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)

Predicting Housing Prices Using Linear Regression

In this chapter, we will introduce supervised learning and predictive modeling by implementing linear regression. In the previous chapter, you learned about exploratory analysis, but haven't looked at modeling yet. In this chapter, we will create a linear regression model to predict housing market prices. Broadly speaking, we are going to predict target variable with the help of its relationship with other variables. Linear regression is very widely used and is a simple model for supervised machine learning algorithms. It's essentially about fitting a line for the observed data. We will start our journey with explaining supervised learning and linear regression. Then, we will analyze the crucial concepts of linear regression such as independent and dependent variables, hyperparameters, loss and error functions, and stochastic...