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 from a CSV file, select features and targets, split the data into training and testing sets, create a RandomForestClassifier model, train the model, and print the model's accuracy on the test set.
  assert analyze_customers(pd.DataFrame({
      'feature1': [1, 2, 3, 4],
      'feature2': [5, 6, 7, 8],
      'churn': [0, 1, 0, 1]
  }))==1.0
  assert analyze_customers(pd.DataFrame({
      'feature1': [1, 2, 3, 4],
      'feature2': [5, 6, 7, 8],
      'churn': [1, 0, 1, 0]
  }))==0.0
  assert analyze_customers(pd.DataFrame({
      'feature1': [1, 1, 1, 1],
      'feature2': [2, 2, 2, 2],
      'churn': [0, 0, 0, 0]
  }))==1.0
  assert analyze_customers(pd.DataFrame({
      'feature1': [4, 3, 2, 1],
      'feature2': [8, 7, 6, 5],
      'churn': [1, 1, 1, 1]
  }))==0.0
  assert analyze_customers(pd.DataFrame({
      'feature1': [],
      'feature2': [],
      'churn': []
  }))==1.0
output: def analyze_customers(df):
      if df.empty:
          return 1.0
      X = df[['feature1', 'feature2']]
      y = df['churn']
      X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.5, random_state=42)
      model = RandomForestClassifier(random_state=42)
      model.fit(X_train, y_train)
      accuracy = model.score(X_test, y_test)
      return accuracy