input: Write a function to analyze a dataset of customers and create a classification model in scikit-learn to determine customer churn. The function should load the dataset from a CSV file, select relevant features, split the data into training and testing sets, create and train a RandomForestClassifier, and then measure and print the accuracy of the model on the test set.
  assert (model := churn_classification_model("customer_data.csv").score(X_test, y_test)) is not None
  assert model > 0
output: def churn_classification_model(file_path):
      import pandas as pd
      from sklearn.model_selection import train_test_split
      from sklearn.ensemble import RandomForestClassifier
      from sklearn.metrics import accuracy_score
  
      data = pd.read_csv(file_path)
      X = data[['feature1', 'feature2', 'feature3']]
      y = data['churn']
      X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, random_state=42)
      model = RandomForestClassifier(random_state=42)
      model.fit(X_train, y_train)
      accuracy = model.score(X_test, y_test)
      print(f"Model Accuracy: {accuracy}")
      return model