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

Linear regression

Finally! We will explore our first true machine learning model. Linear regression is a form of regression, which means that it is a machine learning model that attempts to find a relationship between predictors and a response variable, and that response variable is, you guessed it, continuous! This notion is synonymous with making a line of best fit.

In the case of linear regression, we will attempt to find a linear relationship between our predictors and our response variable. Formally, we wish to solve a formula of the following format:

Linear regression

Let's look at the constituents of this formula:

  • y is our response variable
  • xi is our ith variable (ith column or ith predictor)
  • B0 is the intercept
  • Bi is the coefficient for the xi term

Let's take a look at some data before we go in depth. This dataset is publically available and attempts to predict the number of bikes needed on a particular day for a bike sharing program:

# read the data and set the datetime as the index 
# taken from...