Other posts in this series: Ames Housing - Part 1 - Laying the foundation - Building a Data Pipeline Ames Housing - Part 2 - Walkthrough - An Exploratory Data Analysis In Part 1, we built a data pipeline that cleans the Ames Housing dataset, and in Part 2, we explored the finalized dataset.
In this post, we build linear and non-linear models, and we find out how well they predict the SalePrice of properties. A model is a formula that a computer learns from examples. A linear model adds up the features, each multiplied by a number (a coefficient) that it learns from the data. A non-linear model can also learn curved relationships.
Other posts in this series: Diamonds - Part 1 - In the rough - An Exploratory Data Analysis Diamonds - Part 3 - A polished gem - Building Non-linear Models In a previous post in this series, we explored the diamonds dataset. We found that price has a strong correlation with carat, x, y and z, and that log(price) has an almost straight-line relationship with log(carat). For diamonds of similar weight, clarity and color also seemed to help predict the price.