semvec/pypi-packages huggingface dataset:
resume-classification 1.0
It a simple package for training and classification of resumes.
License: MIT

# 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:

    <img src="pic/Folder_Structure.png" width="600" height="300">  

  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`.