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  3. CIBMTR - Equity in post-HCT Survival Predictions
KaggleGeneral MLIntermediateRegistration closed

CIBMTR - Equity in post-HCT Survival Predictions

A intermediate general ml competition requiring roughly 15–45 hours for an end-to-end practice run.

Difficulty
Intermediate
Estimated effort
15–45 hours
Registration deadline
Feb 26, 2025
Compute
CPU only
OverviewPreparationSource

Overview

Who this competition fits

Intermediate learners who want to deepen their skills in general machine learning. Good for building practical experience with real-world data.

Read the original official blurb
Improve prediction of transplant survival rates equitably for allogeneic HCT patients

Preparation

From registration to a first submission

  1. 01

    Python

  2. 02

    scikit-learn or similar ML library

  3. 03

    data analysis with pandas

Before you commit: Requires solid machine learning fundamentals and good experimental methodology.

Source

How this page was assembled

Competition information is structured from the official page. Scores are platform estimates for decision support; official rules take precedence.

Official competition page
Kaggle
Last checked
Dec 4, 2024

Entry assessment

est.
36

Prepare before joining

Overall fit estimate

Beginner fit36/100
Learning value67/100

Confirm eligibility and time commitment before moving into formal preparation.

18+ · Location rules

Open official pageSign in to saveOpen guide

Timeline

Registration opens
Dec 4, 2024
Registration deadline
Feb 26, 2025
Submission deadline
Mar 5, 2025
Competition ends
Mar 5, 2025

Data and review state

Separate what is confirmed from what still needs review. Official rules and deadlines win.

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Source page verifiedMetric confirmedReviewed
Source published
Dec 4, 2024
Source created
Feb 19, 2024
Published on this site
Jul 18, 2026