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, select features, target variables, split the data into training and testing sets, train a RandomForestClassifier, and finally return the accuracy score of the model's predictions on the test set.
  assert classify_customer_churn("customer_data.csv")==0.85
  assert classify_customer_churn("customer_data.csv")==0.87
  assert classify_customer_churn("customer_data.csv")==0.82
  assert classify_customer_churn("customer_data.csv")==0.80
  assert classify_customer_churn("customer_data.csv")==0.83
output: import pandas as pd
  from sklearn.model_selection import train_test_split
  from sklearn.ensemble import RandomForestClassifier
  from sklearn.metrics import accuracy_score
  
  def classify_customer_churn(file_path):
      data = pd.read_csv(file_path)
      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)