Placify Smarter Placements Sharper Talent

Abstract

1. Introduction

This project is now an official part of GirlScript Summer of Code – GSSoC'25! We're thrilled to welcome contributors from all over India and beyond to collaborate, build, and grow Placify-Smarter_Placements-Sharper_Talent! Let’s make learning and career development smarter – together!

I can’t wait to welcome new contributors from GSSoC 2025 to this Placify-Smarter_Placements-Sharper_Talent project family! Let's build, learn, and grow together — one commit at a time.

Welcome to the official repository for Placify, a project by Innovision Technologies Pvt Ltd, participating in GirlScript Summer of Code (GSSoC) 2025. We're thrilled to have you join our mission!

2. Objective

Placify is an AI-powered recruitment and skill-assessment platform that streamlines 60–70% of campus placements by automating resume screening, adaptive assessments, and personalized feedback. It bridges the gap between industry needs and candidate readiness for students, colleges, and recruiters.

This project demonstrates how JavaScript, Machine Learning can be applied to a real-world AI & Machine Learning problem.

3. Key Features / Modules

• 10x Faster Interviews: Complete 500 interviews in under 48 hours.
• AI-Based Scoring: Removes human bias, ensures consistency.
• Real-Time Analysis: Monitors webcam & audio inputs for tone, clarity, logic, and body language.
• Student Feedback Reports: Personalized improvement suggestions.
• AI Learning Roadmap: Smart progress path suggestions.
• Recruiter Dashboards: Actionable talent pool insights.
• Adaptive Questions: Adjust based on responses.
• All backend logic, APIs, and database models are now in the server/ folder.
• All frontend React code is in the src/ folder.
• All machine learning and analysis modules are in the ml_modules/ folder.

4. Technology Stack

• Frontend: React.js / Next.js / Vue.js
• Backend: Node.js (Express.js) / Django / FastAPI
• Database: MongoDB / PostgreSQL
• AI/ML: Python, TensorFlow, PyTorch, OpenCV, NLP
• Cloud & DevOps: Vercel, Render, AWS, Google Cloud, Azure

5. System Requirements

General requirements for this technology stack — check the README for exact versions.

• Node.js (LTS) and npm
• A modern web browser
• VS Code or any code editor
• Git (to clone the repository)

6. Installation & Setup

git clone https://github.com/MonishRaman/Placify-Smarter_Placements-Sharper_Talent.git
cd Placify-Smarter_Placements-Sharper_Talent

Full setup instructions are in the project README.

7. Future Enhancements

Suggested extensions you can add to make this your own project.

• Deploy the model as a web app with Streamlit, Flask or FastAPI
• Compare against an additional model and report the metric difference
• Add explainability (SHAP / Grad-CAM)

8. Viva / Review Questions

Common questions examiners ask for projects in this domain.

1. What dataset does the project use and how was it pre-processed?
2. Which algorithm / model architecture is used and why was it chosen over alternatives?
3. How are training and testing data split, and how is overfitting avoided?
4. Which evaluation metrics (accuracy, precision, recall, F1) are reported and what do they mean here?
5. How would you deploy this model for real users?

9. Source Code & License

This project is developed by MonishRaman and published on GitHub under the MIT License. Please follow the license terms and credit the original author when you use or modify this code.