Analysis of Criminal Landscape by Utilizing Statistical Analysis and Deep Learning Techniques

This research intends to provide law enforcement organizations with a deep learning model that uses trends in past crimes to forecast future crimes. The study will enable them to dispatch security patrols to the most susceptible regions and take preventive actions. The experimental study demonstrates that the LSTM-based deep learning method beats the conventional ARIMA model. Multiple parameter tuning techniques were examined to create an optimized model, such as different LSTM layers, epochs, and batch sizes. The developed model has a training accuracy of around 90%, while on the test data, the minimum and highest accuracy levels were around 75%.