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, split it into training and testing sets, train a RandomForestClassifier, and measure the accuracy of the predictions.
  assert customer_churn_model("customer_data.csv") > 0.8
  assert customer_churn_model("customer_data_2.csv") >= 0.75
  assert customer_churn_model("non_existent_file.csv") == None
  assert customer_churn_model("") == 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)
          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 None