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 Description
Streamlit 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 for
This 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.
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
Tyler Richards is a data scientist at Facebook, working on community integrity. Before this gig, his focus was on helping bolster the state of US elections for the nonprofit Protect Democracy. He is a data scientist and industrial engineer by training, which he gets to make use of in fun ways such as applying machine learning to local campus elections, creating algorithms to help P&G target Tide Pod users, and finding ways to determine the best ping pong players in friend groups. He is always looking for a new project, a new adventure.
