Book Image

The Data Science Workshop

By : Anthony So, Thomas V. Joseph, Robert Thas John, Andrew Worsley, Dr. Samuel Asare
Book Image

The Data Science Workshop

By: Anthony So, Thomas V. Joseph, Robert Thas John, Andrew Worsley, Dr. Samuel Asare

Overview of this book

You already know you want to learn data science, and a smarter way to learn data science is to learn by doing. The Data Science Workshop focuses on building up your practical skills so that you can understand how to develop simple machine learning models in Python or even build an advanced model for detecting potential bank frauds with effective modern data science. You'll learn from real examples that lead to real results. Throughout The Data Science Workshop, you'll take an engaging step-by-step approach to understanding data science. You won't have to sit through any unnecessary theory. If you're short on time you can jump into a single exercise each day or spend an entire weekend training a model using sci-kit learn. It's your choice. Learning on your terms, you'll build up and reinforce key skills in a way that feels rewarding. Every physical print copy of The Data Science Workshop unlocks access to the interactive edition. With videos detailing all exercises and activities, you'll always have a guided solution. You can also benchmark yourself against assessments, track progress, and receive content updates. You'll even earn a secure credential that you can share and verify online upon completion. It's a premium learning experience that's included with your printed copy. To redeem, follow the instructions located at the start of your data science book. Fast-paced and direct, The Data Science Workshop is the ideal companion for data science beginners. You'll learn about machine learning algorithms like a data scientist, learning along the way. This process means that you'll find that your new skills stick, embedded as best practice. A solid foundation for the years ahead.
Table of Contents (18 chapters)

ML Pipeline for Spot-Checking Multiple Models

Implementing data science projects is predominantly an iterative process. One critical decision point in the data science life cycle is determining what model to try in what scenario. This decision of what model to use in what scenario is arrived at after different experiments with multiple models. This process is called spot-checking models.

Spot-checking models is quite a laborious process. We have to experiment with multiple models and different permutations of model parameters until we can find the best model. The final selection of the model is based on its performance on the test set. All these processes are quite time-consuming when implemented individually.

ML pipelines can be used to make this process easy to implement. We will see this process in action in the next exercise, where we will do the spot-checking of four different models.

Exercise 16.05: Spot-Checking Models Using ML Pipelines

In the previous exercises...