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Outliers can overshadow other data points of a feature. That can negatively influence standard scaling.

Here's why you should use robust scaling to handle outliers.

Robust Scaling Summary Image: A tiny boat and a tall lighthouse.
Sometimes the default statistics provided by pandas describe() method are not enough.

Here's how you can generate custom statistics using the agg() method.

Image: Track and field with numbered tracks
Features with vastly different scales can lead to subpar machine learning models.

We must scale such features. Here’s how sklearn's Standard and MinMax scalers can help.

Image: Half Dome at Yosemite
Overfitted machine learning models can lead to serious performance issues in the real world.

Let's explore how you can stop overfitting dead in it's tracks and train your models with confidence.

Summary Image: Use ‘Train Test Split’ to Beat Overfitting
How can you apply different transformations to different columns of your dataset?

We'll explore a few ways and discuss why using ColumnTransformer is the best approach.

Summary Image: Transforming columns the right way using ColumnTransformer