Book Image

Principles of Data Science - Second Edition

By : Sinan Ozdemir, Sunil Kakade, Marco Tibaldeschi
Book Image

Principles of Data Science - Second Edition

By: Sinan Ozdemir, Sunil Kakade, Marco Tibaldeschi

Overview of this book

Need to turn programming skills into effective data science skills? This book helps you connect mathematics, programming, and business analysis. You’ll feel confident asking—and answering—complex, sophisticated questions of your data, making abstract and raw statistics into actionable ideas. Going through the data science pipeline, you'll clean and prepare data and learn effective data mining strategies and techniques to gain a comprehensive view of how the data science puzzle fits together. You’ll learn fundamentals of computational mathematics and statistics and pseudo-code used by data scientists and analysts. You’ll learn machine learning, discovering statistical models that help control and navigate even the densest datasets, and learn powerful visualizations that communicate what your data means.
Table of Contents (17 chapters)
16
Index

Chapter 11. Predictions Don't Grow on Trees - or Do They?

Our goal in this chapter is to see and apply the concepts learned about in the previous chapters in order to construct and use modern learning algorithms in order to glean insights and make predictions on real datasets. While we explore the following algorithms, we should always remember that we are constantly keeping our metrics in mind.

In this chapter, we will be looking at the following machine learning algorithms:

  • Decision trees
  • Naive Bayes classification
  • k-means clustering

The first two are examples of supervised learning, while the final algorithm is an example of unsupervised learning.

Let's get to it!