text: # Principal Component Analysis (PCA)
  
  # Importing the libraries
  import numpy as np
  import matplotlib.pyplot as plt
  import pandas as pd
  
  # Importing the dataset
  dataset = pd.read_csv('Wine.csv')
  X = dataset.iloc[:, :-1].values
  y = dataset.iloc[:, -1].values
  
  # Feature Scaling
  from sklearn.preprocessing import StandardScaler
  sc = StandardScaler()
  X = sc.fit_transform(X)
  
  # Splitting the dataset into the Training set and Test set
  from sklearn.model_selection import train_test_split
  X_train, X_test, y_train, y_test = train_test_split(X, y, test_size = 0.2, random_state = 0)
  
  # Applying PCA
  from sklearn.decomposition import PCA
  pca = PCA(n_components = 2)
  X_train = pca.fit_transform(X_train)
  X_test = pca.transform(X_test)
  explained_variance = pca.explained_variance_ratio_
  
  # Training the Logistic Regression model on the Training set
  from sklearn.linear_model import LogisticRegression
  classifier = LogisticRegression(random_state = 0)
  classifier.fit(X_train, y_train)
  
  # Predicting the Test set results
  y_pred = classifier.predict(X_test)
  
  # Making the Confusion Matrix
  from sklearn.metrics import confusion_matrix
  cm = confusion_matrix(y_test, y_pred)
  print(cm)
  
  # Visualising the Training set results
  from matplotlib.colors import ListedColormap
  X_set, y_set = X_train, y_train
  X1, X2 = np.meshgrid(np.arange(start = X_set[:, 0].min() - 1, stop = X_set[:, 0].max() + 1, step = 0.01),
                       np.arange(start = X_set[:, 1].min() - 1, stop = X_set[:, 1].max() + 1, step = 0.01))
  plt.contourf(X1, X2, classifier.predict(np.array([X1.ravel(), X2.ravel()]).T).reshape(X1.shape),
               alpha = 0.75, cmap = ListedColormap(('red', 'green', 'blue')))
  plt.xlim(X1.min(), X1.max())
  plt.ylim(X2.min(), X2.max())
  for i, j in enumerate(np.unique(y_set)):
      plt.scatter(X_set[y_set == j, 0], X_set[y_set == j, 1],
                  c = ListedColormap(('red', 'green', 'blue'))(i), label = j)
  plt.title('Logistic Regression (Training set)')
  plt.xlabel('PC1')
  plt.ylabel('PC2')
  plt.legend()
  plt.show()
  
  # Visualising the Test set results
  from matplotlib.colors import ListedColormap
  X_set, y_set = X_test, y_test
  X1, X2 = np.meshgrid(np.arange(start = X_set[:, 0].min() - 1, stop = X_set[:, 0].max() + 1, step = 0.01),
                       np.arange(start = X_set[:, 1].min() - 1, stop = X_set[:, 1].max() + 1, step = 0.01))
  plt.contourf(X1, X2, classifier.predict(np.array([X1.ravel(), X2.ravel()]).T).reshape(X1.shape),
               alpha = 0.75, cmap = ListedColormap(('red', 'green', 'blue')))
  plt.xlim(X1.min(), X1.max())
  plt.ylim(X2.min(), X2.max())
  for i, j in enumerate(np.unique(y_set)):
      plt.scatter(X_set[y_set == j, 0], X_set[y_set == j, 1],
                  c = ListedColormap(('red', 'green', 'blue'))(i), label = j)
  plt.title('Logistic Regression (Test set)')
  plt.xlabel('PC1')
  plt.ylabel('PC2')
  plt.legend()
  plt.show()