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Full Description
Create, deploy, and test your Python applications, analyses, and models with ease using Streamlit
Key Features
Learn how to showcase machine learning models in a Streamlit application effectively and efficiently
Become an expert Streamlit creator by getting hands-on with complex application creation
Discover how Streamlit enables you to create and deploy apps effortlessly
Book DescriptionStreamlit shortens the development time for the creation of data-focused web applications, allowing data scientists to create web app prototypes using Python in hours instead of days. Getting Started with Streamlit for Data Science takes a hands-on approach to helping you learn the tips and tricks that will have you up and running with Streamlit in no time.
You'll start with the fundamentals of Streamlit by creating a basic app and gradually build on the foundation by producing high-quality graphics with data visualization and testing machine learning models. As you advance through the chapters, you'll walk through practical examples of both personal data projects and work-related data-focused web applications, and get to grips with more challenging topics such as using Streamlit Components, beautifying your apps, and quick deployment of your new apps.
By the end of this book, you'll be able to create dynamic web apps in Streamlit quickly and effortlessly using the power of Python.What you will learn
Set up your first development environment and create a basic Streamlit app from scratch
Explore methods for uploading, downloading, and manipulating data in Streamlit apps
Create dynamic visualizations in Streamlit using built-in and imported Python libraries
Discover strategies for creating and deploying machine learning models in Streamlit
Use Streamlit sharing for one-click deployment
Beautify Streamlit apps using themes, Streamlit Components, and Streamlit sidebar
Implement best practices for prototyping your data science work with Streamlit
Who this book is forThis book is for data scientists and machine learning enthusiasts who want to create web apps using Streamlit. Whether you're a junior data scientist looking to deploy your first machine learning project in Python to improve your resume or a senior data scientist who wants to use Streamlit to make convincing and dynamic data analyses, this book will help you get there! Prior knowledge of Python programming will assist with understanding the concepts covered.
Contents
Table of Contents
An Introduction to Streamlit
Uploading, Downloading, and Manipulating Data
Data Visualization
Using Machine Learning with Streamlit
Deploying Streamlit with Streamlit Sharing
Beautifying Streamlit Apps
Exploring Streamlit Components
Deploying Streamlit Apps with Heroku and AWS
Improving Job Applications With Streamlit
The Data Project - Prototyping Projects in Streamlit
Using Streamlit for Teams
Streamlit Power Users
