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EliteMatcher

Introduction

EliteMatcher is an resume screening application designed to streamline the hiring process by leveraging machine learning techniques. This tool helps organizations efficiently analyze and shortlist resumes based on predefined criteria, making the recruitment process faster and more accurate.

Objectives

Automate Resume Screening: Simplify the process of filtering and shortlisting resumes.

Enhance Accuracy: Utilize machine learning algorithms to match candidates' qualifications with job requirements.

Improve Efficiency: Reduce the time and effort required to process large volumes of resumes.

Provide Insights: Generate insights on candidate suitability and potential fit for roles.

Features

Resume Analysis: Parse and analyze resumes using natural language processing (NLP) techniques.

Skill Matching: Compare candidate skills and qualifications against job descriptions.

Data Visualization: Visualize key metrics and insights related to candidate profiles and job requirements.

Integration: Easily integrate with existing HR systems and databases.

Installation

  1. To set up EliteMatcher on your local machine, follow these steps:
  git clone https://github.com/rajsingh565/EliteMatcher.git

2.Navigate to the project directory:

  cd EliteMatcher

3.Install the required dependencies: Make sure you have Python and pip installed. Then, run:

  pip install -r requirements.txt

Usage

  1. Start the application:
   python app.py

2.Upload resumes and job descriptions: Follow the prompts in the application to upload your resume files.

3.Analyze Results: Review the results generated by the application to make informed hiring decisions.

Files

1.EliteMatcher.ipynb: Jupyter notebook containing the main code and analysis.

2.app.py: The main application script.

3.UpdatedResumeDataSet.csv: Sample dataset for resume analysis.

3.tfidf.pkl: Pre-trained TF-IDF model for text processing.

Support

If you encounter any issues or need assistance with EliteMatcher, please feel free to reach out. Contributions and suggestions are welcome!

For support, contact: [email protected]

Acknowledgments

Libraries and Tools: This project uses various Python libraries and tools for machine learning and data processing. See requirements.txt for a complete list.

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