Authors:
G. Sivapriya, B. Vijay Ganesh, U.G. Pradeeshwar, Vishnu Dharshini, Muhammad Al-Amin

Addresses:
1,2,3,4Department of Computer Science and Engineering, SRM Institute of Science and Technology, Ramapuram, Chennai, Tamil Nadu, India. 5Department of International Relations, Sichuan University, Chengdu, China. sg5741@srmist.edu.in1, vb6055@srmist.edu.in2, pg4893@srmist.edu.in3, visnudhs@srmist.edu.in4, alamin2022@stu.scu.edu.cn5  

Abstract:

Crime is a dangerous and global social issue. Crimes influence a nation’s economy, reputation, and quality of life. Innovative technology and novel approaches to crime analytics are needed to safeguard society from Crime. Predicting Crime in a chosen place and visualizing crime data can assist law enforcement in preventing Crime. This paper uses Vancouver crime statistics from crime study resources. As vital as the final prediction, data pre-processing comes first. This paper cleans and feeds data with feature selection, null removal, and label encoding. Pre-processed data is used to build a training model utilizing KNN, Decision trees, classification, linear regression, and random forest algorithms. Sklearn’s matplotlib library predicts and analyses after model creation. The crime dataset is graphed. This work offers a reliable machine-learning criminal case prediction model. Systematic crime analysis and prediction identify crimes. Classification and resolution are difficult due to escalating criminal cases. Knowing local crime patterns helps crime-solving agencies. Machine learning and random forest can reveal local crime tendencies. This article predicts local crimes using crime statistics, speeding up criminal case classification and proceedings. This approach can anticipate high-crime locations and show crime-prone areas. Data mining finds useful information in unstructured data. Existing data anticipates new extraction. Using this method, we can analyze, detect, and estimate regional crime probability. It also discusses crime prediction and analysis using machine learning and data mining. Data mining and machine learning are becoming crucial in almost all fields, including crime prediction. Crime prediction and analysis are essential for detecting and reducing future crimes. 

Keywords: Predictive Analysis; Machine Learning; Artificial Intelligence; Linear Regression; Data Mining; Classification Model; Neural Networks; Visualization Tools; Decision Trees; Crime data; Data Pre-processing.

Received on: 27/12/2022, Revised on: 25/02/2023, Accepted on: 29/03/2023, Published on: 05/04/2023

FMDB Transactions on Sustainable Computer Letters, 2023 Vol. 1 No. 2, Pages: 64-75

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