Book Image

The Data Science Workshop - Second Edition

By : Anthony So, Thomas V. Joseph, Robert Thas John, Andrew Worsley, Dr. Samuel Asare
5 (1)
Book Image

The Data Science Workshop - Second Edition

5 (1)
By: Anthony So, Thomas V. 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)
Preface
12
12. Feature Engineering

2. Regression

Overview

This chapter is an introduction to linear regression analysis and its application to practical problem-solving in data science. You will learn how to use Python, a versatile programming language, to carry out regression analysis and examine the results. The use of the logarithm function to transform inherently non-linear relationships between variables and to enable the application of the linear regression method of analysis will also be introduced.

By the end of this chapter, you will be able to identify and import the Python modules required for regression analysis; use the pandas module to load a dataset and prepare it for regression analysis; create a scatter plot of bivariate data and fit a regression line through it; use the methods available in the Python statsmodels module to fit a regression model to a dataset; explain the results of simple and multiple linear regression analysis; assess the goodness of fit of a linear regression model; and apply linear regression analysis as a tool for practical problem-solving.