A web-based project that predicts suitable locations for placing new Blinkit stores based on area demand, population density, nearby competitors, delivery feasibility, and customer accessibility.
This project is designed to help understand how quick-commerce platforms like Blinkit can use data-driven decision-making for store placement and delivery optimization.
The Blinkit Store Placement Prediction Website is a data-driven web application that helps predict the best possible locations for opening Blinkit dark stores or delivery hubs.
The website takes location-related inputs such as population, demand level, nearby stores, traffic condition, and distance from customers, then predicts whether a location is suitable for a Blinkit store.
- Predicts suitable Blinkit store locations
- User-friendly web interface
- Input fields for location-based data
- Store placement result prediction
- Simple and clean UI design
- Useful for quick-commerce location planning
- Can be extended with machine learning models
- Supports future integration with maps and real-time data
- HTML
- CSS
- JavaScript
- Python
- Flask
- Scikit-learn
- Pandas
- NumPy
- VS Code
- Git
- GitHub
Blinkit-Predict-Website/
│
├── static/
│ ├── css/
│ │ └── style.css
│ ├── js/
│ │ └── script.js
│ └── images/
│
├── templates/
│ ├── index.html
│ └── result.html
│
├── model/
│ └── blinkit_model.pkl
│
├── dataset/
│ └── blinkit_data.csv
│
├── app.py
├── requirements.txt
├── README.md
└── .gitignore