Leveraging Machine Learning for Crime Trend Prediction and Analysis

Abstract

The primary focus of this paper's investigation of different machine learning approaches for crime prediction is using classification and regression techniques to pinpoint high-risk locations and predict crime trends. Artificial Neural Networks (ANN), Random Forest Classifiers, Decision Tree Classifiers, Support Vector Regression (SVR), K-Nearest Neighbors (KNN), Support Vector Machines (SVM), and Kernel Support Vector Machines (Kernel SVM) are some of the methods that are examined. These techniques model and predict criminal activity using a range of data attributes, such as time, location, and demographics. ANN and Kernel SVM handle complex, non-linear relationships, whereas SVM provides dependable performance with unbalanced data. By comparing them to data on known crimes, KNN makes it simple to identify patterns. While logistic regression aids in predicting crime probabilities, Random Forest uses ensemble techniques to decrease overfitting and improve accuracy. Decision trees' interpretability makes them useful for real-time crime analysis. SVR models non-linear crime trends and effectively handles outliers. These models are assessed using performance metrics such as accuracy, precision, and error rates, showing their benefits and drawbacks in real-world crime prediction applications. Using these state-of-the-art machine learning techniques, law enforcement agencies can enhance their crime prevention strategies, more efficiently distribute resources, and more accurately identify at-risk areas.