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

Chapter 3
Supervised Learning Using Python
g, p, dof, expected = chi2_contingency(c_xy, lambda_="loglikelihood")
mi = 0.5 * g / c_xy.sum()
return mi
Classifications with Python
Classification is a well-accepted example of machine learning. It has a
set of a target classes and training data. Each training data is labeled by
a particular target class. The classification model is trained by training
data and predicts the target class of test data. One common application of
classification is in fraud identification in the credit card or loan approval
process. It classifies the applicant as fraud or nonfraud based on data.
Classification is also widely used in image recognition. From a set of
images, if you recognize the image of a computer, it is classifying the image
of a computer and not of a computer class.
Sentiment analysis is a popular application of text classification.
Suppose an airline company wants to analyze its customer textual
feedback. Then each feedback is classified according to sentiment
(positive/negative/neutral) and also according to context (about staff/
timing/food/price). Once this is done, the airline can easily find out
what the strength of that airline’s staff is or its level of punctuality or cost
effectiveness or even its weakness. Broadly, there are three approaches in
classification.
• Rule-based approach: I will discuss the decision tree
and random forest algorithm.
• Probabilistic approach: I will discuss the Naive Bayes
algorithm.
• Distance-based approach: I will discuss the support
vector machine.
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