Advanced Data Analytics Using Python_ With Machine Learning, Deep Learning and NLP Examples ( 2023)

Chapter 3
Supervised Learning Using Python
The following code is an example of the Naive Bayes classification of
numerical data:
#Import Library of Gaussian Naive Bayes model
from sklearn.naive_bayes import GaussianNB
import numpy as np
#assigning predictor and target variables
df = pd.read_csv('csv file path', index_col=0)
y = df[target class column ]
X = df[ col1, col2 ..]
#Create a Gaussian Classifier
model = GaussianNB()
# Train the model using the training sets
model.fit(X, y)
#Predict Output
print model.predict([input array])
Note You’ll see another example of the Naive Bayes classifier in the
“Sentiment Analysis” section.
Support Vector Machine
If you look at Figure 3-2, you can easily understand that the circle and
square points are linearly separable in two dimensions (x1, x2). But they
are not linearly separable in either dimension x1 or x2. The support vector
machine algorithm works on this theory. It increases the dimension of
the data until points are linearly separable. Once that is done, you have
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