Enhancing Customer Satisfaction in the Airline Industry

Created by Fitria Dwi Wulandari – August, 2020

Project Background

In the highly competitive airline industry, customer satisfaction is crucial for maintaining passenger loyalty, enhancing the airline's reputation, and ensuring long-term business success. However, airlines often face challenges in understanding and addressing what causes passenger dissatisfaction. Common concerns among passengers include service quality, comfort, punctuality, and overall travel experience.

Objectives

This project aims to enhance customer satisfaction and pinpoint areas for improvement by:

• Identifying patterns and characteristics of dissatisfied passengers.
• Performing supervised learning algorithms to predict passenger satisfaction.

Methodology

Data Preparation

• Source: Data obtained from Kaggle, which contains airline passenger satisfaction survey.
• Actions: Cleaning and preparing the data for analysis.

Exploratory Data Analysis (EDA)

• Purpose: To discover patterns, spot anomalies, and gain a deeper understanding of the data's characteristics.
• Techniques: Visualization, summary statistics, and correlation analysis.

Machine Learning

• Goal: Build models to predict customer satisfaction.
• Approach: 5 algorithms were evaluated to determine the best prediction model.

Tools

• Programming Language: Python
• Libraries: pandas, numpy, scikit-learn, matplotlib, seaborn

Results

The analysis revealed that the Decision Tree model, achieving an accuracy of 93%, was the most effective in predicting customer satisfaction. This model's high accuracy underscores its potential as a valuable tool for airlines to anticipate and mitigate passenger dissatisfaction. By implementing the findings from this project, airlines can make data-driven decisions to refine their services, ultimately leading to higher customer satisfaction, increased loyalty, and a stronger competitive position in the market.

Future Work

• Model Improvement: Explore more advanced machine learning algorithms and feature engineering techniques to improve model accuracy.
• Broader Application: Extend the analysis to other aspects of airline operations, such as flight delays and baggage handling, to further enhance customer experience.

Repository Contents

• Scripts: Python scripts for data preprocessing, EDA, and model building.