input: Write a function to create a classification model in scikit-learn to determine customer churn. The function should load the dataset, select features and targets, split the data into training and testing sets, create and train a RandomForestClassifier, and finally make predictions and measure accuracy. The function should return the accuracy score.
  assert churn_model() == <expected_accuracy>
  assert churn_model() == <expected_accuracy>
  assert churn_model() == <expected_accuracy>
  assert churn_model() == <expected_accuracy>
  assert churn_model() == <expected_accuracy>
output: def churn_model():
      import pandas as pd
      from sklearn.model_selection import train_test_split
      from sklearn.ensemble import RandomForestClassifier
      from sklearn.metrics import accuracy_score
      
      # Load the dataset
      data = pd.read_csv('churn_data.csv')
      
      # Select features and target
      X = data.drop('Churn', axis=1)
      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 and train a RandomForestClassifier
      model = RandomForestClassifier(random_state=42)
      model.fit(X_train, y_train)
      
      # Make predictions and measure accuracy
      y_pred = model.predict(X_test)
      accuracy = accuracy_score(y_test, y_pred)
      
      return accuracy