Boston house price prediction API made using python flask

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Boston house price prediction using API and dockerising the soution

Automated API deployment for Boston house price prediction on Cluster Kubernetes.

House Price prediction web application using Streamlit and API building using Postman. Though the boston dataset has been removed from scikit-learn.

Developed and deployed a Boston House Price Prediction model using machine learning techniques. The project is hosted on Render with a Flask-based API for real-time predictions. Git was utilized for version control, ensuring efficient collaboration and management of the repository.

Built a machine learning–based house price prediction system using the Boston Housing dataset. Performed data preprocessing, feature engineering, and trained regression models including Random Forest. Achieved an R² score of 0.87 and deployed the model as a Flask REST API for real-time predictions.