Web App Development and Real-Time Web Analytics with Python: Develop and Integrate Machine Learning

Tshepo Chris Nokeri

226 pages, parution le 05/11/2021

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Résumé

Learn to develop and deploy dashboards as web apps using the Python programming language, and how to integrate algorithms into web apps.

Author Tshepo Chris Nokeri begins by introducing you to the basics of constructing and styling static and interactive charts and tables before exploring the basics of HTML, CSS, and Bootstrap, including an approach to building web pages with HTML. From there, he'll show you the key Python web frameworks and techniques for building web apps with them. You'll then see how to style web apps and incorporate themes, including interactive charts and tables to build dashboards, followed by a walkthrough of creating URL routes and securing web apps. You'll then progress to more advanced topics, like building machine learning algorithms and integrating them into a web app. The book concludes with a demonstration of how to deploy web apps in prevalent cloud platforms.

Web App Development and Real-Time Web Analytics with Python is ideal for intermediate data scientists, machine learning engineers, and web developers, who have little or no knowledge about building web apps that implement bootstrap technologies. After completing this book, you will have the knowledge necessary to create added value for your organization, as you will understand how to link front-end and back-end development, including machine learning.

What You Will Learn

Create interactive graphs and render static graphs into interactive ones
Understand the essentials of HTML, CSS, and Bootstrap
Gain insight into the key Python web frameworks, and how to develop web applications using them
Develop machine learning algorithms and integrate them into web apps
Secure web apps and deploy them to cloud platforms

Who This Book Is For

Intermediate data scientists, machine learning engineers, and web developers.Chapter 1: Static 2D and 3D GraphsChapter Goal: This chapter introduces the basics of tabulating data and constructing staticgraphical representations. To begin with, it exhibits an approach of extracting and tabulating data by implementing the pandas and sqlalchemy library. Subsequently, it reveals a prevalent 2D and 3D charting recognized as Matplotlib, then exhibits a technique of constructing basic charts (i.e. box-whisker plot, histogram, line plot, and scatter plot). Tabulating Data 2D Chartingo Box-whisker-ploto Histogramo Line ploto Scatter ploto Density Plot 3D Charting Conclusion
Chapter 2: Interactive ChartingChapter Goal: This chapter introduces an approach for constructing interactive charts byimplementing the most prevalent library, recognized as Plotly. Plotly 2D Chartingo Box-whisker-ploto Histogramo Line ploto Scatter ploto Density Ploto Bar Charto Pie Charto Sunburst 3D Charting Conclusion
Chapter 3: Containing functionality in Interactive GraphsChapter Goal: This chapter extends to the preceding chapter. It introduces an approach toupdating interactive graphs to improve user experience. For instance, you will learn how to add buttons and range sliders, among other functionalities. Besides that, it exhibits an approach for integrating innumerable graphs into one graph with some functionality. Updating Graph Layout Updating Plotly Axes Including Range Slider Including Buttons to a Graph Styling Interactive Graphs Updating Plotly X-Axis Color Sequencing Subplots Conclusions
Chapter 4: Essentials of HTMLChapter Goal: This chapter introduces the most prevalent markup language for developingwebsites. It acquaints you with the essentials of designing websites. Besides that, it contains a richset of code and examples to support you in getting started with coding using HTML. The Communication between a Web Browser and Web Server Domain Hostingo Shared Hostingo Managed Hosting HyperText Markup Languageo HTML Elements Headings Paragraphs Div Span Buttons Text Box Input File Upload Label Form Meta Tag Practical Example Conclusion
Chapter 5: Python Web Frameworks and ApplicationsChapter Goal: The preceding chapter acquainted you with interactive visualization using Plotly. This chapter introduces key Python web frameworks (i.e., flask and dash) and how they differ.Besides that, it provides practical examples and helps you get started with Python web development. Web Frameworks Web Applications Flasko WSGIo Werkzeugo Jinjao Installing Flasko Initializing a Flask Web Applicationo Flask Application Codeo Deploy a Flask Web Application Dasho Installing Dash Dependencieso Initializing a Dash Web Applicationo Dash Application Codeo Deploy a Dash Web Application Jupyter Dash Conclusion
Chapter 6: Dash Bootstrap ComponentsChapter Goal: This chapter covers dash_bootstrap_component. It is a Python library from the Plotly family, which enables us to have key bootstrap functionalities on a dash web application, thussimplifying the web application development. Dash Bootstrap Components (cont.)o Number Inputo Text Areaso Selecto Radio Itemso Checklisto Switcheso Tabso Buttono Table Conclusion
Chapter 7: Styling and ThemingChapter Goal: This chapter introduces the basics of UX design for a web application, dash with plotly. To begin with, it introduces styling an HTML web page. Subsequently, it acquaints you with the Cascade styling sheet (css). Following that, it presents bootstrap. Afterward, it reveals bootstrapping dash web applications. It concludes by demonstrating a way of designing the layout of a dash webapplication. Styling Cascade Styling Sheet Bootstrap Dash Bootstrappingo Dash Core Componentso Dash Bootstrap Componentso Implementing Dash Bootstrap Components Themingo Dash HTML Componentso Dash Web Application Layout Design Responsive Grid System
Chapter 8: Real-Time Web ApplicationChapter Goal: This chapter introduces you to creating a real-time web application with aresponsive navigation bar, sidebar, charts, tables, callbacks, and URL routing. After reading the contents of this chapter you should be able to develop a functional and responsive web application by implementing key Python web frameworks (i.e., dash, dash_core_components, dash_html_components). Besides that, you should be able to use the Input, State, and Output methods available for creating functional application callbacks. Equally, it acquaints you with an approach for implementing CSS to customize as a dash web application.Sub-topics: Creating Iconso Alert Icono Messageso Profile Icon with Dropdown Menu Search Bar Navigation Bar Sidebar Styling Responsive Sidebar Styling Content Styling Sub Menu Finalize Sidebar Menu Footer Navigation Bar Web Application Layout Including Charts Including a Table Enabling Data Download Tabs Collapse Callbackso Callbacks for Responsive Menu URL Routing Conclusion
Chapter 9: Basic AuthenticationChapter Goal: This chapter introduces an approach to securing a dash web application. After reading the contents of this chapter, you should be able to implement user access control, thus providing users access to some web resources and restricting them to others.Sub-topics: Dash Authentications Login Form Login on Home Page Conclusion
Chapter 10: Dash Into a Full WebsiteChapter Goal: Prior chapters introduced a way of building dashboards as web applications,integrated with machine learning models. This chapter takes it a step further. It introduces a way of building the front-end. After reading the contents of this chapter, you should be able to build important pages of a website, such as the home page, about us page, and contact us page, in addition to that, a page for billing/checkout.Sub-topics: Building Home Page Building Contact Us Page Building Billing / Check-Out Building
Chapter 11: Integrate Machine Learning ModelsChapter Goal: This chapter introduces an approach to integrate machine learning models. Initially, it will provide an over of machine learning recognized as linear regression, including ways of preprocessing data and generating predictions. It concludes by exhibiting a technique of implementing it in web applications.Sub-topics: An Introduction to Linear Regression An Introduction to Scikit learn Preprocessing Splitting Data into Training and Test Data Standardization Training an Algorithm Predictions Integrating an Algorithm to a Web App Conclusions
Chapter 12: Deploying Web AppChapter Goal: This chapter concludes the book. It exhibits a way of deploying a web app. Initially, it summarizes an integrated development environment useful for developing, testing, and debugging Python web frameworks. Subsequently, it exhibits an approach of organizing the file structure prior to deploying a web app. Besides that, it provides a practical example that will help you better web app deployment.Sub-topics: Integrated Development Environment PyCharm Virtual Environment File Structure Integration among Multiple Innumerable Python Files Hosting Web App
Tshepo Chris Nokeri harnesses big data, advanced analytics, and artificial intelligence to foster innovation and optimize business performance. In his functional work, he has delivered complex solutions to companies in the mining, petroleum, and manufacturing industries. He initially completed a bachelor's degree in information management. He then graduated with an honors degree in business science at the University of the Witwatersrand on a TATA prestigious scholarship and a Wits Postgraduate Merit Award. They unanimously awarded him the Oxford University Press Prize. He has authored the Apress book Data Science Revealed and Implementing Machine Learning for Finance.

Caractéristiques techniques

	PAPIER
Éditeur(s)	Apress
Auteur(s)	Tshepo Chris Nokeri
Parution	05/11/2021
Nb. de pages	226
EAN13	9781484277829

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Web App Development and Real-Time Web Analytics with Python: Develop and Integrate Machine Learning

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