input: Write a function to develop a decision tree classifier in Python to predict a fourth feature from three other features. Use the given dataset and split it into features and labels. Then, import and fit a DecisionTreeClassifier with entropy as the criterion.
  
  def decision_tree_classifier():
      import pandas as pd
      from sklearn.tree import DecisionTreeClassifier
      
      # 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)
  
  assert decision_tree_classifier() is None
output: def decision_tree_classifier():
      import pandas as pd
      from sklearn.tree import DecisionTreeClassifier
      
      dataset = pd.read_csv('data.csv')
      X = dataset.iloc[:, :-1].values
      y = dataset.iloc[:, 3].values
      classifier = DecisionTreeClassifier(criterion='entropy')
      classifier.fit(X, y)