Without Code: Data Science Predicting the Value of Hong Kong Properties: A Step-By-Step Tutorial Using Azure Machine
Predicting the Value of Hong Kong Properties A Step-By-Step Tutorial Using Azure Machine Learning Overview Learn how companies like Zillow would predict the value of your property in Hong Kong. In this tutorial you will learn how to build a model to predict the real estate sales price of a property based upon various historical features about the house and the sales transaction. About the Data The Hong Kong Island areas of Central, Sheung Wan and Sai Wan private housing dataset is a Excel Comma Separated Value (CSV) text file, which include 29 features and 1139 observations. Each observation represents the sale of a home and each feature is an attribute describing the house or the circumstance of the sale. A data dictionary has been provided to explain the features, click here for the full list of feature descriptions. Objective of this tutorial As a partner of a property investment company, your objective is to make a profit from investing in and the eventual sale of invested properties. To do this, you need a solid property prediction model based on historical property transactions. To enable the prediction of future property prices from your prediction model compared against prevailing asking prices. So that the future sale of a property will bring in a nice profit.