Published Paper Details:

With the increasing population in urban residential complexes, the management of visitors, residents, and maintenance staff has become a critical challenge in ensuring safety, efficiency, and convenience. Traditional manual systems for entry management and maintenance service allocation are time-consuming, prone to errors, and lack centralized control. To address these limitations, this project proposes a Machine Learning-based Gated Community Resident and Visitor Management System, designed to automate visitor approvals, record maintenance, and staff allocation through intelligent data-driven decision-making. The proposed system comprises two primary modules: (1) Visitor Management and Approval, and (2) Intelligent Staff Allocation for Residential Issues. In the first module, visitor access requests are submitted digitally and can be approved by the resident, the security staff, or automatically by a Machine Learning model. The ML model is trained on historical visitor data, including visit frequency, purpose, time of entry, and resident feedback, to predict the likelihood of visitor authorization. This predictive approval mechanism reduces manual dependency and enhances overall community security. All visitor data is stored securely in a MySQL database, enabling real-time monitoring, historical tracking, and anomaly detection. The second module focuses on automated staff allocation for maintenance requests, such as plumbing, electrical work, and housekeeping. A supervised learning algorithm evaluates parameters including staff specialization, current workload, proximity, and previous task efficiency to assign the most suitable staff member for each task. This ensures optimal resource utilization, minimal response time, and improved service quality. The system is developed using Python, Django Framework, and Scikit-learn for machine learning model implementation. The architecture integrates a web-based interface for real time interaction between residents, visitors, and staff. The backend ensures secure authentication, seamless data flow, and predictive analytics capabilities. Experimental testing demonstrates that the system reduces visitor processing time by approximately 40% and increases staff allocation efficiency by 35% compared to traditional manual systems. This project presents a scalable, intelligent, and secure solution for modern gated community management, contributing to the development of smart residential ecosystems aligned with emerging smart city initiatives.