https://github.com/manishkr1754/airline_delay_dashboard_powerbi

Airline Flight Delay Analytics using PowerBI
https://github.com/manishkr1754/airline_delay_dashboard_powerbi

airline-delay-analysis business-analytics business-intelligence dashboard data-visualization dax maven-analytics powerbi-report

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Airline Flight Delay Analytics using PowerBI

• Host: GitHub
• URL: https://github.com/manishkr1754/airline_delay_dashboard_powerbi
• Owner: manishkr1754
• Created: 2023-01-19T18:50:24.000Z (about 2 years ago)
• Default Branch: main
• Last Pushed: 2024-04-04T01:44:46.000Z (10 months ago)
• Last Synced: 2024-12-11T04:06:43.049Z (about 2 months ago)
• Topics: airline-delay-analysis, business-analytics, business-intelligence, dashboard, data-visualization, dax, maven-analytics, powerbi-report
• Homepage:
• Size: 918 KB
• Stars: 3
• Watchers: 1
• Forks: 1
• Open Issues: 0
• Metadata Files:
  • Readme: README.md

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README

        
# Airline_Delay_Dashboard_PowerBI

Airline Flight Delay Analytics using PowerBI

# Problem Statement

Records for **5,000,000+ commercial airline flights** in 2015, compiled for the U.S. DOT Air Travel Consumer Report. Each record represents a single flight, including the airline name, flight number, origin/destination airport and flight distance, as well as scheduled/actual departure and arrival times.

## Recommended Analysis

    1. How does the overall flight volume vary by month? By day of week?

    2. What percentage of flights in experienced a departure delay in 2015? Among those flights, what was the average delay time, in minutes?

    3. How does the % of delayed flights vary throughout the year? What about for flights leaving from Boston (BOS) specifically?

    4. How many flights were cancelled in 2015? What % of cancellations were due to weather? What % were due to the Airline/Carrier?

    5. Which airlines seem to be most and least reliable, in terms of on-time departure?

    

# Approach

Analysed **5.82Mn rows data** across **30 fields** to determine flight volume variations, departure delay percentages and times, cancellations causes and flight reliability of airlines from  **ETL(Extract, Transform & Load) to Visualization**.

# Outcome

Improved understanding of flight volume and delays through **data-driven analysis and recommendations**, enhancing operational efficiency and informed decision-making. 

# Summary Dashboard

![image](https://user-images.githubusercontent.com/114581035/213906136-2b53d6da-30fa-418b-850b-8cd540885354.png)

# Analysis : Delay by  Airline Company

![image](https://user-images.githubusercontent.com/114581035/213906184-0034c781-9dbb-4a16-8dd8-bbb3aecb1b3f.png)

# Analysis : Detailed Delay Time

![image](https://user-images.githubusercontent.com/114581035/213906229-9910b99b-731c-4628-b5c3-703495498433.png)

# Analysis : Delay by Airport

![image](https://user-images.githubusercontent.com/114581035/213906264-450f9e7f-c1a7-47f3-a97a-05a595be5cbf.png)

# Analysis : Delay Type

![image](https://user-images.githubusercontent.com/114581035/213906304-2c903880-be48-4d43-98ad-a60b363a64ac.png)

## Data Source

[Airline Flight Delays](https://mavenanalytics.io/data-playground?tags=6uzM77Svb7DjAk9YQeQzEm)