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utils/data.py

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+"""Data loading and preprocessing utilities for Titanic dataset."""
+import numpy as np
+import pandas as pd
+import seaborn as sns
+from sklearn.model_selection import train_test_split
+from sklearn.preprocessing import StandardScaler
+import streamlit as st
+
+SEED = 42
+
+
+@st.cache_data
+def load_titanic():
+    """Load Titanic dataset from seaborn (no Kaggle account needed)."""
+    return sns.load_dataset('titanic')
+
+
+def preprocess_titanic(df):
+    """타이타닉 데이터 전처리 함수 (노트북 코드 재사용)"""
+    data = df.copy()
+
+    features = ['pclass', 'sex', 'age', 'sibsp', 'parch', 'fare', 'embarked']
+    target = 'survived'
+
+    data = data[features + [target]].copy()
+
+    # 결측값 처리
+    data['age'].fillna(data['age'].median(), inplace=True)
+    data['fare'].fillna(data['fare'].median(), inplace=True)
+    data['embarked'].fillna(data['embarked'].mode()[0], inplace=True)
+
+    # 범주형 → 숫자
+    data['sex'] = (data['sex'] == 'male').astype(int)
+    embarked_map = {'S': 0, 'C': 1, 'Q': 2}
+    data['embarked'] = data['embarked'].map(embarked_map)
+
+    # 파생 피처
+    data['family_size'] = data['sibsp'] + data['parch']
+
+    return data
+
+
+@st.cache_data
+def get_train_test_data():
+    """Return preprocessed train/test split with scaling."""
+    df = load_titanic()
+    data = preprocess_titanic(df)
+
+    X = data.drop('survived', axis=1)
+    y = data['survived']
+
+    X_train, X_test, y_train, y_test = train_test_split(
+        X, y, test_size=0.2, random_state=SEED, stratify=y
+    )
+
+    scaler = StandardScaler()
+    X_train_scaled = scaler.fit_transform(X_train)
+    X_test_scaled = scaler.transform(X_test)
+
+    return X_train, X_test, y_train, y_test, X_train_scaled, X_test_scaled, scaler
+
+
+def get_feature_names():
+    return ['pclass', 'sex', 'age', 'sibsp', 'parch', 'fare', 'embarked', 'family_size']