SECURE BACKEND DEVELOPMENT WITH DATA INTEGRATION FOR A SMART INDUSTRIAL TRAINING PLACEMENT RECOMMENDATION SYSTEM FOR NIGERIAN STUDENTS

This research project developed a Smart Industrial Placement System (SIPS) to revolutionize how Nigerian university students secure industrial training placements. The study was motivated by the persistent challenges in Nigeria's current placement system, which relies heavily on manual processes, personal connections, and often results in mismatched opportunities that fail to align with students' academic disciplines or career aspirations. These inefficiencies undermine the core objective of industrial training to bridge the gap between classroom learning and real-world industry experience. The SIPS platform addresses these gaps through an intelligent, data-driven approach combining machine learning algorithms (including collaborative filtering and TF-IDF-based content analysis) with a robust monolithic architecture. The system architecture integrates Spring Boot for backend services, FastAPI for machine learning model deployment, and a hybrid database system using MySQL for structured data and MongoDB for flexible document storage. Key innovative features include: automated matching based on course relevance, location preferences, and alumni testimonials; real-time updates through web scraping of industrial training portals; and a user-friendly interface for both students and companies. Implementation followed a rigorous methodology including data collection from 33 Nigerian states, preprocessing to clean and normalize industrial training records, and systematic testing. Evaluation metrics demonstrated 85% accuracy in placement matching, with precision and recall scores above 80% in controlled tests. User acceptance testing with 50 students showed 72% satisfaction rates, particularly praising the system's ability to surface previously unknown but relevant opportunities. The study makes both theoretical contributions (demonstrating how hybrid recommendation systems can solve education-industry alignment problems) and practical contributions (delivering a working prototype). Key recommendations include adoption by Nigerian universities, creation of centralized placement databases, and policy interventions to support such technological solutions. Future research directions include incorporating deep learning for resume analysis and expanding the system's coverage across West Africa.