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

Chapter 3
Supervised Learning Using Python
Image Recognition
Image recognition is a common example of image classification. It is easy
to use in Python by applying the opencv library. Here is the sample code:
faceCascade=cv2.CascadeClassifier(cascPath)
image = cv2.imread(imagePath)
gray = cv2.cvtColor(image, cv2.COLOR_BGR2GRAY)
faces = faceCascade.detectMultiScale(
gray,
scaleFactor=1.1,
minNeighbors=5,
minSize=(30, 30),
flags = cv2.cv.CV_HAAR_SCALE_IMAGE
)
print"Found {0} faces!".format(len(faces))
Regression with Python
Regression realizes a variable as a linear or nonlinear polynomial of a set of
independent variables.
Here is an interesting use case: what is the sales price of a product that
maximizes its profit? This is a million-dollar question for any merchant.
The question is not straightforward. Maximizing the sales price may
not result in maximizing the profit because increasing the sales price
sometimes decreases the sales volume, which decreases the total profit.
So, there will be an optimized value of sales price for which the profit will
be at the maximum.
There is N number of records of the transaction with M number of
features called F1, F2, ... Fm (sales price, buy price, cash back, SKU, order
date, and so on). You have to find a subset of K(K<M) features that have an
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