Data Science Projects with Python

A case study approach to successful data science projects using Python, pandas, and scikit-learn

Stephen Klosterman

• Editore: Packt Publishing
• Anno: 2019
• ISBN:
  9781838552602
• Edizione: 1
• Argomento: Computer Science
• Categoria: Programming in Python

Gain hands-on experience with industry-standard data analysis and machine learning tools in Python

Key Features

• Tackle data science problems by identifying the problem to be solved
• Illustrate patterns in data using appropriate visualizations
• Implement suitable machine learning algorithms to gain insights from data

Book Description

Data Science Projects with Python is designed to give you practical guidance on industry-standard data analysis and machine learning tools, by applying them to realistic data problems. You will learn how to use pandas and Matplotlib to critically examine datasets with summary statistics and graphs, and extract the insights you seek to derive. You will build your knowledge as you prepare data using the scikit-learn package and feed it to machine learning algorithms such as regularized logistic regression and random forest. You'll discover how to tune algorithms to provide the most accurate predictions on new and unseen data. As you progress, you'll gain insights into the working and output of these algorithms, building your understanding of both the predictive capabilities of the models and why they make these predictions.

By then end of this book, you will have the necessary skills to confidently use machine learning algorithms to perform detailed data analysis and extract meaningful insights from unstructured data.

What you will learn

• Install the required packages to set up a data science coding environment
• Load data into a Jupyter notebook running Python
• Use Matplotlib to create data visualizations
• Fit machine learning models using scikit-learn
• Use lasso and ridge regression to regularize your models
• Compare performance between models to find the best outcomes
• Use k-fold cross-validation to select model hyperparameters

Who this book is for

If you are a data analyst, data scientist, or business analyst who wants to get started using Python and machine learning techniques to analyze data and predict outcomes, this book is for you. Basic knowledge of Python and data analytics will help you get the most from this book. Familiarity with mathematical concepts such as algebra and basic statistics will also be useful.

Chapter 1:

Data Exploration and Cleaning

Learning Objectives

By the end of this chapter, you will be able to:

• Perform basic operations in Python
• Describe the business context of the case study data and its suitability for the task
• Perform data cleaning operations
• Examine statistical summaries and visualize the case study data
• Implement one-hot encoding on categorical variables

This chapter will get you started with basic operations in Python and shows you how to perform data-related operations such as data verification, data cleaning, datatype conversion, examining statistical summaries, and more.

Introduction

Most businesses possess a wealth of data on their operations and customers. Reporting on these 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 become involved. 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 index-1 refers to the last element of an ordered collection and negative indices count backwards 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 basic list being printed on the console:

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

If you previously had Anaconda installed and were using it prior to this book, you may wish to create a new Python 3.x environment for the 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. For more information, see https://conda.io/docs/user-guide/tasks/manage-environments.html.

Exercise 1: Examining Anaconda and Getting Familiar with Python

In this exercise, you will examine the packages in your Anaconda installation and practice 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 ...