| from sklearn.linear_model import LogisticRegression | |
| from sklearn.model_selection import train_test_split | |
| from sklearn.metrics import classification_report | |
| import joblib | |
| def train_and_evaluate(X, y, test_size=0.2, random_state=42): | |
| X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=test_size, random_state=random_state) | |
| model = LogisticRegression(max_iter=500) | |
| model.fit(X_train, y_train) | |
| y_pred = model.predict(X_test) | |
| print(classification_report(y_test, y_pred)) | |
| return model | |
| def save_model(model, filename): | |
| joblib.dump(model, filename) | |
| def load_model(filename): | |
| return joblib.load(filename) | |