Decoding Passenger Personas

Objective

What can a century-old passenger list tell us about modern customer segmentation? Quite a lot, it turns out. This project dives into the Titanic dataset to uncover unique passenger profiles, exploring how travel behaviors, spending patterns, and socioeconomic factors shaped their journey—and how those insights can inspire smarter strategies today.

Cleaning the Deck

Missing values? Handled. Irrelevant columns? Overboard. I focused on what mattered—key features like age, fare, and travel class.

Turning Variables into Value

Encoded Sex and Embarked for clarity, standardized features like Fare to keep comparisons meaningful, and scaled Age to ensure fair clustering.

Key Insights

The clusters told their own compelling stories:

Setting Sail with Clusters

Using K-means, I grouped passengers based on shared traits, revealing distinct clusters that spoke volumes about their travel priorities.

  • Luxury Globetrotters: First-class passengers, big spenders, and usually traveling solo or in pairs. Think exclusivity and elegance.

  • Family Voyagers: Mid-class passengers traveling in family groups—vacationers who prioritized connection over opulence.

  • Young Adventurers: Budget-conscious travelers making their way in economy, often younger and focused on getting from A to B.

  • Solo Risk-Takers: Independent, cost-efficient, and adventurous, often in a mix of mid to lower-class settings.