ENHANCED CRIME HOTSPOT PREDICTION AND SAFE ROUTE RECOMMENDED USING MACHINE LEARNING AND GEOSPATIAL CRIME ANALYSIS

The Crime Hotspot Detection and Safe Route Recommendation System is an effective solution for analyzing crime data and identifying high-risk locations. By utilizing Machine Learning techniques, the system can detect crime hotspots and present them visually on a map, making it easier for users to understand crime patterns in different areas. The integration of safe route recommendations helps users avoid dangerous locations and travel with greater confidence and security. The system also provides a user-friendly interface for accessing crime information and submitting complaints.   Furthermore, the project supports administrators in managing crime records, monitoring activities, and generating reports for better decision-making. The use of technologies such as Python Flask, MySQL, Google Maps API, and Machine Learning enhances the accuracy and efficiency of the system. Overall, the proposed system contributes to public safety by increasing crime awareness, improving route planning, and assisting authorities in crime analysis. It serves as a valuable tool for creating safer communities

1.                  Ian        Sommerville,       Software Engineering, 10th Edition, Pearson Education, 2016. 2.                  Roger S. Pressman and Bruce R. Maxim, Software Engineering: A Practitioner's Approach, 9th Edition, McGraw-Hill, 2020. 3.                  Efraim Turban, Ramesh Sharda, and Dursun Delen, Decision Support and Business Intelligence Systems, Pearson, 2017. 4.                  Jiawei Han, Micheline Kamber, and Jian Pei, Data Mining: Concepts and Techniques, 3rd Edition, Morgan Kaufmann, 2012. 5.                  Trevor Hastie, Robert Tibshirani, and Jerome Friedman, The Elements of Statistical Learning, Springer, 2017. 6.                  Aurélien Géron, Hands-On Machine Learning with Scikit-Learn, Keras, and TensorFlow, O'Reilly Media, 2022. 7.                  Christopher M. Bishop, Pattern Recognition and Machine Learning, Springer, 2006. 8.                  Tom M. Mitchell, Machine Learning, McGraw-Hill Education, 1997. 9.                  Wes McKinney, Python for Data Analysis, 3rd Edition, O'Reilly Media, 2022. 10.               Jake VanderPlas, Python Data Science Handbook, O'Reilly Media, 2016.