The Power of Predictive Analytics: Forecasting Crime Trends in High-Risk Areas for Crime Prevention using Machine Learning

The main objective of this study is to employ artificial intelligence (AI) and machine learning (ML) techniques to analyze a large crime dataset. The aim is to propose a predictive model that can accurately forecast both the frequency and types of crime that may occur in specific areas within a city. To achieve this, various classification techniques were applied to the crime dataset from Chicago, allowing for the identification of key crime trends and patterns. Machine learning algorithms, including random forest, naive Bayes, decision tree, k nearest neighbor, and logistic regression, were utilized to predict the number of crimes in a given area. Additionally, a neural network was incorporated into the pipeline, introducing an algorithm called neural integrated learning. The performance of the developed models was evaluated using metrics such as accuracy score, precision, recall, and F1 score. The results of this study indicate that the proposed algorithm has the potential to accurately predict crime trends in Chicago with a high degree of precision. This research provides valuable insights into crime patterns in the city, assisting law enforcement agencies in their efforts to prevent crime and enhance public safety.