input: Write a function to create a classification model in scikit-learn that predicts customer churn based on a given dataset. The function should load the dataset, split it into training and testing sets, create and train a RandomForestClassifier, and then return the accuracy of the model on the test set.
  
  def predict_churn():
      import pandas as pd
      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)
      return model.score(X_test, y_test)
  
  assert predict_churn() >= 0.5  # Accuracy should be at least 50%
  assert predict_churn() <= 1.0  # Accuracy cannot exceed 100%
output: def predict_churn():
      import pandas as pd
      from sklearn.ensemble import RandomForestClassifier
      from sklearn.model_selection import train_test_split
  
      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)
      return model.score(X_test, y_test)