input: 
  def classify_customer_churn(data):
      """ 
      Implements a RandomForestClassifier on a given dataset to determine customer churn.
      
      >>> classify_customer_churn(pd.DataFrame({
              'feature1': [1, 2, 3, 4, 5], 
              'feature2': [5, 4, 3, 2, 1], 
              'churn': [0, 1, 0, 1, 0]
          }))
      1.0
      """
      import pandas as pd
      from sklearn.ensemble import RandomForestClassifier
      from sklearn.model_selection import train_test_split
      
      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)
      return model.score(X_test, y_test)
  
output:     import pandas as pd
      from sklearn.ensemble import RandomForestClassifier
      from sklearn.model_selection import train_test_split
      
      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)
      return model.score(X_test, y_test)