How to Build a Simple Machine Learning Web App in Python

As a Data Scientist or Machine Learning Engineer, it is extremely important to be able to deploy our data science project as this would help to complete the data science life cycle. Traditional deployment of machine learning models with established framework such as Django or Flask may be a daunting and/or time-consuming task. This article is based on a video that I made on the same topic on the Data Professor YouTube channel (How to Build a Simple Machine Learning Web App in Python) in which you can watch it alongside reading this article. Today, we will be building a simple machine learning-powered web app for predicting the class label of Iris flowers as being setosa, versicolor and virginica. This will require the use of three Python libraries namely streamlit, pandas and scikit-learn. Let's take a look at the conceptual flow of the app that will include two major components: (1) the front-end and (2) back-end. In the front-end, the sidebar found on the left will accept input parameters pertaining to features (i.e.