CRIME TYPE AND OCCUERANCE PREDICTION USING MACHING LEARNING ALGORITHMS

Abstract

In this modern era, crime has emerged as a significant societal challenge, disrupting
the peace and stability of communities. The escalation of criminal activities not only
poses threats to individuals but also undermines the social fabric of a nation.
Addressing and responding to these criminal activities necessitates a proactive
approach that involves understanding the underlying crime patterns and trends.
To this end, this study focuses on analyzing crime patterns using data obtained from
Kaggle, an open-source platform, to gain insights into the occurrence and distribution
of various types of crimes. By leveraging machine learning algorithms, particularly
Naïve Bayes, this research aims to predict the most recent occurrences of crimes and
identify patterns associated with different crime types.
The primary objective of this project is to ascertain which types of crimes are most
prevalent, as well as to determine the time periods and locations where they occur
with the highest frequency. Through rigorous analysis and classification of crime data,
this study seeks to provide law enforcement agencies and policymakers with valuable
insights for developing targeted crime prevention strategies and allocating resources
effectively.
By harnessing the power of machine learning techniques, such as Naïve Bayes, this
project endeavors to enhance the accuracy of crime pattern prediction compared to
existing methodologies. The findings of this study are expected to contribute to the
advancement of crime analysis and prevention efforts, ultimately fostering safer and
more secure communities.