Exploratory Data Analysis (EDA)
End-to-End Machine Learning: Titanic Survival Prediction
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This section is 1 min read, full guide is 12 min read
Published Nov 18 2025
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KerasMachine LearningMatplotlibNumPyPandasPythonscikit-learnSciPySeabornTensorFlow
Before building models, we explore the data. EDA helps validate assumptions, spot issues, and inspire feature engineering.
Overall survival rate
titanic["survived"].value_counts(normalize=True).plot(kind="bar")
plt.title("Overall Survival Rate")
plt.xticks([0, 1], ["Died", "Survived"])
plt.show()
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Roughly 38% survived, reflecting historical reality.
Survival by gender
The famous “women and children first” rule should be visible:
sns.countplot(data=titanic, x="sex", hue="survived")
plt.title("Survival by Sex")
plt.show()
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Women clearly survived at a far higher rate than men.
Survival by class
sns.countplot(data=titanic, x="pclass", hue="survived")
plt.title("Survival by Passenger Class")
plt.show()
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First class passengers enjoyed significantly higher survival chances, reflecting social and physical advantages (cabin location, priority access to lifeboats, etc.).
Age distribution
sns.kdeplot(data=titanic, x="age", hue="survived", common_norm=False)
plt.title("Age Distribution by Survival")
plt.show()
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Children appear to have somewhat different survival patterns.
We can create an explicit child flag:
titanic["is_child"] = titanic["age"] < 16
sns.countplot(data=titanic, x="is_child", hue="survived")
plt.title("Children vs Adult Survival")
plt.show()
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