input: def customer_churn_model():
      """Given a dataset of customers, create a classification model in scikit-learn to determine customer churn.
      
      Example:
      Load a dataset from "customer_data.csv", train a RandomForestClassifier, and measure its accuracy.
      >>> 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
      from sklearn.model_selection import train_test_split
      from sklearn.ensemble import RandomForestClassifier
  
      data = pd.read_csv("customer_data.csv")
      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))