This experiment serves as a tutorial on building a regression model using Azure ML. We will be using the house prices data set and build a m
Data This version of the house price details dataset can be retrieved from the Kaggle website, specifically their “train” data (4.49KB). The train house prices ship with 1460 rows, each one represent house data related to house sale transaction, such as SalePrice - the property's sale price in dollars. This is the target variable that you're trying to predict. MSSubClass: The building class MSZoning: The general zoning classification LotFrontage: Linear feet of street connected to property LotArea: Lot size in square feet the dataset contain 11 columns, the target column labeled as “SalePrice” , It equals one house sale price. Algorithm Selection We chose to go with Bayesian Linear Regression . The algorithms were trained with their default settings. The model's performance was evaluated and compared together using a single evaluate model module.