Forecasting of Crime Hotspots and Analysis using Machine Learning

One of the most important and concerning aspects of our society is crime. Countless crimes are committed daily, and as a result, the lives of all individuals are becoming increasingly unpleasant. Thus, stopping the crime from happening is difficult. In order to prevent the worldwide community from falling victim to the certain actions of criminals or other disruptive elements, the rate of crime should be reduced through the application of machine learning techniques. As a result, crime prediction techniques are essential for foretell future crimes and decreasing overall crime rates. Understanding the crime pattern is a major step to analyzing and planning a response to any kind of criminal activity. Identifying the type of crime along with the time and place where it has occurred, is the main goal of this system. This work implies the use of machine learning methods, such as decision tree classifier and linear regression, to categorize among different criminal patterns. The accuracy attained was relatively high when compared to previously planned work.