Crime Mapping using Machine Learning Algorithm: K-means Clustering algorithm

Abstract-- This study focuses on the application of data analytics techniques such as clustering to aid in crime prevention and mitigation. The K-means clustering algorithm was utilized in this study after removing rows with missing data. The feature matrix was created using the latitude and longitude data of crimes, and the K-means algorithm was implemented with 5 clusters. The resulting clusters were added to the crime data and visualized on a map using the Folium library. This map can assist law enforcement agencies in identifying high crime areas and developing appropriate strategies. The study highlights the potential of data analytics and visualization techniques in crime prevention and suggests further research to determine the effectiveness of clustering algorithms in identifying crime patterns and trends in large datasets.