input: Write a function to create a classification model using scikit-learn to determine customer churn based on the given dataset of customers. The function should load the dataset from a CSV file, split the data into features and targets, train a RandomForestClassifier, and return the accuracy of the model on the test set.
  assert customer_churn_model("customer_data.csv") > 0.85
  assert customer_churn_model("customer_data.csv") > 0.80
  assert customer_churn_model("customer_data.csv") > 0.75
  assert customer_churn_model("customer_data_empty.csv") == 0.0
  assert customer_churn_model("customer_data_invalid.csv") == 0.0
output: def customer_churn_model(file_path):
      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(file_path)
          if data.empty:
              return 0.0
          
          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)
          accuracy = accuracy_score(y_test, y_pred)
          
          return accuracy
      except:
          return 0.0