This project is an end-to-end web-based stock forecasting tool built with Python, Facebook Prophet, and Streamlit. Users can upload stock price data (CSV format from Yahoo Finance), visualize historical prices, and generate future predictions (up to 1 year) with interactive charts.
Designed for students, data science beginners, and financial enthusiasts, this app demonstrates how time series forecasting works using real-world stock data. It also helps users compare predicted prices with actual prices on selected dates — all through a clean and interactive interface.
⚠️ GitHub does not support embedding actual YouTube videos. Clicking the image will open the video in a new tab.
This project is ideal for:
- 📊 Understanding Time Series Forecasting in a visual and practical way
- 🎓 Learning how to deploy ML models using Streamlit
- 💹 Exploring stock trends and simulating future predictions
- 🧪 Educational demos in data science and machine learning
⚠️ This project is for educational use only and should not be used for real trading or financial decision-making.
👉 Try the app here: Streamlit Live App
| Technology | Role |
|---|---|
| Python | Programming language |
| Prophet | Time series forecasting model |
| Streamlit | Frontend for web-based ML app |
| Plotly | Interactive plotting library |
| Pandas | Data manipulation |
| Pillow | Image display |
- Upload historical stock CSV data from Yahoo Finance
- Cleanly visualize historical trends with candlestick charts
- Generate 365-day future forecasts
- View monthly, weekly, and yearly prediction components
- Compare predicted prices with actual prices on a specific date
- Step-by-step guide for users unfamiliar with stock data sources
This app uses the Facebook Prophet forecasting model. Prophet is robust, easy to use, and handles:
- Seasonality (weekly/monthly)
- Trend changes
- Missing data
- Outliers
Prophet is well-suited for business forecasting, making it a great educational tool.
# Clone the repository
git clone https://github.com/Uvais5/Stock_Price_Prediction_app.git
cd Stock_Price_Prediction_app
# Create virtual environment (optional but recommended)
python -m venv venv
source venv/bin/activate # On Windows: venv\Scripts\activate
# Install dependencies
pip install -r requirements.txt