Basic feature transformations

Instruction and application

In Progress

Basic feature transformations

Many feature engineering techniques depend on transforming data before it reaches the model. Scaling, standardisation and distribution-aware transforms help features behave more consistently and make patterns easier to learn.

In this activity, you will review those transformation techniques in Python through a guided Skillable lab.

What this lab covers

The lab focuses on practical preprocessing steps that appear repeatedly in feature engineering workflows.

Technique	Why it is useful
Standardisation	Centres variables around zero and gives them comparable scale, which helps gradient-based and distance-based models.
Normalisation	Rescales features into a fixed range when relative magnitude matters more than the original units.
Log or root transforms	Reduce skew, compress outliers and make long-tailed variables easier to model.
Power transforms	Improve symmetry and stabilise variance when simple log transforms are not enough.

Why transformations matter

Feature transformations are not just clean-up work. They directly influence model convergence, interpretability and how much signal the algorithm can extract from the data.

Exercise: Run the transformation lab

Launch the Skillable lab and work through the transformation techniques shown in this unit. Pay close attention to how the same feature behaves before and after each preprocessing step.

As you complete the lab, note which transformations improve interpretability, which reduce skew and which make the feature space easier for the model to learn.

Important access information

Please set aside 30 uninterrupted minutes before opening the lab. Your attempt expires after 30 minutes, and you are limited to a maximum of five attempts within 90 days from the first launch.