Step 1 — Input Parameters
Location Analysis
Enter area details to calculate store placement viability
▾
▾
8,000/km²
₹45,000
500/day
⚖️ Scoring Weights — Customize factor importance
30%
25%
25%
20%
Step 2 — Analysis Result
Zone Score Report
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—
—
0
/ 100
Population Score—
Income Score—
Traffic Score—
Competition Score—
Location Bonus—
—
Population Density
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Monthly Income
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Daily Traffic
🧠 AI Verdict
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Run the analysis to see the AI placement verdict.
📋 Action Plan
- ⏳Awaiting analysis…
Geospatial View
Coverage Map
Delivery radius and zone classification visualization
Reference Data
Top Predicted Zones
Pre-analyzed locations across Indian cities
| # | Zone | Score | Population | Income | Cluster | Viability |
|---|
Methodology
How BlinkPredict Works
A 4-step ML-backed pipeline for store placement intelligence
01
📊
Data Collection
Gather population density, demographic data, income levels, professional distribution, and geospatial coordinates for each candidate zone.
02
⚙️
Feature Scoring
Each zone gets scored on 12+ weighted metrics: population, income, foot traffic, competitors, transit access, parking, and growth trajectory.
03
🤖
KMeans Clustering
Scikit-learn KMeans segments zones into clusters — High Potential, Moderate, and Low Viability — based on combined feature vectors.
04
🗺️
Map Visualization
Results render as an interactive map with color-coded zones, delivery radius circles, and ranked placement recommendations.