Predict House Prices

This is a collaborative predictive modeling project built on the ballet framework.

The goal of this project is to build a feature engineering pipeline that will be used to predict house prices for houses in the Ames dataset.

Join the collaboration

Are you interested in joining the collaboration? In the open-source spirit, anyone with an internet connection can contribute features to the collaboration. In this demonstration project, every feature that is well-formed will be accepted and no features will be pruned. (In a practical deployment, Ballet uses a streaming feature selection algorithm to accept only features that provide an information gain above some threshold, and to prune older features that have been made redundant by newer ones.)

• Read the Ballet Contributor Guide
• Read the Ballet Feature Engineering Guide
• Browse the currently accepted features in the contributed features directory (src/ballet_predict_house_prices/features/contrib)
• Look at example features (examples/)
• Launch an interactive Jupyter Lab session to hack on this repository:

Quickstart

Explore the data

You can load the raw data as follows:

from ballet import b  # magical client for this project
X_df, y_df = b.api.load_data()

The resulting variables are pandas DataFrames.

X_df.head()

PID	MS SubClass	MS Zoning	Lot Frontage	Lot Area	Street	Alley	Lot Shape	Land Contour	Utilities	Lot Config	Land Slope	Neighborhood	Condition 1	Condition 2	Bldg Type	House Style	Overall Qual	Overall Cond	Year Built	Year Remod/Add	Roof Style	Roof Matl	Exterior 1st	Exterior 2nd	Mas Vnr Type	Mas Vnr Area	Exter Qual	Exter Cond	Foundation	Bsmt Qual	Bsmt Cond	Bsmt Exposure	BsmtFin Type 1	BsmtFin SF 1	BsmtFin Type 2	BsmtFin SF 2	Bsmt Unf SF	Total Bsmt SF	Heating	Heating QC	Central Air	Electrical	1st Flr SF	2nd Flr SF	Low Qual Fin SF	Gr Liv Area	Bsmt Full Bath	Bsmt Half Bath	Full Bath	Half Bath	Bedroom AbvGr	Kitchen AbvGr	Kitchen Qual	TotRms AbvGrd	Functional	Fireplaces	Fireplace Qu	Garage Type	Garage Yr Blt	Garage Finish	Garage Cars	Garage Area	Garage Qual	Garage Cond	Paved Drive	Wood Deck SF	Open Porch SF	Enclosed Porch	3Ssn Porch	Screen Porch	Pool Area	Pool QC	Fence	Misc Feature	Misc Val	Mo Sold	Yr Sold	Sale Type	Sale Condition
526301100	20	RL	141	31770	Pave	nan	IR1	Lvl	AllPub	Corner	Gtl	NAmes	Norm	Norm	1Fam	1Story	6	5	1960	1960	Hip	CompShg	BrkFace	Plywood	Stone	112	TA	TA	CBlock	TA	Gd	Gd	BLQ	639	Unf	0	441	1080	GasA	Fa	Y	SBrkr	1656	0	0	1656	1	0	1	0	3	1	TA	7	Typ	2	Gd	Attchd	1960	Fin	2	528	TA	TA	P	210	62	0	0	0	0	nan	nan	nan	0	5	2010	WD	Normal
526350040	20	RH	80	11622	Pave	nan	Reg	Lvl	AllPub	Inside	Gtl	NAmes	Feedr	Norm	1Fam	1Story	5	6	1961	1961	Gable	CompShg	VinylSd	VinylSd	None	0	TA	TA	CBlock	TA	TA	No	Rec	468	LwQ	144	270	882	GasA	TA	Y	SBrkr	896	0	0	896	0	0	1	0	2	1	TA	5	Typ	0	nan	Attchd	1961	Unf	1	730	TA	TA	Y	140	0	0	0	120	0	nan	MnPrv	nan	0	6	2010	WD	Normal
526351010	20	RL	81	14267	Pave	nan	IR1	Lvl	AllPub	Corner	Gtl	NAmes	Norm	Norm	1Fam	1Story	6	6	1958	1958	Hip	CompShg	Wd Sdng	Wd Sdng	BrkFace	108	TA	TA	CBlock	TA	TA	No	ALQ	923	Unf	0	406	1329	GasA	TA	Y	SBrkr	1329	0	0	1329	0	0	1	1	3	1	Gd	6	Typ	0	nan	Attchd	1958	Unf	1	312	TA	TA	Y	393	36	0	0	0	0	nan	nan	Gar2	12500	6	2010	WD	Normal
526353030	20	RL	93	11160	Pave	nan	Reg	Lvl	AllPub	Corner	Gtl	NAmes	Norm	Norm	1Fam	1Story	7	5	1968	1968	Hip	CompShg	BrkFace	BrkFace	None	0	Gd	TA	CBlock	TA	TA	No	ALQ	1065	Unf	0	1045	2110	GasA	Ex	Y	SBrkr	2110	0	0	2110	1	0	2	1	3	1	Ex	8	Typ	2	TA	Attchd	1968	Fin	2	522	TA	TA	Y	0	0	0	0	0	0	nan	nan	nan	0	4	2010	WD	Normal
527105010	60	RL	74	13830	Pave	nan	IR1	Lvl	AllPub	Inside	Gtl	Gilbert	Norm	Norm	1Fam	2Story	5	5	1997	1998	Gable	CompShg	VinylSd	VinylSd	None	0	TA	TA	PConc	Gd	TA	No	GLQ	791	Unf	0	137	928	GasA	Gd	Y	SBrkr	928	701	0	1629	0	0	2	1	3	1	TA	6	Typ	1	TA	Attchd	1997	Fin	2	482	TA	TA	Y	212	34	0	0	0	0	nan	MnPrv	nan	0	3	2010	WD	Normal

For a detailed data dictionary, see here.

The resulting target is a pandas Series.

y_df.head()

SalePrice
215000
105000
172000
244000
189900

Run the existing pipeline

You can see the feature values that are extracted by the existing feature engineering pipeline:

from ballet import b  # magical client for this project
result = b.api.engineer_features(X_df, y_df)
X_train, y_train = result.X, result.y

Create your own feature

See detailed info in the Contributor Guide and the Feature Engineering Guide.

Here are some hints on coming up with ideas for new features

1. Treat it like a real data science process, and through exploratory analysis and your own intuition, try to find variables or combinations of variables that when transformed appropriately have high predictive power to the target (house selling price).
2. Look at example features that are provided alongside the project (examples/).
3. Look at notebooks that Kagglers have created for this same problem (https://www.kaggle.com/c/house-prices-advanced-regression-techniques/notebooks).