Algorithmic Guardians: Evaluating Machine Learning for Predicting Criminal Activities

The objectives of this study are to investigate the use of machine learning algorithms to the prediction of criminal behavior. There are many different algorithms that are analyzed and contrasted based on their performance in terms of accuracy, precision, recall, and F1 score. Some of these algorithms include Logistic Regression, K-Nearest Neighbours (KNN), Support Vector Machine (SVM), Random Forest, Naive Bayes, Decision Tree, Multi-Layer Perceptron (MLP), and XG Boost. The evaluation and projection of crime rates in any particular area or nation is of the utmost importance to the authorities who are in charge of governance. These results not only contribute to the identification of methods that may be used to reduce the rates of criminal activity in communities, but they also contribute to the development of practical methods that can finally reduce the number of unlawful activities.