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E-raamat: Web App Development and Real-Time Web Analytics with Python: Develop and Integrate Machine Learning Algorithms into Web Apps

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  • Formaat: EPUB+DRM
  • Ilmumisaeg: 05-Nov-2021
  • Kirjastus: APress
  • Keel: eng
  • ISBN-13: 9781484277836
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Web App Development and Real-Time Web Analytics with Python: Develop and Integrate Machine Learning Algorithms into Web AppsSuurem pilt
  • Formaat: EPUB+DRM
  • Ilmumisaeg: 05-Nov-2021
  • Kirjastus: APress
  • Keel: eng
  • ISBN-13: 9781484277836
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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, hell show you the key Python web frameworks and techniques for building web apps with them. Youll 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. Youll 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 isideal 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.
About the Author xi
About the Technical Reviewer xiii
Acknowledgments xv
Chapter 1 Tabulating Data and Constructing Static 2D and 3D Charts 1(20)
Tabulating the Data
1(3)
2D Charting
4(13)
Box-Whisker Plot
6(1)
Histogram
7(1)
Line Plot
8(1)
Scatter Plot
9(1)
Density Plot
10(1)
Violin Plot
11(2)
Regression Plot
13(1)
Joint Plot
13(1)
Heatmap
14(3)
3D Charting
17(2)
Conclusion
19(2)
Chapter 2 Interactive Tabulation and Charting 21(26)
Plotly
21(1)
Tabulating the Data with Plotly
22(1)
Interactive Charting
23(1)
2D Charting
23(18)
Box Plot
26(1)
Violin Plot
27(1)
Histogram
28(4)
Scatter Plot
32(2)
Density Plot
34(2)
Bar Chart
36(2)
Pie Chart
38(1)
Sunburst
38(3)
Choropleth Map
41(2)
Heatmap
42(1)
3D Charting
43(1)
Indicators
44(1)
Conclusion
45(2)
Chapter 3 Containing Functionality and Styling for Interactive Charts 47(16)
Updating Graph Layout
47(4)
Updating Plotly Axes
48(1)
Including Range Slider
48(1)
Including Buttons to a Graph
49(2)
Subplots
51(5)
Styling Charts
56(5)
Altering Color Schemes
57(1)
Color Sequencing
57(2)
Customizing Traces
59(2)
Conclusion
61(2)
Chapter 4 Essentials of HTML 63(16)
Communication Between a Web Browser and a Web Server
63(1)
URL Structure
63(1)
Domain Hosting
64(2)
Shared Web Hosting
64(1)
Managed Web Hosting
65(1)
Web Servers
66(1)
HyperText Markup Language
66(9)
HTML Elements
67(8)
Meta Tag
75(1)
Practical Example
75(3)
Viewing Web Page Source
78(1)
Conclusion
78(1)
Chapter 5 Python Web Frameworks and Apps 79(8)
Web Frameworks
79(1)
Web Apps
80(1)
Flask
80(2)
WSGI
80(1)
Werkzeug
80(1)
Jinja
81(1)
Installing Flask
81(1)
Initializing a Flask Web App
81(1)
Flask App Code
82(1)
Deploy a Flask Web App
82(1)
Dash
82(2)
Installing Dash Dependencies
83(1)
Initializing a Dash Web App
83(1)
Dash Web App Code
83(1)
Deploy a Dash Web App
84(1)
Jupyter Dash
84(1)
Conclusion
85(2)
Chapter 6 Dash Bootstrap Components 87(12)
Number Input
88(1)
Text Area
89(1)
Select
89(1)
Radio Items
90(1)
Checklist
91(1)
Switches
92(1)
Tabs
93(1)
Button
94(1)
Table
95(2)
Conclusion
97(2)
Chapter 7 Styling and Theming a Web App 99(12)
Styling
99(1)
Cascade Styling Sheet
100(2)
Bootstrap
102(1)
Dash Bootstrapping
103(4)
Dash Core Components
104(1)
Dash Bootstrap Components
104(1)
Implementing Dash Bootstrap Components Theming
104(2)
Dash HTML Components
106(1)
Dash Web Application Layout Design
106(1)
Responsive Grid System
107(2)
Conclusion
109(2)
Chapter 8 Building a Real-Time Web App 111(34)
Dash App Structure
112(1)
Importing Key Dependencies
112(4)
Loading an External CSS File
115(1)
Loading the Bootstrap Icons Library
116(1)
Initializing a Web App
116(1)
Navigation Bars
116(7)
Top Navigation Bar
117(4)
Specifying the Responsive Side Navigation Bar
121(2)
Specifying the Web App CSS Code
123(1)
Side Navigation Bar Menus and Submenus
124(3)
Search Functionality
127(7)
Creating Interactive Charts
129(2)
Containing an Interactive Table and Allowing Generating a Report and Enabling Download
131(3)
Specifying the App Layout
134(1)
Specifying a Callback Function
135(8)
Callback for a Responsive Side Navigation Bar
136(1)
Callback for URL Routing
137(1)
Specifying a Callback Function for Unhiding Content
138(1)
Specifying a Callback Function for Interactive Charts
139(2)
Specifying a Callback Function for Unhiding an Interactive Table
141(1)
Specifying a Callback Function for an Interactive Table
142(1)
Specifying a Callback Function for Callback for Data Download
143(1)
Run the Dash App
143(1)
Conclusion
144(1)
Chapter 9 Basic Web App Authentication 145(14)
Authentication with Dash Auth
145(3)
Authentication with Flask
148(2)
Login Form
150(5)
Login on Home Page
155(3)
Conclusion
158(1)
Chapter 10 Dash into a Full Website 159(30)
Home Page
159(17)
Footer Navigation Bar
168(5)
Banner
173(2)
Callback to Collapse the Navigation for Small Screens
175(1)
Home Page
176(1)
Contact Us
176(7)
Billing/Checkout
183(5)
Conclusion
188(1)
Chapter 11 Integrating a Machine Learning Algorithm into a Web App 189(26)
An Introduction to Linear Regression
189(1)
An Introduction to sklearn
190(1)
Preprocessing
191(1)
Splitting Data into Training and Test Data
192(1)
Standardization
192(1)
Training an Algorithm
192(2)
Predictions
193(1)
Integrating an Algorithm to a Web App
194(1)
Initializing a Web App
195(1)
Navigation Bars
195(12)
Search Functionality
200(1)
Containing Interactive Tables for Results
201(2)
Specifying the App Layout and Callbacks for Responsive Side Menus and URL Routing
203(3)
Specifying a Callback to Load Independent Variables Values
206(1)
Specifying a Callback for Loading the Dependent Variable Values
206(1)
Specifying a Callback for Descriptive Statistics
207(6)
Specifying a Callback for Correlation Analysis Results
208(1)
Specifying a Callback for an Algorithm's Predictions
209(1)
Specifying a Callback for an Algorithm's Intercept and Coefficients
210(1)
Specifying a Callback for an Algorithm's Evaluation Results
211(2)
Running the Dash App
213(1)
Conclusions
213(2)
Chapter 12 Deploying a Web App on the Cloud 215(8)
Integrated Development Environment
215(1)
PyCharm
215(1)
Virtual Environment
216(2)
File Structure
218(1)
Integrating Innumerable Python Files
219(1)
Hosting Web Apps
219(1)
Dash Enterprise
219(2)
Heroku
219(2)
Conclusion
221(2)
Index 223
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 bachelors 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.
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