@vbakotin wrote:
Hello everyone! I’m a newcomer to ML and Im trying to solve the following problem: I have a text data, namely a set of vacancies names with corresponding requirements descriptions (e.g. [ML specialist]–>[Experience in python NLTK, advanced sql et.]). I would like to predict a set of appropriate vacancies for a person with an input set of his/her competences. After doing some NLP operations I got a vocabulary of competencies in such a form: [‘sql’,‘java’,‘python’…] consisting of all appearing in initial data sensible tokens and the same type vocabulary for vacancies names. Then I decided to encode any newcoming competence’s set such as binary string [10101000] (len=len of vocab) where 1 takes place if specific competence appear in a set and 0 otherwise. Now can I use Naive Bayes Classifier to predict suitable vacancies,using probabilities of each class(each vacancy) as a suitability degree? Mb you could advise some more fitting ML approaches?
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