input: def analyze_customer_dataset(data_file: str) -> float:
      """ Analyze a dataset of customers and create a classification model in scikit-learn
      to determine customer churn. The function loads the dataset, selects features, splits
      the data into training and testing sets, creates a RandomForestClassifier, trains
      the model, and then measures the accuracy of the model. 
      
      >>> analyze_customer_dataset("customer_data.csv")
      0.85  # Example output, accuracy may vary based on the actual dataset.
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 the dataset
      df = pd.read_csv(data_file)
  
      # Select features and target
      features = df.drop('Churn', axis=1)
      target = df['Churn']
  
      # Split the data into training and testing sets
      X_train, X_test, y_train, y_test = train_test_split(features, target, test_size=0.2, random_state=42)
  
      # Create a RandomForestClassifier
      model = RandomForestClassifier(random_state=42)
  
      # Train the model
      model.fit(X_train, y_train)
  
      # Predict on the test set
      y_pred = model.predict(X_test)
  
      # Measure the accuracy of the model
      accuracy = accuracy_score(y_test, y_pred)
  
      return accuracy