non linear

Ames Housing - Part 2 - Building Models

In a previous post in this series, we did an exploratory data analysis of the Ames Housing dataset. In this post, we will build linear and non-linear models and see how well they predict the SalePrice of properties. Evaluation Criteria Root-Mean-Squared-Error (RMSE) between the logarithm of the predicted value and the logarithm of the observed SalePrice will be our evaluation criteria. Taking the log ensures that errors in predicting expensive and cheap houses will affect the result equally.

Diamonds - Part 3 - A polished gem - Building Non-linear Models

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 a couple of previous posts, we tried to understand what attributes of diamonds are important to determine their prices. We showed that carat, clarity and color are the most important predictors of price. We arrived at this conclusion after doing a detailed exploratory data analysis.