HousingPricePredictionMLOps

Problem Statement

You are hired by a company, Home Realty Co Ltd. You are provided with a dataset containing details about various houses and their selling prices. The company is looking to optimize its profit by distinguishing between higher and lower priced houses. Your task is to help the company predict the price of a house based on the given attributes, so they can better assess the value of each property and make informed decisions to maximize their profit. Additionally, identify and provide the top 5 attributes that are the most important in determining the house prices.

Data Dictionary

• Area - The total area of the house in square feet.
• Bedrooms - The number of bedrooms in the house.
• Bathrooms - The number of bathrooms in the house.
• Stories - The number of stories (levels) in the house.
• Mainroad - Indicates whether the house is located on a main road (Yes or No).
• Guestroom - Indicates whether the house has a guestroom (Yes or No).
• Basement - Indicates whether the house has a basement (Yes or No).
• Hotwaterheating - Indicates whether the house has hot water heating (Yes or No).
• Airconditioning - Indicates whether the house has air conditioning (Yes or No).
• Parking - The number of parking spaces available with the house.
• Prefarea - Indicates whether the house is in a preferred area (Yes or No).
• Furnishingstatus - The furnishing status of the house, with options: Not Furnished, Semi-Furnished, Furnished.
• Price - The selling price of the house.

Create project template hierarchy

python template.py

Setup development environment

bash init_setup.sh

Acivate environment

source activate ./env

Install project as local package

pip install -r requirement.txt

Pipelines

Training Pipeline

• Data Ingestion (fetched data from source)
• Data Transformation (Feature Engineering, Data Preprocessing)
• Model Builing (Create a model using the processed data)

MLFlow

python training_pipeline.py

mlflow ui

Data version control (DVC)

dvc init

dvc add notebooks/data/Housing.csv

git add .

git commit -m "Add data"

git push

git log

git checkout <commit ID>

dvc checkout

Command to train the pipeline

python training_pipeline.py

Prediction Pipeline

• Two types of prediction pipeline
• Single record prediction
• Batch prediction

Explainer Dashboard

• Feature Importance
• Regression Stats
• Individual Predictions
• What if?
• Feature Dependence

python dashboard.py

Flask App

python app.py

Streamlit App

streamlit run streamlit_app.py

Deployment of DockerImage

docker build -t housing_price_prediction .

docker run -p 8000:5000 housing_price_prediction

docker login

docker tag housing_price_prediction asangkumarsingh/unique_housing_price_prediction

docker push asangkumarsingh/unique_housing_price_prediction

Docker hub repo:

• https://hub.docker.com/r/asangkumarsingh/projecthousinpriceprediction/tags

Dataset Link

• https://www.kaggle.com/datasets/yasserh/housing-prices-dataset

PYI PI Repo

• https://pypi.org/project/uniqueHousingPricePredictionMLOps/