Stock Analyzer - Django Web Application

A modern Django web application for analyzing and comparing stock performance using machine learning predictions.

Features

• Interactive Stock Analysis: Compare two stocks with real-time data from Yahoo Finance
• Machine Learning Predictions: K-Nearest Neighbors (KNN) algorithm for price predictions
• Beautiful Visualizations: Interactive charts using Plotly
• Statistical Analysis: Comprehensive statistics and metrics
• Modern UI: Responsive design with Bootstrap 5
• Model Performance Metrics: MAE, MSE, RMSE, and MAPE calculations

Technologies Used

• Backend: Django 4.2.7
• Data Analysis: Pandas, NumPy, Scikit-learn
• Stock Data: yfinance
• Visualizations: Plotly
• Frontend: Bootstrap 5, Font Awesome
• Machine Learning: K-Nearest Neighbors Regressor

Installation

1. Clone the repository:

   git clone <repository-url>
   cd stock-analyzer

2. Create a virtual environment:

   python -m venv venv

3. Activate the virtual environment:

   • Windows:

     venv\Scripts\activate

   • macOS/Linux:

     source venv/bin/activate

4. Install dependencies:

   pip install -r requirements.txt

5. Run database migrations:

   python manage.py makemigrations
   python manage.py migrate

6. Create a superuser (optional):

   python manage.py createsuperuser

7. Run the development server:

   python manage.py runserver

8. Open your browser and navigate to http://127.0.0.1:8000/

Usage

1. Enter Stock Tickers: Input two stock ticker symbols (e.g., AAPL, GOOGL, MSFT)
2. Click Analyze: The application will fetch real-time data and perform analysis
3. View Results:
   • Current and predicted prices
   • Statistical analysis
   • Interactive charts
   • Model performance metrics

Features Explained

Stock Analysis

• Fetches 90 days of historical data from Yahoo Finance
• Handles missing data with imputation
• Performs comprehensive statistical analysis

Machine Learning

• Uses K-Nearest Neighbors (KNN) algorithm
• Standardizes data for better predictions
• Calculates multiple performance metrics

Visualizations

• Line Chart: Stock price comparison over time
• Scatter Plot: Price distribution analysis
• Histogram: Price frequency distribution
• Correlation Heatmap: Relationship between stocks

Model Metrics

• MAE (Mean Absolute Error): Average absolute difference between predicted and actual values
• MSE (Mean Squared Error): Average squared difference
• RMSE (Root Mean Squared Error): Square root of MSE
• MAPE (Mean Absolute Percentage Error): Average percentage error

Project Structure

stock_analyzer/
├── manage.py
├── requirements.txt
├── README.md
├── stock_analyzer/          # Main Django project
│   ├── __init__.py
│   ├── settings.py
│   ├── urls.py
│   ├── wsgi.py
│   └── asgi.py
├── stock_analysis/          # Django app
│   ├── __init__.py
│   ├── admin.py
│   ├── apps.py
│   ├── models.py
│   ├── urls.py
│   └── views.py
├── templates/
│   └── stock_analysis/
│       └── home.html
└── static/                  # Static files (CSS, JS)

API Endpoints

• GET /: Home page with analysis form
• POST /analyze/: Analyze stocks and return JSON results

Admin Interface

Access the Django admin interface at http://127.0.0.1:8000/admin/ to:

• View analysis history
• Monitor model performance
• Manage stock analysis records

Customization

Adding New Features

1. New Analysis Methods: Add functions in views.py
2. Additional Charts: Extend the create_charts() function
3. Different ML Models: Modify train_models_and_predict()

Styling

• Modify CSS in templates/stock_analysis/home.html
• Add custom Bootstrap classes
• Update color scheme in CSS variables

Troubleshooting

Common Issues

1. Import Errors: Ensure all dependencies are installed

   pip install -r requirements.txt

2. Database Errors: Run migrations

   python manage.py makemigrations
   python manage.py migrate

3. Stock Data Issues: Check internet connection and ticker symbols

4. Chart Display Issues: Ensure JavaScript is enabled in browser

Contributing

1. Fork the repository
2. Create a feature branch
3. Make your changes
4. Add tests if applicable
5. Submit a pull request

License

This project is licensed under the MIT License.

Acknowledgments

• Yahoo Finance for stock data
• Django community for the excellent framework
• Plotly for interactive visualizations
• Bootstrap for responsive design