Search icon CANCEL
Arrow left icon
Explore Products
Best Sellers
New Releases
Books
Videos
Audiobooks
Learning Hub
Conferences
Free Learning
Arrow right icon
Applied Supervised Learning with Python
Applied Supervised Learning with Python

Applied Supervised Learning with Python: Use scikit-learn to build predictive models from real-world datasets and prepare yourself for the future of machine learning

Arrow left icon
Profile Icon Benjamin Johnston Profile Icon Mathur
Arrow right icon
€32.99
Full star icon Full star icon Full star icon Full star icon Full star icon 5 (1 Ratings)
Paperback Apr 2019 404 pages 1st Edition
eBook
€26.99
Paperback
€32.99
Subscription
Free Trial
Renews at €18.99p/m
Arrow left icon
Profile Icon Benjamin Johnston Profile Icon Mathur
Arrow right icon
€32.99
Full star icon Full star icon Full star icon Full star icon Full star icon 5 (1 Ratings)
Paperback Apr 2019 404 pages 1st Edition
eBook
€26.99
Paperback
€32.99
Subscription
Free Trial
Renews at €18.99p/m
eBook
€26.99
Paperback
€32.99
Subscription
Free Trial
Renews at €18.99p/m

What do you get with Print?

Product feature icon Instant access to your digital eBook copy whilst your Print order is Shipped
Product feature icon Paperback book shipped to your preferred address
Product feature icon Download this book in EPUB and PDF formats
Product feature icon Access this title in our online reader with advanced features
Product feature icon DRM FREE - Read whenever, wherever and however you want
Product feature icon AI Assistant (beta) to help accelerate your learning
Table of content icon View table of contents Preview book icon Preview Book

Applied Supervised Learning with Python

Chapter 2. Exploratory Data Analysis and Visualization

Note

Learning Objectives

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

  • Explain the importance of data exploration and communicate the summary statistics of a dataset

  • Visualize patterns in missing values in data and be able to replace null values appropriately

  • Identify continuous features and categorical features

  • Visualize distributions of values across individual variables

  • Describe and analyze relationships between different types of variables using correlation and visualizations

Note

This chapter takes us through how to perform exploration and analysis on a new dataset.

Introduction


Say we have a problem statement that involves predicting whether a particular earthquake caused a tsunami or not. How do we decide what model to use? What do we know about the data we have? Nothing! But if we don't know and understand our data, chances are we'll end up building a model that's not very interpretable or reliable.

When it comes to data science, it's important to have a thorough understanding of the data we're dealing with, in order to generate features that are highly informative and, consequently, to build accurate and powerful models.

In order to gain this understanding, we perform an exploratory analysis on the data to see what the data can tell us about the relationships between the features and the target variable. Getting to know our data will even help us interpret the model we build and identify ways we can improve its accuracy.

The approach we take to achieve this is to allow the data to reveal its structure or model, which helps gain some new, often unsuspected...

Summary Statistics and Central Values


In order to find out what our data really looks like, we use a technique known as data profiling. This is defined as the process of examining the data available from an existing information source (for example, a database or a file) and collecting statistics or informative summaries about that data. The goal is to make sure that you understand your data well and are able to identify any challenges that the data may pose early on in the project, which is done by summarizing the dataset and assessing its structure, content, and quality.

Data profiling includes collecting descriptive statistics and data types. Here are a few commands that are commonly used to get a summary of a dataset:

  • data.info(): This command tells us how many non-null values there are there in each column, along with the data type of the values (non-numeric types are represented as object types).

  • data.describe(): This gives us basic summary statistics for all the numerical columns in the...

Missing Values


When there is no value (that is, a null value) recorded for a particular feature in a data point, we say the data is missing. Having missing values in a real dataset is inevitable; no dataset is ever perfect. However, it is important to understand why the data is missing, and if there is a factor that has affected the loss of data. Appreciating and recognizing this allows us to handle the remaining data in an appropriate manner. For example, if the data is missing randomly, then it's highly likely that the remaining data is still representative of the population. However, if the missing data is not random in nature and we assume that it is, it could bias our analysis and subsequent modeling.

Let's look at the common reasons (or mechanisms) for missing data:

  • Missing Completely at Random (MCAR): Values in a dataset are said to be MCAR if there is no correlation whatsoever between the value missing and any other recorded variable or external parameter. This means that the remaining...

Distribution of Values


In this section, we'll look at how individual variables behave—what kind of values they take, what the distribution across those values is, and how those distributions can be represented visually.

Target Variable

The target variable can either have values that are continuous (in the case of a regression problem) or discrete (as in the case of a classification problem). The problem statement we're looking at in this chapter involves predicting whether or not an earthquake caused a tsunami, that is, the flag_tsunami variable, which takes on two discrete values only—making it a classification problem.

One way of visualizing how many earthquakes resulted in tsunamis and how many didn't is a bar chart, where each bar represents a single discrete value of the variable, and the height of the bars is equal to the count of the data points having the corresponding discrete value. This gives us a good comparison of the absolute counts of each category.

Exercise 16: Plotting a Bar...

Relationships within the Data


There are two reasons why it is important to find relationships between variables in the data:

  • Finding which features are potentially important can be deemed essential, since finding ones that have a strong relationship with the target variable will aid in the feature selection process.

  • Finding relationships between different features themselves can be useful, since variables in the dataset are usually never completely independent of every other variable and this can affect our modeling in a number of ways.

Now, there are a number of ways we can visualize these relationships, and this really depends on the types of variable we are trying to find the relationship between, and how many we are considering as part of the equation or comparison.

Relationship between Two Continuous Variables

To find a relationship between two continuous variables is basically to see how one varies as the value of the other is increased. The most common way to visualize this would be using...

Summary


In this chapter, we started by talking about why data exploration is an important part of the modeling process and how it can help in not only preprocessing the dataset for the modeling process, but also help us engineer informative features and improve model accuracy. This chapter focused on not only gaining a basic overview of the dataset and its features, but also gaining insights by creating visualizations that combine several features.

We looked at how to find the summary statistics of a dataset using core functionality from pandas. We looked at how to find missing values and talked about why they're important, while learning how to use the Missingno library to analyze them and the pandas and scikit-learn libraries to impute the missing values.

Then, we looked at how to study the univariate distributions of variables in the dataset and visualize them for both categorical and continuous variables using bar charts, pie charts, and histograms. Lastly, we learned how to explore relationships...

Left arrow icon Right arrow icon

Key benefits

  • Understand various machine learning concepts with real-world examples
  • Implement a supervised machine learning pipeline from data ingestion to validation
  • Gain insights into how you can use machine learning in everyday life

Description

Machine learning—the ability of a machine to give right answers based on input data—has revolutionized the way we do business. Applied Supervised Learning with Python provides a rich understanding of how you can apply machine learning techniques in your data science projects using Python. You'll explore Jupyter Notebooks, the technology used commonly in academic and commercial circles with in-line code running support. With the help of fun examples, you'll gain experience working on the Python machine learning toolkit—from performing basic data cleaning and processing to working with a range of regression and classification algorithms. Once you’ve grasped the basics, you'll learn how to build and train your own models using advanced techniques such as decision trees, ensemble modeling, validation, and error metrics. You'll also learn data visualization techniques using powerful Python libraries such as Matplotlib and Seaborn. This book also covers ensemble modeling and random forest classifiers along with other methods for combining results from multiple models, and concludes by delving into cross-validation to test your algorithm and check how well the model works on unseen data. By the end of this book, you'll be equipped to not only work with machine learning algorithms, but also be able to create some of your own!

Who is this book for?

Applied Supervised Learning with Python is for you if you want to gain a solid understanding of machine learning using Python. It'll help if you to have some experience in any functional or object-oriented language and a basic understanding of Python libraries and expressions, such as arrays and dictionaries.

What you will learn

  • Understand the concept of supervised learning and its applications
  • Implement common supervised learning algorithms using machine learning Python libraries
  • Validate models using the k-fold technique
  • Build your models with decision trees to get results effortlessly
  • Use ensemble modeling techniques to improve the performance of your model
  • Apply a variety of metrics to compare machine learning models
Estimated delivery fee Deliver to Hungary

Premium delivery 7 - 10 business days

€25.95
(Includes tracking information)

Product Details

Country selected
Publication date, Length, Edition, Language, ISBN-13
Publication date : Apr 27, 2019
Length: 404 pages
Edition : 1st
Language : English
ISBN-13 : 9781789954920
Category :
Languages :

What do you get with Print?

Product feature icon Instant access to your digital eBook copy whilst your Print order is Shipped
Product feature icon Paperback book shipped to your preferred address
Product feature icon Download this book in EPUB and PDF formats
Product feature icon Access this title in our online reader with advanced features
Product feature icon DRM FREE - Read whenever, wherever and however you want
Product feature icon AI Assistant (beta) to help accelerate your learning
Estimated delivery fee Deliver to Hungary

Premium delivery 7 - 10 business days

€25.95
(Includes tracking information)

Product Details

Publication date : Apr 27, 2019
Length: 404 pages
Edition : 1st
Language : English
ISBN-13 : 9781789954920
Category :
Languages :

Packt Subscriptions

See our plans and pricing
Modal Close icon
€18.99 billed monthly
Feature tick icon Unlimited access to Packt's library of 7,000+ practical books and videos
Feature tick icon Constantly refreshed with 50+ new titles a month
Feature tick icon Exclusive Early access to books as they're written
Feature tick icon Solve problems while you work with advanced search and reference features
Feature tick icon Offline reading on the mobile app
Feature tick icon Simple pricing, no contract
€189.99 billed annually
Feature tick icon Unlimited access to Packt's library of 7,000+ practical books and videos
Feature tick icon Constantly refreshed with 50+ new titles a month
Feature tick icon Exclusive Early access to books as they're written
Feature tick icon Solve problems while you work with advanced search and reference features
Feature tick icon Offline reading on the mobile app
Feature tick icon Choose a DRM-free eBook or Video every month to keep
Feature tick icon PLUS own as many other DRM-free eBooks or Videos as you like for just €5 each
Feature tick icon Exclusive print discounts
€264.99 billed in 18 months
Feature tick icon Unlimited access to Packt's library of 7,000+ practical books and videos
Feature tick icon Constantly refreshed with 50+ new titles a month
Feature tick icon Exclusive Early access to books as they're written
Feature tick icon Solve problems while you work with advanced search and reference features
Feature tick icon Offline reading on the mobile app
Feature tick icon Choose a DRM-free eBook or Video every month to keep
Feature tick icon PLUS own as many other DRM-free eBooks or Videos as you like for just €5 each
Feature tick icon Exclusive print discounts

Frequently bought together


Stars icon
Total 94.97
Big Data Analysis with Python
€24.99
Applied Supervised Learning with Python
€32.99
Applied Unsupervised Learning with Python
€36.99
Total 94.97 Stars icon

Table of Contents

6 Chapters
Python Machine Learning Toolkit Chevron down icon Chevron up icon
Exploratory Data Analysis and Visualization Chevron down icon Chevron up icon
Regression Analysis Chevron down icon Chevron up icon
Classification Chevron down icon Chevron up icon
Ensemble Modeling Chevron down icon Chevron up icon
Model Evaluation Chevron down icon Chevron up icon

Customer reviews

Rating distribution
Full star icon Full star icon Full star icon Full star icon Full star icon 5
(1 Ratings)
5 star 100%
4 star 0%
3 star 0%
2 star 0%
1 star 0%
AS Sep 11, 2019
Full star icon Full star icon Full star icon Full star icon Full star icon 5
Wonderful introduction to supervised machine learning. The code provided was also super helpful!
Amazon Verified review Amazon
Get free access to Packt library with over 7500+ books and video courses for 7 days!
Start Free Trial

FAQs

What is the delivery time and cost of print book? Chevron down icon Chevron up icon

Shipping Details

USA:

'

Economy: Delivery to most addresses in the US within 10-15 business days

Premium: Trackable Delivery to most addresses in the US within 3-8 business days

UK:

Economy: Delivery to most addresses in the U.K. within 7-9 business days.
Shipments are not trackable

Premium: Trackable delivery to most addresses in the U.K. within 3-4 business days!
Add one extra business day for deliveries to Northern Ireland and Scottish Highlands and islands

EU:

Premium: Trackable delivery to most EU destinations within 4-9 business days.

Australia:

Economy: Can deliver to P. O. Boxes and private residences.
Trackable service with delivery to addresses in Australia only.
Delivery time ranges from 7-9 business days for VIC and 8-10 business days for Interstate metro
Delivery time is up to 15 business days for remote areas of WA, NT & QLD.

Premium: Delivery to addresses in Australia only
Trackable delivery to most P. O. Boxes and private residences in Australia within 4-5 days based on the distance to a destination following dispatch.

India:

Premium: Delivery to most Indian addresses within 5-6 business days

Rest of the World:

Premium: Countries in the American continent: Trackable delivery to most countries within 4-7 business days

Asia:

Premium: Delivery to most Asian addresses within 5-9 business days

Disclaimer:
All orders received before 5 PM U.K time would start printing from the next business day. So the estimated delivery times start from the next day as well. Orders received after 5 PM U.K time (in our internal systems) on a business day or anytime on the weekend will begin printing the second to next business day. For example, an order placed at 11 AM today will begin printing tomorrow, whereas an order placed at 9 PM tonight will begin printing the day after tomorrow.


Unfortunately, due to several restrictions, we are unable to ship to the following countries:

  1. Afghanistan
  2. American Samoa
  3. Belarus
  4. Brunei Darussalam
  5. Central African Republic
  6. The Democratic Republic of Congo
  7. Eritrea
  8. Guinea-bissau
  9. Iran
  10. Lebanon
  11. Libiya Arab Jamahriya
  12. Somalia
  13. Sudan
  14. Russian Federation
  15. Syrian Arab Republic
  16. Ukraine
  17. Venezuela
What is custom duty/charge? Chevron down icon Chevron up icon

Customs duty are charges levied on goods when they cross international borders. It is a tax that is imposed on imported goods. These duties are charged by special authorities and bodies created by local governments and are meant to protect local industries, economies, and businesses.

Do I have to pay customs charges for the print book order? Chevron down icon Chevron up icon

The orders shipped to the countries that are listed under EU27 will not bear custom charges. They are paid by Packt as part of the order.

List of EU27 countries: www.gov.uk/eu-eea:

A custom duty or localized taxes may be applicable on the shipment and would be charged by the recipient country outside of the EU27 which should be paid by the customer and these duties are not included in the shipping charges been charged on the order.

How do I know my custom duty charges? Chevron down icon Chevron up icon

The amount of duty payable varies greatly depending on the imported goods, the country of origin and several other factors like the total invoice amount or dimensions like weight, and other such criteria applicable in your country.

For example:

  • If you live in Mexico, and the declared value of your ordered items is over $ 50, for you to receive a package, you will have to pay additional import tax of 19% which will be $ 9.50 to the courier service.
  • Whereas if you live in Turkey, and the declared value of your ordered items is over € 22, for you to receive a package, you will have to pay additional import tax of 18% which will be € 3.96 to the courier service.
How can I cancel my order? Chevron down icon Chevron up icon

Cancellation Policy for Published Printed Books:

You can cancel any order within 1 hour of placing the order. Simply contact customercare@packt.com with your order details or payment transaction id. If your order has already started the shipment process, we will do our best to stop it. However, if it is already on the way to you then when you receive it, you can contact us at customercare@packt.com using the returns and refund process.

Please understand that Packt Publishing cannot provide refunds or cancel any order except for the cases described in our Return Policy (i.e. Packt Publishing agrees to replace your printed book because it arrives damaged or material defect in book), Packt Publishing will not accept returns.

What is your returns and refunds policy? Chevron down icon Chevron up icon

Return Policy:

We want you to be happy with your purchase from Packtpub.com. We will not hassle you with returning print books to us. If the print book you receive from us is incorrect, damaged, doesn't work or is unacceptably late, please contact Customer Relations Team on customercare@packt.com with the order number and issue details as explained below:

  1. If you ordered (eBook, Video or Print Book) incorrectly or accidentally, please contact Customer Relations Team on customercare@packt.com within one hour of placing the order and we will replace/refund you the item cost.
  2. Sadly, if your eBook or Video file is faulty or a fault occurs during the eBook or Video being made available to you, i.e. during download then you should contact Customer Relations Team within 14 days of purchase on customercare@packt.com who will be able to resolve this issue for you.
  3. You will have a choice of replacement or refund of the problem items.(damaged, defective or incorrect)
  4. Once Customer Care Team confirms that you will be refunded, you should receive the refund within 10 to 12 working days.
  5. If you are only requesting a refund of one book from a multiple order, then we will refund you the appropriate single item.
  6. Where the items were shipped under a free shipping offer, there will be no shipping costs to refund.

On the off chance your printed book arrives damaged, with book material defect, contact our Customer Relation Team on customercare@packt.com within 14 days of receipt of the book with appropriate evidence of damage and we will work with you to secure a replacement copy, if necessary. Please note that each printed book you order from us is individually made by Packt's professional book-printing partner which is on a print-on-demand basis.

What tax is charged? Chevron down icon Chevron up icon

Currently, no tax is charged on the purchase of any print book (subject to change based on the laws and regulations). A localized VAT fee is charged only to our European and UK customers on eBooks, Video and subscriptions that they buy. GST is charged to Indian customers for eBooks and video purchases.

What payment methods can I use? Chevron down icon Chevron up icon

You can pay with the following card types:

  1. Visa Debit
  2. Visa Credit
  3. MasterCard
  4. PayPal
What is the delivery time and cost of print books? Chevron down icon Chevron up icon

Shipping Details

USA:

'

Economy: Delivery to most addresses in the US within 10-15 business days

Premium: Trackable Delivery to most addresses in the US within 3-8 business days

UK:

Economy: Delivery to most addresses in the U.K. within 7-9 business days.
Shipments are not trackable

Premium: Trackable delivery to most addresses in the U.K. within 3-4 business days!
Add one extra business day for deliveries to Northern Ireland and Scottish Highlands and islands

EU:

Premium: Trackable delivery to most EU destinations within 4-9 business days.

Australia:

Economy: Can deliver to P. O. Boxes and private residences.
Trackable service with delivery to addresses in Australia only.
Delivery time ranges from 7-9 business days for VIC and 8-10 business days for Interstate metro
Delivery time is up to 15 business days for remote areas of WA, NT & QLD.

Premium: Delivery to addresses in Australia only
Trackable delivery to most P. O. Boxes and private residences in Australia within 4-5 days based on the distance to a destination following dispatch.

India:

Premium: Delivery to most Indian addresses within 5-6 business days

Rest of the World:

Premium: Countries in the American continent: Trackable delivery to most countries within 4-7 business days

Asia:

Premium: Delivery to most Asian addresses within 5-9 business days

Disclaimer:
All orders received before 5 PM U.K time would start printing from the next business day. So the estimated delivery times start from the next day as well. Orders received after 5 PM U.K time (in our internal systems) on a business day or anytime on the weekend will begin printing the second to next business day. For example, an order placed at 11 AM today will begin printing tomorrow, whereas an order placed at 9 PM tonight will begin printing the day after tomorrow.


Unfortunately, due to several restrictions, we are unable to ship to the following countries:

  1. Afghanistan
  2. American Samoa
  3. Belarus
  4. Brunei Darussalam
  5. Central African Republic
  6. The Democratic Republic of Congo
  7. Eritrea
  8. Guinea-bissau
  9. Iran
  10. Lebanon
  11. Libiya Arab Jamahriya
  12. Somalia
  13. Sudan
  14. Russian Federation
  15. Syrian Arab Republic
  16. Ukraine
  17. Venezuela