PERSONAL FINANCE MANAGER WITH PREDICTIVE ANALYTICS USING LSTM FOR ENHANCED FINANCIAL DECISION MAKING - Kumar, Jatin; Vishwakarma, Anurag; Sainy, Nikita; Kumar, Arun; Aggarwal, Ruchika (International Journal of Research -GRANTHAALAYAH, 2024)
The Personal Finance Manager project is designed to help individuals effectively manage their finances by tracking income, expenses, and available funds, while leveraging the power of machine learning for predictive analysis. The system features a user-friendly interface where users can input income and expense data, track spending, and visualize financial trends with interactive charts. In addition to traditional budget tracking, the project incorporates Long Short-Term Memory (LSTM) models to analyze past financial data and predict future financial trends, such as potential overspending, savings progress, and expected future expenses. The Personal Finance Manager allows users to set budgets for different categories and provides real-time tracking with graphical representations of their financial habits. The integration of LSTM models helps forecast future income and expenses, enabling users to make more informed financial decisions. Additionally, the system offers goal-setting features, allowing users to set financial goals such as saving for a vacation or paying off debt. By analyzing historical spending patterns, the LSTM model can also suggest personalized budgeting strategies and alert users when they are likely to exceed their financial goals. With its combination of real-time tracking, advanced analytics, and predictive modeling using LSTM, the Personal Finance Manager empowers users to take control of their finances. By anticipating future financial trends, users are equipped to adjust their spending habits proactively, helping them achieve long-term financial stability.