input: Write a function to analyze a dataset of customers and create a classification model using scikit-learn to determine customer churn. If the dataset is empty, return None.
  def customer_churn_classifier(data):
      if data.empty:
          return None
      # Select features
      X = data.drop(['churn'], axis=1)
      # Select targets
      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 the model
      model = RandomForestClassifier()
      # Train the model
      model.fit(X_train, y_train)
      # Return the trained model
      return model
  
  # Test cases
  import pandas as pd
  
  # Sample dataset
  data = pd.DataFrame({
      'feature1': [1, 2, 3, 4],
      'feature2': [5, 6, 7, 8],
      'churn': [0, 1, 0, 1]
  })
  
  # Empty dataset
  empty_data = pd.DataFrame()
  
  assert customer_churn_classifier(data) is not None
  assert customer_churn_classifier(empty_data) is None
output: def customer_churn_classifier(data):
      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()
      model.fit(X_train, y_train)
      return model