# Complete ML workflow
def ml_workflow(data, target_column):
    # 1. Split features and target
    X = data.drop(target_column, axis=1)
    y = data[target_column]

    # 2. Train/test split
    X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2)

    # 3. Create pipeline
    pipeline = Pipeline([
        ('scaler', StandardScaler()),
        ('model', RandomForestRegressor())
    ])

    # 4. Train model
    pipeline.fit(X_train, y_train)

    # 5. Evaluate
    score = pipeline.score(X_test, y_test)
    return pipeline, score

# Usage
model, accuracy = ml_workflow(df, 'weight')
print(f"Model accuracy: {accuracy:.2f}")