Machine learning based analytical approach for geographical analysis and prediction of Boston City crime using geospatial dataset

Machine Learning algorithms has proved its significant contribution in all major domains of technical and non-technical sectors. In present days, the Intelligence Bureau is also using Artificial Intelligence and Machine Learning based analytical approach to predict crime location using past crime data for a given geographical location. Availability of digital records of last few years crimes happened in a certain geographical location is helping crime control division to predict the possible zones for happening next crime and take some precautionary action to reduce the probability of occurring the unwanted event. The government of Boston city initiate to improve city by releasing Crime Incident Report dataset of Boston city to the public. The researcher or analyst can take their initiatives to develop some crime prediction models that can help Boston Police Department to identify the crime prone locations in Boston city and could take some well advance measure to reduce the crime. In this research work, we have analyzed the provided dataset and did an exploratory data analysis to identified the high and low crime prone locations, most severe and least severe crime, year-wise and month-wise crime and shooted and not-shooted crime cases in Boston city. The result presented in this study shows that random forest with Principle Component Analysis (PCA) improve the classification result by 9% in accuracy with comparison to simple decision tree, and PCA with decision tree gives 5% more accuracy than decision tree. Although the computation time is increased in PCA based algorithms in compare to simple decision tree. The proposed research work opens the doors for application of these supervised learning algorithms for prediction and classifying crimes in some other states.