input: Write a function to create a classification model in scikit-learn to determine customer churn by analyzing a given dataset of customers. The function should load the dataset, select features and targets, split the data into training and testing sets, train a RandomForestClassifier model, and print the model's accuracy on the test set.
  
  def create_churn_model(file_path):
      import pandas as pd
      from sklearn.ensemble import RandomForestClassifier
      from sklearn.model_selection import train_test_split
  
      data = pd.read_csv(file_path)
      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)
      return model.score(X_test, y_test)
  
  assert create_churn_model("customer_data.csv") > 0.8
  assert create_churn_model("another_customer_data.csv") > 0.7
  assert create_churn_model("empty_file.csv") is None
  assert create_churn_model("missing_churn_column.csv") is None
  assert create_churn_model("data_with_missing_values.csv") is None
output: def create_churn_model(file_path):
      import pandas as pd
      from sklearn.ensemble import RandomForestClassifier
      from sklearn.model_selection import train_test_split
  
      try:
          data = pd.read_csv(file_path)
          if 'churn' not in data.columns:
              return None
          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)
          return model.score(X_test, y_test)
      except Exception:
          return None