AI Smart Internship Allocation Engine

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  Results after allocation in VS code

Inspiration

We wanted to solve the pain point of industries spending hours manually screening candidates. With AI, matching the right talent to the right opportunity becomes faster, fairer, and more accurate.

What it does

It automatically matches candidates to internships/jobs based on their skills, qualifications, and preferences, while ensuring fairness and scalability for industries.

How we built it

We used Python with ML-based matching logic, CSV datasets for candidates & opportunities, and a prototype front-end to demonstrate seamless allocation.

Challenges we ran into

Integrating multiple criteria (skills, preferences, fairness) into a single matching engine was tricky. We also faced time constraints and limited coding resources.

Accomplishments that we're proud of

We built a working prototype that showcases smart, automated allocation. It demonstrates how AI can make recruitment more efficient and unbiased.

What we learned

We learned how to combine AI/ML techniques with practical industry requirements, and how to quickly turn an idea into a working hackathon-ready prototype.

What's next for AI Smart Internship Allocation Engine

We plan to enhance the model with real-world datasets, build a full-fledged dashboard for industries, and improve fairness metrics to ensure inclusive opportunities.

Built With

• csv
• pandas
• python
• rainbowcsv
• scikit-learn
• streamlit
• vscode