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 Addressing High-Dimensional Datasets

In Activity 14.01, Fitting a Logistic Regression Model on a High-Dimensional Dataset, we witnessed the challenges of high-dimensional datasets. We saw how the resources were challenged when the replication factor was 300. You also saw that the notebook crashes when the replication factor is increased to 500. When the replication factor was 500, the number of features was around 750,000. In our case, our resources would fail to scale up even before we hit the 1 million mark on the number of features. Some modern-day datasets sometimes have hundreds of millions, or in many cases billions, of features. Imagine the kind of resources and time it would take to get any actionable insights from the dataset.

Luckily, we have many robust methods for addressing high-dimensional datasets. Many of these techniques are very effective and have helped to address the challenges raised by huge datasets.

Let's look at some of the techniques...