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The Data Science Workshop

The Data Science Workshop - Second Edition

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

Random State

The key to reproducing the same results is called random state. You simply specify a number, and whenever that number is used, the same results will be produced. This works because computers don't have an actual random number generator. Instead, they have a pseudo-random number generator. This means that you can generate the same sequence of random numbers if you set a random state.

Consider the following figure as an example. The columns are your random states. If you pick 0 as the random state, the following numbers will be generated: 41, 52, 38, 56…

However, if you pick 1 as the random state, a different set of numbers will be generated, and so on.

Figure 7.10: Numbers generated using random state

In the previous exercise, you set the random state to 0 so that the experiment was repeatable.

Exercise 7.02: Setting a Random State When Splitting Data

The goal of this exercise is to have a reproducible way of splitting...

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The Data Science Workshop
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