r/kaggle

I’ve been writing a slow-release research arc on ASL recognition, and before any modeling, I wanted to actually look at Google’s Isolated Sign Language Recognition dataset the way it should’ve been looked at before every Kaggle winner reported 83% accuracy on it.

Notebook 00 of a nine-phase project: What does the Google ASL Signs data actually look like?

https://www.kaggle.com/code/truepathventures/parley-notebook-00-islr-eda

The sharp opinion, drawn from the EDA itself:

The Kaggle-default random 80/10/10 split — which every public winning solution used — puts the same signer’s clips in train, val, and test. That’s measuring how well the model memorizes each signer’s specific missing-landmark pattern, not how well it generalizes. Three numerical reasons:

1. Missing-landmark patterns are structural per-sign, not random. The sign × landmark-type heatmap shows clear one-hand-missing signatures for bilateral-handshape signs and face-adjacent signs. Fork the notebook and scroll to §3.

2. Median clip length varies 2×+ across the 21 signers. Fixed-length padding normalizes away signer-specific timing the model won’t see at inference.

3. Per-signer coverage of signs is high but not uniform. Leave-one-signer-out evaluation is feasible — the coverage histogram in §6 is how we know.

Recommended split: signer-holdout — 17 train / 2 val / 2 test. Notebook 01 (next month) quantifies the accuracy gap against random-split, with error bars across 3+ seeds.

This is notebook 1 of 9. Not a competition entry — a slow-release research project. Feedback welcome, especially from anyone who’s worked with ISLR before or runs signer-holdout evaluation in their own sign-language ML work.

Just submitted Social Friction Bench to the DeepMind AGI competition (Social Cognition track). Wanted to share the methodology since it’s a bit different from most benchmark entries.

The benchmark tests structurally informed social cognition — whether models override socially comfortable responses when safety requires it. Seven scenarios across grief, workplace, coercive control, addiction, and child abuse disclosure. LLM-as-judge with domain-specific rubrics grounded in professional standards (NCTSN, National DV Hotline, Evan Stark’s coercive control framework).

The finding worth discussing: humans baseline at 1.01/2.0 on coercive control detection (N=129, 6 countries). Same scenarios where smaller models fail. The failure mode isn’t AI-specific — it’s a shared gap in structural social knowledge.

A few things that might be interesting to the community:

•	Reasoning Parasitism: Gemini and ChatGPT named the benchmark dimensions when aware they were being tested, rather than responding authentically. V2 will control for this with blind vs. labeled presentation.

•	Thoroughness as failure mode: longer responses buried critical guidance and scored lower than brief correct ones

•	S3 (coercive control) produced the widest variance across all 33 models tested

Writeup:

https://kaggle.com/competitions/kaggle-measuring-agi/writeups/new-writeup-1773797633903

Benchmark:

kaggle.com/benchmarks/benjamynwilson/social-friction-bench

GitHub:

github.com/DataInfamous/social-friction-bench

Happy to discuss methodology, rubric design, or the human baseline approach.

Lately, it feels much harder to get any meaningful engagement or feedback on Kaggle notebooks.

Compared to earlier, the platform seems far less active, and discussions around notebooks are almost non-existent.

Is anyone else experiencing this? Has the engagement on Kaggle dropped, or am I missing something in how the platform is being used now?

I made a JEE Dataset, https://www.kaggle.com/datasets/samyakrajbayar/jee-mains-dataset-includes-2026-jan-attempt/data , pls upvole if u find it useful :)

HELP ME

Right now, I open the kaggle notebook and go through the code and understand the logic and then recreate the solution by myself! I'm noob here, in competitive programming. Am I doing things right? or is there a better way? My end goal is to get a good grasp on medical image analysis! And learn to use an agent to automate some part of the pipeline ( but that's not relevant to this.)

I’m working on my Google Data Analytics Capstone. I’m a Masters of Health Administration student and I am looking for data sets that involve healthcare data. Any suggestions?

Hello reddit community! I am a bioinfo student, my coding proficiency is okayish, I wanna add projects to my resume, I am not gonna claim that I entirely did that project, but, it's going to be a learning experience. Can I do that? or it's a demeaned thing? There are certain cool computer vision projects on kaggle, and I've been learning concepts, but I myself entirely cannot seem to complete the project and I don't have people in the vicinity who could help me complete it, not even my professors. Is that okay?

Hey everyone, I'm participating in a competition where the goal is to predict PM2.5 air quality concentration using Sentinel 5P satellite data (things like NO2, CO, ozone levels) and weather data across hundreds of cities. Competition starts in 4 days so I'm preparing ahead of time.

I want to make sure I'm thinking about the problem the right way before the data drops. Here's what I'd love input on:

1. When you look at a brand new dataset for the first time, what are you actually looking for? What's your thought process before writing any code?

2. How do you decide which features are worth building vs which ones are a waste of time?

3. For tabular data with both location and time dimensions (multiple cities, daily readings), what validation strategy keeps local scores trustworthy?

4. What's the most common mistake in competitions like this that silently kills your score without you realising?

5. What would you prioritise in the first 48 hours after the data drops?

Any advice appreciated, even on just one question. Thanks

I'll explain. I came across Kaggle and decided to relearn python from a different perspective. I haven't coded in years and the way I learned wasn't great because I skipped a lot of basics and when I couldn't understand something I just copy pasted code and hoped it worked. Which was often. So coming into Kaggle I hoped I could learn the basics and bare bones. Which leads me to my issue. There is nothing more infuriating than learning something new and having the module you're working on expect you to know what the fuck to do without clear instructions or even problems that were similar in the tutorial before the exercise. I really let it slide on the beginner's course but as I've gone through the stages I have to uncomment the solutions more often than not because of the asinine way that the lessons are structured. How in the ever loving fuck am I supposed to learn if I don't get all the information I need to practice and effectively complete something? Make the practice questions have the same information as the tutorials so I know that to do in certain problems don't just give me a set of problems that are solved one way and then give me another set of problems in the tutorial that are solved completely different like an asshole. It's a fucking tutorial. And for fuck's sake how fucking hard is it to have two sets of hands-on learning. This read and figure it out bullshit would make anyone not want to learn on Kaggle. The idea Kaggle has is solid and so is it's resources but the execution is brain dead. Make both fucking parts of the lesson interactive so that whoever is fucking learning can do it in a guided way and then make the tutorial be the quiz. This isn't the fucking 80s where you had to have note and go fuck yourself in figure it out in books. If you're gonna fucking do something do it right or not at all. These lessons are half-assed. The idea of free range and creativity in the notebooks is amazing but without all the necessary tools it feels like you're failing every time you ask for the solution. If I'm having to ask for the answer you're failing to teach. Also fuck your hints. if they aren't gonna fucking clearify anything just don't fucking have them there. I've had a better time learning from a glue eating 4 year old then these fucking notebooks. Their only saving grace is that they're free. Now, I'm done complaining so here's the key points:

-Interactive lessons and quizzes

-Full tools and information that will be used in both sections to maximize learning and lessen frustration in new users

-Give multiple problems and solutions that will be used in both sections so that the user can understand the information throughly

-Rather than asinine hints, clarify the question and expand on the outcome wanted so that it can be processed in various perspectives.

Ex. (Compare each element of the list to 2 (i.e. do an 'element-wise' comparison) and give us a list of booleans like [False, False, True, True]. Implement a function that reproduces this behaviour.) vs (Make a function that compares if an element is higher than the threshold given and loops every element in the list to return if that statement is true or false.)

Answer:

def elementwise_greater_than(L, thresh):

return [ele > thresh for ele in L]

Ta-fucking-da.

-Teaching isn't rocket science and neither is a clear and decent explanation. Putting things in this form makes it easier to visualize the outcome rather than "here's what I want figure it out".

Hey guys, I participated in a Kaggle hackathon where judging is based on a write-up, not a leaderboard score. But I’m confused.

A typical write-up is around 1000–1500 words, but Kaggle doesn’t have a single “write-up” field. Instead, it has sections like title, subtitle, card/thumbnail image, media gallery, and project description.

So I’m not sure—does all of this together count as the write-up, or am I supposed to put the full write-up in the “project description”? That section seems meant for a shorter summary.

I’m really confused.

I built and released a free dataset of 12,137 active Texas residential listings for 2026 — structured features (price, sqft, beds, baths, garage, year built) plus NLP-ready listing descriptions with PII redacted. Texas is the #1 volume real estate market in the US and there was nothing clean like this on Kaggle.

Will a submission still count towards the competition if it finishes running after the deadline but was submitted before the deadline?

Thank you

I'm encountering this error constantly when I try to create a new dataset. Earlier, I used to get this error but when I refreshed my dataset was created despite of it, but now I have tried 6-7 times already but nothing works. Does anyone know the fix?

Unexpected token '<', " <html><hea"... is not valid JSON

Unexpected token '<', " <html><hea"... is not valid JSON

https://preview.redd.it/il82z84rflvg1.png?width=819&format=png&auto=webp&s=a9da7f4e6641f1194522f2b888e77ab6fb02c50e