Crime Prediction Using Ensemble Machine Learning Approach

Crime prediction is a critical study area in law enforcement and public safety. This work leverages the power of machine learning (ML) techniques, specifically ensemble learning, to analyze and predict criminal activities based on historical crime datasets. The study begins by collecting and preprocessing a comprehensive dataset, employing ensemble learning techniques such as a Voting Classifier combining Decision Tree Classifier, K-Nearest Neighbors (KNN) Classifier, and Random Forest Classifier to predict crime levels using a dataset comprising 911 call records. With a focus on enhancing public safety, the study explores patterns in criminal incidents, leveraging temporal, spatial, and descriptive attributes. The work’s significance lies in helping law enforcement allocate resources wisely and develop targeted crime prevention strategies. The study provides strong predictions and practical insights by utilizing ensemble models, which combine multiple base models to improve predictive performance. The outcome is expected to benefit law enforcement agencies by providing them with a valuable tool for proactive crime prevention. By anticipating where and when crimes are likely to occur, resources can be allocated efficiently, ultimately leading to safer communities. In conclusion, this study represents a significant step toward advancing crime prediction using ML techniques and demonstrates the potential real-world applications and impact of the proposed crime prediction model.