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If I need to go over the actual data which I have, I have employee data of our company.

Employees are divided into two groups, binary(1,0). Some of them are 1 and the rest are in 0 class. The data set includes a lot of basic data for each employee, such as age, gender, the school they graduated from, time spent in the company or position. I also want to learn and see the common features of those who are in this 1 class, for example, age range, gender, universities, etc.

I know this looks like a classification problem, but isn't it possible to find these similarities without using any machine learning algorithms?

Do you think it is sufficient to perform frequency analysis and chi-square test, or is there any other analysis method or statistical test you can recommend?

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  • $\begingroup$ It sounds like you can just look at histograms and bar charts for your two groups. Plot a bar chart comparing the ages of groups 0 and 1; plot a bar chart comparing the genders;... $\endgroup$
    – Dave
    Commented Dec 21, 2020 at 14:29

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This is really more of a comment than a full response. You could try cross-classifying combinations of the 0,1 features. Next, ranking the frequencies of employees falling into a given bucket would give insight into any redundancy in the groupings. Since it isn't possible to illustrate what I mean by this suggestion within the context of a comment, as noted, it's been expanded into a response.

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While this table shows univariate data, it is possible to convert it into a two-way table upon which odds ratio or chi-square tests, e.g., of independence, can be applied.

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Conversion of more such binary features into three- and higher way tables would be an easy extension, permitting even more complex tests of significance.

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