“Crime Analytics Web Application Using Hybrid Machine Learning And Deep Learning Approach

Authors: Saurabh Sharma, Arun Sharma, Vipin Kumar, Rahul Bharti

Abstract: This paper presents a Crime Analytics Web Ap-plication that leverages a hybrid approach combining Machine Learning and Deep Learning techniques for predictive crime analysis. The primary objective of the system is to transform traditional reactive crime investigation methods into proac-tive, data-driven decision-making frameworks. The proposed system integrates heterogeneous datasets, including historical crime records, temporal attributes, and socio-economic fac-tors, to identify hidden patterns and correlations in criminal activities. A robust data engineering pipeline is designed to pre-process and clean large-scale noisy datasets through feature engineering, normalization, and outlier handling. The system employs a hybrid ensemble model consisting of Light Gradient Boosting Machine (LightGBM) for capturing complex feature interactions and Long Short-Term Memory (LSTM) networks for modeling temporal dependencies in crime trends. This combination enhances prediction accuracy by leveraging both spatial and temporal insights. Furthermore, a user-friendly web application is developed using Streamlit, providing interactive dashboards, real-time prediction capabilities, and advanced data visualizations such as heatmaps and time-series plots. The system enables law enforcement agencies to anticipate crime hotspots, analyze trends, and make informed strategic decisions. The experimental results demonstrate that the hybrid ap-proach outperforms traditional single-model techniques in terms of accuracy and efficiency. The proposed solution has significant potential for real-world deployment in smart polic-ing and urban safety systems, with future scope including real-time data integration, geospatial intelligence, and smart city applications.