https://github.com/hemangsharma/dynamic-pricing
This project provides a dynamic pricing recommendation system using advanced machine learning and big data analytics. The system takes into account local competition, customer reviews, seasonal trends, and other relevant factors. A user-friendly GUI is included for ease of use.
https://github.com/hemangsharma/dynamic-pricing
Last synced: 2 months ago
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This project provides a dynamic pricing recommendation system using advanced machine learning and big data analytics. The system takes into account local competition, customer reviews, seasonal trends, and other relevant factors. A user-friendly GUI is included for ease of use.
- Host: GitHub
- URL: https://github.com/hemangsharma/dynamic-pricing
- Owner: hemangsharma
- Created: 2025-01-27T23:42:03.000Z (4 months ago)
- Default Branch: main
- Last Pushed: 2025-01-27T23:51:43.000Z (4 months ago)
- Last Synced: 2025-01-28T00:31:01.871Z (4 months ago)
- Size: 0 Bytes
- Stars: 0
- Watchers: 1
- Forks: 0
- Open Issues: 0
-
Metadata Files:
- Readme: README.md
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README
# dynamic-pricing
This project provides a dynamic pricing recommendation system using advanced machine learning and big data analytics. The system takes into account local competition, customer reviews, seasonal trends, and other relevant factors. A user-friendly GUI is included for ease of use.
---
## **Key Features**
1. **Dynamic Pricing Suggestions**:
- Daily and weekly price recommendations based on competition and market conditions.
- Incorporates factors like seasons, reviews, and external market data.
2. **Advanced Analytics**:
- Utilizes machine learning (ML) and reinforcement learning (RL) for predictive and adaptive pricing.
- Processes data from multiple APIs (e.g., review platforms, weather APIs for seasonality).
3. **Interactive GUI**:
- User-friendly interface built with Python libraries (e.g., Tkinter, PyQt, or Streamlit).
- Displays pricing suggestions, data visualizations, and historical trends.
4. **Big Data Integration**:
- Leverages large datasets to analyze competition and consumer behavior.
- Includes data preprocessing pipelines for scalability.
## **System Requirements**
- **Python**: Version >= 3.8
- **Libraries**:
- GUI: `streamlit` or `PyQt5`
- Machine Learning: `scikit-learn`, `pandas`, `numpy`
- Visualization: `matplotlib`, `seaborn`, `plotly`
- APIs: `requests`, `beautifulsoup4` (for scraping), `geopy` (for location data)
- **Optional Tools**:
- Big data: `PySpark` or `Dask` for large-scale data processing.
## **Installation**
1. Clone the repository:
```bash
git clone https://github.com/hemangsharma/dynamic-pricing.git
cd dynamic-pricing
```
2. Create a virtual environment and activate it:
```bash
python -m venv venv
source venv/bin/activate # On Windows: venv\Scripts\activate
```
3. Install dependencies:
```bash
pip install -r requirements.txt
```
4. Launch the notebook:
```bash
jupyter notebook
```
## **Usage**
1. **Run the Jupyter Notebook**:
- Open the notebook in Jupyter Lab/Notebook.
- Execute all cells to initialize the system.
2. **Interact with the GUI**:
- Adjust input parameters such as location, season, and customer sentiment.
- View pricing suggestions and analysis in real-time.
3. **Customize**:
- Add new factors or APIs to enhance the analysis.
- Train or fine-tune the ML model for specific datasets.
---
## **Brainstorming and Additional Suggestions**
1. **Data Sources**:
- Use Google Places API or Yelp API to gather competitor pricing and reviews.
- Integrate weather APIs (e.g., OpenWeatherMap) to account for seasonal demand changes.
2. **Machine Learning Techniques**:
- **Regression Models**: Predict prices based on historical data.
- **Clustering**: Identify patterns in local competition.
- **Reinforcement Learning**: Adapt pricing strategies dynamically based on feedback.
3. **GUI Enhancements**:
- Add sliders for selecting price ranges and weights for factors.
- Incorporate real-time graphs to visualize price trends and suggestions.
4. **Scalability**:
- Use distributed data processing frameworks like Apache Spark for large datasets.
- Store data in cloud services (e.g., AWS S3, Google BigQuery).
5. **Automation**:
- Set up a cron job to fetch data and update recommendations automatically.
- Send pricing suggestions via email or notifications.
6. **Performance Metrics**:
- Implement metrics like revenue growth, occupancy rate, or market share to evaluate the pricing strategy.