2.Render
3.[GitCLI]
4.[VsCode]
5.[Postman]
6.[Docker]
''' Boston House Price Prediction
This project is an end-to-end Machine Learning application that predicts Boston house prices based on various housing features. The model is trained using the ElasticNet Regression algorithm and deployed as a Flask web application.
Features Predict house prices using 13 housing attributes REST API support for programmatic predictions User-friendly web interface built with Flask Model saved and loaded using Joblib Containerized using Docker Automated deployment with GitHub Actions Hosted on Render
Tech Stack Python Pandas & NumPy Scikit-Learn Flask Joblib Docker GitHub Actions Render Model
The model is trained using ElasticNet Regression, which combines both L1 (Lasso) and L2 (Ridge) regularization to improve prediction performance and reduce overfitting.
Project Workflow Data Preprocessing ↓ Model Training (ElasticNet) ↓ Model Serialization (Joblib) ↓ Flask Web Application ↓ Docker Containerization ↓ GitHub Repository ↓ GitHub Actions CI/CD ↓ Render Deployment API Endpoint Predict House Price
POST /predict_api
Example JSON:
{ "CRIM": 0.1, "ZN": 18, "INDUS": 2.3, "CHAS": 0, "NOX": 0.5, "RM": 6.5, "AGE": 65, "DIS": 4.0, "RAD": 1, "TAX": 300, "PTRATIO": 15.3, "B": 390, "LSTAT": 5.0 }
Response:
{ "prediction": 28.88 } Running with Docker
Build Image:
docker build -t bostonhousepricing .
Run Container:
docker run -p 5000:10000 -e PORT=10000 bostonhousepricing
Open:
Create a new enviornment.....
''' conda create -p venv python==3.12.4 -y
To activate Enviornment use "venv\Scripts\activate"
Built image:docker build -t bostonhousepricing .
Ran container:
docker run -p 5000:10000 -e PORT=10000 bostonhousepricing
Successfully started:
Gunicorn Listening at 0.0.0.0:10000 '''