CRIMETYPE AND OCCURRENCE PREDICTION USING MACHINE LEARNING

In this era of recent times, crime has become an evident way of making people and society under trouble. An increasing crime factor leads to an imbalance in the constituency of acountry. In order to analyze and have are sponse ahead this type of criminal activities, it is necessary to understand the crime patterns. This study imposes one such crime pattern analysis by using crime data obtained from Kaggle open source which in turn used for the prediction of most recently occurring crimes. The major aspect of this project is to estimate which type of crime contributes the most along with time period and location where it has happened. Some machine learning algorithms such as Naïve Bayes is implied in this work in order to classify among various crime patterns and the accuracy achieved was comparatively high when compared to pre composed works. The rise in urbanization and digitalization has led to an increase in crime rates and the complexity of crime patterns. Traditional methods of crime analysis and prevention are becoming less effective in handling this growing challenge. This study explores the application of machine learning techniques to predict crime types and their occurrences. By leveraging historical crime data, socio- economic factors, and spatial-temporal information, we develop predictive models that can identify potential crime hotspots and the likelihood of different crime types occurring in specific areas. The research aims to enhance law enforcement agencies'ability to allocate resources efficiently, prevent crimes proactively, and improve overall public safety. Our findings demonstrate that machine learning models, particularly ensemble methods and neural networks, provide significant improvements in prediction accuracy compared to traditional statistical approaches.