Skip to content

Latest commit

 

History

History
126 lines (97 loc) · 5.52 KB

Contributing.md

File metadata and controls

126 lines (97 loc) · 5.52 KB
Raw

Contributing to Stock Price Prediction Project

Welcome to the Stock Price Prediction Project! We're thrilled that you're considering contributing. Whether you're a seasoned data scientist, a machine learning enthusiast, or a beginner, your contributions are valuable. Here's a guide to help you contribute effectively.

🚀 Why Contribute?

This project aims to predict the stock prices of "The State Bank of India" using various machine learning algorithms. It's a hands-on opportunity to work with real financial data and cutting-edge algorithms, from Linear Regression to LSTM, and contribute to building models that can potentially power financial predictions.

By contributing, you'll:

  • Sharpen your machine learning skills: Work with real-world data and advanced regression techniques.
  • Collaborate with like-minded developers: Join a growing community of contributors passionate about finance and tech.
  • Build your portfolio: Showcase your work on a practical, impactful project.

📑 How to Contribute

1. Fork the Repository

Start by forking the repository to your GitHub account. This allows you to work on the project independently.

2. Set Up the Project Locally

  • Clone your forked repository: 
    git clone https://github.com/your-username/Stock-Price-Prediction.git
  • Navigate to the project directory: 
    cd Stock-Price-Prediction
  • Install the necessary dependencies: 
    pip install -r requirements.txt

3. Explore Contribution Areas

Here are some areas where you can contribute:

🔍 Data Exploration and Feature Engineering

  • Analyze stock price trends and extract new features that could improve model performance (e.g., technical indicators like RSI or Moving Averages).
  • Clean and preprocess additional financial datasets to enhance prediction accuracy.

🤖 Model Development

  • Experiment with different machine learning algorithms or fine-tune the existing ones like AdaBoost, LSTM, etc.
  • Try implementing Ensemble methods or newer techniques such as AutoML.

📊 Performance Analysis

  • Evaluate models using more detailed metrics or visualizations (e.g., precision-recall curves, ROC curves).
  • Compare performance across different time intervals (e.g., weekly vs. monthly data).

🧠 Deep Learning Integration

  • Integrate deep learning models like CNNs or hybrid approaches combining ANN with financial sentiment data.

4. Submitting Your Contribution

  1. Create a new branch: 
    git checkout -b feature-branch-name
  2. Make your changes and commit them: 
    git add .
git commit -m "Descriptive message about your contribution"
  3. Push your changes to your forked repository: 
    git push origin feature-branch-name
  4. Create a pull request (PR) to the original repository from your GitHub fork.

🛠️ Code Guidelines

  • Write clean, readable code with proper comments and documentation.
  • Follow PEP8 standards for Python code.
  • Ensure your code runs smoothly by testing it with real data from the notebooks/ or src/ directories.

Testing Your Changes

Before submitting your PR, make sure to test your code:

  • Run the notebooks to ensure your models are working as expected.
  • Validate the performance with accuracy, RMSE, MAE, and other relevant metrics.

📝 Documentation

If you're introducing new algorithms or modifying existing ones, please update the documentation. This can include:

  • Adding new markdown cells in Jupyter notebooks.
  • Updating README files or adding comments in the code.

🌟 Looking for Inspiration?

  • Add sentiment analysis or news-based prediction to correlate stock prices with market sentiment.
  • Build a live stock price prediction dashboard using real-time data from Yahoo Finance API.

🧑‍🤝‍🧑 Community

Join the discussion in the Issues section! Share your ideas, ask questions, and collaborate on exciting features with fellow contributors.

🔄 Recent Updates

Changes in Flask Route for Stock Prediction:

  • The route /predict_close has been updated to handle both GET and POST requests.
    • GET request: Renders the stock.html form when the user navigates to the prediction page.
    • POST request: Processes the stock price prediction based on user input (Open, High, Low, and Volume).

Make sure to follow the updated code structure when adding new routes for similar tasks. The current structure is as follows:

@app.route('/predict_close', methods=['GET', 'POST'])
def predict_close():
    if request.method == 'POST':
        try:
            inputs = [
                float(request.form.get('Open')),
                float(request.form.get('High')),
                float(request.form.get('Low')),
                float(request.form.get('Volume'))
            ]

            close_prediction = stock_model.predict(np.array([inputs]))[0] if stock_model else None

            save_data(inputs, close_prediction)
            retrain_model()

            return str(round(close_prediction, 2)) if close_prediction is not None else "Error: Stock model not loaded."
        except Exception as e:
            return f"An error occurred: {e}"
    # For GET request, render the stock.html page
    return render_template('stock.html')


## 🎉 Thank You!
Every contribution counts! Whether you’re fixing a bug, improving documentation, or building a new feature, we appreciate your efforts to make this project better.

Happy coding!