Journal of Artificial Intelligence and Big Data, 2021, 1, 1334 www.scipublications.com/journal/index.php/jaibd DOI: 10.31586/jaibd.2021.1334 DOI: https://doi.org/10.31586/jaibd.2021.1334 Journal of Artificial Intelligence and Big Data Article An Analysis of Crime Prediction and Classification Using Data Mining Techniques Anuj Kumar Gupta 1,* , Dheeraj Varun Kumar Reddy Buddula 2 , Hari Hara Sudheer Patchipulusu 3 , Achuthananda Reddy Polu 4 , Bhumeka Narra 5 , Navya Vattikonda 6 1 Oracle ERP Senior Business Analyst, Genesis Alkali, USA 2 Software Engineer, Anthem Inc, USA 3 Software Engineer, Iheartmedia, USA 4 SDE3, Goldman Sachs, USA 5 Sr Java Developer, Statefarm, USA 6 Business Intelligence Engineer, International Medical Group Inc, USA *Correspondence: Anuj Kumar Gupta Abstract: Crime is a serious and widespread problem in their society, thus preventing it is essential. Assignment. A significant number of crimes are committed every day. One tool for dealing with model crime is data mining. Crimes are costly to society in many ways, and they are also a major source of frustration for its members. A major area of machine learning research is crime detection. This paper analyzes crime prediction and classification using data mining techniques on a crime dataset spanning 2006 to 2016. This approach begins with cleaning and extracting features from raw data for data preparation. Then, machine learning and deep learning models, including RNN-LSTM, ARIMA, and Linear Regression, are applied. The performance of these models is evaluated using metrics like Root Mean Squared Error (RMSE) and Mean Absolute Percentage Error (MAPE). The RNN-LSTM model achieved the lowest RMSE of 18.42, demonstrating superior predictive accuracy among the evaluated models. Data visualization techniques further unveiled crime patterns, offering actionable insights to prevent crime. Keywords: Crime Prediction, Crime Data, Data Mining Visualization, Machine Learning, Deep Learning 1. Introduction Crime poses a significant threat to society, and addressing it effectively has become increasingly complex due to its non-systematic and non-random nature. Modern technologies have not only advanced crime-solving methods but have also empowered criminals to carry out sophisticated offenses. According to the Crime Records Bureau, while some crimes, such as burglary and arson, have decreased, others, including murder, sexual abuse, and gang rape, have shown a significant rise [1]. Understanding the probability of crime in specific hotspot locations is crucial for devising effective preventive measures. Crimes occur at various scales, from small villages to major urban centers, and they encompass a wide range of offenses such as murder, kidnapping, robbery, rape, assault, and more. Rising crime rates increase the urgency for law enforcement to address and resolve cases efficiently [2]. Predictive policing, which utilizes analytical and predictive techniques to identify potential crimes, has proven effective. However, as the crime rate increases and criminals become more technologically advanced, manual analysis of crime data stored in large warehouses becomes impractical. This necessitates the adoption of How to cite this paper: Gupta, A. K., Reddy Buddula, D. V. K., Patchipulusu, H. H. S., Polu, A. R., Narra, B., & Vattikonda, N. (2021). An Analysis of Crime Predic- tion and Classification Using Data Mining Techniques. Journal of Artifi- cial Intelligence and Big Data, 1(1), 156–166. DOI: 10.31586/jaibd.2021.1334 Received: August 22, 2021 Revised: November 26, 2021 Accepted: December 23, 2021 Published: December 27, 2021 Copyright: © 2021 by the authors. Submitted for possible open access publication under the terms and conditions of the Creative Commons Attribution (CC BY) license (http://creativecommons.org/licenses /by/4.0/).