Book Image

Building Data Science Solutions with Anaconda

By : Dan Meador
5 (1)
Book Image

Building Data Science Solutions with Anaconda

5 (1)
By: Dan Meador

Overview of this book

You might already know that there's a wealth of data science and machine learning resources available on the market, but what you might not know is how much is left out by most of these AI resources. This book not only covers everything you need to know about algorithm families but also ensures that you become an expert in everything, from the critical aspects of avoiding bias in data to model interpretability, which have now become must-have skills. In this book, you'll learn how using Anaconda as the easy button, can give you a complete view of the capabilities of tools such as conda, which includes how to specify new channels to pull in any package you want as well as discovering new open source tools at your disposal. You’ll also get a clear picture of how to evaluate which model to train and identify when they have become unusable due to drift. Finally, you’ll learn about the powerful yet simple techniques that you can use to explain how your model works. By the end of this book, you’ll feel confident using conda and Anaconda Navigator to manage dependencies and gain a thorough understanding of the end-to-end data science workflow.
Table of Contents (16 chapters)
1
Part 1: The Data Science Landscape – Open Source to the Rescue
6
Part 2: Data Is the New Oil, Models Are the New Refineries
11
Part 3: Practical Examples and Applications

Clustering problems

In addition to anomaly detection, there is another class of problem that takes an unsupervised approach to trying to group entities together in order to understand more about the dataset. Clustering is the process of finding elements of a dataset that contain enough similar attributes that you can determine clear distinctions from among the individual points.

There are many applications of this technique, and we'll go over the following few examples now:

  • Grouping segments of a customer base
  • Knowing which emails are promotions and which are more important

To achieve this, we can use a few different algorithms such as the following:

  • DBScan
  • K-Means clustering

While there are many more, you can be sure that these have shown promising results across various datasets and are a great place to start.

Let's look at DBscan first.

DBScan

Density-Based Spatial Clustering of Applications with Noise (or DBScan for...