Disney Resorts' Hotels Analysis & Price Prediction

A comprehensive analysis and price prediction system for Disney Resorts hotels worldwide

Overview

This project combines traditional data analysis with machine learning to analyze Disney resort hotels globally. It features real-time data scraping, price prediction using PyTorch, and comprehensive statistical analysis. The system can analyze pricing strategies, occupancy patterns, customer satisfaction, and provide future price predictions.

Project Structure

disney-resorts-hotels/
│
├── data-analysis-disney-hotels.py     # Original analysis script
├── disney_hotel_ml_predictor.py       # ML-based price prediction script
├── requirements.txt                   # Project dependencies
├── .gitignore                        # Git ignore file
├── license.md                        # License information
│
├── data/                             # Data directory
│   ├── raw/                          # Raw scraped data
│   └── processed/                    # Processed datasets
│
├── models/                           # Saved ML models
│   └── trained_models/              # Trained model checkpoints
│
├── tests/                           # Test directory
│   └── test_scraper.py             # Scraper tests
│   └── test_predictor.py           # Predictor tests
│
└── logs/                            # Logging directory
    └── scraping_logs/              # Web scraping logs

Requirements

• Python 3.8+
• Required packages:

numpy>=1.21.0
pandas>=1.3.0
matplotlib>=3.4.0
seaborn>=0.11.0
scipy>=1.7.0
scikit-learn>=0.24.0
beautifulsoup4>=4.9.3
requests>=2.25.1
pytorch>=2.0.0
selenium>=4.1.0
webdriver_manager>=3.8.0
python-dateutil>=2.8.2
tqdm>=4.65.0

Installation

1. Clone the repository:

   git clone https://github.com/yourusername/disney-resorts-hotels.git
   cd disney-resorts-hotels

2. Create and activate a virtual environment:

   python -m venv venv
   source venv/bin/activate  # On Windows: venv\Scripts\activate

3. Install dependencies:

   pip install -r requirements.txt

Usage

The project offers two main scripts:

1. Traditional Analysis:

python data-analysis-disney-hotels.py

2. ML-based Price Prediction:

python disney_hotel_ml_predictor.py

Features

Real-time Data Collection

• Automated web scraping of Disney resort websites
• Real-time price and availability data
• Historical data collection and storage
• Rate limiting and error handling

Data Analysis

• Statistical analysis of hotel metrics
• Price trend analysis
• Occupancy patterns
• Seasonal variations
• Special event impact analysis
• Customer satisfaction correlation

Machine Learning Capabilities

• Neural network-based price prediction
• Feature engineering for ML models
• Real-time price forecasting
• Model performance evaluation
• GPU acceleration support
• Automated model retraining

Visualization & Reporting

• Interactive visualizations
• Price trend charts
• Occupancy heat maps
• Satisfaction correlation matrices
• Performance metrics dashboards

Customization

The system can be customized through various configuration options:

1. Scraping Parameters:

scraper_config = {
    'delay': 2,  # Seconds between requests
    'max_retries': 3,
    'timeout': 30
}

2. Model Parameters:

model_config = {
    'learning_rate': 0.001,
    'batch_size': 32,
    'epochs': 100,
    'hidden_layers': [64, 32]
}

Output

The system generates:

• Processed datasets in CSV format
• Trained ML models
• Detailed analysis reports
• Visualization plots
• Performance metrics
• Log files

Contributing

1. Fork the repository
2. Create a feature branch (git checkout -b feature/AmazingFeature)
3. Commit your changes (git commit -m 'Add some AmazingFeature')
4. Push to the branch (git push origin feature/AmazingFeature)
5. Open a Pull Request

License

This project is licensed under the Apache-2.0 license - see the LICENSE.md file for details.

Future Improvements

• Implementation of advanced ML architectures
• Addition of more Disney resorts
• Integration with booking systems
• Real-time price alerts
• Mobile app development
• API endpoint creation
• Enhanced visualization options

Troubleshooting

Common issues and solutions:

• Scraping errors: Check network connection and update selectors
• Model training issues: Verify GPU availability and memory usage
• Data processing errors: Validate input data format and completeness

Support

For questions and support:

• Open an issue on GitHub
• Check existing documentation
• Review closed issues for solutions

Acknowledgments

• Disney resorts data architecture
• PyTorch community
• Web scraping best practices
• Hospitality industry insights