Naive Bayes

Importing the libraries

import numpy as np
import matplotlib.pyplot as plt
import pandas as pd

Importing the dataset

df = pd.read_csv('Social_Network_Ads.csv')
df.head(10)

	Age	EstimatedSalary	Purchased
0	19	19000	0
1	35	20000	0
2	26	43000	0
3	27	57000	0
4	19	76000	0
5	27	58000	0
6	27	84000	0
7	32	150000	1
8	25	33000	0
9	35	65000	0

X = df.iloc[:, :-1].values
y = df.iloc[:, -1].values

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 = 0.25, random_state = 0)

print(X_train[0:3])

[[    44  39000]
 [    32 120000]
 [    38  50000]]

print(y_train[0:3])

[0 1 0]

Feature Scaling

from sklearn.preprocessing import StandardScaler
sc = StandardScaler()
X_train = sc.fit_transform(X_train)
X_test = sc.transform(X_test)

print(X_train[0:3])

[[ 0.58164944 -0.88670699]
 [-0.60673761  1.46173768]
 [-0.01254409 -0.5677824 ]]

Training the Naive Bayes model on the Training set

from sklearn.naive_bayes import GaussianNB
classifier = GaussianNB()
classifier.fit(X_train, y_train)

GaussianNB()

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Predicting a new result

print(classifier.predict(sc.transform([[30,87000]])))

[0]

Predicting the Test set results

y_pred = classifier.predict(X_test)
#print(np.concatenate((y_pred.reshape(len(y_pred),1), y_test.reshape(len(y_test),1)),1))

Making the Confusion Matrix

from sklearn.metrics import confusion_matrix, accuracy_score
cm = confusion_matrix(y_test, y_pred)
print(cm)

[[65  3]
 [ 7 25]]

accuracy_score(y_test, y_pred)

0.9

Visualising the Training set results

colors = ['#FA8072', '#1E90FF']

from matplotlib.colors import ListedColormap
X_set, y_set = sc.inverse_transform(X_train), y_train
X1, X2 = np.meshgrid(np.arange(start = X_set[:, 0].min() - 10, stop = X_set[:, 0].max() + 10, step = 0.25),
                     np.arange(start = X_set[:, 1].min() - 1000, stop = X_set[:, 1].max() + 1000, step = 0.25))
plt.contourf(X1, X2, classifier.predict(sc.transform(np.array([X1.ravel(), X2.ravel()]).T)).reshape(X1.shape),
             alpha = 0.75, cmap = ListedColormap(colors))
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], color = ListedColormap(colors)(i), label = j)
plt.title('Naive Bayes (Training set)')
plt.xlabel('Age')
plt.ylabel('Estimated Salary')
plt.legend()
plt.show()

Visualising the Test set results

from matplotlib.colors import ListedColormap
X_set, y_set = sc.inverse_transform(X_test), y_test
# Create a grid of points
X1, X2 = np.meshgrid(
    np.arange(start=X_set[:, 0].min() - 1, stop=X_set[:, 0].max() + 1, step=0.25),
    np.arange(start=X_set[:, 1].min() - 1, stop=X_set[:, 1].max() + 1, step=0.25)
)
# Predict for each point on the grid
Z = classifier.predict(sc.transform(np.array([X1.ravel(), X2.ravel()]).T)).reshape(X1.shape)
# Plot the decision boundary
plt.contourf(X1, X2, Z, alpha=0.75, cmap = ListedColormap(colors) )
plt.xlim(X1.min(), X1.max())
plt.ylim(X2.min(), X2.max())
# Define colors for scatter plot
colors = colors
# Plot the test set points
for i, j in enumerate(np.unique(y_set)):
    plt.scatter(
        X_set[y_set == j, 0], X_set[y_set == j, 1],
        color=colors[i], label=j
    )
# Add titles and labels
plt.title('Naive Bayes (Test set)')
plt.xlabel('Age')
plt.ylabel('Estimated Salary')
plt.legend()
plt.show()