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 a model using RandomForestClassifier, train the model, make predictions, and print the accuracy of the model on the test set. If the dataset is empty, return None.
  
  assert customer_churn_model("customer_data.csv") is not None
  assert customer_churn_model("empty_dataset.csv") is None
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 None
          
          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)
          
          print(accuracy)
      except Exception as e:
          return None