Citation

Aziz RM, Sharma P, Hussain A. Ann. Data Sci. 2024; 11(1): 379-410.

Copyright

(Copyright © 2024, Holtzbrinck Springer-Nature)

DOI

10.1007/s40745-022-00424-6

PMID

unavailable

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.

Language: en

Keywords

Decision tree regression (DTR); Indian Penal Code (IPC); Mean absolute percentage error (MAPE); Natural language processing (NLP); Random forest regression (RFR); Support vector regression (SVR)