Advanced-Regression-using-House-Price-Prediction-dataset

In this project, the task is to predict the final price of each home. The dataset has 79 explanatory variables describing (almost) every aspect of the residential homes in Ames, lowa. The application is modelled using Machine learning and explored libraries such as PySpark. Application of containerization principles were used as a better software engineering practice. The model deployed using Docker containers for scalability