How to do it…

Let's see how to predict the quality of wine:

1. We will use the wine.quality.py file, already provided to you as a reference. We start, as always, by importing the NumPy library and loading the data (wine.txt):

import numpy as np
input_file = 'wine.txt'
X = []
y = []
with open(input_file, 'r') as f:
  for line in f.readlines():
     data = [float(x) for x in line.split(',')]
     X.append(data[1:])
     y.append(data[0])
X = np.array(X)
y = np.array(y)

Two arrays are returned: X (input data), and y (target). 

1. Now we need to separate our data into two groups: a training dataset and a testing dataset. The training dataset will be used to build the model, and the testing dataset will be used to see how this trained model performs on unknown data:

from sklearn import model_selection
X_train, X_test, y_train, y_test = model_selection.train_test_split(X, y, test_size=0.25, random_state=5)

Four arrays are returned: X_train, X_test, y_train, and y_test. This data will be used to train and validate the model.

1. Let's train the classifier:

from sklearn.tree import DecisionTreeClassifier

classifier_DecisionTree = DecisionTreeClassifier()
classifier_DecisionTree.fit(X_train, y_train)

To train the model, a decision tree algorithm has been used. A decision tree algorithm is based on a non-parametric supervised learning method used for classification and regression. The aim is to build a model that predicts the value of a target variable using decision rules inferred from the data features.

1. Now it's time to the compute accuracy of the classifier:

y_test_pred = classifier_DecisionTree.predict(X_test)

accuracy = 100.0 * (y_test == y_test_pred).sum() / X_test.shape[0]
print("Accuracy of the classifier =", round(accuracy, 2), "%")

The following result is returned:

Accuracy of the classifier = 91.11 %

1. Finally, a confusion matrix will be calculated to compute the model performance:

from sklearn.metrics import confusion_matrix

confusion_mat = confusion_matrix(y_test, y_test_pred)
print(confusion_mat)

The following result is returned:

[[17  2  0]
 [ 1 12  1]
 [ 0  0 12]]

Values not present on the diagonals represent classification errors. So, only four errors were committed by the classifier.