input: Write a function to analyze a dataset of customers and 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, make predictions, and measure accuracy.
  assert determine_customer_churn("customer_data.csv") == 0.85
  assert determine_customer_churn("another_customer_data.csv") == 0.78
  assert determine_customer_churn("empty_data.csv") == 0.0
  assert determine_customer_churn("random_data.csv") == 0.5
  assert determine_customer_churn("balanced_data.csv") == 1.0
output: def determine_customer_churn(filename):
      import pandas as pd
      from sklearn.model_selection import train_test_split
      from sklearn.ensemble import RandomForestClassifier
      from sklearn.metrics import accuracy_score
  
      try:
          data = pd.read_csv(filename)
      except:
          return 0.0
  
      if data.empty:
          return 0.0
  
      features = data.drop('churn', axis=1)
      target = data['churn']
  
      X_train, X_test, y_train, y_test = train_test_split(features, target, test_size=0.2, random_state=42)
  
      model = RandomForestClassifier(random_state=42)
      model.fit(X_train, y_train)
  
      y_pred = model.predict(X_test)
      accuracy = accuracy_score(y_test, y_pred)
  
      return accuracy