input: 
  def train_churn_model():
      """
      Train a RandomForestClassifier using the provided customer data set to predict customer churn. The dataset should be loaded from 'customer_data.csv'. The features are all columns except for 'churn', and the target is the 'churn' column. The data is split into training and testing sets with a test size of 20% and a random state of 42. After training the model, it predicts on the test set and outputs the accuracy score.
  
      Example:
      train_churn_model()
      (Assuming the data accuracy is 0.85)
      0.85
      """
      import pandas as pd
      import numpy as np
      from sklearn.ensemble import RandomForestClassifier
      from sklearn.model_selection import train_test_split
  
      # Load the data set
      data = pd.read_csv("customer_data.csv")
  
      # Select features
      X = data.drop(['churn'], axis=1)
  
      # Select targets
      y = data['churn']
  
      # Split the data into training and testing sets
      X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, random_state=42)
  
      # Create the model
      model = RandomForestClassifier()
  
      # Train the model
      model.fit(X_train, y_train)
  
      # Make predictions and measure accuracy
      predictions = model.predict(X_test)
      print(model.score(X_test, y_test))
  
output:     import pandas as pd
      import numpy as np
      from sklearn.ensemble import RandomForestClassifier
      from sklearn.model_selection import train_test_split
  
      # Load the data set
      data = pd.read_csv("customer_data.csv")
  
      # Select features
      X = data.drop(['churn'], axis=1)
  
      # Select targets
      y = data['churn']
  
      # Split the data into training and testing sets
      X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, random_state=42)
  
      # Create the model
      model = RandomForestClassifier()
  
      # Train the model
      model.fit(X_train, y_train)
  
      # Make predictions and measure accuracy
      predictions = model.predict(X_test)
      print(model.score(X_test, y_test))