Project Overview

Stock Prophet represents a sophisticated bridge between quantitative finance and modern web engineering. The application is designed to provide traders and analysts with a predictive edge by leveraging a multi-model ensemble approach. Unlike single-model systems, this platform synthesizes predictions from ElasticNet, Random Forest, and Gradient Boosting algorithms to mitigate individual model bias and improve forecast reliability.

The user interface is built with a focus on "Data-to-Decision" efficiency. A persistent "Control Panel" sidebar allows users to select specific analysis dates and trigger fresh predictions with a single click. The primary dashboard displays critical KPIs, such as current market price vs. the AI’s predicted next-day target, alongside a percentage-based variance indicator. A standout feature is the "Signal" indicator (e.g., HOLD, BUY, SELL), which provides immediate directional guidance based on the predicted return. Below the primary metrics, the "Market Context" section hosts an interactive candlestick chart tracking the last 90 days of price action for tickers like AAPL. This ensures that users don't just see a number, but understand the historical volatility and trend that informed the AI's conclusion. This project demonstrates mastery in handling time-series data, integrating Python-based ML backends with responsive frontend frameworks.