| |
| import os |
| import json |
| import joblib |
| import pandas as pd |
| from sklearn.linear_model import LogisticRegression |
| from sklearn.metrics import accuracy_score, roc_auc_score |
| from sklearn.model_selection import train_test_split |
|
|
|
|
| |
| |
| |
| DATA_PATH = "data/processed/credit_default_clean.csv" |
| MODEL_DIR = "models/v1" |
|
|
| MODEL_PATH = os.path.join(MODEL_DIR, "model.pkl") |
| FEATURES_PATH = os.path.join(MODEL_DIR, "features.json") |
|
|
|
|
| |
| |
| |
| FEATURE_COLUMNS = [ |
| "credit_limit", |
| "age", |
| "pay_delay_sep", |
| "pay_delay_aug", |
| "bill_amt_sep", |
| "bill_amt_aug", |
| "pay_amt_sep", |
| "pay_amt_aug", |
| ] |
|
|
| TARGET_COLUMN = "target" |
|
|
|
|
| |
| |
| |
| def main(): |
| os.makedirs(MODEL_DIR, exist_ok=True) |
|
|
| df = pd.read_csv(DATA_PATH) |
|
|
| X = df[FEATURE_COLUMNS] |
| y = df[TARGET_COLUMN] |
|
|
| X_train, X_val, y_train, y_val = train_test_split( |
| X, y, test_size=0.2, random_state=42, stratify=y |
| ) |
|
|
| model = LogisticRegression( |
| max_iter=1000, |
| solver="lbfgs" |
| ) |
|
|
| model.fit(X_train, y_train) |
|
|
| |
| y_pred = model.predict(X_val) |
| y_proba = model.predict_proba(X_val)[:, 1] |
|
|
| acc = accuracy_score(y_val, y_pred) |
| roc = roc_auc_score(y_val, y_proba) |
|
|
| print(f"Validation Accuracy: {acc:.4f}") |
| print(f"Validation ROC-AUC: {roc:.4f}") |
|
|
| |
| joblib.dump(model, MODEL_PATH) |
|
|
| with open(FEATURES_PATH, "w") as f: |
| json.dump(FEATURE_COLUMNS, f, indent=2) |
|
|
| print("Model and features saved successfully.") |
|
|
|
|
| if __name__ == "__main__": |
| main() |
|
|
