input: Write a function to analyze a dataset of customers and create a classification model in scikit-learn to determine customer churn. The function should load the dataset, split it into training and testing sets, create a RandomForestClassifier model, train it, and measure its accuracy by making predictions on the test set. The model's accuracy score should be printed.
  assert customer_churn_model().startswith('# Python code')
  assert 'RandomForestClassifier' in customer_churn_model()
  assert 'model.score(X_test, y_test)' in customer_churn_model()
output: def customer_churn_model():
      import pandas as pd
      from sklearn.model_selection import train_test_split
      from sklearn.ensemble import RandomForestClassifier
      from sklearn.metrics import accuracy_score
      
      # Load the dataset
      data = pd.read_csv('customer_data.csv')
      
      # Split the dataset into features and target
      X = data.drop('churn', axis=1)
      y = data['churn']
      
      # 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)
      
      # Create a RandomForestClassifier model
      model = RandomForestClassifier(random_state=42)
      
      # Train the model
      model.fit(X_train, y_train)
      
      # Make predictions on the test set
      y_pred = model.predict(X_test)
      
      # Measure the accuracy of the model
      accuracy = accuracy_score(y_test, y_pred)
      
      # Print the accuracy score
      print(f'Model Accuracy: {accuracy:.2f}')