Stock Price Prediction Web Application
Description:
This project aims to develop a web application for predicting stock prices using machine learning techniques. Leveraging historical stock data and employing various prediction algorithms, the application provides users with insights into potential future stock movements, aiding in investment decisions.
Features:
Data Collection: Integrates with financial APIs or data providers to gather historical stock price data. Machine Learning Models: Implements predictive models such as linear regression, decision trees, or deep learning architectures to forecast stock prices. User Interface: Designs an interactive web interface allowing users to input stock symbols, select prediction intervals, and view predicted stock prices along with historical data. Performance Metrics: Calculates and displays performance metrics like accuracy, precision, and recall to evaluate the reliability of predictions. Real-Time Updates: Enables periodic updates of stock price predictions based on the latest available data. Responsive Design: Ensures the web application is accessible and usable across various devices including desktops, tablets, and smartphones.