input: from typing import Any
  
  def create_customer_churn_model(data: Any) -> Any:
      """Analyze a dataset of customers and create a classification model in scikit-learn to determine customer churn.
      The function loads the dataset, selects features and targets, splits the data into training and testing sets,
      creates a RandomForestClassifier model, trains the model, and measures its accuracy.
      
      >>> data = pd.read_csv("customer_data.csv")
      >>> create_customer_churn_model(data)
      0.95
      """
output:     import pandas as pd
      from sklearn.model_selection import train_test_split
      from sklearn.ensemble import RandomForestClassifier
      from sklearn.metrics import accuracy_score
  
      # Assuming 'Churn' is the target column and other columns are features
      features = data.drop('Churn', axis=1)
      target = data['Churn']
  
      # Split the data into training and testing sets
      X_train, X_test, y_train, y_test = train_test_split(features, target, test_size=0.2, random_state=42)
  
      # Create a RandomForestClassifier model
      model = RandomForestClassifier(n_estimators=100, random_state=42)
  
      # Train the model
      model.fit(X_train, y_train)
  
      # Predict on the test set
      y_pred = model.predict(X_test)
  
      # Measure the accuracy of the model
      accuracy = accuracy_score(y_test, y_pred)
  
      return accuracy