🌌 India High School Exoplanet Data Challenge — Uncover New Worlds with AI

How do astronomers find planets light-years away?

They don't see them directly. They watch starlight — and look for the faint, telltale dips that betray a planet passing in front of its star.

Now, it's your turn.

We invite high school students across India to step into the role of an astrophysicist and data scientist. Using a curated dataset sourced directly from the NASA Exoplanet Archive, your mission is to build a machine learning classification model that separates real exoplanet candidates from noise and false signals — the exact kind of problem professionals at NASA and research institutions tackle every day.

🚀 The Mission

You'll receive a starter CSV file with real observational data. From there, it's on you:

🔍 Explore — understand distributions, correlations, and anomalies

🧹 Clean — handle missing values and class imbalance thoughtfully

🤖 Model — apply classification algorithms of your choice (Random Forest, XGBoost, Neural Networks — your call)

📊 Interpret — explain what your model learned and why it matters

No astrophysics background required. Just curiosity, code, and commitment :)

🎯 Why This Challenge Exists

Most student competitions give you toy datasets and synthetic problems. This one gives you real data from a real space telescope.

We designed this challenge to bridge the gap between classroom learning and genuine scientific work — and to give students a project that belongs on a college application, a research portfolio, or a GitHub that actually stands out.

This is applied AI for space science, and you don't have to wait until university to do it.

⏱️ Duration: 1.5 months of self-paced, hands-on work.

🏆 Prizes & Recognition

🥇 Top Solutions Published — Winning models and write-ups featured on our platform's newsletter and shared with the STEM community

📜 Certificates of Excellence — Awarded to all top-scoring participants

🎓 Academic Credibility — A real, portfolio-ready project backed by NASA data — the kind that stands out to colleges and research programs

🤩Something exciting — To be soon announced!!

👥 Eligibility

📍 Open to high school students (Grades 9–12) across India

👤 Solo participation or teams of up to 3 members

🆓 Free to enter

• Model Performance (35%)
  F1-score, balance of precision and recall, and overall generalisability.
• EDA & Data Handling (20%)
  Strategies for missing values and class imbalance, plus clear visual insights.
• Feature Engineering (20%)
  Domain-aware derived features and creative feature combinations.
• Explainability (15%):
  Feature importance and plain-language interpretation.
• Write-up Quality (10%)
  Clarity, structure, and reproducibility.