Predicting Home Prices in Ames, IA Using Regression Models

Posted on Mar 24, 2018

This project was the result of a team effort along with Dean GoldmanMichael Chin, and Matthew Sun to enter into the Housing Prices in Ames, Iowa Kaggle Competition.

The objective of the project was to find a model minimizing the mean root squared error between the log of predicted housing sale prices in our model and actual housing models. The log of the sale prices was used as the metric in order to minimize the effect of extremely expensive and cheap sale prices on our model.


Reducing dimensionality of categorical variables to determine feature importance regarding sale prices of homes in Ames, Iowa


We took the steps of cleaning our data set by taking care of missing values. We attempted to impute missing values using mode, mean, classification, and a number of other methods. In order to do this we needed to organize our data into numeric, categorical, and ordinal types. From there, we started our feature engineering by creating dummy values for the categorical variables. We then explored the effects of PCA analysis, lasso regression, and decision trees to find importance of certain field. Our studies are detailed in this presentation.

Feel free to review our project contents in our Github repository.

Ensembling used to test our model


About Author

Kweku Ulzen

A lover of technology and data, Kweku is always interested in exploring how the two intersect to affect society and provide insights into the most pressing and interesting issues. He is a graduate of the University of Alabama...
View all posts by Kweku Ulzen >

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