBE), Research Foundation for Humanity (RFH), vol. 13(2), pages 22-36.

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:spr:aodasc:v:11:y:2024:i:1:d:10.1007_s40745-022-00424-6. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: Sonal Shukla or Springer Nature Abstracting and Indexing (email available below). General contact details of provider: http://www.springer.com .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.