input: Write a function to create a classification model that predicts customer churn using the given dataset and return the accuracy score of the model. The dataset is stored in a CSV file named "customer_data.csv" and contains a 'churn' column that indicates whether a customer has churned or not.
  assert create_churn_model("customer_data.csv") == 0.85
  assert create_churn_model("customer_data_no_churn.csv") == 1.0
  assert create_churn_model("customer_data_all_churn.csv") == 0.5
  assert create_churn_model("non_existent_file.csv") == None
output: def create_churn_model(filename):
      try:
          import pandas as pd
          from sklearn.model_selection import train_test_split
          from sklearn.ensemble import RandomForestClassifier
          from sklearn.metrics import accuracy_score
          
          df = pd.read_csv(filename)
          X = df.drop('churn', axis=1)
          y = df['churn']
          
          X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, random_state=42)
          
          model = RandomForestClassifier(random_state=42)
          model.fit(X_train, y_train)
          
          y_pred = model.predict(X_test)
          return accuracy_score(y_test, y_pred)
      except FileNotFoundError:
          return None