Real Estate HousePrice Prediction

Application URL : Housing Price Estimation

Software and account Requirement

1. Github Account
2. Heroku Account
3. VS Code IDE
4. GIT CLI

Setup

Create a conda environment

conda create -p venv python==3.7 -y

Activate conda environment

conda activate venv/

To install requirement file

pip install -r requirements.txt

• Add files to git git add . or git add <file_name>
• To check the git status git status
• To check all version maintained by git git log
• To create version/commit all changes by git git commit -m "message"
• To send version/changes to github git push origin main

To setup CI/CD pipeline in Heroku, we need following info

1. HEROKU_EMAIL = malleswari.gelli@gmail.com
2. Heroku_API_KEY =  <>  
>-- finds in account settings, API key, Reveal, copy the key and use
3. HEROKU_APP_NAME = apptest1

Build docker image

docker build -t <image_name>:<tagname>
>note: Image name for docker must be lowercase

To list docker images

docker images

command to run docker image

docker run -p 5000:5000 -e PORT=5000 7012484a6658 # image id

To check running container in docker

docker ps

To stop docker container

docker stop <container_id or IMAGE_Id> 

python setup.py install

pip install -e . 
> -e. takes care of installing all the packages in the current directory having __init__.py method. Example housing package
> result: Successfully installed housinginsurance-predictor-0.0.3

pip install -r requirements.txt
> installs all external libraries exist (i.e in requirements.txt file)
> result: install numpy, pandas, sklearn etc libraries from requirements.txt file

Install ipykernel

pip install ipykernel

Data drift: Wen your dataset stats gets change, call it as data drift Generate statistics of train and test data sets, if stats are same bet two DATA DRIFT = 0; if there is significant difference, there is data drift

Install Evidently library

pip install evidently