A comprehensive and interactive Power BI Dashboard designed to analyze India’s domestic aviation ecosystem.
This report transforms 120K+ rows of flight booking data into valuable insights covering pricing trends, airline performance, customer booking patterns, route connectivity, and operational behaviors.
This project is ideal for:
- Business Intelligence Portfolios
- Data Analyst Job Applicants
- Organizations analyzing airline operations
- Students learning Power BI, DAX, and data storytelling
The Indian Airlines Analysis Dashboard helps stakeholders understand India’s domestic airline industry using analytics.
It answers key questions such as:
- How do ticket prices behave as the travel date approaches?
- Which airlines dominate the Indian market?
- What are the busiest source–destination routes?
- What times of day do passengers prefer to travel?
- How does booking demand vary with “days left”?
By combining meaningful KPIs, drill-down visuals, and an intuitive layout, this project demonstrates strong BI skills and analytical thinking.
- Identify patterns in customer booking behavior
- Study airline performance via booking volume and routes
- Analyze flight pricing strategy based on demand and time-to-flight
- Compare class and stop-wise booking distribution
- Build a visually clean and interactive analytics dashboard
- Demonstrate real-world Power BI capabilities for analytics roles
- 🔹 KPI summary with key metrics
- 🔹 Class-wise and Stop-wise Flight Distribution
- 🔹 Dynamic pricing trend analysis (days_left vs price)
- 🔹 Airline booking volume comparison
- 🔹 Route matrix for intercity connectivity
- 🔹 Time-of-day booking preference visualization
- 🔹 Tooltip-based data exploration
- 🔹 Optimized layout for storytelling
- 🔹 Color-coded charts for better readability
- Avg Price: ₹7425
- Airlines: 6
- Source Cities: 6
- Avg Duration: 11.25 hours
- Economy: 68.85% (dominant)
- Business: 31.15%
- One-stop: 83.58%
- Zero-stop: 12%
- Prices drop steadily when customers book early
- Indicates price elasticity based on demand
- Peak booking intervals: days_left 10–30
- Minimum bookings: same-day travel (0 days left)
Shows flight count between metro cities:
- Bangalore
- Chennai
- Delhi
- Mumbai
- Hyderabad
- Kolkata
Used for understanding route demand, fleet planning, and scheduling.
- Vistara: 128K (Top performing airline)
- Air India: 81K
- Indigo: 43K
- GO_FIRST: 23K
- AirAsia: 16K
- SpiceJet: 9K
- Morning & Early Morning flights show highest demand
- Evening also popular for business travelers
| Column Name | Description |
|---|---|
| airline | Airline name |
| flight | Flight number |
| source_city | Departure city |
| departure_time | Departure time category |
| stops | Zero / One stop |
| arrival_time | Arrival time category |
| destination_city | Arrival city |
| class | Business / Economy |
| duration | Flight duration in hours |
| days_left | Days between booking & flight date |
| price | Base ticket price |
| Price_gst | Final price including GST |
| days_left hist | Histogram bucket for visual |
| F1 | Row ID |
-
Data Cleaning
- Removed null values
- Ensured consistent format for categorical fields
- Verified numerical ranges (duration, prices)
-
Feature Engineering
- Created
days_left histfor smoother visuals - Derived time-of-day categories
- Created
-
Data Modeling
- Structured into star-schema style table
- Optimized fields for Power BI visuals
-
Visual Analytics
- Built KPI cards for summary
- Used donut charts for distribution
- Used line chart for pricing trend
- Used matrix for route connectivity
- Used bar charts for airline comparison
- Economy class captures ~70% of domestic bookings
- One-stop flights dominate (limited direct connectivity)
- Ticket prices decrease when booked earlier
- Vistara has the highest booking volume
- Metro routes show highest intercity movement
- Morning flights are preferred by majority of passengers
- Same-day bookings are least common
A simple, optimized model:
This design ensures:
- Faster load time
- Better DAX performance
- Clean visuals without redundancy
DAX: Avg Price = AVERAGE('Indian Airlines'[price])
Airlines Count = DISTINCTCOUNT('Indian Airlines'[airline])
Avg Duration = AVERAGE('Indian Airlines'[duration])
Total Bookings = COUNTROWS('Indian Airlines')
Economy % = DIVIDE( CALCULATE(COUNTROWS('Indian Airlines'), 'Indian Airlines'[class] = "Economy"), [Total Bookings] )
Business % = DIVIDE( CALCULATE(COUNTROWS('Indian Airlines'), 'Indian Airlines'[class] = "Business"), [Total Bookings] )
- Microsoft Power BI Desktop
Download: https://powerbi.microsoft.com/desktop/
- Clone this repository: bash: "https://github.com/prathams0ni/Indian_Airlines_Analysis_Power_BI_Dashboard"
The analysis of Indian domestic airlines reveals clear patterns in customer behavior, airline performance, and pricing strategy. Economy class continues to dominate the market, supported by strong demand for affordable travel options. One-stop flights being the majority indicate limited direct connectivity on several routes, highlighting potential opportunities for airlines to expand non-stop services.
The pricing analysis shows that customers benefit significantly from early bookings, while airlines adjust fares based on demand and time proximity. Vistara emerges as the leading airline in terms of bookings, showcasing strong brand preference and operational efficiency.
Metro cities such as Bangalore, Mumbai, Chennai, Delhi, and Hyderabad form the backbone of domestic air travel, with high intercity movement. Additionally, morning and early-morning flights being the most preferred reflect traveler convenience and business-related travel patterns.
Overall, the dashboard provides actionable insights that can help airlines optimize pricing, improve route planning, enhance customer experience, and strengthen competitive positioning in the Indian aviation market.