def classifier(train_data, train_target, classes, point, k=5):
    data = zip(train_data, train_target)
    # List of distances of all points from the point to be classified
    distances = []
    for data_point in data:
        distance = euclidean_distance(data_point[0], point)
        distances.append((distance, data_point[1]))
    # Choosing 'k' points with the least distances.
    votes = [i[1] for i in sorted(distances)[:k]]
    # Most commonly occurring class among them
    # is the class into which the point is classified
    result = Counter(votes).most_common(1)[0][0]
    return classes[result]