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The Data Science Workshop

The Data Science Workshop

By : Anthony So , Thomas Joseph, Robert Thas John, Andrew Worsley , Dr. Samuel Asare
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The Data Science Workshop

The Data Science Workshop

3 (2)
By: Anthony So , Thomas Joseph, Robert Thas John, Andrew Worsley , Dr. Samuel Asare

Overview of this book

Where there’s data, there’s insight. With so much data being generated, there is immense scope to extract meaningful information that’ll boost business productivity and profitability. By learning to convert raw data into game-changing insights, you’ll open new career paths and opportunities. The Data Science Workshop begins by introducing different types of projects and showing you how to incorporate machine learning algorithms in them. You’ll learn to select a relevant metric and even assess the performance of your model. To tune the hyperparameters of an algorithm and improve its accuracy, you’ll get hands-on with approaches such as grid search and random search. Next, you’ll learn dimensionality reduction techniques to easily handle many variables at once, before exploring how to use model ensembling techniques and create new features to enhance model performance. In a bid to help you automatically create new features that improve your model, the book demonstrates how to use the automated feature engineering tool. You’ll also understand how to use the orchestration and scheduling workflow to deploy machine learning models in batch. By the end of this book, you’ll have the skills to start working on data science projects confidently. By the end of this book, you’ll have the skills to start working on data science projects confidently.
Table of Contents (16 chapters)
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Preface
12
12. Feature Engineering

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...

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