Digital Financial Advisor Using Random Forest Regressor

Abstract

This study presents the Digital Financial Advisor, a web-based application designed to provide personalized invest recommendations by analyzing user specific financial factors such as age, financial knowledge, risk tolerance, investment time horizon, and financial goals. The system uses the Random Forest Regressor algorithm as the core methodology to generate optimal portfolio allocations across diverse asset classes like stocks, fixed deposits, SIPs, bonds, and commodities. The platform features interactive pie chart that makes the investment strategies easier to understand. Initial testing demonstrated system responsiveness and effective port- folio distribution. In conclusion, the system bridges the gap between expert financial advice and everyday users. Future enhancements include integrating real-time financial data for dynamic recommendations and expanding feature sets to incorporate additional financial factors, ensuring more comprehensive and accurate investment guidance.