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https://github.com/abz4375/recommendersystem
A sophisticated recommender system that leverages web mining techniques to help users find hotels that match their preferences.
https://github.com/abz4375/recommendersystem
cosine-similarity css html javascript pandas python scikit-learn selenium selenium-webdriver
Last synced: about 1 month ago
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A sophisticated recommender system that leverages web mining techniques to help users find hotels that match their preferences.
- Host: GitHub
- URL: https://github.com/abz4375/recommendersystem
- Owner: abz4375
- Created: 2024-11-06T13:30:42.000Z (3 months ago)
- Default Branch: master
- Last Pushed: 2024-12-11T14:59:59.000Z (about 1 month ago)
- Last Synced: 2024-12-11T15:47:04.752Z (about 1 month ago)
- Topics: cosine-similarity, css, html, javascript, pandas, python, scikit-learn, selenium, selenium-webdriver
- Language: CSS
- Homepage: https://abz4375.github.io/RecommenderSystem/
- Size: 4.9 MB
- Stars: 1
- Watchers: 1
- Forks: 0
- Open Issues: 0
-
Metadata Files:
- Readme: README.md
Awesome Lists containing this project
README
# Hotel Recommender System 🏨
## Overview 🌟
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.
## Features ✨
- 🔄 Automated web scraping of hotel data from [Google Travel](https://www.google.com/travel/hotels)
- ⚡ Real-time data processing capabilities
- 🤖 Machine learning-based recommendation engine
- 🖥️ Interactive user interface
- 🎯 Personalized hotel recommendations
## Key Hotel Features Analyzed 📋
The system analyzes the following amenities to provide tailored recommendations:
```python
- 🍳 Free breakfast
- 📡 Free Wi-Fi
- ❄️ Air conditioning
- 🍽️ Restaurant
- 🅿️ Free parking
- 🛎️ Room service
- 🏊 Pool
- 👕 Full-service laundry
- 💪 Fitness centre
- 🍳 Kitchen
- 🚌 Airport shuttle
- 💆 Spa
```
## Technical Implementation 🛠️
The project demonstrates the integration of several key components:
1. Web Mining - Automated data collection from hotel listings
2. Data Processing - Real-time analysis of hotel features and amenities
3. Machine Learning - Intelligent recommendation algorithm
4. User Interface - Interactive platform for receiving user preferences
## Project Structure 📁
- Web scraping module for data collection
- Data processing pipeline
- Machine learning recommendation engine
- User interface layer
## Technology Stack 💻
- [Python](https://www.python.org/)-based implementation
- Machine Learning libraries ([scikit-learn](https://scikit-learn.org/stable/))
- Web scraping tools
- Data processing frameworks
## Setup Instructions 🚀
1. Create a virtual environment:
```bash
python -m venv venv
source venv/bin/activate # On Unix/macOS
# or
venv\Scripts\activate # On Windows
```
2. Install required dependencies:
```bash
pip install -r requirements.txt
```
## Usage 📝
1. Start the recommender system:
```bash
python app.py
```
2. 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. 🌐
## Support 💡
For any questions or issues, please open an issue in the repository.