Resume/CV Classifier

Resume Classification is a project that builds a model to sort candidate CVs and predict candidates invited for an Interview and candidates not invited. According to Workopolis, Employers receive averagely between 75 and 250 applications per job posting. This can make it difficult and stressful to identify qualified candidates. In order to avoid having to read through the entire pile of resumes, most employers use applicant tracking systems (ATS).This project used Naive Bayes to build an NLP CV Classification model.

Folder Structure

Resume_classifier
|   |
|   |---data
|   |
|   |---notebook

Data Source

Resumes in this dataset were queried from Indeed.com with keyword 'data scientist', location 'Vermont'. It contains a total of 125 Resumes (33 invited for the Interview and 92 Candidates not invited). If a resume is 'not flagged', the applicant can submit a modified resume version at a later date. If it is 'flagged', the applicant is invited to interview.

Source: https://www.kaggle.com/samdeeplearning/deepnlp

Results

The Naive Bayes Classifer model score a weighted average

	Metrics	Score
1.	Precision	76%
2.	Recall	76%
3.	F1-Score	74%