Get started with Kaggle and submit your first (good) solution
Kaggle is a fun platform that hosts a variety of data science and machine learning competitions, covering topics such as sports, energy, and autonomous driving.
In this post, we'll provide an overview of Kaggle and tackle the Titanic introductory challenge. We explain how we approach and solve such challenges and demonstrate it with the top 7% solutions from Titanic.
The complete code can be found on Github. Follow along as you read this article. You can also reproduce my exact score. In doing so, I'll follow some of what I consider Python best practices and use useful tools like mypy and poetry. That being said, let's get straight to the point.
Kaggle offers a variety of data science/machine learning competitions. See the introduction for an example. This is a great way to test and improve your data science/ML knowledge and learn hands-on how to solve problems. Additionally, you can also get a monetary price. However, Kaggle has some of the best data scientists and ML people, and the pricing is only given to a small number of top solutions (out of hundreds or thousands). So the winner here is…
