"Analyzing Editors Descriptions | Hidden Gems" by Thomas Konstantin:
Time to get meta: This work explores the Hidden Gems series, specifically the text and sentiment of my reviews, with great ideas, narration, and structure. And now it contains itself.
"Evolution of social complexity: A data analysis" by Muskan Jain:
This Notebook studies the changes in social complexity over time. It applies external diagrams to great effect, and features detailed interpretations of its findings.
"Tweet Sentiment Extraction Pytorch" by Aditi Dutta:
A well-described intro to sentiment classification with RoBERTa and huggingface transformers. Note the detailed explanations on model architecture and preprocessing.
"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.
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.