Description

This dataset, titled "Interstellar Travel Customer Satisfaction Analysis," provides a comprehensive view of customer experiences in interstellar space travel. It encompasses approximately 500,000 records, each representing an individual space travel experience. The primary objective of this dataset is to understand and predict the Customer Satisfaction Score, a key indicator of service quality and customer experience in the burgeoning field of interstellar tourism and travel.

Dataset Overview

• Number of Records: ~500,000
• Target Variable: Customer Satisfaction Score

Features

The dataset includes a variety of features that can be leveraged for in-depth analysis and predictive modeling:

1. Age: Age of the traveler.
2. Gender: Gender of the traveler.
3. Occupation: Occupation of the traveler, such as Colonist, Tourist, Businessperson, etc.
4. Travel Class: Class of travel, e.g., Business, Economy, Luxury.
5. Destination: Interstellar destination.
6. Star System: Star system of the destination.
7. Distance to Destination (Light-Years): The distance to the destination measured in light-years.
8. Duration of Stay (Earth Days): Duration of stay at the destination in Earth days.
9. Number of Companions: The number of companions accompanying the traveler.
10. Purpose of Travel: The primary purpose of travel, e.g., Tourism, Research, Colonization.
11. Transportation Type: Type of transportation, e.g., Warp Drive, Solar Sailing, Ion Thruster.
12. Price (Galactic Credits): Price of the trip in Galactic Credits.
13. Booking Date: Date when the trip was booked.
14. Departure Date: Date of departure.
15. Special Requests: Any special requests made by the traveler.
16. Loyalty Program Member: Indicates if the traveler is a member of a loyalty program.
17. Month: Month of travel.

Potential Use-Cases

This dataset is ideal for tasks such as:

• Predictive modeling to determine factors influencing customer satisfaction.
• Analyzing trends in interstellar travel.
• Customer segmentation for targeted marketing.
• Optimizing travel services based on customer feedback.