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, preprocess it, split it into training and testing sets, train a RandomForestClassifier, and then make predictions to measure accuracy. If the dataset is not found, return an error message.
  assert customer_churn_classification("customer_data.csv")==0.85
  assert customer_churn_classification("non_existent_file.csv")=="Error: Dataset file not found."
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 FileNotFoundError:
          return "Error: Dataset file not found."
      
      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)
      accuracy = accuracy_score(y_test, y_pred)
      
      return accuracy