In today’s world, the increasing rate of criminal activities poses a significant challenge to law enforcement agencies. With vast amounts of crime-related data being collected daily, there is an urgent need for intelligent systems that can analyze historical patterns and assist in solving crimes efficiently. Traditional investigative methods, though effective, are time-consuming and often constrained by limited human resources and data interpretation capabilities.

This project, Crime Data Analysis and Perpetrator Identity Prediction, is designed to bridge the gap between raw crime data and actionable insights. By applying machine learning algorithms to crime datasets, the system can predict key attributes of perpetrators—such as age, gender, and their relationship to the victim—based on evidence found at crime scenes. These predictions provide crucial leads in unsolved cases and help prioritize investigation efforts.

Through data preprocessing, model training, and a user-friendly web interface, the system supports law enforcement with intelligent crime analytics. It serves as a foundation for integrating data-driven decision-making into criminal investigations, ultimately contributing to a safer society.

Tags: flask, python

Languages: python