Resume Classification

Objective

Aim of this project is to train a set of resumes of specific domain and create a machine learning model to predict the unseen resumes.

Currently the model is trained using logistic regression on these four domain:

• Java
• Cloud
• Big Data
• Machine Learning

Resumes are read using a package called tika which supports many file formats including the following popular ones:

- doc
- docx
- pdf

NOTE: To know more about tika visit the following link: https://pypi.org/project/tika/

Installation

pip install resume_classification

Dependencies

• numpy==1.17.3
• pandas==0.25.1
• tika==1.24
• nltk==3.4.5

Python version

`Python 3.7.4`

Project Guidelines

• Train

  In order to train a new set of resumes, the project ought to have a defined folder structure given below:

  

  Then run the following commands:

  from resume_classification import train

  train(path-to-resumes-folder)

  Output

  The output will consist of the following metrics

  • Model Accuracy
  • F1 Score
  • Confusion Matrix

• Predict

  Use the following command:

  from resume_classification import predict

  predict(path-to-resumes)

  NOTE for predict module, the path to resume will contain all the unseen resumes in a single folder.

  Output

  The output will be a dataframe consisting of file name and predicted domain.