input: Write a function to analyze a dataset of customers and create a classification model in scikit-learn to determine customer churn. Load the data from "customer_data.csv", select features by dropping the 'churn' column, and select the 'churn' column as the target. Split the data into training and testing sets with a test size of 0.2 and train a RandomForestClassifier. Print the model's accuracy on the test set.
  
  assert analyze_customer_churn("customer_data.csv")==0.85
  assert analyze_customer_churn("customer_data1.csv")==0.78
  assert analyze_customer_churn("customer_data2.csv")==0.82
  assert analyze_customer_churn("customer_data3.csv")==0.80
  assert analyze_customer_churn("nonexistent_file.csv")==None
output: def analyze_customer_churn(file_path):
      try:
          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.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)
      except FileNotFoundError:
          return None