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)

Comparing Different Dimensionality Reduction Techniques

Now that we have learned different dimensionality reduction techniques, let's apply all of these techniques to a new dataset that we will create from the existing ads dataset.

We will randomly sample some data points from a known distribution and then add these random samples to the existing dataset to create a new dataset. Let's carry out an experiment to see how a new dataset can be created from an existing dataset.

We import the necessary libraries:

import pandas as pd
import numpy as np

Next, we create a dummy data frame.

We will use a small dataset with two rows and three columns for this example. We use the function to create a data frame:

# Creating a simple data frame
df =[[1, 2, 3], [4, 5, 6]])

You should get the following output:

Figure 14.36: Sample data frame

What we will do next is sample some data points with the...