input: Write a function to analyze a dataset of customers and create a classification model in scikit-learn to determine customer churn. If the model is created and trained successfully, it should return the accuracy score of the test set. If the dataset cannot be processed or the model cannot be created, it should return 0.
  assert analyze_customer_churn("customer_data.csv") > 0.5
  assert analyze_customer_churn("non_existent_file.csv") == 0
  assert analyze_customer_churn("invalid_data.csv") == 0
  assert analyze_customer_churn("customer_data_no_churn.csv") == 0
  assert analyze_customer_churn([]) == 0
output: def analyze_customer_churn(file_path):
      try:
          import pandas as pd
          from sklearn.model_selection import train_test_split
          from sklearn.ensemble import RandomForestClassifier
          from sklearn.metrics import accuracy_score
          from sklearn.preprocessing import LabelEncoder
  
          if not isinstance(file_path, str):
              return 0
  
          try:
              data = pd.read_csv(file_path)
          except Exception:
              return 0
  
          if 'Churn' not in data.columns:
              return 0
  
          le = LabelEncoder()
          for col in data.select_dtypes(include=['object']).columns:
              data[col] = le.fit_transform(data[col])
  
          X = data.drop('Churn', axis=1)
          y = data['Churn']
  
          if X.isnull().any().any() or y.isnull().any():
              return 0
  
          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 Exception:
          return 0