input: def customer_churn_model():
      """
      Analyzes a dataset of customers and creates a classification model in scikit-learn to determine customer churn.
      Uses a RandomForestClassifier to train the model and measures accuracy on a test set.
      
      >>> data = pd.DataFrame({
      ...    'feature1': [1, 2, 3, 4, 5],
      ...    'feature2': [10, 20, 30, 40, 50],
      ...    'churn': [0, 1, 0, 1, 0] 
      ... })
      >>> 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)
      >>> print(model.score(X_test, y_test))
      0.5
      """
output:     from sklearn.ensemble import RandomForestClassifier
      from sklearn.model_selection import train_test_split
      import pandas as pd
  
      data = pd.DataFrame({
          'feature1': [1, 2, 3, 4, 5],
          'feature2': [10, 20, 30, 40, 50],
          'churn': [0, 1, 0, 1, 0] 
      })
      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)
      print(model.score(X_test, y_test))