Paper Title
HELPING HANDS: A MACHINE LEARNING APPROACH TO PRIORITIZING NGO ASSISTANCE

Abstract
Despite growing willingness to contribute towards social causes, a persistent gap exists between donors and organizations in need due to limited visibility, communication barriers, and lack of trust. Helping hands is a web-based humanitarian aid platform designed to reduce this gap by enabling direct, transparent and efficient interaction between donors, volunteers and Non-Governmental Organizations (NGOs). The platform allows NGOs and community helpers to post real-time requests for resources such as food, clothing, medical supplies and educational support, while donors can easily discover opportunities aligned with their intent to help. A key innovation of this system is the integration of machine learning techniques to improve decision-making and resource allocation. Location-aware recommendation models suggest nearby NGOs to donors, ensuring quicker response times and improved engagement. Additionally, request prioritization based on urgency and resource type enhances visibility for critical needs. By combining real-time data handling, personalization and intelligent recommendations, Helping Hands aims to create a reliable and inclusive ecosystem where assistance is delivered efficiently and ethically to those who need it the most. Keywords - Humanitarian Aid Platform, Ngos, Machine Learning, Transparency, Resource Prioritization.