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.