文章基本信息

标题：Crime Prediction and Forecasting using Machine Learning Algorithms
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作者：Azwad Tamir ; Eric Watson ; Brandon Willett 等
期刊名称：International Journal of Computer Science and Information Technologies
电子版ISSN：0975-9646
出版年度：2021
卷号：12
期号：2
页码：26-33
语种：English
出版社：TechScience Publications
摘要：This research will focus on machine learning algorithms for crime forecasting. In the modern world, crime is becoming a major and complex problem. In this research, we discover the best course of action for teaching a model to forecast crime in major metropolitan cities. The purpose of this study is to provide the Police Department with proper crime forecasting so they can better delegate their resources in response to future crime hotspots. We applied several machine learning models to predict the severity of a reported crime based on whether the crime would lead to an arrest or not. We also did a deep dive into the city districts and studied the crime trends by year. We used Folium to do data visualization for the study of these trends. We discovered trends in the number of crimes and the arrest rate from year to year. The different machine learning models that we developed are the Random Forest, K-Nearest-Neighbours, AdaBoost, and Neural Network. We tested our models on the Chicago Police Department's CLEAR (Citizen Law Enforcement Analysis and Reporting) system, which has more than 6,000,000 records. Among all four models, the Neural Network has the best outcome with an accuracy of 90.77%. This study also provides an insight into the applicability of different machine learning models in analyzing crime report datasets from large metropolitan cities.
关键词：AdaBoost;crime forecasting;deep neural network;folium;future crime;KNN;machine learning;prediction;random forest.