Test the Application
The final step is to test the web application to ensure it works as expected. This involves navigating to http://localhost:5000, entering values for RM, LSTAT, and CRIM into the form, and submitting it. The application should display the predicted housing price based on the input values.
http://localhost:5000
Deploying a Linear Regression ML model as a Web Application on Docker
Docker is a set of platforms as a service (PaaS) products that use Operating system-level virtualization to deliver software in packages called containers. Containers are isolated from one another and bundle their software, libraries, and configuration files; they can communicate through well-defined channels.
Linear regression is a supervised machine learning algorithm that computes the linear relationship between a dependent variable and one or more independent features.
We can deploy an ML model as a Web Application on Docker. Deploying a Linear Regression ML model as a web application on Docker involves several steps.
- Train the Linear Regression Model
- Build the Flask Web Application
- Dockerize the Flask App
- Build and Run Docker Image
- Test the Application