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)

Splitting Data

You will learn more about splitting data in Chapter 7, The Generalization of Machine Learning Models, where we will cover the following:

  • Simple data splits using train_test_split
  • Multiple data splits using cross-validation

For now, you will learn how to split data using a function from sklearn called train_test_split.

It is very important that you do not use all of your data to train a model. You must set aside some data for validation, and this data must not have been used previously for training. When you train a model, it tries to generate an equation that fits your data. The longer you train, the more complex the equation becomes so that it passes through as many of the data points as possible.

When you shuffle the data and set some aside for validation, it ensures that the model learns to not overfit the hypotheses you are trying to generate.

Exercise 6.01: Importing and Splitting Data

In this exercise, you will import data from a...