Enhancing Infrastructure and Accessibility

Enhancing infrastructure and accessibility for healthcare and education is essential to bridging gaps in underserved regions. This project leverages AI-driven solutions to provide real-time demand scores and actionable insights for effective infrastructure planning. Using advanced machine learning models and APIs, the system helps stakeholders prioritize and optimize efforts in these sectors. For education connectivity, the system evaluates indicators like out-of-school rates, enrollment rates, and geographic data to identify regions with inadequate infrastructure. It uses Random Forest models to predict demand and recommend initiatives like building schools, establishing digital learning hubs, or allocating educational resources in high-need areas. In healthcare, the platform incorporates factors like facility type, ownership type, and population density. It assigns weighted scores to calculate demand and leverages K-Means clustering for regional analysis, combined with Gradient Boosting for accurate predictions. This helps recommend expanding networks, upgrading hospitals, or deploying telemedicine in underserved areas. The project features a user-friendly frontend visualization tool built with React and Google Maps APIs. Users can interactively select regions on a map, view demand scores, explore clusters, and access recommendations in real time. A key highlight is the LLM-based recommendation engine, which generates tailored suggestions based on real-time data. For education, it may suggest building schools or launching e-learning platforms, while for healthcare, it can recommend facility upgrades or mobile healthcare units. These recommendations adapt dynamically to evolving needs, making the platform a valuable tool for governments, NGOs, and private organizations to drive change. This system showcases the potential of AI and large language models in improving infrastructure, fostering inclusivity, and addressing challenges in global healthcare and education.