What is the best algorithm to solve the regression problem of predicting the number of languages a Wikipedia article can be translated to? I'm doing a student project where I construct a model predicting the number of languages that a given Wikipedia article is translated into (for example, the article TOYOTA is translated into 93 languages). I've tried extracting basic info (article length, number of links, etc.) to create a simple regression model, but can't get the $R^2$ value above $0.25$ or so.
What's the most appropriate NLP algorithm for regression problems? Almost all examples I find online are classification problems. FYI I'm aware of the basics of NLP preprocessing (tokenization, lemmatization, bag of words, etc).
I think it's difficult to tell wich algorithm is &quot;the best&quot; or &quot;the simplest&quot;.
I had the same issue of choosing the suited NLP algorithm for my dataset and I used :
https://scikit-learn.org/stable/tutorial/machine_learning_map/index.html
Then I recommanded you to test many algorithms as you can to find the best for your needed.