Submit your paper : editorIJETjournal@gmail.com

Paper Title : Crime Prediction using Machine Learning

ISSN : 2395-1303
Year of Publication : 2021
10.29126/23951303/IJET-V7I3P30

Authors: T.Prathima, Madan Vijay Karnati, Vislavath Srinath

         


Abstract
- For a developing country like India, it is not new that people hear of crimes happening quite often. With the rapid urbanization of cities, we have to constantly be aware of our surroundings. In order to avoid the unfortunate, we will try to observe crime rates by the KNN prediction method. It will predict, tentatively, the type of crime, when, where and at what time it may take place. Recognizing the patterns of a criminal activity of a place is paramount in order to prevent it. Law enforcement agencies can work effectively and respond faster if they have better knowledge about crime patterns in different geological points of a city. The aim is to use machine learning with the help of python to classify a criminal incident by type, depending on its occurrence at a given time and location. To be better prepared to respond to criminal activity, it is important to understand patterns in crime. In our project, we analyze crime data from the city of Indore, scraped from publicly available Kaggle website. At the outset, the task is to predict which category of crime is most likely to occur given a time and place in Indore. The use of AI/ML in predicting crimes or an individual’s likelihood for committing a crime has promise but is still more of an unknown. The biggest challenge will probably be “proving” to politicians that it works. When a system is designed to stop something from happening, it is difficult to prove the negative. Companies that are directly involved in providing governments with AI tools to monitor areas or predict crime will likely benefit from a positive feedback loop. Improvements in crime prevention technology will likely spur increased total spending on this technology. We also attempt to make our classification task more meaningful by merging multiple classes into larger classes. Finally, we report and reflect on our results with different classifiers, and dwell on avenues for future work.

Reference
[1] Wang, Tong and Rudin, Cynthia and Wagner, Daniel and Sevieri, Rich. 2013. pages 515- 530, Machine Learning and Knowledge Discovery in Databases [2] Jiawei Han, Micheline Kamber and Jian Pei, "Data Mining: Concepts and Techniques", The Morgan Kaufmann Series in Data Management Systems, Morgan Kaufmann Publishers, 3rd Ed, ISBN 978-0123814791, (2011) [3] Akash Kumar, Aniket Verma, Gandhali Shinde, Yash Sukhdev, Nidhi lal , “A KNN under sampling approach for data balancing” 2020, IEEE [4] Bogomolov, Andrey and Lepri, Bruno and Staiano, Jacopo and Oliver, Nuria and Pianesi,Fabio and Pentland, Alex.2014. Once upon a crime: Towards crime prediction from demographics and mobile data, Proceedings of the 16th International Conference on Multimodal Interaction. [5] Nurul Hazwani Mohd Shamsuddin, Nor Azizah Ali, Razana Alwee, “An overview on crime prediction methods” 2017, IEEE [6] Yu, Chung-Hsien and Ward, Max W and Morabito, Melissa and Ding, Wei.2011. Crime forecasting using data mining techniques, pages 779-786, IEEE 11th International Conference on Data Mining Workshops (ICDMW) [7] Kianmehr, Keivan and Alhajj, Reda. 2008. Effectiveness of support vector machine for crime hot-spots prediction, pages 433-458, Applied Artificial Intelligence, volume 22, number 5. [8] Toole, Jameson L and Eagle, Nathan and Plotkin, Joshua B. 2011 (TIST), “Spatiotemporal correlations in criminal offense records”,volume 2, number 4, pages 38, ACM Transactions on Intelligent Systems and Technology [9] Friedman, Jerome H. ”Stochastic gradient boosting.” Computational Statistics and Data Analysis 38.4 (2002): 367-378
Keywords
— Naïve Bayes, K-NN, Random Forest, SVM, Crime Prediction