Application of Classification Techniques for Prediction and Analysis of Crime in India

  • Priyanka DasEmail author
  • Asit Kumar Das
Conference paper
Part of the Advances in Intelligent Systems and Computing book series (AISC, volume 711)


Due to dramatic increase of crime rate, human skills for accessing the massive volume of data is about to diminish. So application of several data mining techniques can be beneficial for achieving insights on the crime patterns which will help the law enforcement prevent the crime with proper crime prevention strategies. This present work collects crime records for kidnapping, murder, rape and dowry death and analyses the crime trend in Indian states and union territories by applying various classification techniques. Analysing the crime would be much easier by the prediction rates shown in this work, and the effectiveness of these techniques is evaluated by accuracy, precision, recall and F-measure. This work also describes a comparative study for different classification algorithms used.


Crime prediction Classification Naïve Bayes Random Forest Precision Recall 


  1. 1.
    Anisha Agarwal, Dhanashree Chougule, A.A.D.C.: Application for analysis and prediction of crime data using data mining. In: Proceedings of IRF-ieeeforum International Conference. (2016) 35–38Google Scholar
  2. 2.
    Chandrasekar, A., Raj, A.S., Kumar, P.: Crime prediction and classification in San Francisco cityGoogle Scholar
  3. 3.
    Subhash Tatale, N.B.: Crime prediction based on crime types and using spatial and temporal criminal hotspots. International Journal of Data Mining & Knowledge Management Process 5(4) (2015) 1–19Google Scholar
  4. 4.
    Subhash Tatale, N.B.: Criminal data analysis in a crime investigation system using data mining. Journal of Data Mining and Management 1(1) (2016) 1–13Google Scholar
  5. 5.
    Lawrence McClendon, N.M.: Using machine learning algorithms to analyze crime data. Machine Learning and Applications: An International Journal (MLAIJ) 2(1) (2015) 1–12CrossRefGoogle Scholar
  6. 6.
    Yu, C.H., Ding, W., Chen, P., Morabito, M.: Crime forecasting using spatio-temporal pattern with ensemble learning. In: Pacific-Asia Conference on Knowledge Discovery and Data Mining. (2014) 174–185Google Scholar
  7. 7.
    Abba Babakura, Md Nasir Sulaiman, M.A.Y.: Improved method of classification algorithms for crime prediction. In: Proceedings of International Symposium on Biometrics and Security Technologies (ISBAST). (2014) 250–255Google Scholar
  8. 8.
    S. Yamuna, N.B. Chang: Datamining techniques to analyze and predict crimes. The International Journal of Engineering And Science (IJES) 1(2) (2012) 243–247Google Scholar
  9. 9.
    Sathyadevan, S., Devan, M.S., Surya Gangadharan, S.: Crime analysis and prediction using data mining. In: 2014 First International Conference on Networks Soft Computing (ICNSC2014). (Aug 2014) 406–412Google Scholar
  10. 10.
    Demšar, J., Curk, T., Erjavec, A., Črt Gorup, Hočevar, T., Milutinovič, M., Možina, M., Polajnar, M., Toplak, M., Starič, A., Štajdohar, M., Umek, L., Žagar, L., Žbontar, J., Žitnik, M., Zupan, B.: Orange: Data mining toolbox in python. Journal of Machine Learning Research 14 (2013) 2349–2353Google Scholar

Copyright information

© Springer Nature Singapore Pte Ltd. 2019

Authors and Affiliations

  1. 1.Department of Computer Science and TechnologyIndian Institute of Engineering Science and TechnologyHowrahIndia

Personalised recommendations