In this article, I will show you how to build a simple machine learning powered data science web app in Python using the streamlit library in less than 50 lines of code.

The data science life cycle is essentially comprised of data collection, data cleaning, exploratory data analysis, model building and model deployment. For more information, please check out the excellent video by Ken Jee on Different Data Science Roles Explained (by a Data Scientist). A summary infographic of this life cycle is shown below:

Data science lifecycle. Drawn by Chanin Nantasenamat.

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.

#data science #machine learning #python #streamlit library #web app