Preparing Data for Machine Learning (ETL)
End-to-End Machine Learning: Titanic Survival Prediction
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Published Nov 18 2025
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KerasMachine LearningMatplotlibNumPyPandasPythonscikit-learnSciPySeabornTensorFlow
Machine learning requires:
- No missing values
- All numeric inputs
- Encoded categorical variables
- Train/test split
We define feature lists:
features = [
"pclass", "sex", "age", "sibsp", "parch",
"fare", "embarked", "class", "who", "alone"
]
target = "survived"
data = titanic[features + [target]].copy()
data["age"] = data["age"].fillna(data["age"].median())
data["fare"] = data["fare"].fillna(data["fare"].median())
data["embarked"] = data["embarked"].fillna(data["embarked"].mode()[0])
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Split the data:
X_train, X_test, y_train, y_test = train_test_split(
data[features], data[target],
test_size=0.2, random_state=42, stratify=data[target]
)
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Define numeric and categorical transformers:
numeric_features = ["age", "sibsp", "parch", "fare", "pclass"]
categorical_features = ["sex", "embarked", "class", "who", "alone"]
preprocessor = ColumnTransformer(
transformers=[
("num", StandardScaler(), numeric_features),
("cat", OneHotEncoder(handle_unknown="ignore"), categorical_features)
]
)
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