Predicting High-Risk Areas for Crime Hotspot Using Hybrid KNN Machine Learning Framework

This research presents the application of the K-Nearest Neighbour (KNN) Machine Learning algorithm for predicting crime hotspots. The KNN algorithm is a supervised learning method that uses distance between data points to forecast outcomes. The system was used to find trends and forecast potential hotspots after being trained on previous crime data. The study also takes into account social and environmental elements that influence the frequency of crimes in hotspots. The study's findings give insight into the fundamental causes of criminal activity and show that the KNN algorithm may be successfully used to identify crime hotspots. In order to lower crime rates in high-risk locations, law enforcement agencies and urban planners can benefit from this research in establishing focused crime prevention efforts. However, the quality of the data and the algorithm's capacity to recognize pertinent patterns both affect how accurate the forecasts turn out to be. Exploring the potential and restrictions of this method to crime prediction will require more research. In general, using the KNN algorithm to anticipate crime hotspots has shown promise and has the potential to help create efficient crime prevention tactics.