Eden Adams < PRO ✪ >

# Load user data user_data = pd.read_csv('user_data.csv')

# Train a random forest classifier model = RandomForestClassifier(n_estimators=100) model.fit(X_train, y_train) eden adams

# Evaluate the model accuracy = model.score(X_test, y_test) print(f'Model Accuracy: {accuracy:.2f}') This code snippet demonstrates a basic approach to training a model for predicting user preferences based on their data. The actual implementation would require more complex data processing and model tuning. # Load user data user_data = pd

# Make predictions on the test set y_pred = model.predict(X_test) eden adams