input: Write a function to create a classification model in scikit-learn to determine customer churn based on a given dataset. The function should load the dataset, select appropriate features and targets, split the data into training and testing sets, create a RandomForestClassifier model, train the model, make predictions, and measure the accuracy of the model. Return the accuracy score.
  assert churn_classification("customer_data.csv")==0.85
  assert churn_classification("customer_data_2.csv")==0.90
  assert churn_classification("customer_data_3.csv")==0.78
  assert churn_classification("customer_data_empty.csv")==0.0
  assert churn_classification("")==0.0
output: def churn_classification(file_path):
      if not file_path:
          return 0.0
      
      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)
      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)
      
      return accuracy_score(y_test, y_pred)