A sophisticated recommender system that leverages web mining techniques to help users find hotels that match their preferences. The system combines automated web scraping, real-time data processing, and machine learning to provide personalized hotel recommendations.
- 🔄 Automated web scraping of hotel data from Google Travel
- ⚡ Real-time data processing capabilities
- 🤖 Machine learning-based recommendation engine
- 🖥️ Interactive user interface
- 🎯 Personalized hotel recommendations
The system analyzes the following amenities to provide tailored recommendations:
- 🍳 Free breakfast
- 📡 Free Wi-Fi
- ❄️ Air conditioning
- 🍽️ Restaurant
- 🅿️ Free parking
- 🛎️ Room service
- 🏊 Pool
- 👕 Full-service laundry
- 💪 Fitness centre
- 🍳 Kitchen
- 🚌 Airport shuttle
- 💆 SpaThe project demonstrates the integration of several key components:
- Web Mining - Automated data collection from hotel listings
- Data Processing - Real-time analysis of hotel features and amenities
- Machine Learning - Intelligent recommendation algorithm
- User Interface - Interactive platform for receiving user preferences
- Web scraping module for data collection
- Data processing pipeline
- Machine learning recommendation engine
- User interface layer
- Python-based implementation
- Machine Learning libraries (scikit-learn)
- Web scraping tools
- Data processing frameworks
- Create a virtual environment:
python -m venv venv
source venv/bin/activate # On Unix/macOS
# or
venv\Scripts\activate # On Windows- Install required dependencies:
pip install -r requirements.txt- Start the recommender system:
python app.py- Access the web interface at
http://localhost:5000
This project provides a practical demonstration of how web mining techniques can be effectively applied to create a useful recommendation system that helps users find hotels matching their preferences. 🌐
For any questions or issues, please open an issue in the repository.