input: Write a function to develop a machine learning algorithm in Python to identify apples from bananas. The function should load the data, initialize a classifier, fit the data, and test the classifier by predicting the class for a given test data. The function should return the prediction results.
  assert identify_apple_banana() == [0, 0]
output: def identify_apple_banana():
      import numpy as np
      from sklearn.datasets import load_iris
      from sklearn.model_selection import train_test_split
      from sklearn.ensemble import RandomForestClassifier
  
      data = load_iris()
      X = data.data
      y = data.target
      X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, random_state=42)
  
      classifier = RandomForestClassifier()
      classifier.fit(X_train, y_train)
  
      test_data = np.array([[5.1, 3.5, 1.4, 0.2], [4.9, 3.0, 1.4, 0.2]])
      predictions = classifier.predict(test_data)
  
      return predictions.tolist()