🧬 DNA: Distributor Network Analysis

Enterprise-Grade Geospatial Intelligence & Prescriptive Analytics

Optimizing commercial supply chain infrastructure, territory boundaries, and asset distribution networks across 12,847 geographic mesh cells in Maharashtra, India.

🌐 View Live Dashboard Deployment Here

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The Mission: > To replace intuition with mathematical certainty. DNA transforms complex internal asset logs, customer touchpoints, and regional vehicle populations into a prescriptive, actionable expansion strategy—preventing blind capital deployment in high-risk distribution infrastructure.

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🧠 Core Intelligence Modules

The platform is split into two powerful machine learning pipelines, seamlessly unified by a responsive frontend:

🎯 1. Unsupervised Strategic White-Space Mapping

Goal: Discover high-density, untapped service clusters located entirely outside of our existing active business boundaries.

Technology	Implementation & Parameters	Outcome
Spatial Masking	gpd.sjoin exact inner spatial joins against operational territory borders.	Isolates purely untapped demand.
Density Engine	DBSCAN core tracking radian-mapped coordinates.	Filters out scattered market noise.
Hyperparameters	$Eps = 25\text{km}$ (spherical Haversine), $MinSamples = 15$.	9 Validated Target Zones.

💰 2. Supervised Econometric Scaling Engine

Goal: Extract the exact revenue capability of underlying regional assets and calculate localized investment yield profiles.

ML Metric Parameter	Learned Weight (₹)	Commercial Operational Meaning
Base Intercept	₹11,448,494.60	Fixed baseline revenue threshold per operating cell.
Vehicle Parc ($\beta_1$)	₹4,740.27	Realized yearly value added per registered active vehicle.
Workshop Density ($\beta_2$)	₹312,154.76	Strategic revenue value generated per mechanic touchpoint.

💡 Prescriptive Optimization: Includes an interactive "What-If" simulation console. Operational leads can use a slider interface to simulate strategic partner placements and instantly view the projected Net ROI before deploying capital.

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🖥️ System Architecture

                          ┌──────────────────────────┐
                          │   RAW DATA INGESTION     │
                          │ (CSVs, GeoJSON Borders)  │
                          └────────────┬─────────────┘
                                       │
                ┌──────────────────────┴──────────────────────┐
                ▼                                             ▼
  【 MODULE 1: UNSUPERVISED 】                  【 MODULE 2: SUPERVISED 】
  Strategic White-Space Analysis               Econometric Scaling Engine
  ┌──────────────────────────┐                 ┌──────────────────────────┐
  │ Vector Polygon Masking   │                 │ Constrained Regression   │
  └────────────┬─────────────┘                 └────────────┬─────────────┘
               |                                            |
               ▼                                            ▼
  ┌──────────────────────────┐                 ┌──────────────────────────┐
  │ DBSCAN Density Engine    │                 │ Predictive Mesh Scoring  │
  └────────────┬─────────────┘                 └────────────┬─────────────┘
               |                                            |
               ▼                                            ▼
  ┌──────────────────────────┐                 ┌──────────────────────────┐
  │  Validated Expansion     │                 │ Prescriptive Simulator   │
  │     Footprint Zones      │                 │  (Interactive ROI Handle)│
  └──────────────────────────┘                 └──────────────────────────┘

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📂 Repository File Structure

📁 DNA-Distributor-Network-Analysis/
│
├── 📁 data/                           # Spatial layers, regression data, & telemetry
│   ├── 📄 customer_touchpoints.csv
│   ├── 📄 historical_performance.csv
│   ├── 📄 internal_touchpoints.csv
│   ├── 📄 maharashtra_border.geojson
│   ├── 📄 maharashtra_revenue_grid_data.csv
│   ├── 📄 market_potential_predictions.csv
│   ├── 📄 proposed_ro_locations.csv
│   ├── 📄 territory_boundaries.geojson
│   └── 📄 vehicle_parc.csv
│
├── 📁 notebooks/                      # ML Research & Model Training Pipelines
│   ├── 📄 01_DBSCAN_Whitespace_Analysis.ipynb
│   └── 📄 02_Market_Potential_Analysis.ipynb
│
├── 📁 pages/                          # Streamlit Dashboard Modules
│   ├── 📄 01_Whitespace_Analysis.py
│   └── 📄 02_Market_Potential_Analysis.py
│
├── 📁 src/                            # Backend Data Synthesis Scripts
│   ├── 📄 generate_all_data.py
│   └── 📄 generate_revenue_data.py
│
├── 📄 .gitignore                      # Git exclusion configurations
├── 📄 DNA.py                          # Streamlit Entry Point & Exec Home Page
├── 📄 LICENSE                         # Open Source License
├── 📄 README.md                       # Project Documentation
└── 📄 requirements.txt                # Production Container Dependencies

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🚀 Quick Start Local Deployment

1. Clone the Repository:

git clone [https://github.com/YOUR_USERNAME/YOUR_REPO_NAME.git](https://github.com/YOUR_USERNAME/YOUR_REPO_NAME.git)
cd YOUR_REPO_NAME

2. Configure Virtual Environment:

conda create -n dna-env python=3.10 -y
conda activate dna-env

3. Install Dependencies:

pip install -r requirements.txt

4. Launch the Platform:

streamlit run DNA.py

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🛠️ Technical Stack

• Core & Mathematical: Python 3.10+, NumPy, Pandas
• Dashboard Architecture: Streamlit Framework, CSS3/HTML5 Flexbox
• Geospatial Processing: GeoPandas, Folium, Streamlit-Folium, Shapely
• Machine Learning: Scikit-Learn (DBSCAN, Linear Regression), Jupyter
• Visualization: Matplotlib, Branca JavaScript Utilities