Published Paper Details:

The rise of criminal activities worldwide has created a need for law enforcement agencies to implement more efficient methods for preventing crime. Traditional crime-solving techniques have been shown to be inadequate in the face of increasing crime rates. As a solution, the integration of machine learning (ML) and computer vision algorithms and techniques is a promising approach for predicting and preventing crime. The main objective of this study is to illustrate the potential of ML and computer vision in assisting law enforcement agencies in detecting, preventing, and solving crimes with greater speed and accuracy. The study highlights previous successful cases where these techniques have been applied, demonstrating their impact on law enforcement agencies through statistical analysis. The significance of this research lies in the potential to revolutionize law enforcement by significantly improving crime detection and prevention. If implemented successfully, the use of these techniques has the potential to ease the burden on police officers and lead to more effective crime prevention measures. The primary objective of this study is to create a machine learning-based predictive model for crime rates. The dataset utilized in this research encompasses various socio-economic, demographic, and geographic attributes that may influence crime rates. Different machine learning algorithms such as decision trees, random forests, and neural networks are used in this study to build and compare predictive models. The findings reveal that the predictive model based on the random forests algorithm delivers the highest accuracy for predicting crime rates. This research highlights the potential of machine learning techniques as a valuable tool to aid law enforcement agencies and policymakers in making informed decisions to prevent and reduce crime rates.