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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

Interpreting k-means Results

After training our k-means algorithm, we will likely be interested in analyzing its results in more detail. Remember, the objective of cluster analysis is to group observations with similar patterns together. But how can we see whether the groupings found by the algorithm are meaningful? We will be looking at this in this section by using the dataset results we just generated.

One way of investigating this is to analyze the dataset row by row with the assigned cluster for each observation. This can be quite tedious, especially if the size of your dataset is quite big, so it would be better to have a kind of summary of the cluster results.

If you are familiar with Excel spreadsheets, you are probably thinking about using a pivot table to get the average of the variables for each cluster. In SQL, you would have probably used a GROUP BY statement. If you are not familiar with either of these, you may think of grouping each cluster together and then...

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