Demo Video
Stock Price Prediction Using Machine Learning
Stock Price Prediction Using Machine Learning is a Python Flask web application that retrieves stock market data from Yahoo Finance and presents price trends, RSI-based BUY, HOLD, or SELL recommendations, next-close estimates, interactive charts, financial metrics, historical data, CSV downloads, and a Stock Assistant through a modern dashboard.
What You Will Get
Project Overview
Stock Price Prediction Using Machine Learning is a Python Flask-based web application presented through the StockSense AI interface. It retrieves historical stock market information using the yfinance library, processes financial data with Pandas and NumPy, and displays market insights through an interactive dashboard.
Users can enter a stock symbol to explore the latest available price, price changes, historical trends, trading volume, technical indicators, and company financial information. The application also generates an RSI-based BUY, HOLD, or SELL recommendation and estimates the next closing price using a simple linear trend.
Key Features
- Stock Symbol Search: Analyze supported stock symbols such as AAPL, TSLA, and MSFT.
- Stock Price Analysis: View the latest available closing price, price changes, and percentage movement.
- RSI Technical Analysis: Calculate the Relative Strength Index using historical closing prices.
- BUY, HOLD, and SELL Recommendations: Generate recommendations using the project's RSI thresholds.
- Next Closing Price Estimate: Project a simple linear trend from recent closing prices to estimate the next trading-day close.
- Interactive Charts: Visualize approximately one year of price history and historical trading volume.
- Financial Information: Display available market capitalization, trailing P/E ratio, dividend yield, 52-week high and low, average volume, sector, and industry.
- Historical Data Table: Review recent historical stock market records.
- CSV Export: Download historical stock data for further analysis.
- Stock Assistant: Ask supported questions about predictions, RSI, and recommendations.
- JSON API: Access stock summaries, chart data, and insight responses through Flask endpoints.
- Responsive Dashboard: Explore stock information through a modern dark-themed interface.
Stock Analysis and Forecasting
The application retrieves historical market data through Yahoo Finance using yfinance. It calculates the Relative Strength Index (RSI) and uses the following thresholds for its recommendations:
- RSI below 30: BUY
- RSI above 70: SELL
- RSI between 30 and 70: HOLD
For its next-close estimate, the application fits a straight-line trend to approximately the latest 30 closing prices and projects that trend forward. These calculations are transparent and suitable for educational exploration of stock analysis.
Dashboard and Stock Assistant
The dashboard combines price information, technical indicators, historical charts, company metrics, and generated insights in one interface. The Stock Assistant communicates with the application's insights endpoint and provides short responses about supported stock analysis topics.
Technology Stack
- Programming Language: Python
- Backend Framework: Flask
- Market Data: yfinance and Yahoo Finance
- Data Processing: Pandas and NumPy
- Frontend: HTML, CSS, JavaScript, and Bootstrap
- Charts: Chart.js
- Template Engine: Jinja2
- API: Flask JSON endpoints
Software Requirements
- Python
- Visual Studio Code
- A modern web browser
- Internet connection for retrieving market data
Project Applications
This project is useful for students who want to understand Python web development, financial data retrieval, technical indicators, API communication, data visualization, and basic trend estimation. It can also serve as a learning example for building interactive financial dashboards.
Important Project Limitations
Although the interface uses AI-focused labels, the documented prediction logic relies on RSI thresholds and a simple linear trend calculation rather than a trained machine learning or deep learning model. The application does not provide guaranteed stock predictions or professional investment advice. Its outputs should be treated as educational analysis, not as a basis for making financial decisions.
Watch Project Demo
Watch Stock Price Prediction Project Demo
More Details and Full Documentation
For the complete project overview, feature details, modules, setup instructions, and documentation, visit the official UPDATEGADH project page:
Project Modules
2. Stock Data Retrieval Module
3. RSI Technical Analysis Module
4. Next Closing Price Estimation Module
5. Interactive Dashboard Module
6. Stock Assistant Module
7. Financial Metrics and Historical Data Module
8. Charts and Data Visualization Module
9. CSV Export Module
10. Flask JSON API Module
Installation Guide
1. Extract the project ZIP file and open the project folder in Visual Studio Code. 2. Ensure Python is installed and available from the terminal. 3. Open the VS Code terminal in the project directory. 4. Create a virtual environment using: python -m venv venv 5. Activate the virtual environment on Windows using: venv\Scripts\activate 6. Install the required dependencies using: pip install -r requi.txt 7. Ensure the computer has an active internet connection to retrieve stock market data from Yahoo Finance. 8. Start the application using: python main.py 9. Wait for Flask to start and note the local address displayed in the terminal. 10. Open that address in your web browser. 11. Enter a supported stock symbol, such as AAPL, TSLA, or MSFT, and start the analysis. 12. Explore price charts, RSI recommendations, financial metrics, historical records, and CSV downloads.
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