Flights Delay Using power bi

Problem Statement:

Analyze over 5 million flight records from 2015, covering key metrics like flight volume, delays, and cancellations.

Key Analysis:

Flight Volume Analysis:

Objective: Determine how flight volumes vary by month and day of the week.

Departure Delay Analysis:

Objective: Calculate the percentage of flights that experienced departure delays and the average delay time in minutes.

Yearly and Location-Based Delay Trends:

Objective: Analyze how the percentage of delayed flights fluctuates throughout the year, with a special focus on flights departing from Boston (BOS).

Cancellation Analysis:

Objective: Quantify the number of flight cancellations and determine the percentage caused by weather versus airline-related issues.

Airline Reliability:

Objective: Identify the most and least reliable airlines in terms of on-time departures.

Approach:

Data: Analyzed 5.82 million rows across 40 fields.

Process: From ETL (Extract, Transform & Load) to Visualization using data sourced from Kaggle.

Tools: Used Power BI for data modeling, analysis, and creating dynamic, interactive dashboards.

Outcome:

This project led to a deeper understanding of flight volume variations, delay patterns, and cancellation causes, providing actionable insights for improving airline operations.

Deliverables:

Four Interactive Power BI Pages:

Summary Dashboard: High-level overview of flight volumes, delays, and cancellations.

Delay by Airline Company: Comparison of on-time performance across airlines.

Detailed Delay Time: In-depth analysis of delay durations.

Delay by Airport: Airport-specific delay analysis, highlighting performance at key hubs.

Next Steps:

This analysis not only aids in enhancing operational efficiency but also empowers stakeholders with data-driven decision-making. The report is designed to be refreshable, ensuring up-to-date insights as new data is added.

Data Source: Kaggle - U.S. DOT Air Travel Consumer Report