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

Summary

In this chapter, we learned about binary classification using logistic regression from the perspective of solving a use case. Let's summarize our learnings in this chapter. We were introduced to classification problems and specifically binary classification problems. We also looked at the classification problem from the perspective of predicting term deposit propensity through a business discovery process. In the business discovery process, we identified different business drivers that influence business outcomes.

Intuitions derived from the exploratory analysis were used to create new features from the raw variables. A benchmark logistic regression model was built, and the metrics were analyzed to identify a future course of action, and we iterated on the benchmark model by building a second model by incorporating the feature engineered variables.

Having equipped yourselves to solve binary classification problems, it is time to take the next step forward. In the...