Crime Prediction and Analysis Using Machine Learning

Abstract

Preventing criminal activity is crucial since crime is a major issue in modern society. Several violent crimes are perpetrated every day. For this reason, it is necessary to retain a record of any criminal activity for possible future reference. Current challenges include keeping accurate crime datasets and evaluating these datasets to aid in future crime prediction and resolution. This study aims to forecast future crime types based on an analysis of large datasets including information about past crimes. The goal of this research is to use data analysis and machine learning science to anticipate crimes committed in Chicago based on that city's extensive crime database. The statistics are taken directly from the Chicago Police Department's website. Details about the location, time, date, latitude, and longitude of a crime are all included. The data will be preprocessed before the model is trained, and then features will be selected and scaled for optimal accuracy. We will evaluate many different crime prediction algorithms, including K-Nearest Neighbor (KNN), and utilise the most effective one in our training procedures. Several scenarios, such as when crime rates or criminal activities tend to spike, will be graphically represented as part of the dataset's visualisation. This project's overarching goal is to provide a conceptual framework for how law enforcement organisations may make use of machine learning to improve their ability to identify, anticipate, and catch criminals at an accelerated pace, hence lowering the crime rate. This is not unique to Chicago; it may be used elsewhere, too, if a suitable dataset is made available.