"RANZCR 1st Place Soluiton Cls Model (small ver.)" by Qishen Ha:
Another underrated 1st place competition notebook: this well-structured work demonstrates a part of the 2-stage segmentation + classification approach that won the recent imaging challenge.
A narrated introduction to using the Biopython library on a genome dataset. Note the way in which the code is enriched with detailed explanations and interpretations.
This Notebook presents a visual exploration of a grim topic. A variety of charts are accompanied by their interpretations by the author, which help the reader to access the displayed information.
I'm well on track for my 500 @kaggle hours. Got a bit carried away in March, with a few free weekends. Some of this work isn't public yet, but will be soon.
Competition wise, things are going less well. I've joined a few comps late, but my results aren't anything to write home (or write tweets) about, so far. No teaming up yet, either.
I've learnt a few new tricks, though; especially for imaging data. Hoping to build on those.
Featuring great narration and well-crafted visuals, this excellent #rstats notebook based on the 2020 Kaggle Survey analyses its captivating title question from different angles.
"A Very Extensive Porto Exploratory Analysis" by @CaptCalculator:
A compact visual EDA and baseline model that deals with the challenges of anonymised features & imbalanced targets. Clear organisation helps the reader to navigate the feature set.
A compact work providing adversarial validation of the rainforest competition data together with interpretable Shapely values via GPU-powered #XGBoost in the @RAPIDSai framework.