input: 
  def analyze_customer_churn():
      """
      This function loads a customer dataset, selects features and targets, splits the
      data into training and testing sets, and trains a RandomForestClassifier to
      determine customer churn. The function then makes predictions and measures the
      accuracy of the model.
      Example:
      analyze_customer_churn()
      0.91
      """
  
output:     
      import pandas as pd
      from sklearn.model_selection import train_test_split
      from sklearn.ensemble import RandomForestClassifier
      from sklearn.metrics import accuracy_score
  
      # Load the dataset
      data = pd.read_csv('customer_data.csv')
  
      # Select features and target
      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)
  
      # Train a RandomForestClassifier
      model = RandomForestClassifier(random_state=42)
      model.fit(X_train, y_train)
  
      # Make predictions
      y_pred = model.predict(X_test)
  
      # Measure the accuracy of the model
      accuracy = accuracy_score(y_test, y_pred)
      return accuracy
  