Lok Sabha 2019 Election Dashboard

Business Question

What does the 2019 Lok Sabha result actually look like across multiple dimensions — not just who won, but how voter turnout varied by region, what the winner demographics looked like, and where individual parties performed relative to their alliance?

Data Sources

Publicly available election data from the Election Commission of India covering all 543 parliamentary constituencies — candidate details, party affiliation, votes received, winner status, and supplementary data on winner demographics (age, gender, education, declared criminal cases).

KPIs

Total seats, seats won by alliance and party, voter turnout percentage (overall and by zone), total votes cast, gender and age distribution of winners, and education and criminal-record disclosure rates among elected candidates.

Dashboard Design

A multi-panel Power BI report with a summary KPI row, party-wise seat bars, alliance-share visualizations with vote share breakdown, zone-wise turnout columns, a state results table, and a winner demographics panel covering gender, age, education and declared criminal cases.

Key Responsibilities

• Sourcing and structuring the public election dataset for Power BI
• Building zone-level geographic aggregations in Power Query
• Designing the multi-panel layout balancing overview and detail
• Creating DAX measures for turnout rate, seat share, vote share and demographic breakdowns

Technologies I Personally Used

Insights

• NDA won 303 of 543 seats — a clear majority; INC+ took 91; the remaining 149 went to regional parties and independents
• North-East zones showed the highest turnout at 78%+; Western India was lowest at 63%
• 85.6% of winners were male; 78 women won seats
• 43% of winners had declared criminal cases — a data point that generated discussion in the context of electoral reform
• Employees with 0–5 years tenure showed the highest attrition in the HR dashboard; analogously, constituencies with first-time incumbents showed higher variance in vote margins

Business Impact

This was a learning project. It was used as a portfolio piece during the SRCC-GBO programme, demonstrating the ability to work with large public datasets and extract multi-dimensional analytical views using Power BI.

Lessons Learned

• Large public datasets require significant cleaning — constituency name standardization and state boundary reconciliation took more time than the visualization itself
• Geographic aggregation (by zone) required building a reference table manually when the dataset didn't include it
• Demographic panels on winners added a layer of analysis not commonly seen in standard election coverage, which made the dashboard more interesting to viewers

Reflection

What I learned: working with a fully public, high-interest dataset forced precision — any number could be checked against public sources, which raised the bar for data validation.

What I would improve today: add an interactive map visual for the constituency-level result and a trend comparison with the 2014 election for seats and vote share movement.