input: Write a function to create a classification model using scikit-learn to determine customer churn based on a given dataset. The function should load the data, preprocess it, split it into training and testing sets, train a RandomForestClassifier, and then make predictions on the test set. The function should return the accuracy score of the model.
  assert customer_churn_model("customer_data.csv")>0.75
  assert customer_churn_model("")==None
  assert customer_churn_model("nonexistent_file.csv")==None
  assert customer_churn_model("small_dataset.csv")==1.0
  assert customer_churn_model("imbalanced_dataset.csv")<0.5
output: def customer_churn_model(file_path):
      if not file_path:
          return None
      try:
          import pandas as pd
          from sklearn.model_selection import train_test_split
          from sklearn.ensemble import RandomForestClassifier
          from sklearn.metrics import accuracy_score
          from sklearn.preprocessing import StandardScaler
          
          data = pd.read_csv(file_path)
          X = data.drop('Churn', axis=1)
          y = data['Churn']
          
          scaler = StandardScaler()
          X = scaler.fit_transform(X)
          
          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)
          return accuracy_score(y_test, y_pred)
      except FileNotFoundError:
          return None