Crime Rate Prediction Using Machine Learning Techniques

Crime is a pervasive and concerning issue in modern society, with a multitude of crimes being committed on a daily basis, causing great unrest among the general populace. Preventing crime is therefore an essential task. In recent times, the significance of artificial intelligence has been observed in almost every field, including crime prediction. To achieve this, it is necessary to maintain a comprehensive data of past crimes, as this information can be utilized for future reference. Predicting the likelihood of future crimes can help law enforcement agencies prevent them before they occur, providing valuable strategic information based on factors such as time and location. However, accurately predicting crime is a challenging task, given the increasing rate at which crimes are being committed (Dutta in Random forests regression in python [1]). Therefore, the development of effective crime prediction and analysis methods is crucial in detecting and reducing future crimes. To this end, numerous researchers have conducted experiments to predict crimes using various machine learning techniques and input parameters such as random forest and decision trees. The ultimate goal is to demonstrate the value and efficiency of machine learning in predicting crimes occurring in a particular place, so that it can be utilized by the police to reduce crime rates and promote societal well-being.