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)

Strategies for Dealing with Imbalanced Datasets

Now that we have identified the challenges of imbalanced datasets, let's look at strategies for combatting imbalanced datasets:

Figure 13.7: Strategies for dealing with imbalanced datasets

Collecting More Data

Having encountered an imbalanced dataset, one of the first questions you need to ask is whether it is possible to get more data. This might appear naïve, but collecting more data, especially from the minority class, and then balancing the dataset should be the first strategy for addressing the class imbalance.

Resampling Data

In many circumstances, collecting more data, especially from minority classes, can be challenging as data points for the minority class will be very minimal. In such circumstances, we need to adopt different strategies to work with our constraints and still strive to balance our dataset. One effective strategy is to resample our dataset to make the dataset...