Metadata-Version: 2.1
Name: resume_classification
Version: 1.0
Summary: It a simple package for training and classification of resumes.
Home-page: https://github.com/shreyas2306/ResumeParser
Author: Shreyas Nanaware/Krushna Kumar Nalange
License: MIT
Description: # 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`.
Platform: UNKNOWN
Classifier: License :: OSI Approved :: MIT License
Classifier: Programming Language :: Python :: 3
Classifier: Programming Language :: Python :: 3.7
Description-Content-Type: text/markdown
