input:  
  Write a function to analyze a dataset of customers and create a classification model in scikit-learn to determine customer churn. The function should load a dataset, select features and targets, split the data into training and testing sets, create a RandomForestClassifier model, train the model, and measure the accuracy of predictions. If the dataset file does not exist, return an error message.
  assert analyze_churn_dataset("customer_data.csv")=="Customer churn prediction model created successfully with accuracy: [accuracy_value]"
  assert analyze_churn_dataset("nonexistent_file.csv")=="Error: File not found"
  
output:     
      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(dataset_file)
      except FileNotFoundError:
          return "Error: File not found"
  
      features = data.drop('Churn', axis=1)
      target = data['Churn']
  
      X_train, X_test, y_train, y_test = train_test_split(features, target, 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 f"Customer churn prediction model created successfully with accuracy: {accuracy}"
  