Book Image

Applying Math with Python

By : Sam Morley
Book Image

Applying Math with Python

By: Sam Morley

Overview of this book

Python, one of the world's most popular programming languages, has a number of powerful packages to help you tackle complex mathematical problems in a simple and efficient way. These core capabilities help programmers pave the way for building exciting applications in various domains, such as machine learning and data science, using knowledge in the computational mathematics domain. The book teaches you how to solve problems faced in a wide variety of mathematical fields, including calculus, probability, statistics and data science, graph theory, optimization, and geometry. You'll start by developing core skills and learning about packages covered in Python’s scientific stack, including NumPy, SciPy, and Matplotlib. As you advance, you'll get to grips with more advanced topics of calculus, probability, and networks (graph theory). After you gain a solid understanding of these topics, you'll discover Python's applications in data science and statistics, forecasting, geometry, and optimization. The final chapters will take you through a collection of miscellaneous problems, including working with specific data formats and accelerating code. By the end of this book, you'll have an arsenal of practical coding solutions that can be used and modified to solve a wide range of practical problems in computational mathematics and data science.
Table of Contents (12 chapters)

Using multilinear regression

Simple linear regression, as seen in the previous recipe, is excellent for producing simple models of a relationship between one response variable and one predictor variable. Unfortunately, it is far more common to have a single response variable that depends on many predictor variables. Moreover, we might not know which variables from a collection make good predictor variables. For this task, we need multilinear regression.

In this recipe, we will learn how to use multilinear regression to explore the relationship between a response variable and several predictor variables.

Getting ready

For this recipe, we will need the NumPy package imported as np, the Matplotlib pyplot module imported as plt, the Pandas package imported as pd, and an instance of the NumPy default random number generator created using the following commands:

from numpy.random import default_rng
rng = default_rng(12345)

We will also need the...