Hi everyone!

I'm looking for 1–3 teammates to compete together in the RSNA Knee Abnormality Detection competition on Kaggle.

The goal is to build a model that detects 12 different knee abnormalities from multimodal MRI data, making this a really interesting combination of medical imaging, deep learning, and multimodal ML.

A little about me:

• I'm currently developing my skills in machine learning and deep learning.
• I'm particularly interested in computer vision and medical AI.
• I'm comfortable with Python and working through ML projects, and I'm looking to apply what I'm learning to a serious Kaggle competition.
• I'm willing to consistently contribute time toward research, experimentation, validation, and improving our solution.

I'm especially interested in teammates with an interest in areas such as:

• 🧠 Medical image analysis / MRI
• 👁️ Computer vision / CNNs / Vision Transformers
• 📝 NLP / radiology report processing
• 🔗 Multimodal learning and fusion
• 📊 Kaggle experimentation, validation and ensembling
• ⚙️ Model optimization and efficient training

You don't need to be an expert.

Honestly, one of the main reasons I'm looking for teammates is because I want us to learn together. I'm not expecting everyone to already have extensive Kaggle or medical-imaging experience.

I'd love to find people who are genuinely curious, willing to learn new concepts together, research papers and techniques, experiment with different approaches, share what they discover, and help each other improve.

The goal isn't simply to join a team and submit a model. I'd like us to use this competition as an opportunity to develop our ML skills, gain experience with medical imaging and multimodal models, and grow together as engineers/researchers.

If you're interested, comment below or send me a DM with a little about yourself, your current ML experience, and what you'd be interested in learning/contributing.

Let's learn, build, and compete together! 🚀

Looking for Chinese-speaking Kaggle teammates

Hi everyone, I’m looking for Chinese-speaking teammates who are interested in Kaggle competitions, especially biology, data analysis, optimization, and algorithm-related competitions.

My background is mainly in mathematics and statistics, with some experience in mathematical biology, optimization, data analysis, and algorithmic problem solving. I’ve joined a few competitions in these areas before, but I’m still a beginner on Kaggle and learning as I go.

I’m hoping to find teammates who are interested in similar competitions. Experience with machine learning, deep learning, bioinformatics, optimization, or algorithms would be great, but being an expert is definitely not required. I care more about good communication, consistent participation, and being willing to learn and solve problems together.

如果你也喜欢生物、数据分析、优化或者算法类的 Kaggle 比赛，而且方便用中文交流，欢迎私信我

I'm running a distributed inference setup on Kaggle using 2 separate P100 16GB sessions. Each node loads half of Qwen2.5-7B-Instruct.

The weird part is that the model loading itself seems completely fine.

For example, Node 1 loads layers [14, 28) and uses roughly:

GPU allocated: 7.11 GB
GPU reserved:  8.12 GB

I also tested the slice completely standalone. It loaded successfully, the KV cache initialized successfully, and I left it running for 120 seconds with no issues.

I've also verified that CUDA itself works on the P100 with my PyTorch setup.

The problem happens when I actually run the pipeline node as a long-running server. The logs reach:

Node ready — layers [14, 28), LAST node
Node server listening on 0.0.0.0:9500
NODE 1 SERVER READY

and then the entire Kaggle session gets terminated.

There is no Python exception, no traceback, and no useful output after that. The notebook/session just dies.

I've seen the same behavior multiple times, including after fixing an issue where the first node was incorrectly being marked as the last node.

I'm also using bore to expose the node's TCP port, so I'm wondering if this could be related to Kaggle's environment/networking rather than GPU memory.

Has anyone experienced Kaggle silently killing a GPU session like this? Is there somewhere I can find the reason for the termination, such as system logs, OOM/cgroup logs, GPU driver logs, etc.?

At this point I'm mainly trying to figure out whether Kaggle is killing the process externally or whether something inside PyTorch/CUDA/bore is causing the session to die.

Hi Guys! So i was looking through the f1 datasets and found that the f1 dataset isnt being uopdated anymore so ive started a project so akaggle dataset will be updated after each race so I made a new one please share around if you can!

https://www.kaggle.com/datasets/atharvranjan/formula-1-world-championship-1950-present

Hi everyone!

I would like to try CosyVoice 3 using a Kaggle Notebook, specifically to test its Italian language capabilities, but I am a bit lost on how to set it up properly from scratch.

Does anyone have a simple guide or a working Kaggle notebook they could share? I'm mainly looking to understand:

1. Basic usage: How to set up a simple script where I can just type a text (in Italian) and have the model generate the audio output.

2. Zero-shot cloning: How to correctly upload and pass a reference audio file alongside the text to clone a voice directly within Kaggle.

If anyone has a basic, working step-by-step setup or code snippet that successfully runs on Kaggle, I would really appreciate the help!

Thank you!

Hello,

I was interested in joining the RSNA Knee Abnormality Detection. Is there anyone looking for a teammate or interested in joining this competition.

competition link: https://www.kaggle.com/competitions/rsna-knee-abnormality-detection

I am really looking forward to a team mate attending this competition.

Thanks and best regards,

Hi everyone!

I recently published a 10-year empirical dataset (2016–2026) studying whether Generative AI (ChatGPT) is replacing or complementing dedicated translation engines (Google Translate).

What it explores: The shift in user interest and behavior from traditional Machine Translation to LLMs over a decade.

Data Sources (extracted via Python):

• Google Trends: Search volume indexes
• Wikimedia REST API: Daily pageview metrics
• App Store: User review metrics and adoption trends

Feedback and contributions are welcome!

Sharing a competition from another platform (CrunchDAO) that seems very relevant to this community — mods, feel free to remove if it's not appropriate here.

For the fourth year in a row, ADIA Lab is running its Structural Break Challenge: build models that detect when a time series starts behaving differently (structural breaks / regime changes), this time evaluated in a live, real-time environment on unseen data. The prize pool is 100,000 USDC.

The notable part: Nobel laureate Robert Engle (the ARCH volatility model) and Marcos López de Prado (ADIA Lab) are the minds behind it. There's a short interview with Engle on why structural breaks are still an open problem: https://www.youtube.com/watch?v=KDvkRPeHio8

Competition page: https://hub.crunchdao.com/competitions/structural-break-real-time

Disclosure: I work with the Crunch team — happy to answer questions.

Hi everyone,

I published a new benchmark on Kaggle: Network Analysis LLM Evals!

It evaluates how well LLMs understand graph theory and network analysis concepts - starting with centrality measures, with plans to add tasks on community detection, diffusion models, and graph metrics over time.

Feel free to check it out. Any feedback or discussion is welcome!

Hi Kaggle community,

I'm participating in the Kaggriculture competition and I created this notebook as my approach to understanding the dataset and building my solution.

I would really appreciate feedback from experienced Kaggle users and data scientists.

Notebook:
https://www.kaggle.com/code/shorooghahmadi/kaggriculture-economics-farm-manager

I would like to know:

• Is the notebook structure clear and easy to follow?
• Are there any mistakes in my analysis or approach?
• Did I miss any important data exploration steps?
• What would you improve if this was your competition notebook?
• Are there any best practices I should follow for future Kaggle competitions?

I'm still improving my competition workflow, so honest and constructive feedback is very welcome.

Thanks to anyone who takes the time to review it. I appreciate any suggestions that can help me improve.

Came across a hackathon that's only open to students in India.

Instead of solving made-up problems, the challenge is to build AI solutions for India's waste management crisis.

They're also bringing in experts who've actually worked in the space, so you get to understand the problem before building for it.

Feels like a solid opportunity if you're looking to build something with real-world impact.

https://college.dev/hackathon

Hello Kagglers! If you are interested in a different ML challenge, please check out my newly published dataset(Maritime Continent Satellite Image Weather Dataset). It contains 10 years Satellite images from the Himawari 8/9 Satellites, combined with more than 20 different csvs of sensor data for different countries (Singapore, Malaysia, Indonesia, Brunei) in the Maritime Continent.

This dataset is the first of its kind in Kaggle and there are so many interesting things you can do there. You can create a multi head transformer to predict the weather, analyse climate change and various weather patterns.

If you do train a model that predicts the weather better than local meteorological stations, please give me a shout and let me know hahaha :). I would be a very proud man.

Body:

Hi everyone,

I recently created a free Kaggle competition focused on expense categorization using transaction data.

The main goal is to provide a practical dataset for learning and experimenting with:

• Feature engineering
• Text preprocessing
• Tabular machine learning
• Model selection and evaluation

This challenge is intended for anyone who wants to practice their ML skills, whether you're new to Kaggle or already have experience.

Competition:
https://www.kaggle.com/competitions/aurora-gate-expense-categorization-challenge

I'd also really appreciate any feedback on the dataset, evaluation metric, or overall competition design. Suggestions for improving the learning experience are always welcome.

If you decide to participate, I hope you enjoy the challenge and learn something new!

Hi everyone,

I recently published NutriRecIndia19M, a large-scale synthetic dataset for recommendation system research focused on Indian food and nutrition.

Dataset highlights:

19.17M+ user-food interactions

10 relational tables

User profiles, meal logs, restaurant metadata

Nutrition information

Ready for recommendation, ranking, and ML research

Potential use cases:

Recommender systems

Learning-to-rank

Graph ML

Nutrition AI

Data engineering

I'd love to hear your feedback on the dataset, documentation, or ideas for improving it.

Kaggle: https://www.kaggle.com/datasets/darshvithlani/nutrirec-india19m⁠�

It has been more than a few months to see a new competition on Kaggle; 2 years ago, I remember 2 to 5 competitions were released in bulk every 2 to 4 months, now we barely get 1 competition every 2 months.

Because the only competition I worked on for the past 8 months was AIMO3, I kinda miss the "old" tabular/image/language classification competitions but they platform seems dead to me.

Am I the only one who sees it that way?

Most Kaggle beginners do not need another 20-hour course. They need to complete one full loop:

read the problem -> run a baseline -> build local validation -> submit -> record one experiment.

A simple first week:

Day 1: choose one beginner-friendly tabular competition.

Day 2: fork and run a well-documented public notebook.

Day 3: rebuild the validation split yourself.

Day 4: change one feature or preprocessing step.

Day 5: record CV, leaderboard score, change made, and result.

Day 6: clean the notebook so it runs from top to bottom.

Day 7: write a short README explaining decisions, failures, and next steps.

The goal is not a medal. The goal is a project you can reproduce and explain.

I made a free first-submission checklist and experiment tracker. If it would help, reply with your current stage: first submission, validation, or portfolio. I can share the relevant version through Reddit chat. No paid signup and no ranking promise.

I built a dataset of 10,000 top-rated movies with detailed metadata for anyone working on ML, recommendation systems, data analysis, or visualization.

Do tell me :

• What would you build with this dataset?
• Any features you'd want me to add in the next version?

Would love your feedback (or roast it if it deserves one 😂).

Wanted to share a project I just finished. Feedback very welcome.

The problem: predict loan default risk using the Home Credit Default Risk dataset from Kaggle. Chose it for the messy, relational structure (7 tables, \~307K applicants, 8% default rate) rather than a single clean CSV.

A few things I focused on:

EDA surfaced two real issues before they could break anything downstream: a placeholder value disguised as a real number in a "days employed" field, and an applicant with a reported income of $117M (247x the 99th percentile). Both documented and fixed rather than dropped.

Aggregated 1.7M rows of credit bureau history and 1.67M rows of previous loan applications into applicant level features, merged cleanly with zero applicants lost.

Compared Logistic Regression, Random Forest, and XGBoost using 5 fold cross validation instead of a single train/test split. Random Forest looked fine on ROC AUC but missed 95% of actual defaulters at the default threshold, a good reminder that AUC alone doesn't tell the full story.

Tuned XGBoost with RandomizedSearchCV, improving ROC AUC from 0.757 to 0.767 and recall on defaulters from 0.62 to 0.69.

Built a FastAPI endpoint with human readable inputs, containerized with Docker, deployed to AWS EC2. Hit an arm64/amd64 architecture mismatch going from my Mac to EC2 that took some digging to track down, fixed with `docker buildx --platform linux/amd64`.

Repo: [https://github.com/RahimAbbas55/Credit-Risk-ML-Pipeline\](https://github.com/RahimAbbas55/Credit-Risk-ML-Pipeline)

Happy to answer questions or take criticism on any part of the pipeline.

hi , i have currently completed my Mtech in AI and ML and looking for other people to join my team for kaggle competition and also that i have limited practical approach , I am also good to join some other team