https://github.com/nayakpenguin/quickscan-realtime-pricing

Leveraging machine learning to determine the optimal prices for restaurant menu items. The goal is to balance profitability and customer satisfaction.
https://github.com/nayakpenguin/quickscan-realtime-pricing

aws-ec2 flask google-gemini-ai machine-learning reactjs

Last synced: 13 days ago
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Leveraging machine learning to determine the optimal prices for restaurant menu items. The goal is to balance profitability and customer satisfaction.

• Host: GitHub
• URL: https://github.com/nayakpenguin/quickscan-realtime-pricing
• Owner: NayakPenguin
• Created: 2024-04-12T07:22:22.000Z (7 months ago)
• Default Branch: main
• Last Pushed: 2024-07-04T11:09:25.000Z (4 months ago)
• Last Synced: 2024-10-11T05:46:44.288Z (27 days ago)
• Topics: aws-ec2, flask, google-gemini-ai, machine-learning, reactjs
• Language: Jupyter Notebook
• Homepage: https://quickscan-alpha-v1.web.app/
• Size: 9.8 MB
• Stars: 1
• Watchers: 1
• Forks: 0
• Open Issues: 0
• Metadata Files:
  • Readme: README.md

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# Quickscan - Realtime Machine Learning Based Pricing for Restaurant Menu

  

    


	

         

  	

  

	

		

	

 

	

		

	

 

	

		

	

  








## About

Welcome to the QuickScan - Realtime Machine Learning Based Pricing project! This project aims to help restaurant owners optimize their menu prices dynamically using advanced machine learning techniques. The system leverages QR codes for a contactless menu, integrates WhatsApp OTP verification, and utilizes datasets from Zomato and Uber Eats to adjust prices based on various factors.

## Features

- **QR Code-Based Menu**: Customers can scan a QR code to access the restaurant menu on their devices.

- **WhatsApp OTP Verification**: Integrated with Facebook API and Twilio for secure user verification.

- **Dynamic Pricing**: Prices are adjusted in real-time based on time of day, weather conditions, day of the week, and historical data.

- **Machine Learning Models**: Utilizes K-Means, Random Forest, and XGBoost algorithms to predict optimal prices with over 92% accuracy.

- **Cloud Deployment**: The system is deployed on AWS EC2 with Firebase for real-time database management.

## Machine Learning Explanation

### Factors Considered for Dynamic Pricing

The dynamic pricing strategy takes into account multiple factors to optimize menu prices effectively:

1. **Time of Day**: Different prices are set for breakfast, lunch, and dinner based on historical demand patterns.

2. **Weather Conditions**: Weather data is used to predict changes in customer behavior. For example, rainy days might see a higher demand for comfort foods.

3. **Day of the Week**: Prices vary between weekdays and weekends to capitalize on higher weekend traffic.

4. **Historical Sales Data**: Past sales data helps in understanding trends and setting prices that maximize revenue.

5. **External Datasets**: Leveraged datasets from Zomato and Uber Eats to incorporate broader market trends and competitive pricing.

### Machine Learning Models

- **K-Means Clustering**: Used to segment customers based on their ordering patterns and preferences, allowing for targeted pricing strategies.

- **Random Forest**: Implemented to predict the impact of different factors on sales and optimize pricing decisions.

- **XGBoost**: Applied for high-accuracy price predictions, factoring in complex interactions between variables.

### Model Performance

- Achieved over 92% accuracy in predicting optimal menu item prices.

- Continuous monitoring and refinement of models to adapt to new data and changing market conditions.

### Future Work

We are currently working on developing an automatic feedback loop pipeline. This pipeline will:

- **Automatically Collect Data**: Gather real-time data on sales, customer behavior, and external factors.

- **Model Retraining**: Continuously retrain models with new data to improve accuracy and adapt to changing conditions.

- **Feedback Integration**: Incorporate customer feedback and sales performance to fine-tune pricing strategies dynamically.

## Technologies Used

- **Frontend**: React.js

- **Backend**: Node.js, Express.js

- **Database**: MongoDB, Firebase

- **Machine Learning**: Python (scikit-learn, XGBoost)

- **Deployment**: AWS EC2

- **APIs**: Facebook API, Twilio API, Zomato API, Uber Eats API