input: 
  def train_churn_model(data_path: str) -> float:
      """ Train a classification model using Random Forest to determine customer churn. The function loads the dataset from a CSV file, splits it into training and testing sets, trains the model, and returns the accuracy of the model on the test set.
      >>> train_churn_model("customer_data.csv")
      0.95
      """
      import pandas as pd
      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)
  
      # Make predictions and measure accuracy
      return model.score(X_test, y_test)
output:     import pandas as pd
      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)
  
      # Make predictions and measure accuracy
      return model.score(X_test, y_test)