When it comes to appraisal of individual real estate, the quality and quantity of many physical property attributes must be considered for accuracy. Examples of typical questions are not limited to location, size, and the ratio of bedrooms-to-bathrooms. In this project, supervised machine learning is used for the prediction of more than 800 housing prices in Ames, Iowa from 2006-2010. It is demonstrated that the baseline null model can be improved upon by the inclusion of carefully chosen explanatory features. Furthermore, the model produces data-driven insights capable of informing decisions to optimize ROI as it relates to the sale of, or investment in, individual properties.
- Are location and size the most important factors in predicting the selling price of individual real estate?
- What data-driven decisions can property owners make to maximize ROI?
- EDA & Data Cleaning
- Preprocessing & Feature Engineering
- Model Benchmarks
- Model Tuning
- Production Model & Insights
- Recommendations and Next Steps
- Kaggle Submissions
- Software Requirements
- Acknowledgements and Contact
train.csv: Journal of Statistics Education (source | data dictionary)
2,051 homes/properties: ~70%
test.csv: Journal of Statistics Education (source | data dictionary)
878 homes/properties: ~30%
clean_train.csv: Subset of train.csv after cleaning in Notebook 01, saved for use in all notebooks.cat_select_test_m4.csv: Subset of data from categorical features included in Model 4, saved for Kaggle submission.cat_select_train.csv: Subset of data from categorical features included in Model 4, saved for Kaggle submission.
Notes about the data:
- 80 Explanatory Variables:
- 34 Numerical:
- 14 Discrete
- General Description: Number of types of items present, or dates of construction/remodeling.
- NOTE: Consider whether values are better represented as categorical or continuous.
- 20 Continuous
- General Description: Various area-dimensions of each property. * 46 Categorical:
- 23 Ordinal
- General Description: Various ratings of items specific to each property.
- 23 Nominal
- General Description: Various types of items/materials/conditions.
-
Time was spent searching for null values, dropping outliers, and recognizing the simplest approach was a first model with only integer features.
Feature Selection Methodology
- By referring to the Data Library for definitions of sub-categorical features, we can follow best judgement for an initial prediction to beat the baseline Null.
- Successive feature selection was conducted iteratively by adjusting parameters after visualizing feature distributions with box and violin plots in Notebook 02.
- feedback from statsmodels in Notebook 03 I interpretted p-values to improve my RMSE computed in Notebook 04
Left: Sale Price, Right: Log-Level Transformation of Sale Price
NOTICE: The distribution of Sale Price approaches NORMAL following the transfromation. Q: How much does this improve the model?
- Missing Values:
- Garage Area
- Identify Outliers:
- Are relationships to the target linear?
- Null Value Imputation:
- Manage Outliers:
- Combining Features:
- Interaction Terms:
- Do you want to manually drop collinear features?
-with Neighborhoods grouped into classes and visualized by hue 0(blue), 1(orange), or 2(green)
Class 2: Mean Sale Price (All sales) < 1st Quartile (Class 2)
Class 1: Mean Sale Price (All sales) > 3rd Quartile (Class 1)
Class 0: Mean Sale Price (All sales) ~ 2nd Quartile (Class 0 or "other neighborhoods")
Pre-processing
- Set-up Models > Unsupervised Clustering
- One-hot encode categorical variables
- Train/test split the data
- Scale the data
- Consider using automated feature selection
Various metrics, such as MSE, MAE, and adjusted R-Squared, are displayed for evaluation of four linear regression models. The "regression_metrics()" function streamlines the performance evaluation of each model included for comparison.
Pave your driveway! There are many things to consider when selling your home, and yes size plays a role in the final Sale Price, but there are many things that can be done to improve the value of your home rgardless of size. Installing A/C, investing in quality insulation and heating improve the expected worth of homes in this dataset. Since the sample data were representative of the housing population in Ames at the time, property owners would have benefitted from fixing them up before selling.
- [
Journal of Statistics Education] (Taylor & Francis Online): (source) - [
Feature Engineering and Selection: A Practical Approach for Predictive Models] (Online Text): (source) - [
Title] (Platform): (source)
Ames, Iowa: Alternative to the Boston Housing Data(Journal of Statistics Education): (source)Title(Journal/Blog): (source)
| Feature | Type | Dataset | Category | Description |
|---|---|---|---|---|
| Bldg TypeC | integer | 2006-10 Ames, Iowa Assessor’s Office |
'1Fam':0 'TwnhsE':0 'Twnhs':1 '2fmCon':1 'Duplex':1 |
Type of building |
| Central AirC | integer | 2006-10 Ames, Iowa Assessor’s Office |
'Y':0, 'N':1 | Central Air-Conditioning |
| Condition 1C | integer | 2006-10 Ames, Iowa Assessor’s Office |
0: Other, 1:Near/adjacent to major streets/Railroads, 2:Near/Adjacent to poz off-site feature | Proximity to various conditions |
| Exterior 1stC | integer | 2006-10 Ames, Iowa Assessor’s Office |
Asbestos Shingles:1, else:0 | Exterior covering on house |
| Exter QualC | integer | 2006-10 Ames, Iowa Assessor’s Office |
Good/Typical:0, Fair:1, Excellent:2 | Quality of material on exterior |
| FoundationC | integer | 2006-10 Ames, Iowa Assessor’s Office |
CinderBlock/Stone/Wood:0, Brick/Slab:1, PouredConcrete:2 | Type of Foundation |
| Land ContourC | integer | 2006-10 Ames, Iowa Assessor’s Office |
Banked-Quick and significant rise from street grade to building:1, Other:0 | Flatness of the property |
| Lot ConfigC | integer | 2006-10 Ames, Iowa Assessor’s Office |
Cul-de-sac:1, Other:0 | Lot Configuration |
| Lot ShapeC | integer | 2006-10 Ames, Iowa Assessor’s Office |
Most Irregular: 1, Otherwise:0 | General shape of property |
| 1st Flr SF | integer | 2006-10 Ames, Iowa Assessor’s Office |
Continuous | First Floor Square-Feet |
| Garage Area | integer | 2006-10 Ames, Iowa Assessor’s Office |
Continuous | Size of garage in Square-Feet |
| Gr Liv Area | integer | 2006-10 Ames, Iowa Assessor’s Office |
Continuous | Above grade living area in Square-Feet |
| Heating QCC | integer | 2006-10 Ames, Iowa Assessor’s Office |
_1:No 2:Yes | Heating Quality & Condition |
| NeighborhoodC | integer | 2006-10 Ames, Iowa Assessor’s Office |
2: StoneBr, NridgHt, NoRidge 1: Sawyer, BrDale, IDOTRR, MeadowV, SWISU, BrkSide, NPkVill, Blueste, Landmrk 0: All others |
Physical locations within the Ames city limits classified by sample average-'Sale Price'. Class 0 avg Sale Prices were less than 1 stdev above/below the sample avg., Class 02 were above, Class 01 below. |
| Overall Qual | integer | 2006-10 Ames, Iowa Assessor’s Office |
Ordinal: 10-Very Excellent to 1-Very Poor | Overall quality of material and finish |
| Paved DriveC | integer | 2006-10 Ames, Iowa Assessor’s Office |
1:No/Partial, 0:Yes | Paved driveway |
| Total Bsmt SF | integer | 2006-10 Ames, Iowa Assessor’s Office |
Continuous | Total Square-Feet of Basement Area |
| Year Built | integer | 2006-10 Ames, Iowa Assessor’s Office |
Discrete, range: 1872 - 2010 | Original Construction Date |
| Year Remod/Add | integer | 2006-10 Ames, Iowa Assessor’s Office |
Discrete, range: 1950 - 2010 | Remodel date or construction date if no remodel or additions |