CareerLens AI

Intelligent Placement Prediction and Career Insight System

CareerLens AI is an intelligent web platform designed to help students evaluate their placement readiness using data-driven insights. The system analyzes academic performance, technical skills, internships, and project experience to estimate placement probability and provide personalized career recommendations.

You can test out the application yourself by visiting our Live Deployment.

Project Demo Video

📺 Click here to watch the CareerLens AI Demo Video

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The platform integrates:

• Machine Learning for placement prediction
• Machine Learning for stress well being monitor(random forest classifier)
• Generative AI (Gemini API) for resume analysis and career navigation
• Explainable AI using SHAP to identify key factors affecting placement probability by Logistic regression

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Team Details

• Team Number: 16

Team Members

• Pradeep Kumar N — CB.EN.U4ECE22137
• Yuvan Dhurkesh SJ — CB.SC.U4CSE23365
• Gugan SS — CB.SC.U4CSE23416
• Kokul M — CB.SC.U4CSE23462

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Project Modules

1. Placement Prediction Module

Predicts placement probability using a Logistic Regression model trained on student academic and skill data.

2. Resume Analyzer

Uses the Gemini API to analyze resumes and generate an ATS compatibility score with improvement suggestions.

3. Career Navigator

Uses the Gemini API to analyze student skills and recommend learning paths and technologies for career growth.

4. Wellbeing Monitoring Module

Tracks study patterns and behavioral indicators to detect stress or imbalance during placement preparation.

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Technologies Used

Frontend

• React.js

Backend

• Node.js
• Express.js

Database

• MongoDB

Machine Learning

• Python
• Scikit-learn
• SHAP Explainability

AI Integration

• Gemini API

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Project Folder Structure

CareerLens/
|- frontend/
|  |- src/
|  |  |- components/
|  |  |- pages/
|  |  |- redux/
|  |- index.html
|  |- package.json
|
|- backend/
|  |- config/
|  |- controllers/
|  |- data/
|  |- middleware/
|  |- models/
|  |- routes/
|  |- services/
|  |- uploads/
|  |- package.json
|  |- server.js
|
|- README.md
|- PROJECT_SUMMARY.md
|- start.ps1

Folder Explanation

• /frontend: Contains the React.js user interface, pages, components, and Redux state management.
• /backend: Contains the Node.js and Express.js API server and business logic.
• /backend/models: MongoDB schema definitions and model files.
• /backend/routes: API endpoint definitions.
• /backend/controllers: Request handling logic for authentication, career, resume, placement, and stress modules.
• /backend/data: Datasets and cached model data used by prediction and stress analysis services.
• /backend/services: Machine learning utility services used by backend modules.
• /backend/config: Configuration files for database connection, authentication, and Gemini integration.
• /backend/middleware: Middleware for security and route protection.
• /backend/uploads: Uploaded files such as resumes for analysis.

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Installation Instructions

1. Clone the repository.

git clone <repository-link>

2. Navigate to the project directory.

cd careerlens-ai

3. Install dependencies for backend.

cd backend
npm install

4. Install dependencies for frontend.

cd ../frontend
npm install

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Running the Project

Start the backend server

cd backend
npm start

Start the frontend application

cd frontend
npm run dev

The application will run locally on the configured development server.

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Features

• Placement probability prediction
• Resume ATS score analysis
• AI-based career learning recommendations
• SHAP explainability visualization
• Student wellbeing monitoring

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Notes

• Configure environment variables in backend/.env before running the backend server.
• Ensure MongoDB is available locally or via a cloud connection string.
• A valid Gemini API key is required for AI-based modules.