input: def classify_customer_churn_model(data_path: str) -> RandomForestClassifier:
      """ Analyze a dataset of customers and create a classification model in scikit-learn to determine customer churn.
      >>> classify_customer_churn_model("customer_data.csv").score(X_test, y_test)
      0.95
      """
      import pandas as pd
      import numpy as np
      from sklearn.ensemble import RandomForestClassifier
      from sklearn.model_selection import train_test_split
  
      # Load the data set
      data = pd.read_csv(data_path)
  
      # 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)
  
      return model
output:     # Evaluate the model
      score = model.score(X_test, y_test)
      
      # Print the score for verification
      print(f"Model accuracy: {score:.2f}")
      
      return model