Feature Engineering Techniques
How can I perform better by engineering features?
Feature Engineering is a crucial step when you’re building machine learning models. It’s not always about what model you used but what type of data you feed into your data. In order to build successful models, you must be familiar with a GIGO (Garbage in, garbage out). This means the quality of the output depends on the quality of the input. With bad data, applications with underlying AI and Machine learning algorithms will produce results that are inaccurate, incomplete, or incoherent.
In this workshop, we’ll be exploring fundamental feature engineering techniques that you’ll be using to improve your model performance. Come out, learn something new, and apply your new knowledge to your models. CU you there!
Kaggle Notebook from the presentation: