Integration of a ML model in Render using Streamlit - Step by step guide

Find and understand a new dataset or use the model from the previous project.
Integrate it into Render using a Streamlit based application.

🌱 How to start this project

Follow the instructions below:

Create a new repository based on machine learning project by clicking here.
Open the newly created repository in Codespace using the Codespace button extension.
Once the Codespace VSCode has finished opening, start your project by following the instructions below.

🚛 How to deliver this project

Once you have finished solving the exercises, be sure to commit your changes, push to your repository and go to 4Geeks.com to upload the repository link.

📝 Instructions

Step 1: Train a new model or choose the one from the previous project.

In the Deployment with Flask module we search for a dataset and train a model that we later use in a web application developed in Flask and deploy in Render. In this project you can use the same model and change only the web application, or find another dataset and train a new model.

Step 2: Develop a web application using Streamlit

With the knowledge acquired in this module, develop an interface to be able to use the model. Style it as you see fit and note any external resources you have used for development.

Step 3: Integrate the model and the application in Render

Create a free service in Render and integrate the work you have done to be able to deploy the web application online. Don't forget to include the link to the service in your repository.