For unsupervised learning, we try to reorganize data or classify it into different groups based on certain traits or characteristics. For this purpose, we can use certain rules to categorize our dataset. For example, we could classify them into different groups based on investors' characteristics, such as age, education level, background, job types, living city, profession, salary level, and house ownership. For instance, they could be classified into four types of investors: aggressive, risk averse, risk neutral, and extremely risk averse. After that, financial institutions could design and market specific financial products targeting different groups.
To plan an equitable income tax policy, governments could classify potential taxpayers based on various criteria, such as income level and whether a person has a certain disability. Then...