input: from typing import List
  
  def analyze_and_predict_churn(file_path: str, feature_columns: List[str]) -> float:
      """Analyze a customer dataset from a CSV file and create a classification model using 
      scikit-learn's RandomForestClassifier to predict customer churn. The function will 
      return the accuracy score of the model on the test set.
  
      >>> analyze_and_predict_churn("customer_data.csv", ['age', 'balance', 'active'])
      0.85
      >>> analyze_and_predict_churn("customer_data.csv", ['age', 'balance', 'active', 'duration'])
      0.87
      """
      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(file_path)
  
      # Select features
      X = data[feature_columns]
  
      # 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)
output:     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(file_path)
  
      # Select features
      X = data[feature_columns]
  
      # 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)