Optimizing Crime Prevention Strategies Using Machine Learning Models

Crime rates are on the rise, necessitating effective strategies for prevention. Machine learning has emerged as a promising tool for forecasting crime incidents due to its ability to learn from data autonomously. The research on the Utilization of Machine Learning Algorithms in Crime Forecasting investigates innovative approaches to anticipate and diminish crime rates. By utilizing information from the San Francisco Police Department, diverse machine learning algorithms are employed to scrutinize and predict crime patterns. The methodology includes data collection, preprocessing, and the implementation of predictive models like K-Nearest Neighbors (KNN), Support Vector Machines (SVM), and Decision Trees. These models were trained on a preprocessed dataset of crime statistics from 2014 to 2017, and their performance was evaluated using accuracy and F-beta score metrics. Key results show that the Decision Tree algorithm achieved the highest accuracy at 88%, with KNN following closely at 87%. SVM, although effective in other scenarios, attained a lower accuracy of 66% in this study. These findings indicate that Decision Trees and KNN are particularly effective for classifying and predicting crime data.