📍 Heat Map Based Store Placement Analysis

Overview

This project is a data-driven decision support system designed to identify optimal store locations using heat map analysis.
It visualises customer density, demand intensity, and geographic patterns to support strategic retail placement decisions.

The application is built as a full-stack data application, combining backend analytics with interactive map-based visualisation.

---

🚀 Key Features

• 📊 Heat map visualisation of customer/activity density
• 🗺️ Geographic mapping for city and store-level analysis
• 📈 Data-driven store placement insights
• ⚡ Interactive and high-performance web interface
• 🔧 Modular, scalable architecture

---

🛠️ Tech Stack

Backend

• Python 3.10+
• FastAPI / Streamlit
• Pandas, NumPy
• Scikit-learn (if ML used)
• PostgreSQL / CSV-based data

Frontend

• React (Vite)
• JavaScript, HTML, CSS
• Leaflet / Mapbox for map visualisation

---

📂 Project Structure

HEATMAP/
│
├── backend/                # Backend source code
│   ├── app.py / main.py
│   ├── requirements.txt
│   └── utils/
│
├── frontend/               # Frontend (React + Vite)
│   ├── src/
│   ├── package.json
│   └── vite.config.js
│
├── data/                   # Dataset files (CSV)
│
├── .gitignore
├── README.md
└── requirements.txt