The objective of this project is to develop a web-based application that can predict future stock prices using historical market data. The application is built using Python and Streamlit, providing an interactive and user-friendly interface for analysis and visualization.
This project focuses on understanding time-series forecasting and applying machine learning concepts to real-world financial data.
Stock price prediction is a challenging task due to market volatility and various external influencing factors. The goal of this project is to analyze past stock data and generate future predictions using a forecasting model.
- Python
- Streamlit (for web interface)
- Prophet (for time-series forecasting)
- Pandas & NumPy (data processing)
- yfinance (stock data collection)
- Plotly (interactive visualization)
- User selects a stock ticker symbol.
- Historical data is fetched using the yfinance library.
- Data is processed and formatted for the forecasting model.
- Prophet model is trained on historical data.
- Future stock prices are predicted.
- Results are displayed using interactive graphs.
- app.py → Main application file
- requirements.txt → Required dependencies
- README.md → Project documentation
Step 1: Clone the repository
git clone https://github.com/PraveenGitGenius/stock_prediction.git
Step 2: Navigate to project directory
cd stock_prediction
Step 3: Install dependencies
pip install -r requirements.txt
Step 4: Run the application
streamlit run app.py
- Understanding of time-series forecasting
- Hands-on experience with Streamlit
- Integration of APIs for real-time data
- Data visualization using Plotly
- Model training and prediction workflow
- Predictions are based only on historical trends.
- Does not consider external economic factors.
- Market behavior can be highly unpredictable.
- Integration of multiple ML models for comparison
- Deployment on cloud platform
- Adding performance metrics
- Real-time news sentiment analysis
Praveen
Electronics and Communication Engineering