Integrating Streamlit, Facebook prophet, and machine learning models to create an interactive stock price and risk prediction dashboard

The goal of this project was to create an interactive dashboard for stock price forecasting and risk analysis using an integration between Streamlit, Facebook Prophet, and machine learning techniques. The project aimed to help investors understand potential stock price changes and risk levels by providing interpretable time-series predictions. The dashboard was built entirely using free and open-source technologies, allowing users to view price trends, future estimates, and significant risk signals. The project employed a Research and Development (R&D) approach and incorporated the ADDIE framework, which consists of five stages: analysis, design, development, implementation, and evaluation. The model was tested using Bank Rakyat Indonesia (BBRI) and Bank Central Asia (BBCA) as examples of liquid and highly traded emerging market stocks. The experimental results verified the efficiency of this method, with consistent forecast credibility and satisfactory classification performance for risk prediction. The findings demonstrate that the Streamlit-based dashboard successfully integrates the Prophet and machine learning models into a cohesive and user-friendly web interface, effectively communicating pricing dynamics and emphasizing risk exposure. The study contributes to making financial forecasting more accessible by combining an explainable model, an interactive interface, and integrated risk metrics. Future work may expand the dashboard to include deep learning forecasting models and cross-asset analytics.