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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

3. Binary Classification

Overview

In this chapter, we will be using a real-world dataset and a supervised learning technique called classification to generate business outcomes.

By the end of this chapter, you will be able to formulate a data science problem statement from a business perspective; build hypotheses from various business drivers influencing a use case and verify the hypotheses using exploratory data analysis; derive features based on intuitions that are derived from exploratory analysis through feature engineering; build binary classification models using a logistic regression function and analyze classification metrics and formulate action plans for the improvement of the model.

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The Data Science Workshop
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