| # app/model.py | |
| from sklearn.datasets import load_iris | |
| from sklearn.model_selection import train_test_split | |
| from sklearn.ensemble import RandomForestClassifier | |
| from joblib import dump | |
| # Load the Iris dataset | |
| iris = load_iris() | |
| X, y = iris.data, iris.target | |
| # Split the dataset into training and testing sets | |
| X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, random_state=42) | |
| # Train a Random Forest classifier | |
| model = RandomForestClassifier(n_estimators=100, random_state=42) | |
| model.fit(X_train, y_train) | |
| # Evaluate the model | |
| accuracy = model.score(X_test, y_test) | |
| print("Model accuracy:", accuracy) | |
| # Save the trained model as a joblib file | |
| dump(model, "model.joblib") | |