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Practical Discrete Mathematics

You're reading from   Practical Discrete Mathematics Discover math principles that fuel algorithms for computer science and machine learning with Python

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Product type Paperback
Published in Feb 2021
Publisher Packt
ISBN-13 9781838983147
Length 330 pages
Edition 1st Edition
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Authors (2):
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Ryan T. White Ryan T. White
Author Profile Icon Ryan T. White
Ryan T. White
Archana Tikayat Ray Archana Tikayat Ray
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Archana Tikayat Ray
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Toc

Table of Contents (17) Chapters Close

Preface 1. Part I – Basic Concepts of Discrete Math
2. Chapter 1: Key Concepts, Notation, Set Theory, Relations, and Functions FREE CHAPTER 3. Chapter 2: Formal Logic and Constructing Mathematical Proofs 4. Chapter 3: Computing with Base-n Numbers 5. Chapter 4: Combinatorics Using SciPy 6. Chapter 5: Elements of Discrete Probability 7. Part II – Implementing Discrete Mathematics in Data and Computer Science
8. Chapter 6: Computational Algorithms in Linear Algebra 9. Chapter 7: Computational Requirements for Algorithms 10. Chapter 8: Storage and Feature Extraction of Graphs, Trees, and Networks 11. Chapter 9: Searching Data Structures and Finding Shortest Paths 12. Part III – Real-World Applications of Discrete Mathematics
13. Chapter 10: Regression Analysis with NumPy and Scikit-Learn 14. Chapter 11: Web Searches with PageRank 15. Chapter 12: Principal Component Analysis with Scikit-Learn 16. Other Books You May Enjoy

Dataset

For this chapter, we will be using a dataset that contains technical specifications for different cars. This dataset is a modified version of the MPG_dataset.csv available here: https://www.kaggle.com/uciml/autompg-dataset. Some of the columns of the original dataset were removed since they are not relevant to this chapter.

The columns of the dataset are as follows:

  • mpg: Miles per gallon (continuous variable)
  • cylinders: Number of cylinders in the car (multi-valued discrete variable)
  • displacement: Combined volume of all the cylinders (continuous variable)
  • horsepower: Unit of power (continuous variable) – target/dependent variable
  • weight: Weight of the car (continuous variable)
  • acceleration: Acceleration of the car (continuous variable)

Let's say that we are trying to buy a car and our deciding factor is horsepower. However, we have access to values for all other variables (mpg, displacement, weight, and acceleration...

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