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 St