Machine Learning Applications in Offense Type and Incidence Prediction

Abstract

In today's rapidly evolving world, detrimental behaviour has undeniably emerged as a significant factor leading to the downfall of individuals and communities. The rising prevalence of such behaviour creates substantial disruptions within a country's population, affecting social stability and economic progress. To mitigate the impact of these harmful actions, it is crucial to identify and address them promptly and effectively. This study evaluates specific patterns of detrimental behaviour using data from Kaggle to predict and analyze prevalent negative behaviours. Recent incidents of theft, for example, have underscored the importance of understanding the most common types of misconduct, as well as their timing and locations. We can develop targeted strategies to prevent and respond to such incidents by analyzing these patterns. Artificial Intelligence (AI) techniques encompass various computational methods and algorithms designed to enable machines to perform tasks that typically require human intelligence. These techniques are used in various applications, from natural language processing to image recognition, and offer powerful tools for behavioral analysis. This project employs advanced AI techniques, such as Naive Bayes, to model and identify patterns in detrimental behavior. Naive Bayes, a probabilistic classifier based on Bayes' theorem, is particularly effective in handling large datasets and making accurate predictions. By applying this algorithm, the study achieves a high level of precision in predicting various types of detrimental behavior, enabling a better understanding of their underlying patterns. This knowledge can inform the development of more effective prevention and intervention strategies, ultimately contributing to the reduction of harmful behaviors and the enhancement of community well-being.