Budget Buddy: A Geo-Location Based Budget Activity Recommender

Abstract

Planning travel activities is often a challenging process, as individuals must consider both their geographical lo- cation and financial constraints while exploring available options. Traditional methods, such as online searches, guidebooks, or fragmented platforms, tend to be inefficient, lack personalization, and do not meet budget limits, leading to missed opportunities and suboptimal travel experiences. This project introduces GeoBudget Explorer, an AI-driven application that recommends affordable and location-sensitive activities tailored to a user’s budget. The system integrates geospatial filtering, cost-based recommendation algorithms, and machine learning models for activity ranking and personalization. External APIs (e.g., Google Maps, TripAdvisor) provide real-time activity data, while a back-end powered by Flask/FastAPI and a MongoDB/PostgreSQL database ensures secure storage and efficient retrieval of user and activity information. In the front-end, Geo Budget Explorer provides a dynamic activity list, interactive maps, and budget-sensitive dashboards to visualize expenditure trends. A feedback module enables users to share their experiences, which are analyzed using sentiment analysis to refine future recommendations. The primary goal of Geo Budget Explorer is to simplify travel planning by combining location intelligence with budget optimization, making activity discovery more accessible, cost- effective, and user-friendly. This improves user satisfaction, supports better decision making, and fosters more meaningful travel experiences