input: from typing import List
  
  def create_classification_model_for_customer_churn(dataset_path: str) -> float:
      """
      Load a dataset of customers, preprocess it, create a RandomForestClassifier model 
      using scikit-learn to determine customer churn, and return the accuracy of the 
      model on the test set.
      
      >>> create_classification_model_for_customer_churn("customer_data.csv")
      0.85
      """
output:     
      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
  
      # Load the dataset
      data = pd.read_csv(dataset_path)
  
      # Preprocess the data
      X = data.drop('Churn', axis=1)
      y = data['Churn']
  
      # Split the data into training and testing sets
      X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, random_state=42)
  
      # Standardize the features
      scaler = StandardScaler()
      X_train = scaler.fit_transform(X_train)
      X_test = scaler.transform(X_test)
  
      # Create and train the model
      model = RandomForestClassifier(random_state=42)
      model.fit(X_train, y_train)
  
      # Predict on the test set
      y_pred = model.predict(X_test)
  
      # Calculate and return the accuracy
      return accuracy_score(y_test, y_pred)
  