Stock Price Prediction Using Python

Stock Price Prediction Using Python is a professional web application designed for short-term stock price prediction, market analysis and stock trend visualization. Developed using Python, FastAPI, Streamlit, Scikit-learn and XGBoost, the platform combines machine learning models, technical indicators, market news analysis and stock market data to provide users with data-driven insights.

The application provides a complete platform for users to analyze stock performance, visualize historical prices, generate predictions and manage their portfolios. It also includes secure authentication, subscription plans with Stripe payments, custom stock alerts and an AI-powered sentiment analysis feature for market news.

Key Features

• User Authentication: Users can securely create accounts and log in to access the stock prediction platform.
• Subscription Plans: The platform provides different subscription tiers with prediction limits and forecasting capabilities, including integrated Stripe payments.
• Portfolio Management: Users can manage and track their stock portfolios through a dedicated dashboard.
• Custom Stock Alerts: Users can create custom alerts to monitor selected stocks and price movements.
• AI-Powered Sentiment Analysis: Market news can be analyzed to provide sentiment-based insights for stock analysis.
• Interactive Historical Charts: Users can visualize historical stock prices through interactive charts for better market analysis.
• Technical Indicators: The system provides popular technical indicators including Bollinger Bands, MACD, RSI, SMA and EMA.
• Multiple Machine Learning Models: The platform supports Linear Regression, Random Forest, Extra Trees, K-Nearest Neighbors and XGBoost models for stock price prediction.
• RESTful API: The application provides a RESTful API for accessing platform functionality.
• User Dashboard: Users can track their predictions and manage their preferences from a centralized dashboard.
• Stock Market Data: The application uses Yahoo Finance API to retrieve stock market data.
• Modern Deployment: The platform is designed for containerized deployment using Docker and Docker Compose.

Technology Stack

System Requirements

• Python environment
• FastAPI
• Streamlit
• Scikit-learn and XGBoost
• Redis
• Docker and Docker Compose
• Modern web browser such as Chrome, Firefox or Edge
• Internet connection for accessing stock market data through the Yahoo Finance API

How It Works

The Stock Price Prediction platform collects stock market data through the Yahoo Finance API and processes the information for market analysis and prediction. Users can select stocks, view historical price charts and analyze technical indicators such as RSI, MACD, Bollinger Bands, SMA and EMA.

The machine learning component uses multiple models including Linear Regression, Random Forest, Extra Trees, K-Nearest Neighbors and XGBoost to generate stock price predictions. Users can access the prediction results through the web interface and track their predictions from the dashboard.

The platform also provides portfolio management, custom stock alerts and AI-powered sentiment analysis of market news. Different subscription plans control prediction limits and forecast periods, with Stripe integrated for subscription payments.

Perfect For

This project is suitable for BCA, MCA, B.Tech CS/IT students, Python developers, Machine Learning learners, Data Science students and educators who want to understand how machine learning can be combined with financial market data and web application development.

Watch Project Demo

Watch the project walkthrough and setup tutorial on our YouTube channel.

▶ Watch on YouTube – Decode It

More Details & Full Documentation

For complete project details, source code and setup information, visit the official project page.

🔗 View Full Project Details – UpdateGadh