Predicting Crime Types and Occurrences Using Machine Learning Algorithms

This study aims to analyze crime data from the Kaggle platform to identify the main crime categories and explore the impact of geographical and temporal factors, such as location and time, on crime occurrences. The goal is to provide actionable insights that can help law enforcement agencies develop more targeted strategies for crime prevention and response. To achieve this, advanced machine learning techniques, particularly the Random Forest Classifier, are employed to classify and predict crime trends. The random forest model, known for its effectiveness in managing complex and non-linear data relationships, reveals key patterns in crime data. The analysis demonstrates a high level of accuracy, showcasing the potential of this method in forecasting crime frequencies. The findings will help law enforcement better allocate resources, enabling them to implement preventative measures and interventions in high-risk areas and at critical times. Ultimately, this data-driven approach can enhance public safety and inform evidence-based policymaking for crime reduction.