Crime Rate Prediction using Machine Learning

Abstract

A common problem in the world is crime, and predicting crime rates is an important element in providing and predicting crime rates is an important effective crime prevention and resource management. This paper examines the use of machine learning in prediction of crime rates in order to prevent crime and allocate resources more efficiently. This study uses dataset of crime statistics and demographic information for specific regions and applies various machine learning algorithms such as K-Nearest Neighbor, Support Vector Machine and Decision tree to classify given region as high, medium, and low crime rate region. Each algorithm is evaluated based on metrics such as accuracy, precision and recall. This study provides insight of machine learning potential in predicting crime and suggests future research options in this field. Ultimately, these findings could have important implications for crime prevention and resource allocation. Therefore, helping policy makers and law enforcement to accurately, efficiently forecast and reduce crime rate. Crime rates can change over time due to changes in social, economic, or political factors, and machine learning algorithms can adapt to these changes and make more accurate predictions. However, there are also potential ethical issues associated with using machine learning to predict crime rates. In addition, privacy and traceability issues may arise when models use sensitive data such as personal information or criminal records. This is a risk of bias or discrimination if the data used to train the model is not representative of the general population. The research emphasizes the value of interdisciplinary cooperation between data scientists and law enforcement agencies and shows the potential of machine learning in crime prediction.