This project is open-source and available under the MIT License.
Feel free to connect:
- GitHub: Ronit049
- LinkedIn: Ronit Raj
- X (Twitter): @its_rsr04
A data-driven decision support tool to help businesses identify ideal locations for new store placement using predictive modeling and geospatial visualization.
In competitive markets, selecting the right store location is crucial for business success. Our project, Store Placement Visualizer, uses geospatial data, clustering algorithms, and a scoring system to recommend the most optimal areas for placing a new store.
By analyzing key factors such as population density, competitor presence, income levels, and foot traffic, we provide a visual map showing the best zones for expansion.
“Where should we open our next store to maximize visibility, reach, and profitability?”
Many businesses struggle with store placement due to a lack of actionable data insights. Our solution helps visualize the best locations using intelligent algorithms and interactive maps.
- 📍 Visualize areas on a map based on store potential
- 📊 Score locations based on weighted metrics (population, competition, traffic)
- 🤖 Use clustering (KMeans) to segment zones
- 🌍 Interactive map with color-coded zones (good, average, bad)
- 📈 Predict top
nbest zones for opening a store - 💻 Optional frontend with Streamlit UI
| Area | Tools Used |
|---|---|
| Language | Python |
| Data Handling | Pandas, NumPy |
| Visualization | Folium, Matplotlib, Plotly |
| Machine Learning | Scikit-learn (KMeans, scoring) |
| UI (optional) | Streamlit |
| Version Control | Git + GitHub |
store-placement-visualizer/
│
├── data/ # Datasets (raw & processed)
├── notebooks/ # Jupyter notebooks
├── src/ # Core logic (scripts)
├── ui/ # Streamlit app (optional)
├── images/ # Output map visuals
├── requirements.txt # Python dependencies
├── README.md # Project overview
└── .gitignore # Ignore temp/system files
- Load and clean location + demographic data
- Score each zone using a weighted formula
- Use KMeans clustering to group similar regions
- Visualize results on an interactive map
- Highlight high-potential zones in green
git clone https://github.com/yourusername/store-placement-visualizer.git
cd store-placement-visualizer
pip install -r requirements.txt
To run the optional Streamlit UI:
streamlit run ui/app.py
📊 Sample Data Columns (CSV)
Area Name Latitude Longitude Population Competitor_Count Avg_Income Traffic
Zone A 26.9 75.8 15000 2 48000 450
🔮 Future Enhancements
Add real-time data integration (e.g., Google Places API)
Support for different business types (e.g., café vs pharmacy)
ROI prediction based on rent and expected revenue
Heatmap animation over time
<!DOCTYPE html>
<html lang="en">
<head>
<meta charset="UTF-8">
<title>Store Placement Visualizer</title>
</head>
<body>
<!-- 📬 Contact Section -->
<h2>📬 Contact</h2>
<p>Feel free to connect:</p>
<ul>
<li><strong>GitHub:</strong> <a href="https://github.com/Ronit049" target="_blank">Ronit049</a></li>
<li><strong>LinkedIn:</strong> <a href="https://www.linkedin.com/in/ronit-raj-114181315" target="_blank">Ronit Raj</a></li>
<li><strong>X (Twitter):</strong> <a href="https://x.com/its_rsr04" target="_blank">@its_rsr04</a></li>
</ul>
<!-- 📃 License Section -->
<h2>📃 License</h2>
<p>This project is open-source and available under the <a href="https://opensource.org/licenses/MIT" target="_blank">MIT License</a>.</p>
<!-- 📦 Requirements.txt Section -->
<h2>📦 Requirements</h2>
<p>To install all necessary Python libraries, use the following <code>requirements.txt</code> file:</p>
<pre><code>pandas
numpy
scikit-learn
folium
matplotlib
plotly
streamlit
seaborn
</code></pre>
<p><strong>Optional additions (if needed):</strong></p>
<ul>
<li><code>geopandas</code> – for geospatial operations</li>
<li><code>sqlalchemy</code> – for database connections</li>
<li><code>faker</code> – for generating fake data</li>
</ul>
<p>💡 Install with:</p>
<pre><code>pip install -r requirements.txt</code></pre>
</body>
</html>
👥 Team
Ronit Raj – Data Science & Visualization