/CI-CD-Pipeline-for-house-prediction

CI/CD Pipeline for house prediction

Primary LanguagePythonMIT LicenseMIT

CI-CD-Pipeline-for-house-prediction

Python application test with Github Actions

CI/CD Pipeline for house price prediction

Project Plan

  • A link to a Trello board for the project
  • A link to a Spreadsheet that includes the original and final project plan

Instructions

  • Architectural Diagram that shows how key parts of the system work Architectural Diagram

Instructions for running the Python project.

  • Project running on Azure App Service img

First start by cloning this repository

foo@bar:~$ git clone https://github.com/Rbaibi/CI-CD-Pipeline-for-house-prediction.git
  • Project cloned into Azure Cloud Shell img

Run the make all command in azure bash

foo@bar:~$ make all
  • Passing tests that are displayed after running the make all command from the Makefile img

Create the serverless webapp by running the following command

foo@bar:~$ az webapp up --name flaskserverlesswebapp --resource-group Azuredevops --runtime "PYTHON:3.7"

To get an output of the streamed log files use

foo@bar:~$ az webapp log tail
  • Output of streamed log files from deployed application img

Test out whether you can make a prediction

foo@bar:~$ ./make_predict_azure_app.sh
  • Successful prediction from deployed flask app in Azure Cloud Shell.

The output should look similar to this: img

  • Output of a test run img

  • Successful deploy of the project in Azure Pipelines.
    img

  • Running Azure App Service from Azure Pipelines automatic deployment img

  • Locust test img img

Enhancements

How to improve the project in the future

  • Collect more data
  • Improve the ML model
  • Create a Graphical User Interface

Demo

Link Screencast on YouTube