Classification

Course Description

Machine learning classification challenges demand the classification of a given data set into two or more categories. There are several different sorts of classification problems:

1. Binary classification divides data into two categories: yes/no, good/bad, high/low, suffers from a specific ailment or not, and so on.
2. Classification on a multinomial scale: Organizes data into three or more categories; Document categorization, product categorization, and malware categorization are all examples of classification.

Classification issues are supervised learning problems in which the training data set includes data from both independent and response variables (label). Some of the following algorithms are used to train the classification models: Logistic regression, decision tree, Naive bayes, support vector machine, artificial neural network. Here are some real-world examples of classification problems:

• Customer behavior prediction: Customers can be divided into groups based on their purchasing habits, online shop browsing habits, and other factors.
• Image classification: To categorize photos into distinct categories, a multinomial classification model can be developed.
• Malware classification: A multinomial classification can be used to categorize new/emerging-malware based on similar malware traits.
• Image sentiment analysis: Machine learning binary classification models based on machine learning algorithms can be created to classify whether an image has a good or negative emotion/sentiment.
• Customer behavior assessment for promotional offers: In the context of a given business scenario such as upselling, cross-selling, and so on, a binary classification model can be used to determine whether an account is customer-friendly or not.
• Validation of deductions: A binary classification model can be used to determine if a deduction claimed by the buyer on a certain invoice is valid or invalid.