input: 
  def customer_churn_model():
      """ Analyze a dataset of customers and create a classification model in scikit-learn to determine customer churn.
      >>> customer_churn_model()
      0.95
      """
  
output:     
      import pandas as pd
      from sklearn.model_selection import train_test_split
      from sklearn.ensemble import RandomForestClassifier
      from sklearn.metrics import accuracy_score
  
      # 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)
  
      # Train model
      model = RandomForestClassifier(random_state=42)
      model.fit(X_train, y_train)
  
      # Predict and evaluate
      y_pred = model.predict(X_test)
      accuracy = accuracy_score(y_test, y_pred)
  
      return accuracy
  