Data Patterns in Ames Housing

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Posted on Jan 8, 2022

The skills I demoed here can be learned through taking Data Science with Machine Learning bootcamp with NYC Data Science Academy.

Ames Housing

There are many many parties who are greatly interested in accurate understanding of house prices for reasons both personal and financial. Therefore it is a subject of immense value to create good (or in some cases better) models for house prices, as well as deepening our conceptual understanding of the housing market in general.

We use various ML techniques to model the Ames, IA, housing price data, thereby considering different issues and tackling different problems associated with the same data set.

Neighborhood-based Demographic Visualization

We do the usual data cleaning and imputation that you would typically do for any data analysis project.

But in addition, we also do an exploratory analysis of Ames housing development. Specifically, we do a neighborhood and distance-based analysis to aggregate Ames houses and create a visualization of construction trends across over time and how they overlay neighborhoods and price.

House Values in different geographical areas

Data Patterns in Ames Housing

Data Patterns in Ames Housing

Linear Models

We use a generic multi-linear correlation model (implicitly without regularization) and compare that to a LASSO model with L1 penalties. Specifically, we compare the performance of each model on train/test sets to evaluate the possibility of overfitting.

The important features from the lasso regression is below

Data Patterns in Ames Housing

Data Patterns in Ames Housing

Tree Models

We also consider a generic Random Forest model and Gradient Boosting model along with a modified Random Forest model with a Term Structure adjustment. We evaluate the Random Forest vs. the Gradient Boosting model in terms of accuracy and feature importance, and we evaluate the Term Structure adjustment in terms of ensemble improvement to the Random Forest.

About Authors

john kosmicke

John is a quantitative technologist with experience in high frequency trading, recruiting, a master's degree from Iowa State and a bachelors's from Chicago.
View all posts by john kosmicke >

Cherie Wang

I worked in the Pharmaceutical industry and primarily focused on model building for oncology clinical trials. I am excited to learn more about machine learning as I pivot to a career in data science.
View all posts by Cherie Wang >

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