MACHINE LEARNING IN CRIME HOTSPOT FORECASTING: OPTIMIZING RESOURCE DISTRIBUTION FOR PUBLIC SAFETY

Predicting crime hotspots using Machine Learning has emerged as a promising technique for boosting law enforcement strategies. This assignment examines a dataset of over 1.7 million historical crime records, utilizing temporal and spatial analysis techniques to identify patterns in crime occurrences. The crime facts are labeled into three levels—low, medium, and immoderate—based mostly on crime rates. Several tool-studying algorithms have been accomplished and compared, which include Decision Trees, Random Forest, Naive Bayes, K-Nearest Neighbor (KNN), Logistic Regression, Linear Discriminant Analysis (LDA), Quadratic Discriminant Analysis (QDA), and Gradient Boosting Trees. The Overall performance metrics of the model were assessed using metrics such as accuracy, confusion matrices, and Unweighted Average Recall (UAR). The Random Forest classifier exhibited superior average overall performance, achieving immoderate accuracy and UAR scores for every balanced and imbalanced dataset. Additionally, a "God Test" was modified into brought to evaluate the model`s predictive energy using historical facts, further validating its effectiveness on unseen datasets. The findings highlight the functionality of tool studying to assist facts-driven selections in law enforcement, allowing optimized police patrols and better useful resource allocation to reduce crime correctly.