Paper Contents
Abstract
Crime is a major challenge faced by modern societies, impacting social stability, economic growth, and individual safety. With the increasing availability of digital records and open crime datasets, data-driven approaches can provide valuable insights for law enforcement agencies. This project focuses on Crime Data Analysis and Prediction using Machine Learning to identify crime patterns, analyze trends, and predict future crime occurrences.The proposed system applies data preprocessing techniques to clean and normalize raw crime datasets, followed by exploratory data analysis (EDA) to uncover hidden patterns such as crime hotspots, peak occurrence times, and correlations between crime categories. Various machine learning algorithmsincluding Decision Trees, Random Forests, Support Vector Machines, and Neural Networksare implemented to build predictive models that can classify and forecast potential crime occurrences based on historical data.The system aims to support predictive policing by providing accurate insights into when and where crimes are more likely to happen, enabling authorities to allocate resources efficiently and improve public safety measures. Evaluation metrics such as accuracy, precision, recall, and F1-score are used to measure model performance.By integrating machine learning with crime data analysis, this project contributes to evidence-based decision-making, helps law enforcement in proactive crime prevention, and enhances community safety through technology-driven solutions.
Copyright
Copyright © 2025 Sai sireesh d . This is an open access article distributed under the Creative Commons Attribution License.
