input: Write a function to develop a decision tree classifier in Python to predict a fourth feature from three other features. The function should take in a dataset stored in 'data.csv' and use features A, B, and C to predict the fourth feature. The function should return the trained classifier.
  import pandas as pd
  from sklearn.tree import DecisionTreeClassifier
  
  def decision_tree_classifier():
      # importing the dataset 
      dataset = pd.read_csv('data.csv')
      
      # splitting the dataset into features and labels 
      X = dataset.iloc[:, :-1].values 
      y = dataset.iloc[:, 3].values
      
      # importing DecisionTreeClassifier 
      classifier = DecisionTreeClassifier(criterion = 'entropy')
      
      # fitting the classifier to the dataset
      classifier.fit(X, y)
      
      return classifier
  
  assert isinstance(decision_tree_classifier(), DecisionTreeClassifier) == True
  assert decision_tree_classifier().predict([[0, 1, 2]]) is not None
  assert decision_tree_classifier().predict([[3, 4, 5]]) is not None
output: import pandas as pd
  from sklearn.tree import DecisionTreeClassifier
  
  def decision_tree_classifier():
      dataset = pd.read_csv('data.csv')
      X = dataset.iloc[:, :-1].values 
      y = dataset.iloc[:, 3].values
      classifier = DecisionTreeClassifier(criterion = 'entropy')
      classifier.fit(X, y)
      return classifier