A Practical Guide to Feature Engineering in Python

Now that we understand what feature engineering is, let's go straight into the practical aspect of this article. The first is the Loan Default Prediction dataset hosted on Zindi by Data Science Nigeria, and the second -- also hosted on Zindi -- is the Sendy Logistics dataset by Sendy. You can find the descriptions of the dataset and the corresponding machine learning tasks in the links above. If you have cloned the repo, you'll have a folder of the datasets and the notebook used for this article and can follow along easily. First, let's import some libraries and the datasets: We can see that the loan dataset has three tables.