A prediction analysis for the case of a Korean police dataset

In the evolving landscape of law enforcement, predictive policing, which leverages data analysis and machine learning to anticipate crimes and optimise police responses, has emerged as a critical tool. This paper explores the application of machine learning techniques in predictive policing through a detailed analysis of a Korean police dataset. Focusing on predicting the patterns and duration of police responses, the study employs various algorithms such as RandomForest, Gaussian Naive Bayes, Decision Tree and K-Nearest Neighbors. These models are evaluated based on accuracy, precision, recall, and F-score to determine their efficacy in different response scenarios. Our findings indicate that RandomForest has a much better performance in forecasting response duration, whilst Decision Tree and K-Nearest Neighbour models are particularly effective in predicting the type of response for incidents. The study underscores the significance of specific features like incident severity and police response type in influencing prediction outcomes. Through this research, we contribute to understanding the potential and challenges of machine learning in enhancing the efficiency of police operations in Korea, providing a framework applicable to broader contexts.