Airlines Customer Experience Dataset

Dataset Overview

The dataset includes feedback from passengers regarding various service aspects such as in-flight service, seat comfort, check-in efficiency, and more. Each record represents a passenger’s experience with multiple factors that contribute to overall satisfaction.

Predictive Purpose

The primary goal of this dataset is to train machine learning models that can predict whether future passengers will be satisfied with the airline’s services based on the provided parameter values. This will help the airline refine its customer experience strategies.

Service Improvement

Beyond prediction, the dataset can be analyzed to pinpoint specific service areas that require enhancement. Airlines can use this information to focus on aspects that impact customer satisfaction the most, thereby increasing customer retention and improving their overall reputation.

Data Applications

Researchers and data scientists can leverage this dataset for a variety of machine learning tasks, such as:

• Classification Models: Predict whether a customer is likely to be satisfied based on flight and service data.
• Sentiment Analysis: Understand which areas of service receive the most positive or negative feedback.
• Service Optimization: Help airlines prioritize improvements in key areas like in-flight entertainment, seat quality, or punctuality.