A platform to help vegetable sellers maximize profits by analyzing and comparing the prices of perishable vegetables across various cities. This tool recommends the most profitable transportation routes based on real-time and historical data.
The Vegetable Price Optimizer analyzes the prices of vegetables in different cities and suggests the most profitable locations for sellers to transport their goods. The tool aims to provide insights for better supply chain management by using machine learning models and data analysis.
- Price Comparison: Analyze and compare vegetable prices across various cities.
- Optimal Route Suggestions: Suggest profitable transport routes based on current market prices.
- Real-Time Data Updates: Use dynamic data to provide up-to-date recommendations.
- User-Friendly Interface: Display insights and recommendations through an intuitive frontend.
The dataset contains synthetic data of vegetable prices across different cities. Key attributes include:
- City: The city where the vegetable price is recorded.
- Vegetable: Type of vegetable.
- Price_per_kg: Price per kilogram of the vegetable.
- Date: Date when the price was recorded.
- Transport_Mode: Suggested transportation method (Road, Rail, Air, Sea).
- Distance_to_Nearest_City: Distance to the nearest city in kilometers.
- IBM Watson Studio: For data analysis and model training.
- IBM Watson Machine Learning: To create and deploy machine learning models.
- Python: Backend development using Flask/Django.
- React: Frontend development.
- IBM Cloud: Deployment and hosting of the full-stack application.
- Clone the repository:
git clone https://github.com/yourusername/vegetable-price-optimizer.git
- Navigate to the project directory and follow the setup instructions provided in the backend and frontend folders.
Special thanks to IBM Watson and lablab.ai for organizing this hackathon and providing the tools necessary for this project.