I am building an automated cleaning process that clean null values from the dataset. I discovered few functions like mode, median, mean which could be used to fill NaN values in given data. But which one I should select? if data is categorical it has to be either mode or median while for continuous it has to be mean or median. So to define whether data is categorical or continuous I decided to make a machine learning classification model.

I took few features like,

1) standard deviation of data

2) Number of unique values in data

3) total number of rows of data

4) ratio of unique number of total rows

5) minimum value of data

6) maximum value of data

7) number of data between median and 75th percentile

8) number of data between median and 25th percentile

9) number of data between 75th percentile and upper whiskers

10) number of data between 25th percentile and lower whiskers

11) number of data above upper whisker

12) number of data below lower whisker

First with this 12 features and around 55 training data I used the logistic regression model on **Normalized form** to predict label 1(continuous) and 0(categorical).

Fun part is it worked!!

But, did I do it the right way? Is it a correct method to predict nature of data? Please advise me if I could improve it further.