Criminal investigate tracker with suspect prediction

Abstract

Crime investigation is a complex and time-consuming process that requires the analysis of large volumes of data, including crime records, suspect information, evidence, and location details. Traditional investigation methods often rely on manual analysis, which can lead to delays and inefficiencies in identifying suspects and solving cases. With the rapid advancement of data analytics and machine learning technologies, intelligent systems can now assist investigators in analyzing crime patterns and predicting potential suspects. The Criminal Investigation Tracker With Suspect Prediction system is designed to support law enforcement agencies by providing an intelligent platform for managing crime records and predicting suspect involvement using machine learning techniques. The system stores detailed information about crime incidents, suspects, evidence, and investigation activities. By analyzing this data, the system can identify patterns and relationships that may not be easily detected through manual investigation. Machine learning algorithms such as Random Forest, Decision Tree, and Logistic Regression are used to analyze historical crime data and predict whether a suspect is likely involved in a particular crime. The system evaluates various factors including crime location, time, suspect history, distance from the crime scene, and evidence type to generate predictions. In addition to prediction capabilities, the system also functions as an investigation management platform that helps track crime cases, monitor investigation progress, and maintain organized records of suspects and evidence. By combining crime tracking with predictive analytics, the system assists investigators in making faster, more informed decisions. Ultimately, this project aims to improve investigation efficiency, reduce investigation time, and support law enforcement agencies in solving crimes more effectively.