input: from typing import List, Any
  
  def custom_ml_model(data: Any) -> Any:
      """Create a classification model in scikit-learn to determine customer churn from a dataset.
      This function loads a CSV dataset, selects features and targets, splits the data into training 
      and testing sets, creates a RandomForestClassifier, trains the model, and then makes predictions 
      and measures the accuracy.
      >>> custom_ml_model(pd.read_csv("customer_data.csv"))
      0.85
      """
output:     
      import pandas as pd
      from sklearn.model_selection import train_test_split
      from sklearn.ensemble import RandomForestClassifier
      from sklearn.metrics import accuracy_score
  
      # Assuming the dataset has a column 'Churn' indicating customer churn
      features = data.drop('Churn', axis=1)
      target = data['Churn']
  
      # Split the data into training and testing sets
      X_train, X_test, y_train, y_test = train_test_split(features, target, test_size=0.2, random_state=42)
  
      # Create a RandomForestClassifier
      model = RandomForestClassifier(n_estimators=100, random_state=42)
  
      # Train the model
      model.fit(X_train, y_train)
  
      # Make predictions
      y_pred = model.predict(X_test)
  
      # Measure the accuracy
      accuracy = accuracy_score(y_test, y_pred)
      return accuracy
  