human
What is a code to 4 given the following:
4. The Real Deal : Building (Coding up) the classifier
The details are explained in comment form line-by-line in the following section."
Please write code following the instructions in jupyter notebook style.
assistant
#Here is code to 4.

# First we implement a class. Classes let you structure code in a specific way.(source --> https://learnpythonthehardway.org/book/ex40.html)

class OneNeighborClassifier():                # This 'class' has 2 Methods : Fit and Predict
    
    #Each step is followed by a comment which explains how the classifier is working 
    
    def fit(self, X_train, y_train):          # Takes features and labels as input
        self.X_train = X_train                # Storing the X_train in self.X_train
        self.y_train = y_train                # Storing the y_train in self.y_train
                                              # This is like the ML classifier will memorize the values 
        
    def predict (self, X_test):               # Receives features from the testing data and returns predictions
        predictions = []                      # List of predictions, since X_test is a 2D array or a list of lists.
        for row in X_test:                    # Each row contains the features for one testing example
            label = self.closest(row)         # We are calling the function that we are creating in the next block
                                              # to find the closest training point from the test point
            predictions.append(label)         # Add the labels to the predictions list to fill it.
        return predictions                    # Return predictions as the output
    
    def closest(self, row):                   # Create the function closest such that -->
        best_dist = euc(row, self.X_train[0]) # Measure the shortest distance a test point and the first train point
        best_index = 0                        # Keep track of the index of the train point that is closest
        for i in range (1, len(self.X_train)):# Iterate over the different training points
            dist = euc(row, self.X_train[i])
            if dist < best_dist:              # The moment we find a closer one, we update our variables.
                best_dist = dist              # If dist is...