Welcome to the official problem statement repository for Praxis 2.0, organized by GDG on Campus – GB Pant.
This repository contains the problem statements, rules, timelines, and submission expectations for the event.
Praxis 2.0 is a GenAI + Machine Learning innovation showcase where student teams design and build functional prototypes that address real-world challenges.
Teams are expected to deliver a working prototype, not just a concept.
A complete submission should demonstrate:
- A clear understanding of the problem
- Effective use of Machine Learning for analysis or prediction
- Meaningful use of Generative AI for reasoning, explanation, or insight generation
- Practical relevance and usability
Each team must submit the following:
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Public GitHub Repository
- Complete source code
- Clear README explaining the approach, architecture, and assumptions
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Demo Video
- Short walkthrough of the working prototype
- Explanation of the problem, solution, and key insights
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Live Link (if hosted)
- Web app, dashboard, or deployed service (optional but encouraged)
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Documentation
- Model details and evaluation metrics
- Explanation of how ML + GenAI are integrated
- Any ethical, bias, or limitation considerations
For each problem statement, a curated dataset will be provided to participants as a baseline.
Teams are expected to apply Machine Learning techniques for tasks such as prediction, forecasting, segmentation, classification, or pattern discovery, using appropriate evaluation metrics.
To support explanation, reasoning, summarization, and insight generation, teams may use Generative AI, including:
- Open-source large language models
- Free-tier APIs such as Gemini
The choice of tools, frameworks, or architectures is left open.
Submissions should demonstrate meaningful integration of ML + GenAI, where analytical outputs are translated into clear, human-understandable insights.
GenAI may be used in any form that improves reasoning, visualization or decision support.
Praxis 2.0 values:
- Thoughtful problem framing
- Sound technical reasoning
- Responsible use of AI
- Clear communication of insights
There is no single correct solution. Creativity and depth will be rewarded alongside technical rigor.
Good luck, and we look forward to seeing what you build 🚀