michaelplug/airline-passenger-satisfaction

My individual project for the course Big Data Computing of the academic year 2021/22, held by professor Gabriele Tolomei.

Big Data Computing’s Project proposal 2021/22

This repository contains my individual project for the course Big Data Computing of the academic year 2021/22, held by professor Gabriele Tolomei at La Sapeinza univerisity.

The task:

The goal of this project is to predict if a passenger is satisfied air travel, based on travel and costumer data. This is an example of binary classification: satisfied or not.

The dataset:

Airline Passenger Satisfaction. Source: https://www.kaggle.com/datasets/teejmahal20/airline-passenger-satisfaction?select=train.csv This dataset contains nearly 130,000 rows, and 23 attributes.

This is a short description of dataset’s attributes:

• Gender: Gender of the passengers (Female, Male)
• Customer Type: The customer type (Loyal customer, disloyal customer)
• Age: The actual age of the passengers
• Type of Travel: Purpose of the flight of the passengers (Personal Travel, Business Travel)
• Class: Travel class in the plane of the passengers (Business, Eco, Eco Plus)
• Flight distance: The flight distance of this journey
• nflight wifi service: Satisfaction level of the inflight wifi service (0:Not Applicable;1-5)
• Departure/Arrival time convenient: Satisfaction level of Departure/Arrival time convenient
• Ease of Online booking: Satisfaction level of online booking
• Gate location: Satisfaction level of Gate location
• Food and drink: Satisfaction level of Food and drink
• Online boarding: Satisfaction level of online boarding
• Seat comfort: Satisfaction level of Seat comfort
• Inflight entertainment: Satisfaction level of inflight entertainment
• On-board service: Satisfaction level of On-board service
• Leg room service: Satisfaction level of Leg room service
• Baggage handling: Satisfaction level of baggage handling
• Check-in service: Satisfaction level of Check-in service
• Inflight service: Satisfaction level of inflight service
• Cleanliness: Satisfaction level of Cleanliness
• Departure Delay in Minutes: Minutes delayed when departure
• Arrival Delay in Minutes: Minutes delayed when Arrival
• Satisfaction: Airline satisfaction level (Satisfaction, neutral or dissatisfaction)

The methods:

To reach the goal I’m going to apply these methods:

• Logistic Regression
• Gaussian Naïve-Bayes
• Random Forest

The evaluation framework:

To evaluate the performance, I’m going to use the evaluation metrics:

• AUROC
• Precision