Building a Machine Learning Model API with Streamlit: Easy, Interactive, and Deployable

In today’s data-driven world, serving machine learning models through an API is a critical aspect of making predictive models accessible and useful. It allows businesses and developers to integrate machine learning into various applications, from recommendation systems to fraud detection. In this tutorial, we’ll explore the power of Streamlit, a user-friendly Python framework, to create interactive data-driven apps. Our goal is to guide you through the process of building an interactive Streamlit app that serves machine learning models via an API, making your models easily accessible and actionable.

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Let’s embark on this journey of building a practical and impactful Streamlit application with machine learning at its core.

Getting Started with Streamlit

Streamlit is an open-source Python library that simplifies the process of creating web applications for data science and machine learning. It’s known for its ease of use and allows you to turn data scripts into shareable web apps quickly. In this section, we’ll introduce Streamlit and guide you through the setup of a basic Streamlit app.

Overview of Streamlit:

• User-Friendly: Streamlit is designed for users with little to no web…