DEVELOPMENT AND IMPLEMENTATION OF A VOLUNTEER RECRUIMENT SYSTEM USING A SMART MACHINE LEARNING DATA DRIVEN MODEL

This thesis presents the design and implementation of Civic Pulse, a smart, machine learning-driven volunteer recruitment platform. The project addresses the inefficiencies in traditional volunteer matching by introducing an intelligent, automated system that analyzes user profiles, preferences, and skills to pair them with relevant opportunities. The platform integrates a hybrid matching algorithm combining profile-based and collaborative filtering methods to improve matching accuracy. Built with a Django-React architecture, it provides a modern web interface, real-time notifications, and a scalable backend. Data was sourced from Kaggle and preprocessed using Python libraries such as Pandas, NumPy, and Scikit-learn. The system was deployed using Docker and hosted on PythonAnywhere. The results demonstrate that machine learning can significantly enhance the efficiency, personalization, and accuracy of volunteer placement systems, offering value to NGOs and community-focused organizations.