The Most Renowned Tabular Competition – Porto Seguro’s Safe Driver Prediction
Learning how to reach the top on the leaderboard in any Kaggle competition requires patience, diligence, and many attempts to learn the best way to compete and achieve top results. For this reason, we have thought of a workbook that can help you build those skills faster by trying some Kaggle competitions of the past and learning how to reach the top of the leaderboard by reading discussions, reusing notebooks, engineering features, and training various models.
We start with one of the most renowned tabular competitions, Porto Seguro’s Safe Driver Prediction. In this competition, you are asked to solve a common problem in insurance and figure out who is going to have a car insurance claim in the next year. Such information is useful to increase the insurance fee for drivers more likely to have a claim and to lower it for those less likely to.
In illustrating the key insights and technicalities necessary for cracking this competition, we will show you the necessary code and ask you to study topics and answer questions found in The Kaggle Book itself. Therefore, without much more ado, let’s start this new learning path of yours.
In this chapter, you will learn:
- How to tune and train a LightGBM model
- How to build a denoising autoencoder and how to use it to feed a neural network
- How to effectively blend models that are quite different from each other
All the code files for this chapter can be found at Change to https://packt.link/kwbchp1
