Curriculum Vitae Clustering using k-means

A repository of datasets of curriculum vitaes and an example of clustering curriculum Vitae/resume using k-means. If interest is only on datasets, read this README.md file.

Requirement:

• scikit-learn
• nltk
• textract
• PyPDF2

Description:

• data/word directory contains the word(.docx) files of Curriculum Vitae.
• resource/common_cities_state_countries_names.txt files contains the common cities, states, countries names. The main routine includes the contains of this file as stopwords.
• resource/human_names.txt contains common human names. The main routine includes the contains of this file as stopwords.
• resource/specific_stopwords.txt contains some user defined stopwords. The main routine includes the contains of this file as stopwords.
• The main routine folderize the docx files of same cluster in a new folder with in output\ directory . The name of new directory is the first 15 most frequent features of that cluster.

Run:

• python main.py for clustering docx file in data/word
• python clean_resources.py to remove duplicate words in resource/human_names.txt & resource/common_cities_state_countries_names.txt files