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 2 - A cut above - Building Linear Models In the previous posts, we found out which attributes of a diamond are important for its price. An exploratory data analysis showed that carat, clarity and color are the most important features to predict price. Then we built linear models to predict the prices, and we chose the best model by its metrics.
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.