The exponential growth of financial markets and the increasing complexity of investment decision-making demand intelligent, automated tools capable of transforming raw market data into actionable insights. This paper presents an AI-Powered Stock Analysis and Prediction Application, a full-stack web system designed to democratize stock market analysis for beginner investors and financial enthusiasts. The system integrates Facebook Prophet — a robust time-series forecasting model — with real-time data retrieved via the Yahoo Finance API (yfinance) to deliver personalized stock predictions, confidence scores, and trend directions. The backend is implemented using FastAPI, while the frontend employs React.js, Vite, Tailwind CSS, and ShadCN UI components to deliver a clean, responsive, and interactive user experience. The application is fully containerized using Docker and Docker Compose, enabling seamless deployment. Experimental results demonstrate that the system consistently produces directionally accurate forecasts and outperforms conventional market platforms in terms of AI-driven interpretability and ease of use. Future work includes integration of sentiment analysis, multi-stock portfolio tracking, and advanced deep learning forecasting models.

Keywords: Stock Prediction, Facebook Prophet, FastAPI, React.js, Time-Series Forecasting, Yahoo Finance API, Machine Learning, Financial Technology, Full-Stack Application, Docker.