Author: Xavier M. Puspus

I used a sample dataset on insurance pricing and built a simple web application with a machine learning backend to see live changes to predicted/suggested prices based on changes in input data.

You can get the data here.

I only used a low-order machine learning technique for demo purposes. The web app's backend can work for more complex models.

I used the most recently released API of Streamlit to deploy the ml model and locally serve the web app.

In order to run the app, you must have the basic data science packages available on your machine, (pandas, numpy, seaborn, matplotlib, sklearn and install streamlit using:

foo@bar:~$ pip install streamlit

Afterwards, cd into the directory of app.py and run this on the terminal:

foo@bar:~$ streamlit run app.py

The web app should look something like this: