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InternMate – AI Internship Recommendation Engine



Problem Statement ID: 25034

Title: AI-Based Internship Recommendation Engine for PM Internship Scheme

1️. Problem Understanding

  • Students often struggle to find internships that match their skills.
  • The PM Internship Scheme provides opportunities, but students → internships mapping is not personalized.
  • Institutions lack AI-powered career guidance tools.

📌 Need: An AI system that matches students’ skills, interests, and career goals with internships listed in the PM Internship Portal.

2️. Solution Concept

InternMate → An AI-driven hybrid recommendation engine that:

  1. Reads student data (academics, skills, extracurriculars).
  2. Reads internship requirements (from PM Internship database).
  3. Uses AI/ML to recommend the top 5 best-fit internships.
  4. Provides skill-gap analysis → “You need to improve X skill, take Y course.”

3️. System Architecture

Data Sources

  • Student Data:
    • Academic records, resume, skills, courses.
    • Extracurricular activity (hackathons, coding clubs, NSS, projects).
  • Internship Data:
    • Job descriptions from PM Internship Portal (domain, skills required, duration).
  • Additional Data:
    • Skill-to-career mapping datasets (e.g., O*NET, NCS India).

Workflow

  1. Student Profile Ingestion

    • Upload resume / fill form / auto-sync from EduVault (future).
    • NLP extracts skills, certifications, projects.

  2. Internship Data Processing

    • Scrape/import internship descriptions.
    • Preprocess text → extract required skills, eligibility, domain.

  3. Recommendation Engine

    • Content-Based Filtering → Match student skills with internship skills (Cosine Similarity, TF-IDF, embeddings).
    • Collaborative Filtering → Learn from what similar students applied for.
    • Hybrid Model → Combine both for accuracy.

  4. Explainable AI Output

    • Internship recommendations ranked by match percentage.
    • Example:
      • Internship A → 92% match (Skills: Python, Data Analysis ✔️)
      • Internship B → 75% match (Missing: Cloud Computing ❌)

  5. Skill-Gap Analysis

    • If skills missing → recommend online courses (NPTEL, SWAYAM, Coursera).

4️. Core Tech Stack

  • Frontend: React / Next.js (clean UI, fast demo).
  • Backend: Python (Flask/FastAPI).
  • Database: PostgreSQL / MongoDB.
  • AI/ML:
    • NLP → spaCy, HuggingFace Transformers (for resume parsing + internship description parsing).
    • Recommendation System → Scikit-learn / PyTorch (hybrid filtering).
    • Vector Embeddings → Sentence-BERT for semantic matching.
  • Integration: API with PM Internship Portal (or demo with dummy dataset).

5️. Demo Flow for Judges

  1. Step 1: Student logs in → uploads resume.
  2. Step 2: System auto-extracts skills (NLP pipeline).
  3. Step 3: Student clicks “Find Internships.”
  4. Step 4: AI recommends Top 5 internships with % match.
    • Internship A (92% match ✅) → Required: Python, SQL (You have both).
    • Internship B (75% match ⚠️) → Missing: Cloud Computing.
  5. Step 5: “Skill Advisor” suggests → Enroll in NPTEL Cloud Computing Course.
  6. Step 6: Student can directly apply with 1 click (future integration).

6️. Value Proposition (Judges’ Angle)

  • For Students → Saves time, personalized, reduces mismatch.
  • For Govt (PM Internship Scheme) → Higher placement rate, better student engagement.
  • For Institutions → AI-driven career guidance, better placement metrics.

7️. USP (Unique Selling Points)

  • Explainable AI → Shows why a recommendation was made.
  • Skill-Gap Analysis → Adds actionable value beyond recommendations.
  • National Scale Ready → Directly pluggable into PM Internship Scheme portal.
  • Integration Friendly → Can merge with “Centralised Student Record Platform” (Problem 25093).

8️. Future Enhancements

  • Integration with DigiLocker for verified student data.
  • Use Reinforcement Learning → improve recommendations based on student feedback.
  • AI Chatbot Mentor → helps students choose wisely.
  • Blockchain Validation → verify internship completion certificates.

✅ Why This Wins:
Judges love AI/ML + real-world impact + alignment with Govt scheme. This project has technical depth + social relevance + scalability → strong winning potential.