https://github.com/atharvbyadav/dark-store-feasibility-analysis
A hackathon project analyzing the feasibility of setting up dark stores using data-driven insights. Focuses on demand clustering, location intelligence and logistics optimization.
https://github.com/atharvbyadav/dark-store-feasibility-analysis
business-intelligence dark-store data-analysis geospatial-analysis hackathon hackathon-project location-intelligence logistics pandas python retail-analytics urban-planning visualization
Last synced: 5 months ago
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A hackathon project analyzing the feasibility of setting up dark stores using data-driven insights. Focuses on demand clustering, location intelligence and logistics optimization.
- Host: GitHub
- URL: https://github.com/atharvbyadav/dark-store-feasibility-analysis
- Owner: atharvbyadav
- License: mit
- Created: 2025-03-05T04:43:38.000Z (over 1 year ago)
- Default Branch: main
- Last Pushed: 2025-06-25T18:14:42.000Z (12 months ago)
- Last Synced: 2025-07-30T04:36:27.340Z (10 months ago)
- Topics: business-intelligence, dark-store, data-analysis, geospatial-analysis, hackathon, hackathon-project, location-intelligence, logistics, pandas, python, retail-analytics, urban-planning, visualization
- Language: Jupyter Notebook
- Homepage: https://dark-store.streamlit.app/
- Size: 85.9 KB
- Stars: 1
- Watchers: 1
- Forks: 3
- Open Issues: 0
-
Metadata Files:
- Readme: README.md
- License: LICENSE
Awesome Lists containing this project
README
# **🛒 Dark Store Feasibility Analysis 📊**
**An Interactive AI-Powered Tool for Strategic Dark Store Placement**
[](https://www.python.org/)
[](https://streamlit.io/)
[](./LICENSE)
[](https://github.com/atharvbyadav/Dark-Store-Feasibility-Analysis)
[](https://github.com/atharvbyadav)
[](https://github.com/atharvbyadav)
[](https://dark-store.streamlit.app/)
[](https://github.com/atharvbyadav/Dark-Store-Feasibility-Analysis/commits/main)
---
## 🚀 Live Demo
🔗 **Try it out now**: [dark-store.streamlit.app](https://dark-store.streamlit.app/)
🔗 **GitHub Repository**: [Dark Store Feasibility Analysis](https://github.com/atharvbyadav/Dark-Store-Feasibility-Analysis)
---
## 🚀 **Project Overview**
Dark Stores are **closed fulfillment centers** designed exclusively for **online orders**, enabling faster deliveries and efficient inventory management. **Where should these stores be located to maximize efficiency and revenue?**
This project provides a **data-driven solution** to **analyze, predict and recommend optimal locations** for Dark Stores using **Machine Learning, Data Visualization and Interactive Maps**.
🔹 **Key Features:**
✅ **Predict Demand for Different Neighborhoods**
✅ **Recommend Top 6 Locations to Open Dark Stores**
✅ **Identify High-Demand Areas Needing Multiple Stores**
✅ **Visualize Trends with Interactive Graphs & Maps**
✅ **Machine Learning-Based Demand Forecasting**
---
## 🏗️ **How It Works**
1️⃣ **Data Collection & Cleaning**
- Raw data is processed in **Google Colab** notebooks (included in this repo).
- The **cleaned, processed dataset** is used for predictions.
2️⃣ **Data Analysis & Visualization**
- Population growth, order volume trends and demand spikes analyzed.
- Graphs & charts provide insights into neighborhood potential.
3️⃣ **Machine Learning Model**
- Uses **Linear Regression** to predict future demand.
- Evaluated with **Mean Absolute Error (MAE) & RMSE** for accuracy.
4️⃣ **Streamlit Web App**
- Users can **interact with data, view recommendations and explore maps**.
---
## 🔥 **Tech Stack**
| **Component** | **Technology Used** |
|--------------|------------------|
| Programming | Python 🐍 |
| Web Framework | Streamlit 🎈 |
| Data Processing | Pandas, NumPy |
| Machine Learning | Scikit-Learn 🤖 |
| Visualization | Plotly, Matplotlib 📊 |
| Mapping | Folium 🗺️ |
| Data Cleaning | Google Colab 🚀 |
---
## 🖥️ **Installation & Setup**
### **🔹 Clone the Repository**
```bash
git clone https://github.com/atharvbyadav/Dark-Store-Feasibility-Analysis.git
```
### **🔹 Run the Streamlit App**
```bash
streamlit run MainScript.py
```
---
## 📂 **Project Structure**
Only for this repo. You can change data as per your need and upload your own Data Sets for Analysis.
```
📦 Dark-Store-Feasibility
│-- 📂 data
│ │-- 📂 processed
│ │ │-- Merged_Pune_Dark_Store_Data.csv
│ │ │-- Pune_Climate_Delivery_Impact.csv
│ │ │-- Pune_Neighborhood_Population_Analysis.csv
│ │ │-- Pune_Online_Activity_Prediction.csv
│ │ │-- pune_dark_stores.csv
│ │
│ │-- 📂 raw
│ │ │-- Pune_Raw_Climate_Data.csv
│ │ │-- Pune_Raw_Online_Activity_Data.csv
│ │ │-- Pune_Raw_Population_Data.csv
│ │ │-- pune-ward-wise-census-2011.csv
│
│-- 📂 notebooks
│ │-- Clean_Climate.ipynb # Cleans climate data
│ │-- DataCleaner.ipynb # Processes raw data
│
│-- 📂 app
│ │-- app.py # Streamlit app
│ │-- model.py # Machine Learning model
│
│-- LICENSE
│-- MainScript.py
│-- README.md
│-- index.html # GitHub Pages support
│-- requirements.txt
```
---
## 🎯 **Key Features **
### **📊 Data Insights & Visualization**
- **Population & order volume trends per neighborhood**
- **Bar charts, scatter plots & interactive graphs**
### **🏆 Top 6 Neighborhood Recommendations**
- **Find the best locations for opening Dark Stores**
- **See order volume projections**
### **🚦 High-Demand Locations (Requiring 2 Stores)**
- **Identifies areas where 1 store isn't enough**
- **Helps optimize store placement**
### **📈 Machine Learning Demand Prediction**
- **Forecasts future demand trends**
- **Improves decision-making for dark store expansion**
### **🗺️ Interactive Dark Store Map**
- **View existing & recommended store locations**
- **Zoom in for neighborhood-level analysis**
---
## 🔍 **Machine Learning Model**
📌 **Algorithm Used:** **Linear Regression**
📌 **Evaluation Metrics:**
- **Mean Absolute Error (MAE)**: Measures prediction accuracy.
- **Root Mean Squared Error (RMSE)**: Checks for large deviations.
---
## 🔮 **Future Improvements**
💡 **Better ML Models**: Try **XGBoost, Random Forest** for higher accuracy.
🌍 **Live Data Feeds**: Integrate **real-time order tracking & traffic analysis**.
📊 **Competitor Heatmaps**: Identify areas with **less competition** for strategic placement.
---
## 🤝 Contribution
Contributions are welcome!
Feel free to fork this repo, suggest improvements or submit a pull request.
---
## 📜 **License**
This project is licensed under the **MIT License** – feel free to use, modify and distribute it.
See the [LICENSE](LICENSE) file for full details.
---
## ⭐ **Like This Project? Give It a Star!** ⭐
If you found this useful, **consider giving it a star ⭐** on GitHub!
---