March 15, 2025•8 min read

Would You Have Survived the Titanic?

What this historic disaster reveals about inequality, decision-making, and leadership under pressure.

On April 14, 1912, the RMS Titanic struck an iceberg and sank in the North Atlantic Ocean. Of the estimated 2,224 passengers and crew aboard, more than 1,500 died, making it one of the deadliest peacetime maritime disasters in history.

But here's what makes the Titanic more than just a tragic story: the passenger manifest and survival records provide a stark data-driven view of how social class, gender, and age determined who lived and who died. This wasn't just about being in the right place at the right time, it was about who you were, not what you did.

Demographics Trumped Decisions

The data reveals clear patterns in survival rates based on passenger demographics. Using machine learning analysis, we can see how gender, class, and age significantly influenced who survived the Titanic disaster.

Titanic Survival by Gender

Women had a 74.2% survival rate compared to just 18.9% for men

Titanic Survival by Passenger Class

First-class passengers had a 63% survival rate, while third-class had only 24.2%

Titanic Survival by Age Group

Children had the highest survival rate at 42.2%, while elderly passengers had only 14.3%

Technical Approach

I trained a Decision Tree Classifier to predict survival based on passenger characteristics including age, gender, passenger class, number of siblings/spouses, number of parents/children, ticket fare, and port of embarkation. After tuning hyperparameters with GridSearchCV, the model reached strong predictive accuracy and provided clear insights into which factors most influenced survival chances.

Key Insights From the Decision Tree

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Gender was the strongest predictor

The "women and children first" protocol was largely followed, with women having nearly 4x higher survival rates than men.

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Class determined access to lifeboats

First-class passengers had better access to lifeboats and information, with survival rates decreasing dramatically by class.

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Family size mattered

Passengers with 1-3 family members had higher survival rates than those traveling alone or in very large groups.

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Age created a hierarchy within gender

Among women, younger passengers had slightly better survival rates, while among men, age had less impact.

So Why Does This Matter Today?

Because in every crisis, from natural disasters to economic downturns to global pandemics, we see the same patterns emerge. Those with more resources, better access to information, and higher social status consistently have better outcomes. The Titanic wasn't unique in this regard; it was just unusually well-documented.

When we build predictive models today, whether for loan approvals, hiring decisions, or resource allocation, we're essentially creating modern versions of the Titanic's passenger manifest. The question isn't whether these systems will reflect existing inequalities; it's whether we'll acknowledge that they do and work to address it.

The data science lesson here isn't just about feature importance or model accuracy. It's about understanding that our algorithms don't just predict the future, they can perpetuate the past. Every time we deploy a model, we should ask ourselves: are we building lifeboats for everyone, or just determining which "class" our systems are really designed for?