Boston House Pricing

Project aim: The aim of the project includes the following:

1. Training a house price prediction Regression model
2. Creating a simple flask app to make predictions based on user inputs
3. Dockerizing it
4. Creating a github actions
5. Deploying it on Heroku cloud

Project requirements: python, pandas, numpy, flask, scikit-learn and docker

Tools used: VS Code and Linux CLI

Software and Tools Download Links:

1. Github Account
2. Hereku Account
3. VSCode IDE
4. GitCLI

Steps involved in the project:

1. Create a new environment for the project

conda create -p venv python==3.7 -y

2. Activate the environment conda activate venv/

3. Create a requirements.txt file run pip install -r requirements.txt

4. Create flask based application

• Creating a home template home.html
• Added a form based input and prediction api

5. Create a github repository and push all files

6. Deploying it to heroku cloud

• Deploy using github repo option

7. Docker based Deployment

• Create a Procfile
• Create a Dockerfile

8. Creating github actions CI/CD pipeline

• Create a .github/workflows directory
• Add a github action main.yaml file

9. Push files to github and deploy the container on Heroku cloud

Application link: House Price Prediction Application