Other posts in this series: Ames Housing - Part 2 - Walkthrough - An Exploratory Data Analysis Ames Housing - Part 3 - Closing the deal - Building Models In this case study, we use the Ames Housing dataset to predict the sale prices of houses. The case study has three posts:
In this post, we build a data pipeline: a series of steps that fixes errors in the data, fills in missing values, creates new features and drops features that we do not need. At the end, we save the finalized dataset. In Part 2, we explore the finalized dataset to find which features affect the sale price. In Part 3, we build models that predict the prices. A model is a formula that a computer learns from examples. Neighborhoods of Ames Location is very important for the price of a house. The version of the dataset that we use (from Kaggle) does not have the location of each property. But the full dataset by Dean De Cock, which is in the R package modeldata, has the latitude and longitude of 2930 properties. We use it to show where the neighborhoods are.