input: Write a function to create a classification model in scikit-learn that predicts customer churn based on a provided dataset of customers. The function should load the dataset, select the necessary features and targets, split the data into training and testing sets, create a RandomForestClassifier model, train the model, and then make predictions to measure accuracy.
  assert determine_customer_churn("customer_data.csv")==0.85
  assert determine_customer_churn("customer_data2.csv")==0.90
  assert determine_customer_churn("customer_data3.csv")==0.78
  assert determine_customer_churn("customer_data4.csv")==0.82
  assert determine_customer_churn("")==None
output: def determine_customer_churn(filename):
      if not filename:
          return None
      import pandas as pd
      from sklearn.model_selection import train_test_split
      from sklearn.ensemble import RandomForestClassifier
      from sklearn.metrics import accuracy_score
      
      data = pd.read_csv(filename)
      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(random_state=42)
      model.fit(X_train, y_train)
      
      y_pred = model.predict(X_test)
      return accuracy_score(y_test, y_pred)