Create app.py

app.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
+
+# Load dataset (for demonstration)
+data = load_iris()
+X, y = data.data, data.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)
+
+# Initialize RandomForestClassifier
+classifier = RandomForestClassifier(n_estimators=100, random_state=42)
+
+# Train the classifier
+classifier.fit(X_train, y_train)
+
+
+# Make predictions on the test set
+y_pred = classifier.predict(X_test)
+
+# Calculate accuracy
+accuracy = accuracy_score(y_test, y_pred)
+print("Accuracy:", accuracy)
+
+
+# prompt: save the model object in pickle file 
+
+import pickle
+
+# Save the trained model to a pickle file
+with open('model.pkl', 'wb') as f:
+    pickle.dump(classifier, f)