Data Science Projects with Python

A case study approach to gaining valuable insights from real data with machine learning, 2nd Edition

Stephen Klosterman

• Publisher: Packt Publishing
• Year: 2021
• eBook ISBN: 9781800569447
• Edition: 2
• Topic: Computer Science
• Subtopic: Data Processing
• Index: Computer Science

Gain hands-on experience of Python programming with industry-standard machine learning techniques using pandas, scikit-learn, and XGBoost

Key Features

• Think critically about data and use it to form and test a hypothesis
• Choose an appropriate machine learning model and train it on your data
• Communicate data-driven insights with confidence and clarity

Book Description

If data is the new oil, then machine learning is the drill. As companies gain access to ever-increasing quantities of raw data, the ability to deliver state-of-the-art predictive models that support business decision-making becomes more and more valuable.

In this book, you'll work on an end-to-end project based around a realistic data set and split up into bite-sized practical exercises. This creates a case-study approach that simulates the working conditions you'll experience in real-world data science projects.

You'll learn how to use key Python packages, including pandas, Matplotlib, and scikit-learn, and master the process of data exploration and data processing, before moving on to fitting, evaluating, and tuning algorithms such as regularized logistic regression and random forest.

Now in its second edition, this book will take you through the end-to-end process of exploring data and delivering machine learning models. Updated for 2021, this edition includes brand new content on XGBoost, SHAP values, algorithmic fairness, and the ethical concerns of deploying a model in the real world.

By the end of this data science book, you'll have the skills, understanding, and confidence to build your own machine learning models and gain insights from real data.

What you will learn

• Load, explore, and process data using the pandas Python package
• Use Matplotlib to create compelling data visualizations
• Implement predictive machine learning models with scikit-learn
• Use lasso and ridge regression to reduce model overfitting
• Evaluate random forest and logistic regression model performance
• Deliver business insights by presenting clear, convincing conclusions

Who this book is for

Data Science Projects with Python – Second Edition is for anyone who wants to get started with data science and machine learning. If you're keen to advance your career by using data analysis and predictive modeling to generate business insights, then this book is the perfect place to begin. To quickly grasp the concepts covered, it is recommended that you have basic experience of programming with Python or another similar language, and a general interest in statistics.

1. Data Exploration and Cleaning

Overview

In this chapter, you will take your first steps with Python and Jupyter notebooks, some of the most common tools data scientists use. You'll then take the first look at the dataset for the case study project that will form the core of this book. You will begin to develop an intuition for quality assurance checks that data needs to be put through before model building. By the end of the chapter, you will be able to use pandas, the top package for wrangling tabular data in Python, to do exploratory data analysis, quality assurance, and data cleaning.

Introduction

Most businesses possess a wealth of data on their operations and customers. Reporting on this data in the form of descriptive charts, graphs, and tables is a good way to understand the current state of the business. However, in order to provide quantitative guidance on future business strategies and operations, it is necessary to go a step further. This is where the practices of machine learning and predictive modeling are needed. In this book, we will show how to go from descriptive analyses to concrete guidance for future operations, using predictive models.

To accomplish this goal, we'll introduce some of the most widely used machine learning tools via Python and many of its packages. You will also get a sense of the practical skills necessary to execute successful projects: inquisitiveness when examining data and communication with the client. Time spent looking in detail at a dataset and critically examining whether it accurately meets its intended purpose is time well spent. You will learn several techniques for assessing data quality here.

In this chapter, after getting familiar with the basic tools for data exploration, we will discuss a few typical working scenarios for how you may receive data. Then, we will begin a thorough exploration of the case study dataset and help you learn how you can uncover possible issues, so that when you are ready for modeling, you may proceed with confidence.

Python and the Anaconda Package Management System

In this book, we will use the Python programming language. Python is a top language for data science and is one of the fastest-growing programming languages. A commonly cited reason for Python's popularity is that it is easy to learn. If you have Python experience, that's great; however, if you have experience with other languages, such as C, Matlab, or R, you shouldn't have much trouble using Python. You should be familiar with the general constructs of computer programming to get the most out of this book. Examples of such constructs are for loops and if statements that guide the control flow of a program. No matter what language you have used, you are likely familiar with these constructs, which you will also find in Python.

A key feature of Python that is different from some other languages is that it is zero-indexed; in other words, the first element of an ordered collection has an index of 0. Python also supports negative indexing, where the index -1 refers to the last element of an ordered collection and negative indices count backward from the end. The slice operator, :, can be used to select multiple elements of an ordered collection from within a range, starting from the beginning, or going to the end of the collection.

Indexing and the Slice Operator

Here, we demonstrate how indexing and the slice operator work. To have something to index, we will create a list, which is a mutable ordered collection that can contain any type of data, including numerical and string types. "Mutable" just means the elements of the list can be changed after they are first assigned. To create the numbers for our list, which will be consecutive integers, we'll use the built-in range() Python function. The range() function technically creates an iterator that we'll convert to a list using the list() function, although you need not be concerned with that detail here. The following screenshot shows a list of the first five positive integers being printed on the console, as well as a few indexing operations, and changing the first item of the list to a new value of a different data type:

Figure 1.1: List creation and indexing

A few things to notice about Figure 1.1: the endpoint of an interval is open for both slice indexing and the range() function, while the starting point is closed. In other words, notice how when we specify the start and end of range(), endpoint 6 is not included in the result but starting point 1 is. Similarly, when indexing the list with the slice [:3], this includes all elements of the list with indices up to, but not including, 3.

We've referred to ordered collections, but Python also includes unordered collections. An important one of these is called a dictionary. A dictionary is an unordered collection of key:value pairs. Instead of looking up the values of a dictionary by integer indices, you look them up by keys, which could be numbers or strings. A dictionary can be created using curly braces {} and with the key:value pairs separated by commas. The following screenshot is an example of how we can create a dictionary with counts of fruit – examine the number of apples, then add a new type of fruit and its count:

Figure 1.2: An example dictionary

There are many other distinctive features of Python and we just want to give you a flavor here, without getting into too much detail. In fact, you will probably use packages such as pandas (pandas) and NumPy (numpy) for most of your data handling in Python. NumPy provides fast numerical computation on arrays and matrices, while pandas provides a wealth of data wrangling and exploration capabilities on tables of data called DataFrames. However, it's good to be familiar with some of the basics of Python—the language that sits at the foundation of all of this. For example, indexing works the same in NumPy and pandas as it does in Python.

One of the strengths of Python is that it is open source and has an active community of developers creating amazing tools. We will use several of these tools in this book. A potential pitfall of having open source packages from different contributors is the dependencies between various packages. For example, if you want to install pandas, it may rely on a certain version of NumPy, which you may or may not have installed. Package management systems make life easier in this respect. When you install a new package through the package management system, it will ensure that all the dependencies are met. If they aren't, you will be prompted to upgrade or install new packages as necessary.

For this book, we will use the Anaconda package management system, which you should already have installed. While we will only use Python here, it is also possible to run R with Anaconda.

Note: Environments

It is recommended to create a new Python 3.x environment for this book. Environments are like separate installations of Python, where the set of packages you have installed can be different, as well as the version of Python. Environments are useful for developing projects that need to be deployed in different versions of Python, possibly with different dependencies. For general information on this, see https://docs.conda.io/projects/conda/en/latest/user-guide/tasks/manage-environments.html. See the Preface for specific instructions on setting up an Anaconda environment for this book before you begin the upcoming exercises.

Exercise 1.01: Examining Anaconda and Getting Familiar with Python

In this exercise, you will examine the packages in your Anaconda installation and practice with some basic Python control flow and data structures, including a for loop, dict, and list. This will confirm that you have completed the installation steps in the preface and show you how Python syntax and data structures may be a little different from other programming languages you may be familiar with. Perform the following steps to complete the exercise:

Note

Before executing the exercises a...

Table of contents

• Data Science Projects with Python
• Preface
• 1. Data Exploration and Cleaning
• 2. Introduction to Scikit-Learn and Model Evaluation
• 3. Details of Logistic Regression and Feature Exploration
• 4. The Bias-Variance Trade-Off
• 5. Decision Trees and Random Forests
• 6. Gradient Boosting, XGBoost, and SHAP Values
• 7. Test Set Analysis, Financial Insights, and Delivery to the Client
• Appendix