"COVID-19 World Vaccination Progress" by Ivanna Chovhan:
A well organised Notebook which demonstrates how readability is drastically improved through section headers, compact code, and nuggets of interpretation and narration that accompany visuals.
"Forecast with N-BEATS || Interpretable model" by Gaétan Dubuc:
This work presents a detailed introduction on a neural network time series forecasting method, complete with applied examples. Note the clean structure and helpful visuals.
"World Happiness Index Report" by Madhan Chandrasekharan:
This Notebook assembles a large set of diverse visualisations to study happiness indices in a global context; in particular the various heatmaps, panel layouts, and density plots.
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.
"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.
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.