You're reading from The Data Science Workshop Learn how you can build machine learning models and create your own real-world data science projects

Product type Paperback

Published in Aug 2020

Publisher Packt

ISBN-13 9781800566927

Length 824 pages

Edition 2nd Edition

Languages

Python

Tools

Scikit-learn

Concepts

Data Science

Authors (5):

Anthony So

Thomas Joseph

Robert Thas John

Andrew Worsley

Dr. Samuel Asare

+1 more

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Table of Contents (16) Chapters

Preface

1. Introduction to Data Science in Python

2. Regression FREE CHAPTER

3. Binary Classification

4. Multiclass Classification with RandomForest

5. Performing Your First Cluster Analysis

6. How to Assess Performance

7. The Generalization of Machine Learning Models

8. Hyperparameter Tuning

9. Interpreting a Machine Learning Model

10. Analyzing a Dataset

11. Data Preparation

12. Feature Engineering

Introduction

13. Imbalanced Datasets

14. Dimensionality Reduction

15. Ensemble Learning

Python for Data Science

Python offers an incredible number of packages for data science. A package is a collection of prebuilt functions and classes shared publicly by its author(s). These packages extend the core functionalities of Python. The Python Package Index (https://packt.live/37iTRXc) lists all the packages available in Python.

In this section, we will present to you two of the most popular ones: pandas and scikit-learn.

The pandas Package

The pandas package provides an incredible amount of APIs for manipulating data structures. The two main data structures defined in the pandas package are DataFrame and Series.

DataFrame and Series

A DataFrame is a tabular data structure that is represented as a two-dimensional table. It is composed of rows, columns, indexes, and cells. It is very similar to a sheet in Excel or a table in a database:

Figure 1.28: Components of a DataFrame

In Figure 1.28, there are three different columns: algorithm...