input: Write a function to analyze a dataset of customers and create a classification model in scikit-learn to determine customer churn. The dataset should be loaded from a CSV file named "customer_data.csv". The function should split the data into training and testing sets, create and train a RandomForestClassifier, make predictions, and print the model's accuracy.
  assert predict_churn([{'churn': 0, 'feature_1': 5, 'feature_2': 3}, {'churn': 1, 'feature_1': 2, 'feature_2': 4}], test_size=0.5)==0.5
  assert predict_churn([{'churn': 1, 'feature_1': 1, 'feature_2': 1}, {'churn': 1, 'feature_1': 2, 'feature_2': 2}], test_size=0.5)==1.0
  assert predict_churn([{'churn': 0, 'feature_1': 5, 'feature_2': 5}, {'churn': 0, 'feature_1': 5, 'feature_2': 5}], test_size=0.5)==0.0
  assert predict_churn([], test_size=0.5)==None
  assert predict_churn([{'churn': 1, 'feature_1': 1, 'feature_2': 1}], test_size=0.5)==None
output: def predict_churn(data, test_size=0.5):
      if not data:
          return None
      df = pd.DataFrame(data)
      X = df[['feature_1', 'feature_2']]
      y = df['churn']
      X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=test_size, random_state=42)
      if len(X_train) == 0 or len(X_test) == 0:
          return None
      model = RandomForestClassifier(random_state=42)
      model.fit(X_train, y_train)
      y_pred = model.predict(X_test)
      return accuracy_score(y_test, y_pred)