Asset Details
Do you wish to reserve the book?
Machine Learning Algorithms for Crime Prediction under Indian Penal Code
Hey, we have placed the reservation for you!
By the way, why not check out events that you can attend while you pick your title.
Oops! Something went wrong.
Looks like we were not able to place the reservation. Kindly try again later.
Are you sure you want to remove the book from the shelf?
Machine Learning Algorithms for Crime Prediction under Indian Penal Code
Do you wish to request the book?
Machine Learning Algorithms for Crime Prediction under Indian Penal Code
Please be aware that the book you have requested cannot be checked out. If you would like to checkout this book, you can reserve another copy
We have requested the book for you!
Your request is successful and it will be processed during the Library working hours. Please check the status of your request in My Requests.
Oops! Something went wrong.
Looks like we were not able to place your request. Kindly try again later.
Journal Article
Machine Learning Algorithms for Crime Prediction under Indian Penal Code
2024
Overview
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.
Publisher
Springer Berlin Heidelberg,Springer Nature B.V
Subject
