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✈️ Airline Passenger Satisfaction — Unsupervised Learning

Project Overview

This project applies unsupervised learning to analyze a dataset of airline passenger satisfaction surveys. The goal is to uncover hidden patterns, segment passengers, and understand what drives satisfaction using clustering and dimensionality reduction techniques.

Tools & Libraries Used

  • Python (Jupyter Notebook)
  • pandas, numpy
  • seaborn, matplotlib
  • scikit-learn (KMeans, PCA, KernelPCA, Silhouette Score, Preprocessing Pipelines)
  • scipy (Box-Cox transformation)

Dataset Description

The dataset contains:

  • Demographic Info: Gender, Age, Customer Type
  • Travel Details: Flight Class, Distance, Travel Type
  • Service Ratings: Seat Comfort, Boarding, Entertainment, etc.
  • Timing: Departure/Arrival Delay
  • Target: Passenger Satisfaction

Initial preprocessing steps:

  • Removed unused columns
  • Renamed columns for clarity
  • Checked for missing values and outliers
  • Analyzed distribution of features using histograms and describe()

Exploratory Data Analysis (EDA)

  • Class vs Satisfaction: Created proportional stacked bar charts to explore satisfaction levels by travel class.
  • Correlation Matrix:
    • Strong correlation between Online Booking and Time Convenience (0.72)
    • Extremely high correlation (0.97) between Departure Delay and Arrival Delay
  • FacetGrid Visualizations: Used histograms to compare distributions of all categorical features against satisfaction.

Feature Engineering

  • Applied log and Box-Cox transformations to address skewness in delay variables
  • Created preprocessing pipelines using ColumnTransformer and OrdinalEncoder
  • Scaled numerical features with StandardScaler

Clustering Analysis

  • Started with 2-cluster and then 3-cluster KMeans models.
  • Evaluated cluster quality using Inertia and the Elbow Method.
  • Determined 3 clusters as optimal.

Updated Cluster Summary (3 Clusters)

  • Cluster 0: Majority neutral/dissatisfied (23,828 vs 4,679 satisfied)
  • Cluster 1: Majority satisfied (32,207 vs 11,954 neutral/dissatisfied)
  • Cluster 2: More neutral/dissatisfied than satisfied, but not as extreme as Cluster 0

Insights:

  • Cluster 1 is the most satisfied group, ideal for retention strategies.
  • Cluster 0 & 2 show dissatisfaction trends, indicating areas for improvement.

Dimensionality Reduction with PCA & KernelPCA

  • Applied PCA with 14 components to calculate feature contributions
  • Compared KernelPCA (3 components) vs PCA (14 components) using Silhouette Scores
  • Visualized clusters with color gradients based on distance in PCA space

Conclusion:

  • Both methods provide good separation, but PCA with 14 components slightly outperforms KernelPCA based on Silhouette Score

Final Thoughts

This project demonstrates end-to-end unsupervised learning using real-world survey data:

  • Cleaned and explored data thoroughly
  • Reduced skewness and scaled features
  • Identified optimal clusters
  • Applied and compared dimensionality reduction techniques

It highlights the power of clustering to understand customer satisfaction and create actionable insights.

Next Steps

  • Deep dive into features within each cluster
  • Try DBSCAN or Gaussian Mixture Models
  • Visualize with t-SNE or UMAP
  • Build interactive dashboard to explore segments

👩‍💼 Author

Project completed by Shiva Dorri as part of her unsupervised learning journey into data science.

Tags: #KMeans #PCA #KernelPCA #CustomerSegmentation #UnsupervisedLearning #DataScience