# Linear Discriminant Analysis (LDA) in Python – Step 8.) Visualize the Results of LDA Model

# Import the libraries
import numpy as np
import matplotlib.pyplot as plt
import pandas as pd

# Import the dataset
X = dataset.iloc[:, 0:13].values
y = dataset.iloc[:, 13].values

# Splitting the dataset into the Training set and Test set
from sklearn.cross_validation import train_test_split
X_train, X_test, y_train, y_test = train_test_split(X, y, test_size = 0.2)

# Feature Scaling to Dataset
from sklearn.preprocessing import StandardScaler
sc = StandardScaler()
X_train = sc.fit_transform(X_train)
X_test = sc.transform(X_test)

# Implement LDA
from sklearn.discriminant_analysis import LinearDiscriminantAnalysis as LDA
lda = LDA(n_components = 2)
X_train = lda.fit_transform(X_train, y_train)
X_test = lda.transform(X_test)

# Train Logistic Regression with LDA
from sklearn.linear_model import LogisticRegression
classifier = LogisticRegression()
classifier.fit(X_train, y_train)

# Predict Results of Regression with LDA
y_pred = classifier.predict(X_test)

# Confusion Matrix 3X3
from sklearn.metrics import confusion_matrix
cm = confusion_matrix(y_test, y_pred)

# Visualising the Regression Result on Training Set
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(‘LD1’)
plt.ylabel(‘LD2’)
plt.legend()
plt.show()

# Visualising the Regression Result on Testing Set
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(‘LD1’)
plt.ylabel(‘LD2’)
plt.legend()
plt.show()

##### Other Sections on Linear Discriminant Analysis :

Step 8.) Visualize the Results of LDA Model

##### Other Topics – Artifical Inteligent :
• Upper Confidence Bound
• Thompson Sampling