Book Image

The Data Science Workshop

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

The Data Science Workshop

By: Anthony So, Thomas V. Joseph, Robert Thas John, Andrew Worsley, Dr. Samuel Asare

Overview of this book

You already know you want to learn data science, and a smarter way to learn data science is to learn by doing. The Data Science Workshop focuses on building up your practical skills so that you can understand how to develop simple machine learning models in Python or even build an advanced model for detecting potential bank frauds with effective modern data science. You'll learn from real examples that lead to real results. Throughout The Data Science Workshop, you'll take an engaging step-by-step approach to understanding data science. You won't have to sit through any unnecessary theory. If you're short on time you can jump into a single exercise each day or spend an entire weekend training a model using sci-kit learn. It's your choice. Learning on your terms, you'll build up and reinforce key skills in a way that feels rewarding. Every physical print copy of The Data Science Workshop unlocks access to the interactive edition. With videos detailing all exercises and activities, you'll always have a guided solution. You can also benchmark yourself against assessments, track progress, and receive content updates. You'll even earn a secure credential that you can share and verify online upon completion. It's a premium learning experience that's included with your printed copy. To redeem, follow the instructions located at the start of your data science book. Fast-paced and direct, The Data Science Workshop is the ideal companion for data science beginners. You'll learn about machine learning algorithms like a data scientist, learning along the way. This process means that you'll find that your new skills stick, embedded as best practice. A solid foundation for the years ahead.
Table of Contents (18 chapters)


In the previous chapter, Chapter 12, Feature Engineering, where we dealt with data points related to dates, we were addressing scenarios pertaining to features. In this chapter, we will deal with scenarios where the proportions of examples in the overall dataset pose challenges.

Let's revisit the dataset we dealt with in Chapter 3, Binary Classification, in which the examples pertaining to 'No' for term deposits far outnumbered the ones with 'Yes' with a ratio of 88% to 12%. We also determined that one reason for suboptimal results with a logistic regression model on that dataset was the skewed proportion of examples. Datasets like the one we analyzed in Chapter 3, Binary Classification, which are called imbalanced datasets, are very common in real-world use cases.

Some of the use cases where we encounter imbalanced datasets include the following:

  • Fraud detection for credit cards or insurance claims
  • Medical diagnoses where we...