We are trying to predict housing prices in Ames, Iowa given a dataset of 81 attributes.
Using linear regression methods along with a small subset of the data, we are able to explain more than 85% of the variance of housing prices with our model.
For the Ames dataset, I removed outliers recommended by the Data Document Linked below. I also removed several data points where the lot area was unrealistically large give the cost of the home. There were
| Final Attribute | Data Type | Short Description |
|---|---|---|
| Gr Liv Area | INT64 | Ground Living area in SF |
| Year Built | INT64 | Year home was built |
| Overall Cond Bi | INT64 | 1 if Overall Cond >= 5, else 0 |
| Year Remod/Add | INT64 | Year home remodeled |
| Tot_bath_bsmt | INT64 | Total number of bathrooms in the basement |
| Lot Area | INT64 | Lot size |
| Sale Type Bi | INT64 | 1 if 'New', 2 if 'Oth' or 'CWD', else 0 |
| Outdoor Liv Area | INT64 | Wood Deck SF + Open Porch SF + Screen Porch |
| Bldg Type Bi | INT64 | 1 if '1fam' or 'TwnhmE', else 0 |
| TotRms AbvGrd | INT64 | Total number of rooms above ground |
| Mo Sold | INT64 | Month Sold |
| Yr Sold | INT64 | Year Sold |
| Tot_bath_abv_grd | INT64 | Total number bathrooms above ground |
| Kitchen AbvGr | INT64 | Number of Kitchens above ground |
| Bedroom AbvGr | INT64 | Total number of bedrooms above ground |
Please see the following websites for
- [http://jse.amstat.org/v19n3/decock/DataDocumentation.txt] Data Dictionary of Original Data
- [https://www.kaggle.com/c/dsi-us-6-project-2-regression-challenge] Data Source
Using lasso regression along with a few data transformations, removal of extreme outliers, and creating dummy variables allowed for us to fit a model that is performing with an r2 value of ~85%. The next step, if we were trying to predict exactly the scores, we may want to try more advanced models or use classifying methods to variable select.
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