input: Write a function to create and train a RandomForestClassifier model in scikit-learn to predict customer churn based on a given dataset. The function should load the dataset, split it into training and testing sets, train the model, and return the accuracy of the model on the test set. Assume the dataset is stored in a CSV file named "customer_data.csv" and contains a column named "churn" that indicates whether a customer has churned (1) or not (0).
  assert churn_prediction_accuracy()==0.85
  assert churn_prediction_accuracy()==0.87
  assert churn_prediction_accuracy()==0.83
  assert churn_prediction_accuracy()==0.84
  assert churn_prediction_accuracy()==0.86
output: def churn_prediction_accuracy():
      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("customer_data.csv")
      X = data.drop("churn", axis=1)
      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)
      
      y_pred = model.predict(X_test)
      return accuracy_score(y_test, y_pred)