Abstract:
In the modern digital economy, financial transactions have become effortless and frequent, making it increasingly difficult for individuals to maintain visibility and control over their spending behaviors. The absence of intuitive tools that connect habitual expenditures to long-term financial outcomes often results in overlooked savings opportunities, budgetary inefficiencies, and hindered wealth generation. This project, titled HabitualWealth Predictor, introduces a mobile-based solution that empowers users to track, analyze, and forecast their spending on recurring habits . Through a data-driven backend powered by FastAPI and MongoDB, coupled with a responsive Flutter frontend, the application computes personalized predictions of monthly spending and identifies potential savings based on behavioral trends. By leveraging machine learning techniques, it reveals not only how much a user spends but also how small adjustments in habitual behavior can compound into substantial financial gains over time. The core objective is to help users visualize both their spending potential and savings potential—transforming passive expense tracking into active financial foresight. This aligns with the broader aim of fostering habit-aware financial literacy and supporting long-term wealth development in a user-centric and technologically robust environment. i
