input: 
  import pandas as pd
  from sklearn.ensemble import RandomForestClassifier
  from sklearn.model_selection import train_test_split
  
  def customer_churn_model(data_path: str):
      """ Create a RandomForestClassifier model to predict customer churn based on a dataset.
      The function will load the dataset, select features, split the data into training and 
      testing sets, train the model, and print the accuracy of the model on the test set.
      >>> customer_churn_model("customer_data.csv")
      0.92
      """
      # Load the data set
      data = pd.read_csv(data_path)
      
      # 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)
      return model.score(X_test, y_test)
  
output:     # Print the accuracy of the model on the test set
      accuracy = model.score(X_test, y_test)
      print(accuracy)
      return accuracy