Correlations Among Numeric Features
End-to-End Machine Learning: Titanic Survival Prediction
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Published Nov 18 2025, updated Aug 17 2026
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KerasMachine LearningMatplotlibNumPyPandasPythonscikit-learnSciPySeabornTensorFlow
We compute correlations to understand linear relationships:
numeric_cols = ["survived", "pclass", "age", "sibsp", "parch", "fare"]sns.heatmap(titanic[numeric_cols].corr(), annot=True, cmap="coolwarm")plt.title("Correlation Matrix")plt.show()
Key observations:
- Higher class (lower
pclassnumber) correlates with survival. - Higher fares correlate with survival.
- Age has a weak negative correlation.
These correlations help form hypotheses for statistical testing and modelling.