Maharashtra QSR Site Selection Analysis

A data-driven approach to identifying optimal locations for Quick Service Restaurant (QSR) expansion in Maharashtra, India.

Overview

This project combines pincode-level census data with Point of Interest (POI) competition data to identify high-potential areas for QSR expansion across Maharashtra. The analysis uses demographic indicators, population density, and competitive landscape mapping to score and rank potential sites.

Project Objectives

• Data Integration: Merge multiple data layers (census, POI, competition data) at pincode granularity
• Market Analysis: Identify underserved markets with high population density
• Competition Mapping: Assess competitive intensity across different pincodes
• Site Scoring: Develop a multi-factor scoring system for site selection
• Actionable Insights: Generate ranked recommendations for expansion

Data Layers

Layer 1: Pincode/Census Data

• Source: Census demographic data
• Granularity: Pincode level
• Key Fields:
  • Pincode
  • District
  • Population/Households
  • State information

Layer 2: POI/Competition Data

• Source: Points of Interest database
• Granularity: Pincode level
• Key Fields:
  • Pincode
  • POI locations (restaurants, competitors, etc.)
  • Competition density

Data Requirements

Place the following CSV files in your working directory:

• 5c2f62fe-5afa-4119-a499-fec9d604d5bd.csv - Pincode/Census data
• consolidated_pincode_census.csv - POI/Competition data

Project Structure

maharashtra-qsr-site-selection/
│
├── README.md
├── requirements.txt
├── data/
│   ├── raw/
│   │   ├── pincode_census.csv
│   │   └── poi_competition.csv
│   
├── notebooks/
│   ├── 01_qsr_model.ipynb
│  
│
│
└── outputs/
    ├── reports/

Analysis Pipeline

Phase 1: Data Acquisition and Preparation

Key Steps:

1. Load raw datasets
2. Filter Maharashtra records (pincode prefix '4')
3. Standardize pincode format (6-digit strings)
4. Remove invalid/missing pincodes
5. Merge datasets on pincode

Output: Clean master dataset with standardized pincodes

Phase 2: Feature Engineering

• Demographic Features: Population density, household counts
• Competition Features: POI density, competitive intensity
• Market Features: Population-to-POI ratio, market saturation
• Geographic Features: District-level aggregations

Phase 3: Scoring and Ranking

Multi-factor scoring based on:

• Population potential (40% weight)
• Competition intensity (30% weight)
• Market gaps (20% weight)
• Accessibility indicators (10% weight)

Phase 4: Visualization and Reporting

• Heatmaps of opportunity scores
• District-level comparisons
• Top-N site recommendations
• Interactive dashboards

Key Metrics

Metric	Description	Calculation
Population Density	People per pincode area	Total Population / Area
POI Density	Competition concentration	POI Count / Pincode
Market Gap Score	Underserved market indicator	Population / POI_Count
Opportunity Score	Overall site potential	Weighted combination

Sample Outputs

Top 10 Recommended Pincodes

Rank | Pincode | District  | Population | POI_Count | Opportunity_Score
-----|---------|-----------|------------|-----------|------------------
1    | 400001  | Mumbai    | 125,000    | 15        | 92.5
2    | 411014  | Pune      | 98,000     | 12        | 88.3
3    | 422001  | Nashik    | 87,500     | 8         | 85.7
...

Data Processing Steps

1. Pincode Standardization

def standardize_pincode(df, pincode_col):
    """Standardize pincode to 6-digit string format"""
    df[pincode_col] = df[pincode_col].astype(str).str.replace('.0', '', regex=False)
    df[pincode_col] = df[pincode_col].str.strip()
    df[pincode_col] = df[pincode_col].apply(
        lambda x: x if len(x) == 6 and x.isdigit() else np.nan
    )
    return df.dropna(subset=[pincode_col])

2. Data Aggregation

# Aggregate by pincode
df_master = df_pincode.groupby('Pincode').agg({
    'District': 'first',
    'Population': 'sum',
    'POI_Count': 'sum'
}).reset_index()

Data Quality Checks

✓ Pincode format validation (6 digits)
✓ State filtering (Maharashtra only)
✓ Duplicate removal
✓ Missing value handling
✓ Data type consistency
✓ Outlier detection

Use Cases

1. QSR Chain Expansion: Identify next 10-50 locations for expansion
2. Market Entry Strategy: Prioritize districts for new market entry
3. Competitive Analysis: Understand market saturation by area
4. Investment Planning: Allocate resources based on opportunity scores
5. Performance Benchmarking: Compare actual vs. predicted performance

Notes and Assumptions

• Pincode Prefix: Maharashtra pincodes start with '4'
• Population Estimation: When unavailable, estimated at 5,000 people per post office
• POI Count: Missing values filled with 0 (assumes no competition)
• Geographic Scope: Analysis limited to Maharashtra state
• Data Currency: Results depend on data freshness; recommend quarterly updates

Contributing

This is an analytical framework. To adapt for your needs:

1. Update file paths to match your data sources
2. Adjust scoring weights based on business priorities
3. Add additional data layers (income, traffic, real estate)
4. Customize visualization preferences
5. Extend feature engineering based on domain knowledge

Contact & Support

For questions about the methodology or implementation:

• Review the code comments for detailed explanations
• Check data quality reports in the outputs folder
• Consult domain experts for market-specific insights

License

This project is designed for internal business analysis and site selection purposes which i took as a problem statement.

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Last Updated: February 2026

By Amirtha Ganesh R