PyCaret and Streamlit: How to Create and Deploy Data Science Web App
Building a wine classifier with PyCaret and using Streamlit to create and deploy the web app
Building and deploying a machine learning model have never been easier. Right now, we have a lot of frameworks and libraries that enable us to build machine learning models with just a few lines of code. Among all of them, PyCaret is one of the best. To create and deploy a web app for our data science project, Streamlit has become very popular lately.
In this article, we will use these two libraries to create a data science web app. We’re going to use PyCaret to build a wine quality classifier. Next, we’re going to use Streamlit to create and deploy this wine classifier. You’ll be surprised of how easy and quick it is to build the classifier and deploy the web app with these two libraries. So, let’s get started!
Load and Preprocess the Data
The data that we will use in this article is the Wine Quality dataset, which you can download for free here. This dataset consists of 1599 instances with 12 features. Let’s load the dataset with Pandas.