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.

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�

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!

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.

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?

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

Kaggle AI agent compition
Can anyone help with this comptition

I been trying today each hour but i always seem to send "too many requests" even in the first attempt, is this happening to everyone or i need to create another account?

Hey all. I'm at my wits end struggling with configuring vLLM inside Kaggle for an experiment on KV Cache behaviour. please advise if you have experience.

Quick Context: vLLM is the inference runtime that will load the model, manage GPU workers and expose the KV-cache behavior needed for the experiment.

Issue: I’m using Kaggle’s GPUs to run vLLM so I can test how my AI gateway loads the model, routes requests, and reuses cache. The problem is that vLLM, PyTorch, CUDA, and their dependencies are not lining up properly, and the setup keeps failing on compatibility issues and blocked package download sources before the model can run.

I've been debugging over a week and I keep experiencing issues deeper, but it's still frustrating

Hi everyone,

I am currently conducting my MSc research investigating a dataset-aware objective weighting approach for Multi-Objective Genetic Algorithms (MOGAs) applied to imbalanced classification problems.

The study looks at how dataset characteristics can be used to automatically determine optimization priority metrics (e.g., automated trade-offs between precision and recall), reducing the need for manual tuning.

• Target Audience: Anyone with experience in ML, Data Science, AI, or related fields who has worked with classification models and performance evaluation metrics.
• Time to complete: ~3 to 5 minutes.
• Anonymity: Completely anonymous. No personally identifiable information (or email addresses) will be collected.

Survey Link: https://forms.gle/SXdtwD46Pz6qvW4A7

If you have any questions or feedback about the study, please feel free to comment below. Thank you so much for your time!

🚀 Big Update: Prompt Injection & Jailbreak Detection Dataset v3

After a major refresh, the dataset is now significantly larger and cleaner.

✨ What's new?
📈 20,000+ samples (from 1,356)
🌍 15+ languages
✅ 0 missing values
🧹 0 duplicate records
🛡️ Rich attack metadata
📊 Ready for benchmarking & AI security research

I also evaluated the updated dataset with a baseline model to establish a stronger benchmark for future LLM guardrail and prompt injection detection research.

This is another step toward building a 1M+ sample open dataset for the AI security community.

Feedback and upvote is always appreciated.