How to handle large number of categories in a dataset? I have one dataset of "Books" which contains 8 columns initially and out of which 3 of them contains text values which can be categorized. The 3 columns contains "Language-code", "Author Name" and "title" of the book. As sklearn LinearRegression don't take text as input so i decided to categorize these 3 columns by using "pandas_getdummies(...)" but after categorizing it the columns number exceeded to 20072 from 8 which is way too high. 

The dataset url is: https://www.kaggle.com/jealousleopard/goodreadsbooks/downloads/goodreadsbooks.zip/6

So my queries are:


What to do with the title name? Categorizing it doesn't seems right.
What to with the rest 2 columns? If i leave the title name then the number of columns exceeds to 7646. Is there any other algorithm where i can directly feed the dataset without categorization?
How to handle these large number of features after categorizing?
Algorithm like Decision Tree, can also work well on ordinal values, i.e. without OneHotEncoding. You can try this one.

Also Im not sure the importance of Title Name, so you have to take decision based upon the requirements. But I have avoided in my use case.


  Also, generally when you have large number of categories, you can give
  a try by clubbing uncommon categories into one.