input: def determine_customer_churn(data: pd.DataFrame) -> float:
      """ Analyze a dataset of customers and create a classification model in scikit-learn to determine customer churn.
      The function loads the dataset, selects appropriate features, splits the data into training and testing sets,
      creates a RandomForestClassifier model, trains it, and measures its accuracy.
      >>> data = pd.DataFrame({
      ...     'feature1': [0, 1, 0, 1, 0, 1],
      ...     'feature2': [0, 0, 1, 1, 0, 1],
      ...     'churn': [0, 1, 1, 0, 1, 0]
      ... })
      >>> determine_customer_churn(data)
      0.6666666666666666
      """
      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()
      model.fit(X_train, y_train)
      predictions = model.predict(X_test)
      return model.score(X_test, y_test)
output:     from sklearn.model_selection import train_test_split
      from sklearn.ensemble import RandomForestClassifier