Book Image

Principles of Data Science

Book Image

Principles of Data Science

Overview of this book

Need to turn your skills at programming into effective data science skills? Principles of Data Science is created to help you join the dots between mathematics, programming, and business analysis. With this book, you’ll feel confident about asking—and answering—complex and sophisticated questions of your data to move from abstract and raw statistics to actionable ideas. With a unique approach that bridges the gap between mathematics and computer science, this books takes you through the entire data science pipeline. Beginning with cleaning and preparing data, and effective data mining strategies and techniques, you’ll move on to build a comprehensive picture of how every piece of the data science puzzle fits together. Learn the fundamentals of computational mathematics and statistics, as well as some pseudocode being used today by data scientists and analysts. You’ll get to grips with machine learning, discover the statistical models that help you take control and navigate even the densest datasets, and find out how to create powerful visualizations that communicate what your data means.
Table of Contents (20 chapters)
Principles of Data Science
Credits
About the Author
About the Reviewers
www.PacktPub.com
Preface
Index

Linear regression


Finally! We will explore our first true machine learning model. Linear regressions are 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 for a formula of the following format:

  • 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 Kaggle: https://www.kaggle.com/c/bike-sharing-demand...