Create train_model.py

train_model.py

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+from sklearn.datasets import load_iris
+from sklearn.model_selection import train_test_split
+from sklearn.ensemble import RandomForestClassifier
+from sklearn.metrics import accuracy_score, classification_report
+import joblib
+
+# Load the iris dataset
+iris = load_iris()
+X, y = iris.data, iris.target
+
+# Split the data 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 RandomForest classifier
+clf = RandomForestClassifier(n_estimators=100, random_state=42)
+clf.fit(X_train, y_train)
+
+# Evaluate the model
+y_pred = clf.predict(X_test)
+accuracy = accuracy_score(y_test, y_pred)
+report = classification_report(y_test, y_pred, target_names=iris.target_names)
+
+print(f"Model Accuracy: {accuracy}")
+print("Classification Report:")
+print(report)
+
+# Save the trained model to a file
+joblib.dump(clf, "iris_model.pkl")