Data Hackathon · Submission

Submit your entry

Attach your notebook (.ipynb), trained model (.joblib), requirements.txt, test features (test_x.csv), predictions, and metrics — preview them here, then submit for judging.

Before you upload

Submission format

The judging API actually runs your model against a hidden test set — get these right and it scores cleanly instead of coming back failed_run.

Model — .joblib only

Save it with the snippet below. It has to unpickle with the exact library versions listed in your requirements.txt — a version mismatch (e.g. training with one scikit-learn version, listing another) is the most common cause of a failed run.

joblib.dump(model, "model.joblib")

requirements.txt — only what you import, pinned

List only the packages your notebook actually imports — check with the command below — pinned to the exact version you trained with. Unrelated packages just slow down environment setup on the judge's side.

pip freeze | grep -iE "scikit-learn|numpy|pandas|joblib"

predictions.csv — your model's predictions on the test set

One row per test-set id, in the same row order as the test CSV above (or with your own id column matching it).

metrics.csv — your own self-reported evaluation

Whatever you measured locally on a held-out split — accuracy, precision, recall, f1, and anything else relevant (specificity, ROC-AUC, confusion-matrix counts, …). This is a sanity check for the admin against the judge's own run, not what determines your score.

The problem

Problem statement & Submission guideline

Read this before you start — it explains the columns in the test CSV and the prediction format expected.

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Event-time submission

Submission details

Choose what you're submitting, then attach your notebook and predictions below.

Team nameTeam / registration IDMembersInstitution

Choose an .ipynb notebook

Choose a model file (.joblib)

Choose a requirements.txt

Pinned dependencies the judging API builds your environment from before running your model.

Choose a test_x.csv (test features)

The test features your model runs on — sent to the judging API with your submission.

Choose a .csv file

Choose a metrics.csv

Your own self-reported evaluation of the model above — accuracy, precision, recall, f1, and anything else relevant. A sanity check for the admin, not what determines your score.