Book Image

Mastering Numerical Computing with NumPy

By : Umit Mert Cakmak, Tiago Antao, Mert Cuhadaroglu
Book Image

Mastering Numerical Computing with NumPy

By: Umit Mert Cakmak, Tiago Antao, Mert Cuhadaroglu

Overview of this book

NumPy is one of the most important scientific computing libraries available for Python. Mastering Numerical Computing with NumPy teaches you how to achieve expert level competency to perform complex operations, with in-depth coverage of advanced concepts. Beginning with NumPy's arrays and functions, you will familiarize yourself with linear algebra concepts to perform vector and matrix math operations. You will thoroughly understand and practice data processing, exploratory data analysis (EDA), and predictive modeling. You will then move on to working on practical examples which will teach you how to use NumPy statistics in order to explore US housing data and develop a predictive model using simple and multiple linear regression techniques. Once you have got to grips with the basics, you will explore unsupervised learning and clustering algorithms, followed by understanding how to write better NumPy code while keeping advanced considerations in mind. The book also demonstrates the use of different high-performance numerical computing libraries and their relationship with NumPy. You will study how to benchmark the performance of different configurations and choose the best for your system. By the end of this book, you will have become an expert in handling and performing complex data manipulations.
Table of Contents (11 chapters)

Independent and dependent variables

As we mentioned in the previous subsection, linear regression is used to predict a value of a variable based on other variables. We are investigating the relationship between input variables, X and the output variable, Y.

In linear regression, dependent variable is the variable that we want to predict. The reason that we call it the dependent variable is because of the assumption behind linear regression. The model assumes that these variables depend on the variables that are on the other side of the equation, which are called independent variables.

In simple regression model, model will explain how the dependent variable changes based on independent variable.

As an example, let's imagine that we want to analyze how the sales values are effected based on changes in prices for a given product. If you read this sentence carefully, you can...