trees1.py

import pandas as pd
import numpy as np
import graphviz
import pydotplus
import matplotlib.image as mpimg
import io
import random

from matplotlib import pyplot as plt
from sklearn.tree import DecisionTreeClassifier, DecisionTreeRegressor
from sklearn.model_selection import train_test_split, GridSearchCV
from sklearn import preprocessing, tree, datasets
from dtreeviz.trees import dtreeviz

pd.set_option('display.max_rows', 10)
random.seed(24)

trees2.py

df_red_wine = pd.read_csv('https://archive.ics.uci.edu/ml/machine-learning-databases/wine-quality/winequality-red.csv', sep=';')
df_white_wine = pd.read_csv('https://archive.ics.uci.edu/ml/machine-learning-databases/wine-quality/winequality-white.csv', sep=';')

df_red_wine['label'] = 1
df_white_wine['label'] = 0

df_merged_wine = pd.concat([df_red_wine, df_white_wine])
df_merged_wine