input: 
  from typing import List
  
  def analyze_customer_data(data_path: str, feature_columns: List[str], target_column: str) -> float:
      """ Analyzes a customer dataset from a given CSV file path and creates a classification model using scikit-learn's RandomForestClassifier to predict customer churn.
      Returns the accuracy of the model on the test set.
      >>> analyze_customer_data("customer_data.csv", ['feature1', 'feature2', 'feature3'], 'churn')
      0.85
      >>> analyze_customer_data("customer_data.csv", ['feature1', 'feature2'], 'churn')
      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(data_path)
  
      # Select features
      X = data[feature_columns]
  
      # Select targets
      y = data[target_column]
  
      # 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
      accuracy = model.score(X_test, y_test)
      return accuracy
  
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(data_path)
  
      # Select features
      X = data[feature_columns]
  
      # Select targets
      y = data[target_column]
  
      # 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
      accuracy = model.score(X_test, y_test)
      return accuracy