1. Fork this repository
2. Clone your fork of this repo
3. Create a new conda environment from the yml file in this repo
   • In your terminal, navigate to your clone of this repo, then run:

   conda env create -n streamlit --file st-environment.yml
   

BONUS: The session will go a lot smoother if you have a code editor installed! I'll be using VS Code.

By default for Windows users, VS Code will create a code command you can use from Git Bash to launch VS Code easily.

Mac users can set up the same command by following the instructions found here.

1. Explore Streamlit documentation and create a simple app.py file

   • Using st.write()
   • Creating and displaying data
   • Creating and displaying visualizations
   • Utilizing inputs through input widgets

2. Walk through an example project to be deployed

   • How to think about model inputs when designing your model
   • How to pickle a fit sklearn model
   • How to test using a pickled model before deployment

3. Create a Streamlit app to take in inputs, transform data appropriately, load a pickled model, and make predictions on new incoming data