Editorial
Crime Prediction using Machine Learning
@ARTICLE{10.4108/eetiot.5123, author={Sridharan S and Srish N and Vigneswaran S and Santhi P}, title={Crime Prediction using Machine Learning}, journal={EAI Endorsed Transactions on Internet of Things}, volume={10}, number={1}, publisher={EAI}, journal_a={IOT}, year={2024}, month={2}, keywords={Crime prediction, Linear regression, Visualization, Geographic mapping, Crime analysis, Random Forest Classifier, Machine Learning}, doi={10.4108/eetiot.5123} }- Sridharan S
Srish N
Vigneswaran S
Santhi P
Year: 2024
Crime Prediction using Machine Learning
IOT
EAI
DOI: 10.4108/eetiot.5123
Abstract
The process of researching crime patterns and trends in order to find underlying issues and potential solutions to crime prevention is known as crime analysis. This includes using statistical analysis, geographic mapping, and other approaches of type and scope of crime in their areas. Crime analysis can also entail the creation of predictive models that use previous data to anticipate future crime tendencies. Law enforcement authorities can more efficiently allocate resources and target initiatives to reduce crime and increase public safety by evaluating crime data and finding trends. For prediction, this data was fed into algorithms such as Linear Regression and Random Forest. Using data from 2001 to 2016, crime-type projections are made for each state as well as all states in India. Simple visualisation charts are used to represent these predictions. One critical feature of these algorithms is identifying the trend-changing year in order to boost the accuracy of the predictions. The main aim is to predict crime cases from 2017 to 2020 by using the dataset from 2001 to 2016.
Copyright © 2024 Sridharan S. et al., licensed to EAI. This is an open-access article distributed under the terms of the CC BY-NCSA 4.0, which permits copying, redistributing, remixing, transformation, and building upon the material in any medium so long as the original work is properly cited.
