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)

Generating Synthetic Samples

In the previous section, we looked at the undersampling method, where we downsized the majority class to make the dataset balanced. However, when undersampling, we reduced the size of the dataset. In many circumstances, downsizing the dataset can have adverse effects on the predictive power of the classifier. An effective way to counter the downsizing of the dataset is to oversample the minority class. Oversampling is done by generating new synthetic data points similar to those of the minority class, thereby balancing the dataset.

Two very popular methods for generating such synthetic points are:

  • Synthetic Minority Oversampling Technique (SMOTE)
  • Modified SMOTE (MSMOTE)

The way the SMOTE algorithm generates synthetic data is by looking at the neighborhood of minority classes and generating new data points within the neighborhood:

Figure 13.15: Dataset with two classes

Let's explain the concept of generating...