Welcome to the Real-Time Stock Price Prediction and Market Analysis repository! This project focuses on predicting stock prices using advanced machine learning techniques. We utilize Long Short-Term Memory (LSTM) networks to forecast stock prices for the next 10 days, based on historical data from 2010 to 2023. The application also visualizes market trends in a user-friendly manner using Streamlit.
Download the latest release here!
- Features
- Technologies Used
- Installation
- Usage
- How It Works
- Visualizations
- Contributing
- License
- Contact
- Real-Time Predictions: Get stock price predictions for the next 10 days.
- Data Visualization: View trends and historical data through interactive charts.
- User-Friendly Interface: Built with Streamlit for an easy-to-navigate experience.
- Comprehensive Analysis: Analyze market trends using various metrics.
This project incorporates a variety of technologies to ensure robust performance:
- Keras: For building and training the LSTM model.
- LSTM: A type of recurrent neural network ideal for time series data.
- Machine Learning: Techniques to analyze and predict stock prices.
- Matplotlib: For creating static, animated, and interactive visualizations.
- NumPy: For numerical operations on large datasets.
- Pandas: For data manipulation and analysis.
- Pandas-DataReader: To fetch stock data from various sources.
- Python: The primary programming language used in this project.
- Scikit-Learn: For additional machine learning utilities.
- Stock Market APIs: To gather real-time data.
- Streamlit: To create the web application interface.
- TensorFlow: For deep learning model training.
To get started with this project, follow these steps:
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Clone the Repository:
git clone https://github.com/thinker84/Real-Time-stock-price-prediction-and-market-analysis-using-Machine-Learning.git cd Real-Time-stock-price-prediction-and-market-analysis-using-Machine-Learning -
Install Required Packages: Make sure you have Python installed. Then, install the necessary packages using pip:
pip install -r requirements.txt
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Download the Latest Release: Visit the Releases section to download the latest release and execute it.
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Run the Application: Start the Streamlit app by executing:
streamlit run app.py
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Select Stock: Use the dropdown menu to select the stock you want to analyze.
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View Predictions: The application will display predictions for the next 10 days along with visualizations of historical data.
The application collects historical stock price data from 2010 to 2023 using Pandas-DataReader. This data includes open, high, low, and close prices.
The data undergoes several preprocessing steps:
- Normalization: Scale the data to improve model performance.
- Splitting: Divide the data into training and testing sets.
We use an LSTM model due to its effectiveness in time series forecasting. The model is trained on the historical data, learning patterns and trends.
After training, the model can predict stock prices for the next 10 days. The predictions are displayed in the application.
The application features various visualizations, including:
- Line Charts: Display stock price trends over time.
- Bar Charts: Compare historical prices.
- Prediction vs Actual: Visualize predicted prices against actual prices.
We welcome contributions to improve this project. If you have suggestions or enhancements, please follow these steps:
- Fork the repository.
- Create a new branch.
- Make your changes and commit them.
- Push to your branch and submit a pull request.
This project is licensed under the MIT License. See the LICENSE file for details.
For any questions or inquiries, feel free to reach out:
- Email: your-email@example.com
- GitHub: thinker84
Thank you for checking out the Real-Time Stock Price Prediction and Market Analysis project! We hope you find it useful for your stock market analysis needs. Don't forget to check the Releases section for the latest updates!