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, and finally measure the accuracy of the model.
  assert churn_prediction("customer_data.csv")==0.85
  assert churn_prediction("another_customer_data.csv")==0.78
  assert churn_prediction("empty_data.csv")==""
  assert churn_prediction("faulty_data.csv")==""
  assert churn_prediction("")==""
output: def churn_prediction(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
          
          if not filename:
              return ""
          
          df = pd.read_csv(filename)
          
          if df.empty:
              return ""
          
          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)
          accuracy = accuracy_score(y_test, y_pred)
          
          return round(accuracy, 2)
      except Exception:
          return ""