input: from typing import List
  
  def create_churn_model(data: pd.DataFrame) -> RandomForestClassifier:
      """
      Analyze a dataset of customers and create a classification model to determine customer churn.
      Split the dataset into training and testing sets, train the model with a RandomForestClassifier,
      and return the trained model.
      
      Example:
      >>> data = pd.DataFrame({...})
      >>> model = create_churn_model(data)
      >>> predictions = model.predict(X_test)
      >>> print(model.score(X_test, y_test))
      """
output:     from sklearn.model_selection import train_test_split
      from sklearn.ensemble import RandomForestClassifier
      from sklearn.preprocessing import LabelEncoder
  
      # Assuming 'Churn' is the target column
      X = data.drop('Churn', axis=1)
      y = data['Churn']
  
      # Encode categorical variables
      le = LabelEncoder()
      for col in X.select_dtypes(include=['object']).columns:
          X[col] = le.fit_transform(X[col])
  
      # 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)
  
      # Initialize and train the RandomForestClassifier
      model = RandomForestClassifier(random_state=42)
      model.fit(X_train, y_train)
  
      return model