" Crime Analysis and Prediction System"

Abstract

In contemporary times, the prevalence of crime has become a pressing concern, significantly impacting individuals and societal well-being. The escalation of criminal activities disrupts the harmony within a nation. Consequently, there arises a crucial need to comprehensively analyse crime patterns to effectively respond to such nefarious acts. This study undertakes the task of crime pattern analysis utilizing data sourced from Kaggle, an open-access platform, facilitating the prediction of the most recent criminal occurrences.

A fundamental objective of this project is to discern the primary types of crimes that significantly contribute to the overall criminal landscape. Additionally, the study seeks to identify the temporal and spatial characteristics associated with these crimes, aiding in the formulation of targeted preventive measures. Leveraging machine learning algorithms, notably Naïve Bayes, this research endeavours to classify diverse crime patterns with a notable emphasis on enhancing predictive accuracy, surpassing prior endeavours in this domain.

The utilization of Naïve Bayes algorithm in this context offers a robust framework for distinguishing among various crime patterns, thereby facilitating a nuanced understanding of criminal behaviours. Notably, the achieved accuracy in classification surpasses the benchmarks set by previous studies, signifying the efficacy of this approach in discerning intricate crime patterns.

In summary, this research endeavours to provide valuable insights into contemporary crime dynamics, aiding in the development of proactive strategies aimed at mitigating criminal activities. By harnessing data-driven methodologies and advanced machine learning algorithms, the study aims to enhance our understanding of crime patterns, thereby fostering safer and more secure communities