input: Write a function to classify customer churn using a dataset of customers and a scikit-learn model. Load the dataset from a CSV file, prepare the features and targets, split the data into training and testing sets, create and train a RandomForestClassifier, and evaluate the model's accuracy.
  import pandas as pd
  import numpy as np
  from sklearn.ensemble import RandomForestClassifier
  from sklearn.model_selection import train_test_split
  
  def classify_customer_churn():
      # Load the data set
      data = pd.read_csv("customer_data.csv")
      
      # Select features
      X = data.drop(['churn'], axis=1)
      
      # Select targets
      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)
      
      # Create the model
      model = RandomForestClassifier()
      
      # Train the model
      model.fit(X_train, y_train)
      
      # Make predictions and measure accuracy
      predictions = model.predict(X_test)
      return model.score(X_test, y_test)
  
  assert classify_customer_churn() >= 0
  assert classify_customer_churn() <= 1
output: 
  def classify_customer_churn():
      data = pd.read_csv("customer_data.csv")
      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()
      model.fit(X_train, y_train)
      predictions = model.predict(X_test)
      return model.score(X_test, y_test)
  