Use SkLearn Pipelines to build Machine Learning Models
In a typical Machine Learning project a Data Scientist is expected to apply
different Data Transformations with
various Data Scaling techniques combining a
variety of Machine Learning algorithms
to dish out the best Machine Learning model for your data
Often the most important aspect of this process is the
ability to reproduce the same results and
optimise the time to evaluate
various combinations of Machine Learning models
Learning to use Pipelines from scikit-learn ( sklearn ) library in Python
gives you the edge to build a ton of such Machine Learning models in
a standardised,
well structured and
extremely easy method