input: def create_churn_model():
      """ Analyze a dataset of customers and create a classification model in scikit-learn
      to determine customer churn. The model uses RandomForestClassifier and evaluates
      its accuracy on a test set.
      
      Example:
      >>> create_churn_model()
      0.85
      """
output:     
      from sklearn.ensemble import RandomForestClassifier
      from sklearn.model_selection import train_test_split
      from sklearn.metrics import accuracy_score
      import pandas as pd
  
      # Load dataset
      data = pd.read_csv('customer_churn_dataset.csv')
  
      # Preprocess data
      X = data.drop('Churn', axis=1)
      y = data['Churn']
  
      # Split 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 model
      model = RandomForestClassifier(random_state=42)
      model.fit(X_train, y_train)
  
      # Predict on the test set
      y_pred = model.predict(X_test)
  
      # Evaluate accuracy
      accuracy = accuracy_score(y_test, y_pred)
      return accuracy
  