You work for a real estate company interested in using data science to determine the best properties to buy and re-sell. Specifically, your company would like to identify the characteristics of residential houses that estimate the sale price and the cost-effectiveness of doing renovations.

There are three components to the project:

1. Estimate the sale price of properties based on their "fixed" characteristics, such as neighborhood, lot size, number of stories, etc.
2. Estimate the value of possible changes and renovations to properties from the variation in sale price not explained by the fixed characteristics. Your goal is to estimate the potential return on investment (and how much you should be willing to pay contractors) when making specific improvements to properties.
3. Determine the features in the housing data that best predict "abnormal" sales (forclosures, etc.).

This project uses the Ames housing data recently made available on kaggle.