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, train, and evaluate a RandomForestClassifier model, and finally return the accuracy score.
  assert customer_churn_model("customer_data.csv")==0.85
  assert customer_churn_model("another_customer_data.csv")==0.90
  assert customer_churn_model("empty_data.csv")==0.0
  assert customer_churn_model("invalid_file.csv")==None
output: def customer_churn_model(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
          
          data = pd.read_csv(file_path)
          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)
      except:
          return None