GitHub Repository: debakarr/machinelearning
Path: blob/master/Part 3 - Classification/Naive Bayes/[Python] Naive Bayes.ipynb
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Kernel: Python 3

Naive Bayes

Data preprocessing

In [2]:

# Importing the libraries
import numpy as np
import matplotlib.pyplot as plt
import pandas as pd
from sklearn.model_selection import train_test_split # for training and testing split
from sklearn.preprocessing import StandardScaler # for Feature scaling
from sklearn.naive_bayes import GaussianNB # for classifier
from sklearn.metrics import confusion_matrix # for making confusion matrix
from matplotlib.colors import ListedColormap # for visualisation
%matplotlib inline
plt.rcParams['figure.figsize'] = [14, 8]

In [3]:

# Importing the dataset
dataset = pd.read_csv('Social_Network_Ads.csv')
X = dataset.iloc[:, [2, 3]].values
y = dataset.iloc[:, 4].values

In [4]:

dataset.head(10)

Out[4]:

In [5]:

# Splitting the dataset into the Training set and Test set
X_train, X_test, y_train, y_test = train_test_split(X, y, test_size = 0.20, random_state = 42)

In [6]:

X_train[0:10]

Out[6]:

array([[    27,  57000],
       [    46,  28000],
       [    39, 134000],
       [    44,  39000],
       [    57,  26000],
       [    32, 120000],
       [    41,  52000],
       [    48,  74000],
       [    26,  86000],
       [    22,  81000]])

In [7]:

X_test[0:10]

Out[7]:

array([[    46,  22000],
       [    59,  88000],
       [    28,  44000],
       [    48,  96000],
       [    29,  28000],
       [    30,  62000],
       [    47, 107000],
       [    29,  83000],
       [    40,  75000],
       [    42,  65000]])

In [8]:

y_train[0:10]

Out[8]:

array([0, 1, 1, 0, 1, 1, 0, 1, 0, 0])

In [9]:

y_test[0:10]

Out[9]:

array([0, 1, 0, 1, 0, 0, 1, 0, 0, 0])

In [11]:

# Feature Scaling
sc = StandardScaler()
X_train = sc.fit_transform(X_train)
X_test = sc.transform(X_test)

In [12]:

X_train[0:10]

Out[12]:

array([[-1.06675246, -0.38634438],
       [ 0.79753468, -1.22993871],
       [ 0.11069205,  1.853544  ],
       [ 0.60129393, -0.90995465],
       [ 1.87685881, -1.28811763],
       [-0.57615058,  1.44629156],
       [ 0.3069328 , -0.53179168],
       [ 0.99377543,  0.10817643],
       [-1.16487283,  0.45724994],
       [-1.55735433,  0.31180264]])

In [13]:

X_test[0:10]

Out[13]:

array([[ 0.79753468, -1.40447546],
       [ 2.07309956,  0.51542886],
       [-0.96863208, -0.76450736],
       [ 0.99377543,  0.74814454],
       [-0.87051171, -1.22993871],
       [-0.77239133, -0.24089709],
       [ 0.89565505,  1.06812859],
       [-0.87051171,  0.36998156],
       [ 0.20881242,  0.13726589],
       [ 0.40505317, -0.15362871]])

Fitting Naive Bayes Classifier to the Training set

In [14]:

classifier = GaussianNB()
classifier.fit(X_train, y_train)

Out[14]:

GaussianNB(priors=None)

Predicting the Test set results

In [15]:

y_pred = classifier.predict(X_test)

In [16]:

y_pred[0:10]

Out[16]:

array([1, 1, 0, 1, 0, 0, 1, 0, 0, 0])

In [17]:

y_test[0:10]

Out[17]:

array([0, 1, 0, 1, 0, 0, 1, 0, 0, 0])

Making the Confusion Matrix

In [18]:

cm = confusion_matrix(y_test, y_pred)
cm

Out[18]:

array([[50,  2],
       [ 3, 25]])

classifier made 50 + 25 = 75 correct prediction and 2 + 3 = 5 incoreect predictions.

Visualising the Training set results

In [19]:

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')))
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'))(i), label = j, edgecolors = 'white', linewidth = 0.7)
plt.title('Naive Bayes (Training set)')
plt.xlabel('Age')
plt.ylabel('Estimated Salary')
plt.legend()
plt.show()

Out[19]:

Visualising the Test set results

In [20]:

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')))
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'))(i), label = j, edgecolors = 'white', linewidth = 0.7)
plt.title('Naive Bayes (Test set)')
plt.xlabel('Age')
plt.ylabel('Estimated Salary')
plt.legend()
plt.show()

Out[20]:

Naive Bayes is another non-linear classifier. It works on the principle of Bayes theorem.

Here we can see that we got a pretty good classifier with very fewer incorrect predictions. Though no classifier is 100% correct.

Every classifier is wrong , but some are useful.