Demo Video
Stock Price Prediction Using Python
Stock Price Prediction Using Python is a machine learning web application for stock trend analysis and short-term price prediction with authentication, portfolio management, technical indicators, sentiment analysis and interactive charts.
What You Will Get
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
| Component | Technology |
|---|---|
| Backend | Python, FastAPI |
| Frontend | Streamlit |
| Machine Learning | Scikit-learn, XGBoost |
| Visualization | Plotly |
| Caching | Redis |
| Authentication | JWT |
| Deployment | Docker, Docker Compose |
| Payment Gateway | Stripe |
| Stock Data Provider | Yahoo Finance API |
| Database | Not Applicable / NA |
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.
It can be used for academic submissions, portfolio projects, learning purposes and demonstrating practical concepts such as machine learning, technical analysis, API integration, data visualization and financial application development.
Subscription Plans
| Plan | Features |
|---|---|
| Free | 5 predictions/day, 7-day forecast, Linear Regression model only |
| Basic | 20 predictions/day, 14-day forecast, Linear Regression + Random Forest |
| Professional | 50 predictions/day, 30-day forecast, 4 models, Priority Support |
| Enterprise | 200 predictions/day, 60-day forecast, All models, Premium Support, Bulk API access |
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.
Project Modules
- User registration and login
- Portfolio management
- Stock predictions
- Custom stock alerts
- Prediction history
- User preferences
- Subscription management
Prediction Module:
- Linear Regression
- Random Forest
- Extra Trees
- K-Nearest Neighbors
- XGBoost
- Short-term stock price prediction
Market Analysis Module:
- Historical price charts
- Bollinger Bands
- MACD
- RSI
- SMA
- EMA
- Market news sentiment analysis
API Module:
- RESTful API
- Yahoo Finance API integration
Installation Guide
User Module: - User registration and login - Portfolio management - Stock predictions - Custom stock alerts - Prediction history - User preferences - Subscription management Prediction Module: - Linear Regression - Random Forest - Extra Trees - K-Nearest Neighbors - XGBoost - Short-term stock price prediction Market Analysis Module: - Historical price charts - Bollinger Bands - MACD - RSI - SMA - EMA - Market news sentiment analysis API Module: - RESTful API - Yahoo Finance API integration
FAQ for this project coming soon.
No reviews yet. Be the first to review!