⚠️ DISCLAIMER: ⚠️ This project uses simulated electoral data for educational and portfolio purposes only. It is not intended for official or production use.

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Tamil Nadu Electoral Roll Analysis 2025

A comprehensive Power BI analytics dashboard analyzing the Tamil Nadu Electoral Roll Revision, comparing Special Summary Revision (SSR) and Special Intensive Revision (SIR) data across 38 districts and 234 assembly constituencies.

Status: ✅ Complete | Last Updated: January 3, 2026

📋 Table of Contents

• Dashboard Glimpse
• Overview
• Data Transformation
• Key Features
• Dashboard Pages
• Project Structure
• Technical Stack
• Data Model
• Key Metrics
• Documentation
• Contact & Support

📋 Dashboard Glimpse

Executive Overview

District Drill-Down

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🎯 Overview

This project analyzes the 2025 Tamil Nadu electoral roll revision, examining:

• Voter Count Changes: 64M (SSR, Jan 2025) → 54M (SIR, Dec 2025) = -14.52% change
• Cleanup Impact: 9.24M voters removed (15.31% of original roll)
• Gender Representation: Stable ~47% female ratio (no bias detected)
• Geographic Variation: District-level and AC-level breakdowns

Timeframe: January 6, 2025 – December 19, 2025
Geographic Scope: 38 districts, 234 assembly constituencies, entire Tamil Nadu state

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🛠️ Data Transformation

Raw Data Sources

Cleaned Data After Power Query ETL

The Power Query transformations were applied to clean and structure the data for analysis. It was attached as documentation in the docs/power_query_steps.md file.

✨ Key Features

1. Interactive Navigation Sidebar

• Hamburger menu for compact UX
• Quick access to 5 analysis pages
• Consistent styling across report

2. 5 Comprehensive Pages

• Executive Overview – High-level KPIs and state trends
• Gender Analysis – Demographic shifts and ratio analysis
• Cleanup Analysis – Breakdown by removal category (deceased, shifted, duplicates)
• District Drill-Down – AC-level deep dive with mapping
• Information – Dashboard metadata and documentation

3. 35+ DAX Measures

• Core voter metrics (SSR, SIR, growth)
• Gender-specific calculations
• Cleanup analysis by category
• Advanced ranking and categorization
• Performance-optimized formulas

4. Professional Design

• Responsive layouts adapting to filters
• Color-coded KPI cards
• Intuitive visualizations (stacked charts, treemaps, area charts)
• Data quality badges

5. Complete Documentation

• DAX formula reference
• Information page with FAQ
• Recent updates timeline
• Data quality indicators

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📊 Dashboard Pages

Page 1: Executive Overview

What it does: State-level summary with key metrics and district comparison

Visualizations:

• KPI Cards: SSR (64M), SIR (54M), Loss (-9.24M), Cleanup Impact (15.31%)
• Stacked bar chart: Gender composition (SSR vs SIR)
• Horizontal bar chart: Voter loss by district (top 6 impacted)
• Area chart: Voter count comparison by constituency

Key Interactions: District & AC slicers for filtering

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Page 2: Gender Analysis

What it does: Examine gender representation changes and detect bias

Visualizations:

• Stacked column chart: Male/Female/Third Gender by district (SSR vs SIR)
• Data matrix: Gender ratios and percentage point changes
• KPI cards: Male/Female growth metrics
• Conditional formatting: Green (stable), Yellow (small shift), Red (significant shift)

Business Value: Confirms cleanup process was non-discriminatory

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Page 3: Cleanup Analysis

What it does: Break down removal categories and identify cleanup patterns

Visualizations:

• 100% stacked bar chart: Cleanup composition by district (Deceased, Shifted, Duplicates)
• Treemap: Total cleanup volume by district with color-coded intensity
• Stacked bar chart: Cleanup type percentage breakdown
• Data table: Cleanup statistics and rates

Business Value: Identify which districts have highest duplicate/shifted voter issues

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Page 4: District Drill-Down

What it does: Interactive AC-level analysis for deep investigation

Visualizations:

• Slicer: Select district to filter to all ACs
• Clustered column chart: Voter counts (SSR vs SIR) for each AC
• Data matrix: AC metrics (growth %, cleanup rate, cleanup intensity, rank)
• Map: Geographic visualization of selected district

Business Value: Pinpoint specific constituencies needing further investigation

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Page 5: Information

What it does: Self-documenting metadata and user support

Content:

• Project overview and data scope
• Key metrics summary
• Page guide (what each page does)
• Recent updates timeline
• Data quality status badges
• Contact information

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🏗️ Project Structure

TN-voters-SIR-Analytics-2025/
├── README.md                          
├── TN_Voters_Analysis_2025-26.pbix   
│
├── docs/
│   ├── dax_formulas.md               
│   ├── data_model.md    
│   ├── data_sources.md
│   └── power_query_steps.md
│
├── data/
│   ├── tn-voters-datasets-cleaned.xlsx
│
└── assets/
    ├── data_model.png
    ├── data-source.png
    ├── executive_overview.png
    ├── gender_analysis.png
    ├── cleanup_analysis.png
    ├── district_detail.png
    └── information_page.png
    

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🛠️ Technical Stack

Component	Technology	Version
BI Platform	Power BI Desktop	Latest (2025)
Data Modeling	DAX (Data Analysis Expressions)	Power BI Standard
ETL	Power Query	Built-in Power BI
Database	Star Schema (In-memory)	Fact + Dimension tables
Visualizations	Power BI Native Visuals	Cards, Charts, Maps
Version Control	Git + GitHub	Standard workflow

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🗄️ Data Model

Star Schema Architecture

Fact Tables:
├── fact_Voters
│   ├── AC_No.
│   ├── Snapshot_Date
│   ├── Snapshot_Revision          -- SSR / SIR flag
│   ├── Male
│   ├── Female
│   ├── Third_Gender
│   └── Total                      -- Total voters by AC & snapshot
│
└── fact_ASD
    ├── AC_No.
    ├── Snapshot_Date
    ├── Deceased
    ├── Enrolled_Multiple_Places   -- Duplicate registrations
    ├── Shifted_Absent             -- Shifted / not found
    └── Total                      -- Total cleanup voters by AC & snapshot


Dimension Tables:
└── dim_AC
    ├── AC_No.
    ├── AC_Name
    └── District                   -- District name for each AC

Key Relationships

• dim_District 1:N dim_AC
• dim_AC 1:N fact_Voters
• dim_AC 1:N fact_Cleanup

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📈 Key Metrics

Core Metrics

Metric	Value	Significance
SSR Total (Jan 2025)	64M	Baseline voter count
SIR Total (Dec 2025)	54M	Final voter count post-cleanup
Voter Loss (Absolute)	-9.24M	Total removed voters
Voter Loss (%)	-14.52%	Cleanup impact as % of SSR
Cleanup Rate	15.31%	Standard benchmark
Female Ratio (SSR)	~47.5%	Gender representation (baseline)
Female Ratio (SIR)	~47.5%	Gender representation (final)
Districts Analyzed	38	Complete state coverage
ACs Analyzed	234	Full AC coverage

Top 3 Most Impacted Constituencies

1. Chennai – Lost 1.44M voters
2. Chengalpattu – Lost 660K voters
3. Salem – Lost 330K voters

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📞 Contact & Support

For questions about this project:

• 📧 Email: mohamedkhasim.16@gmail.com
• 🔗 GitHub: https://github.com/k3XD16
• 💼 LinkedIn: linkedin.com/in/mohamedkhasim16

For suggestions or improvements:

• Open an issue on GitHub
• Submit a pull request with improvements

External Resources

• Microsoft DAX Documentation
• Power BI Best Practices
• Data Modeling Concepts

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Made with ❤️ for Data Excellence

Last updated: January 3, 2026 | Power BI 2025

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