Hackit 2.0

Team name : CodeCrafters 😎

• Problem-Statement
• Need of the Project
• How ML Helps Us?
• Tasks
• System Architecture
• Tech stack
• Folder Structure
• Clone git repository
• You tube video
• License
• Contributors

Problem Statement

Recruitment Assisting platform which will help recruiters filter out resumes for a particular job profile. By Team CodeCrafters

Need of the Project

Motto- By evaluating all candidates against the same screening standards, … the process will be more objective, fair and accurate.

• Evaluator should set list of standards and criteria to compare resumes
• Job Description/ any other relevant information should also be compared with resume
• Standards should not be bend, as they were created to meet the job expectations
• There are hundreds of millions of candidate profiles and CVs online
• Manual screening of resumes is still the most time-consuming part of recruiting
• 75% to 88% of the resumes received for a role are unqualified
• Screening resumes and shortlisting candidates to interview is estimated to take 23 hours of a recruiter’s time for a single hire.

How ML Helps Us?

Machines are better at certain things such as:

1. Sourcing
2. Screening
3. Matching
4. Assessing
5. Helps to eliminate human bias, like candidate’s age, race, and gender by assessing candidates purely on their merits.
6. Low-value, time consuming recruiting tasks will become streamlined and automated.
7. Recruiter’s role will become more strategic.

Tasks

• Apply ML to segregate the resumes into different classes or groups. These classes or groups must be utilised to enhance the Resume based Search Engine.
• Create a recommender system which will prompt the recruiter with skills related to the filters provided to enhance the search.
• Create an automated Scraper to fetch profiles from public recruitment based platforms.
• Association based mining to be used. The recruiter just needs to provide the post or position in the organisation and the tool should provide the recommended candidates

System Architecture

Tech stack

1️⃣ Client Side : ReactJs, Firebase, React Hooks.

2️⃣ Server Side : Flask, Node JS, cheerio(web Scraper), request .

3️⃣ ML Model : Scikit-learn, nltk, ml5.js(npm module) .

4️⃣ Database and Storage : Firebase Cloud Firestore .

Clone git repository

$ git clone "https://github.com/Hackit-2-0/Team-CodeCrafters"

Folder Structure

📁 Team-CodeCrafters :
       :file_folder: assets
       :file_folder: Classificatioons
       :file_folder: clientform
       :file_folder: server
       :file_folder: github-finder

Render React UI

$ cd clientform

install node modules

$ npm i 

npm start

Runs the app in the development mode.
Open http://localhost:3000 to view it in the browser.

$ npm start 

Start Server

$ cd server

$ node app.js

You tube video

License

MIT License

Contributors

Name	Email 📧
Vedang parasnis	vedang.parasnis@somaiya.edu
Priya mane	priya.hm@somaiya.edu
Pratik merchant	pratik.merchant@somaiya.edu
Hritik Jaiswal	hritik.jaiswal@somaiya.edu

Developed with ❤️ by Vedang Parasnis , Priya mane , Pratik merchant and Hritik jaiswal.