Real Estate Supervised ML/AI Linear Regression Revisited - USA House Price Prediction
ETL Workflow Linear regression is an algorithm of supervised Machine Learning (ML) in which the predicted output is continuous with having a constant slope [1]. Consider a company of real estate with datasets containing the property prices of a specific region. The price of a property is based on essential factors like bedrooms, areas, and parking. Majorly, a real estate company requires [1]: Define the set of variables (model features like areas, number of rooms and bathroom, etc.) that affects the price of a house; Creating a linear model quantitatively related to the house price with model va riables; Examine the accuracy of an output model, i.e. how well the model variables can predict the prices of a house for training, test and validation data. So real estate experts assume that the trained, tested and deployed ML model would be capable of learning and predicting how far people would go in the bidding in order to buy a house, based on selected features. That’s the ML housin