Data Science
How to Build a Data Science Web App in Python (Penguin Classifier)
Part 3: ML-Powered Web App in a Little Over 100 Lines of Code
This is Part 3 and I will be showing you how to build a machine learning powered data science web app in Python using the Streamlit library in a little over 100 lines of code.
The web app that we will be building today is the Penguins Classifier. The demo of this Penguins Classifier web app that we are building is available at http://dp-penguins.herokuapp.com/.
Previously, in Part 1 of this Streamlit tutorial series, I have shown you how to build your first data science web app in Python that is able to fetch stock price data from Yahoo! Finance followed by displaying a simple line chart. In Part 2, I have shown you how to build a machine learning web app using the Iris dataset.
As also explained in previous articles of this Streamlit Tutorial Series, model deployment is an essential and final component of the data science life cycle that helps to bring the power of data-driven insights to the hands of end users whether it be business stakeholders, managers or customers.