The objective of this project is to conduct an in-depth exploratory data analysis on a subset of the flight delay dataset, with the aim of investigating the potential influence of a set of variables on flight arrival delay times. It gave me a good learning on using statsmodels module for Multiple Linear Regression, creating and interpreting residuals v/s fitted value plots and QQ plots to test Linear Models.
The entire dataset can be found here: 2015 Flight Delays and Cancellations
I am working with a randomized subset of 'flights.csv' data file to perform my analysis and building a regression model. Attaching the CSV file, the python file as well as my approach and findings in a easy to understand report :)
The flights.csv file contains the following features along with some data definitions & values
YEAR (2015)
MONTH (1 through 12)
DAY (1 through 31)
DAY_OF_WEEK (1 throught 7)
AIRLINE
FLIGHT_NUMBER
TAIL_NUMBER
ORIGIN_AIRPORT
DESTINATION_AIRPORT
SCHEDULED_DEPARTURE (Time - in XXYY format)
DEPARTURE_TIME (WHEELS_OFF - TAXI_OUT)
DEPARTURE_DELAY (DEPARTURE_TIME - SCHEDULED_DEPARTURE)
TAXI_OUT (Time - The time duration elapsed between departure from the origin airport gate and wheels off)
WHEELS_OFF (Time - The time point that the aircraft's wheels leave the ground)
SCHEDULED_TIME (Time - in XXYY format)
ELAPSED_TIME (AIR_TIME + TAXI_IN + TAXI_OUT)
AIR_TIME (The time duration between WHEELS_OFF and WHEELS_ON time)
DISTANCE
WHEELS_ON (Time - The time point that the aircraft's wheels touch on the ground)
TAXI_IN (Time - The time duration elapsed between wheels-on and gate arrival at the destination airport)
SCHEDULED_ARRIVAL (Time - in XXYY format)
ARRIVAL_TIME (Time - in XXYY format) (WHEELS_ON + TAXI_IN)
ARRIVAL_DELAY (ARRIVAL_TIME - SCHEDULED_TIME)
DIVERTED (Yes/No - Boolean)
CANCELLED (Yes/No - Boolean)
CANCELLATION_REASON (Reason for Cancellation of flight: A - Airline/Carrier; B - Weather; C - National Air System; D - Security)
AIR_SYSTEM_DELAY
SECURITY_DELAY
AIRLINE_DELAY
LATE_AIRCRAFT_DELAY
WEATHER_DELAY