The Kaggle Blueprints
An article series analyzing Kaggle competitions’ winning solutions for lessons we can apply to our own data science projects
If you ask any successful Kaggler what tips they have to improve your data science skill set, they all have the same answer. They will tell you to study the top solutions of completed Kaggle competitions.
Kaggle is a platform for data science competitions for various types of problems. Competitors compete by building Machine Learning models and submitting their predictions. The competitor with the most accurate predictions takes home a prize.
Despite the competitive surrounding, the Kaggle community nurtures a mindset of learning. The platform itself encourages public sharing of approaches during and after the competitions.
As a result, a completed Kaggle competition is a pool of learning resources of state-of-the-art Machine Learning techniques.
We can differentiate between two types of resources:
- Approaches shared during the competitions (in form of discussions or Notebooks): resources showing a variety of different techniques to approach the problem
- Solutions shared after the competition deadline (in form of high-level write-ups and code on GitHub): resources showing which techniques worked well for the problem
While the resources in themselves are usually well structured, it may be challenging to navigate the number of resources to extract the relevant information after the competition has ended.
Thus, this article series reviews and summarizes the most popular and successful techniques used in Kaggle competitions. But it won’t review the exact solutions for the specific problem setting. Instead, we will analyze the Kaggle competition’s winning solutions and extract the “blueprints” for lessons we can apply to our data science projects.
… [W]e will analyze the Kaggle competition’s winning solutions and extract the “blueprints” for lessons we can apply to our data science projects.
If you don’t want to miss a new article in this series, you can subscribe for free to get notified whenever I publish a new story.
You can find the collection of articles in this series here:
The Kaggle Blueprints: Unlocking Winning Approaches
The Kaggle Blueprints
An article series analyzing Kaggle competitions’ winning solutions for lessons we can apply to our own data science projects
If you ask any successful Kaggler what tips they have to improve your data science skill set, they all have the same answer. They will tell you to study the top solutions of completed Kaggle competitions.
Kaggle is a platform for data science competitions for various types of problems. Competitors compete by building Machine Learning models and submitting their predictions. The competitor with the most accurate predictions takes home a prize.
Despite the competitive surrounding, the Kaggle community nurtures a mindset of learning. The platform itself encourages public sharing of approaches during and after the competitions.
As a result, a completed Kaggle competition is a pool of learning resources of state-of-the-art Machine Learning techniques.
We can differentiate between two types of resources:
- Approaches shared during the competitions (in form of discussions or Notebooks): resources showing a variety of different techniques to approach the problem
- Solutions shared after the competition deadline (in form of high-level write-ups and code on GitHub): resources showing which techniques worked well for the problem
While the resources in themselves are usually well structured, it may be challenging to navigate the number of resources to extract the relevant information after the competition has ended.
Thus, this article series reviews and summarizes the most popular and successful techniques used in Kaggle competitions. But it won’t review the exact solutions for the specific problem setting. Instead, we will analyze the Kaggle competition’s winning solutions and extract the “blueprints” for lessons we can apply to our data science projects.
… [W]e will analyze the Kaggle competition’s winning solutions and extract the “blueprints” for lessons we can apply to our data science projects.
If you don’t want to miss a new article in this series, you can subscribe for free to get notified whenever I publish a new story.
You can find the collection of articles in this series here: