[Kaggle](https://www.kaggle.com/) is a web platform that hosts data science competitions. Companies with data science problems to solve will host a competition and offer prize money for the winning solutions. You can even host your own competition if you'd like. If you need to [[publish a dataset]], you can do that on Kaggle. Kaggle also hosts a selection of courses covering beginning and advanced topics in data science and guides with resources and walk-throughs to learn new technologies. ## how to compete The first thing you need to know about Kaggle competitions--you do not need to win a Kaggle competition to get a job as a data scientist. Winning a competition, or even placing in the top few spots, requires many hours of dedicated time tweaking and optimizing your code, often simply to eke out an additional 0.1% accuracy, as you compete with others doing the same thing. Read one of the many blog posts from Kaggle winners to better understand the time and effort required to stay at the top. So, don't be afraid to get started with competitions because you don't think you'll place highly. First, **pick a competition**. Make sure there is plenty of time remaining so you can get the full benefit of working with the community and learning from others as the competition progresses. Sign up to get emails as new competitions are launched. **Read the overview section** closely to understand what the competition is about and if it interests you. **Check the data section** to see what you'll be working with. Next, **check out the discussion board and review the Q&A**. Throughout the course of the competition, many of the tips and tricks that will be used by winning submissions will be discussed in these forums. This is also the place to **find a team**. Look for a post where others are soliciting teams and team members. It's often against the rules to discuss the competition outside of these forums. Speaking of rules, **check the rules section** to find important rules for the competition and what you can and can't do with the provided datasets. You don't want to get kicked out of a competition or off the platform accidentally. **Review others' code** to see what's working so far in the code section. Sort by "Hotness" to see what's generating buzz or "Public Score" to see what's doing the best. Now it's time to **develop your model**. Use best practices in data science to explore the dataset, clean the dataset, ask questions, build models and evaluate results. You'll find that many competitions are won by ensembles of models. The publicly available test dataset can be used to test your models, but the final results will be based on a hidden, private test dataset so be careful of overfitting. **Submit your notebook** or solutions to check your score (note most competitions have a daily submission limit). Finally, check the leaderboards to see how your solution compares. You can typically select only one or two of your submissions to be assessed as your final solution in the competition. Once the competition closes, you'll be able to see your score against the private test dataset. That's the one that counts. ## load Kaggle datasets To load a Kaggle dataset outside of the Kaggle platform, use the Kaggle API. First, you must download a Kaggle API token (find instructions [here](https://www.kaggle.com/docs/api#authentication)). From the dataset you are interested in, click the **Download** button and select *Download via*: `kagglehub` to get the required code snippet. > [!Tip]- Additional Resources > - [Rob Mulla - Kaggle Competitions: A Beginner's Guide to Winning](https://youtu.be/4BOtr1PZ2D8?si=vvfcUmnhCx6ZCjL7)