Airline passenger Satisfaction prediction

Keywords: passenger, Satisfaction

Background

Airline passenger satisfaction plays a crucial role in the success of airlines in today's competitive market. Understanding and predicting passenger satisfaction is essential for airlines to improve their services, enhance customer experience, and maintain a loyal customer base. This study proposes a novel approach to predict airline passenger satisfaction using Artificial Neural Networks (ANN). The research leverages a comprehensive dataset comprising various factors influencing passenger satisfaction, including flight punctuality, in-flight services, seat comfort, and customer demographics. A multi-layered ANN model is designed and trained on the dataset to learn the complex relationships between these factors and passenger satisfaction. The findings of this study provide valuable insights for airline operators and decision-makers to identify areas for improvement and prioritize efforts to enhance passenger satisfaction.

Aim & Objectives

Aim
To predict Airline passenger Satisfaction Using DL Technique
Objective
Develop an innovative Artificial Neural Network (ANN) model to accurately predict airline passenger satisfaction by analyzing a diverse dataset encompassing factors like flight punctuality, services, comfort, and demographics. The goal is to provide airlines with actionable insights to enhance services, improve customer experience, and strategically prioritize areas of improvement, thereby driving higher levels of passenger satisfaction and maintaining a competitive edge in the airline industry.

Research Methodology

ANN Technique used

Software & Tools

Jupyter, Google Collab

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