# Kaggle Community Datasts Models
## Kaggle: Community Datasts Models
Join over 15M+ machine learners to share, stress test, and stay up-to-date on all the latest ML techniques and technologies. Discover a huge repository of community-published models, data & code for your next project.
Who's on Kaggle?
## **Datasets**
[**View all**](https://www.kaggle.com/datasets)
261K high-quality public datasets. Everything from avocado prices to video game sales.
\*\*[US Accidents (2016 - 2023)\*\* Usability **10.0** · 685 MB A Countrywide Traffic Accident Dataset (2016 - 2023)](https://www.kaggle.com/datasets/sobhanmoosavi/us-accidents)
\*\*[International football results from 1872 to 2023\*\* Usability **10.0** · 1 MB An up-to-date dataset of over 40,000 international football results](https://www.kaggle.com/datasets/martj42/international-football-results-from-1872-to-2017)
\*\*[Formula 1 World Championship (1950 - 2023)\*\* Usability **10.0** · 6 MB F1 race data from 1950 to 2023](https://www.kaggle.com/datasets/rohanrao/formula-1-world-championship-1950-2020)
\*\*[UNCOVER COVID-19 Challenge\*\* Usability 8.8 · 269 MB United Network for COVID Data Exploration and Research](https://www.kaggle.com/datasets/roche-data-science-coalition/uncover)
## code**Notebooks**
[**View all**](https://www.kaggle.com/code)
887K public notebooks and access to a powerful notebook environment with no cost GPUs & TPUs.
**Create Series & Pandas DataFrame**
Python
1000 upvotes · 2 comments
**ICR competition analysis and findings**
Python
485 upvotes · 54 comments[ICR - Identifying Age-Related Conditions](https://www.kaggle.com/competitions/icr-identify-age-related-conditions) +1
**Identifying Age-Related Conditions w/ TFDF**
Python
370 upvotes · 91 comments[ICR - Identifying Age-Related Conditions](https://www.kaggle.com/competitions/icr-identify-age-related-conditions)
**Markdown for Colab**
Python
288 upvotes · 3 comments
## tenancy**Models**
[**View all**](https://www.kaggle.com/models)
1,900 pre-trained, ready-to-deploy ML models.
\*\*[Segment Anything\*\* Meta Segment Anything Model (SAM) is a promptable segmentation system with zero-shot generalization to unfamiliar objects and images, without the need for additional training.](https://www.kaggle.com/models/metaresearch/segment-anything)
\*\*[flan-t5\*\* Google Scaling Instruction-Finetuned Language Models](https://www.kaggle.com/models/google/flan-t5)
\*\*[toxicity\*\* TensorFlow · Transformer Toxicity classifier trained on civil comments dataset.](https://www.kaggle.com/models/tensorflow/toxicity)
\*\*[resnet\_v2\*\* Google Imagenet (ILSVRC-2012-CLS) classification with ResNet V2 50.](https://www.kaggle.com/models/google/resnet-v2)
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