U.S DEPARTMENT OF TRANSPORTATION AIRLINE ANALYSIS

Tool Used: Microsoft Power BI
Objective: To determine the reliability of different airlines across the United States
Data Analyst: Bello Mueezah Olajumoke

DATA OVERVIEW

The data was obtained from a table containing a total of 5,800,675 rows and 36 columns. It was taken and compiled in the year 2015 in the United States for the U.S. Department of Transportation (DOT). The analysis was carried out to compare the details of air transport over the past years with the findings of the recent years. Hence, annual analysis was to be carried out to determine the similarities, improvements and shortcomings over the years.
The body needed a summary of their monthly, weekly and daily performances in the year 2015. I was to also obtain various information which included the total number of flights, the total number and percentage of early departure, total number and percentage of delayed departure, percentage of cancelled flights amongst others. Each row of the data represented a single flight, including the airline name, flight number, origin/destination airport, and flight distance, as well as scheduled/actual departure and arrival times.

This Analysis involved the following Airlines with the IATA CODE:
UA — United Airlines Inc.
AA — American Airlines Inc.
US — US Airways Inc.
F9 — Frontier Airlines Inc.
OO — Skywest Airlines Inc.
AS — Alaska Airlines Inc.
NK — Spirit Airlines
WN — Southwest Airlines Co.
DL — Delta Airlines Inc.
EV — Atlantic Southeast Airlines
HA — Hawaiian Airlines Inc.
MQ — American Eagle Airlines Inc.
VX — Virgin America

DATA ANALYSIS PROCESS

The airline dataset contained irregular values and characters. Some of the details of the table were also in codes such as reasons for cancellations and also airline names (although the full meaning of these codes were given in a separate document).
The data was cleaned using Power query editor. Some blank spaces were also identified in the table which were replaced with N/A and 0 for alphabet and numeric data types respectively. The codes and abbreviations were replaced with their full names using the conditional columns. Months and Weekdays coded with numbers were also replaced with their actual names using the conditional column. Cancellation reasons which were initially coded with A, B, C, D were also renamed to assume the original reasons for cancellation.

CANCELLATION REASON

A- Airline/Carrier

B- Weather

C- National Air System

D- Security

Months

1. January
2. February
3. March
4. April
5. May
6. June
7. July
8. August
9. September
10. October
11. November
12. December

Weekdays

1. Sunday
2. Monday
3. Tuesday
4. Wednesday
5. Thursday
6. Friday
7. Saturday

I used a Conditional Column under the “Add Column” pane to transform these.

Key Findings

— Wednesdays had the highest volume of flights while Fridays had the least.

— Volume of flights were at their highest level in July and the least volume was recorded in February.

— June had the highest level of delay throughout the U.S. The level of delay in February was also high.

— In a specific location (Boston), February also had the highest level of flight delays.

Recommendations

— Activities at the beginning of the year might not be well monitored. Hence, appropriate measures should be carried out to ensure that there is an increased efficiency, to ensure smooth running and that the best service is offered.

— Volume of flights seemed to be high in the middle of the week (Tuesday, Wednesday and Thursday) and low during weekends. Weekend flights might be increased if special offers are out in place on Fridays, Saturdays and Sundays.

DASHBOARD