input: Write a function to analyze a dataset of customers and create a classification model using scikit-learn to determine customer churn. The function should load the dataset, split it into training and testing sets, create and train a RandomForestClassifier model, and return the accuracy of the model on the test set. If the dataset is empty, the function should return None.
  assert customer_churn_classification("customer_data.csv") == 0.85
  assert customer_churn_classification("another_customer_data.csv") == 0.90
  assert customer_churn_classification("empty_data.csv") == None
output: def customer_churn_classification(filename):
      import pandas as pd
      from sklearn.model_selection import train_test_split
      from sklearn.ensemble import RandomForestClassifier
      from sklearn.metrics import accuracy_score
  
      try:
          data = pd.read_csv(filename)
      except pd.errors.EmptyDataError:
          return None
  
      if data.empty:
          return None
  
      X = data.drop('Churn', axis=1)
      y = data['Churn']
  
      X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, random_state=42)
  
      model = RandomForestClassifier(random_state=42)
      model.fit(X_train, y_train)
  
      y_pred = model.predict(X_test)
      return accuracy_score(y_test, y_pred)