House prices report

This document is a data science report of the kaggle house prices tutorial project. It was generated using the Shapash library.

General Information

Version : 0.7

Name : House Prices Prediction Project

Purpose : Predicting the sale price of houses

Date : 2026-07-09

Contributors : Yann Golhen, Sebastien Bidault, Thomas Bouche, Guillaume Vignal, Thibaud Real

Description : This work is a data science project that tries to predict the sale of houses based on 79 explanatory variables. It was designed inside the data science team at X. and improved since the beggining of the project in 2019. The model was put into production since February 2021.

Source Code : https://github.com/MAIF/shapash/tree/master/tutorial

Git Commit : 1ff46e83beafba8949a7f3b7de27586acd6ae99e

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Dataset Information

Path : https://www.kaggle.com/c/house-prices-advanced-regression-techniques/

Origin : The Assessor’s Office

Description : the sale of individual residential property in Ames, Iowa

Depth : from 2006 to 2010

Perimeter : only residential sales

Target Variable : SalePrice

Target Description : The property's sale price in dollars

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Data Preparation

Variable Filetring : All variables that required special knowledge or previous calculations for their use were removed

Individual Filtering : only the most recent sales data on any property were kept (for houses that were sold multiple times during this period)

Missing Values : were replaced by 0

Feature Engineering : No feature was created. All features are directly taken from the kaggle dataset. Categorical features were transformed using an ordinal encoder.

Path To Script : https://github.com/MAIF/shapash/tree/master/tutorial/

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Model Training

Used Algorithm : We used a RandomForestRegressor algorithm (scikit-learn) but this model could be challenged with other interesting models such as XGBRegressor, Neural Networks, ...

Parameters Choice : We did not perform any hyperparameter optimisation and chose to use n_estimators=50. Future works should be planned to perform gridsearch optimizations

Metrics : Mean Squared Error metric

Validation Strategy : We splitted our data into train (75%) and test (25%)

Path To Script : https://github.com/MAIF/shapash/tree/master/tutorial/

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Model analysis

Model used : RandomForestRegressor

Library : sklearn.ensemble._forest

Library version : 1.8.0

Model parameters :

Parameter key	Parameter value
estimator	DecisionTreeRegressor()
n_estimators	50
estimator_params	('criterion', 'max_depth', 'min_samples_split', 'min_samples_leaf', 'min_weight_fraction_leaf', 'max_features', 'max_leaf_nodes', 'min_impurity_decrease', 'random_state', 'ccp_alpha', 'monotonic_cst')
bootstrap	True
oob_score	False
n_jobs	None
random_state	None
verbose	0
warm_start	False
class_weight	None
max_samples	None
criterion	squared_error
max_depth	None
min_samples_split	2

Parameter key	Parameter value
min_samples_leaf	1
min_weight_fraction_leaf	0.0
max_features	1.0
max_leaf_nodes	None
min_impurity_decrease	0.0
ccp_alpha	0.0
monotonic_cst	None
feature_names_in_	['1stFlrSF' '2ndFlrSF' '3SsnPorch' 'BedroomAbvGr' 'BldgType' 'BsmtCond' 'BsmtExposure' 'BsmtFinSF1' 'BsmtFinSF2' 'BsmtFinType1' 'BsmtFinType2' 'BsmtFullBath' 'BsmtHalfBath' 'BsmtQual' 'BsmtUnfSF' 'CentralAir' 'Condition1' 'Condition2' 'Electrical' 'EnclosedPorch' 'ExterCond' 'ExterQual' 'Exterior1st' 'Exterior2nd'...
n_features_in_	72
_n_samples	1095
n_outputs_	1
_n_samples_bootstrap	1095
estimator_	DecisionTreeRegressor()
estimators_	[DecisionTreeRegressor(max_features=1.0, random_state=653785649), DecisionTreeRegressor(max_features=1.0, random_state=1433934617), DecisionTreeRegressor(max_features=1.0, random_state=1136066427), DecisionTreeRegressor(max_features=1.0, random_state=2030394820), DecisionTreeRegressor(max_features=1.0,...

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Dataset analysis

Global analysis

	Training dataset	Prediction dataset
number of features	72	72
number of observations	1,095	365
missing values	0	0
% missing values	0	0

Univariate analysis

Feature

• $Value of miscellaneous feature
• Basement full bathrooms
• Basement half bathrooms
• Bedrooms above grade
• Building Class
• Central air conditioning
• Condition of sale
• Electrical system
• Enclosed porch area in square feet
• Exterior covering on house
• Exterior materials' condition
• Exterior materials' quality
• First Floor square feet
• Flatness of the property
• Full bathrooms above grade
• Garage condition
• Garage location
• Garage quality
• General condition of the basement
• General shape of property
• General zoning classification
• Ground living area square feet
• Half baths above grade
• Heating quality and condition
• Height of the basement
• Home functionality
• Interior finish of the garage?
• Kitchen quality
• Kitchens above grade
• Lot configuration
• Lot size square feet
• Low quality finished square feet
• Masonry veneer area in square feet
• Masonry veneer type
• Month Sold
• Number of fireplaces
• Open porch area in square feet
• Original construction date
• Other exterior covering on house
• Overall condition of the house
• Overall material and finish of the house
• Paved driveway
• Physical locations within Ames city limits
• Pool area in square feet
• Proximity to other various conditions
• Proximity to various conditions
• Rating of basement finished area
• Rating of basement finished area (if present)
• Refers to walkout or garden level walls
• Remodel date
• Roof material
• Screen porch area in square feet
• Second floor square feet
• Size of garage in square feet
• Slope of property
• Style of dwelling
• Three season porch area in square feet
• Total rooms above grade
• Total square feet of basement area
• Type 1 finished square feet
• Type 2 finished square feet
• Type of dwelling
• Type of foundation
• Type of heating
• Type of road access
• Type of roof
• Type of sale
• Type of utilities available
• Unfinished square feet of basement area
• Wood deck area in square feet
• Year Sold
• Year garage was built

MiscVal - Numeric

$Value of miscellaneous feature

	Training dataset	Prediction dataset
count	1,095	365
mean	51.5	19.3
std	569	111
min	0	0
25%	0	0
50%	0	0
75%	0	0
max	15,500	1,200

BsmtFullBath - Categorical

Basement full bathrooms

	Training dataset	Prediction dataset
distinct values	4	3
missing values	0	0

BsmtHalfBath - Categorical

Basement half bathrooms

	Training dataset	Prediction dataset
distinct values	3	3
missing values	0	0

BedroomAbvGr - Categorical

Bedrooms above grade

	Training dataset	Prediction dataset
distinct values	8	7
missing values	0	0

MSSubClass - Categorical

Building Class

	Training dataset	Prediction dataset
distinct values	15	15
missing values	0	0

CentralAir - Categorical

Central air conditioning

	Training dataset	Prediction dataset
distinct values	2	2
missing values	0	0

SaleCondition - Categorical

Condition of sale

	Training dataset	Prediction dataset
distinct values	6	6
missing values	0	0

Electrical - Categorical

Electrical system

	Training dataset	Prediction dataset
distinct values	5	4
missing values	0	0

EnclosedPorch - Numeric

Enclosed porch area in square feet

	Training dataset	Prediction dataset
count	1,095	365
mean	23	18.8
std	63.2	54.5
min	0	0
25%	0	0
50%	0	0
75%	0	0
max	552	272

Exterior1st - Categorical

Exterior covering on house

	Training dataset	Prediction dataset
distinct values	14	12
missing values	0	0

ExterCond - Categorical

Exterior materials' condition

	Training dataset	Prediction dataset
distinct values	5	3
missing values	0	0

ExterQual - Categorical

Exterior materials' quality

	Training dataset	Prediction dataset
distinct values	4	4
missing values	0	0

1stFlrSF - Numeric

First Floor square feet

	Training dataset	Prediction dataset
count	1,095	365
mean	1,180	1,120
std	400	341
min	334	483
25%	886	864
50%	1,100	1,050
75%	1,420	1,320
max	4,690	2,630

LandContour - Categorical

Flatness of the property

	Training dataset	Prediction dataset
distinct values	4	4
missing values	0	0

FullBath - Categorical

Full bathrooms above grade

	Training dataset	Prediction dataset
distinct values	4	4
missing values	0	0

GarageCond - Categorical

Garage condition

	Training dataset	Prediction dataset
distinct values	5	4
missing values	0	0

GarageType - Categorical

Garage location

	Training dataset	Prediction dataset
distinct values	6	6
missing values	0	0

GarageQual - Categorical

Garage quality

	Training dataset	Prediction dataset
distinct values	5	4
missing values	0	0

BsmtCond - Categorical

General condition of the basement

	Training dataset	Prediction dataset
distinct values	4	4
missing values	0	0

LotShape - Categorical

General shape of property

	Training dataset	Prediction dataset
distinct values	4	4
missing values	0	0

MSZoning - Categorical

General zoning classification

	Training dataset	Prediction dataset
distinct values	5	5
missing values	0	0

GrLivArea - Numeric

Ground living area square feet

	Training dataset	Prediction dataset
count	1,095	365
mean	1,520	1,490
std	530	512
min	334	605
25%	1,130	1,130
50%	1,470	1,460
75%	1,790	1,730
max	5,640	4,480

HalfBath - Categorical

Half baths above grade

	Training dataset	Prediction dataset
distinct values	3	3
missing values	0	0

HeatingQC - Categorical

Heating quality and condition

	Training dataset	Prediction dataset
distinct values	4	5
missing values	0	0

BsmtQual - Categorical

Height of the basement

	Training dataset	Prediction dataset
distinct values	4	4
missing values	0	0

Functional - Categorical

Home functionality

	Training dataset	Prediction dataset
distinct values	7	5
missing values	0	0

GarageFinish - Categorical

Interior finish of the garage?

	Training dataset	Prediction dataset
distinct values	3	3
missing values	0	0

KitchenQual - Categorical

Kitchen quality

	Training dataset	Prediction dataset
distinct values	4	4
missing values	0	0

KitchenAbvGr - Categorical

Kitchens above grade

	Training dataset	Prediction dataset
distinct values	3	3
missing values	0	0

LotConfig - Categorical

Lot configuration

	Training dataset	Prediction dataset
distinct values	5	5
missing values	0	0

LotArea - Numeric

Lot size square feet

	Training dataset	Prediction dataset
count	1,095	365
mean	10,600	10,300
std	10,200	9,410
min	1,300	1,600
25%	7,500	7,740
50%	9,500	9,300
75%	11,600	11,500
max	215,000	165,000

LowQualFinSF - Numeric

Low quality finished square feet

	Training dataset	Prediction dataset
count	1,095	365
mean	5.18	7.83
std	46.4	54.7
min	0	0
25%	0	0
50%	0	0
75%	0	0
max	572	513

MasVnrArea - Numeric

Masonry veneer area in square feet

	Training dataset	Prediction dataset
count	1,095	365
mean	101	109
std	173	203
min	0	0
25%	0	0
50%	0	0
75%	168	145
max	1,600	1,380

MasVnrType - Categorical

Masonry veneer type

	Training dataset	Prediction dataset
distinct values	4	4
missing values	0	0

MoSold - Categorical

Month Sold

	Training dataset	Prediction dataset
distinct values	12	12
missing values	0	0

Fireplaces - Categorical

Number of fireplaces

	Training dataset	Prediction dataset
distinct values	4	4
missing values	0	0

OpenPorchSF - Numeric

Open porch area in square feet

	Training dataset	Prediction dataset
count	1,095	365
mean	46.9	46.1
std	67.6	62.1
min	0	0
25%	0	0
50%	26	24
75%	66	72
max	547	341

YearBuilt - Numeric

Original construction date

	Training dataset	Prediction dataset
count	1,095	365
mean	1,970	1,970
std	30.3	29.8
min	1,870	1,880
25%	1,950	1,950
50%	1,970	1,970
75%	2,000	2,000
max	2,010	2,010

Exterior2nd - Categorical

Other exterior covering on house

	Training dataset	Prediction dataset
distinct values	16	14
missing values	0	0

OverallCond - Categorical

Overall condition of the house

	Training dataset	Prediction dataset
distinct values	8	9
missing values	0	0

OverallQual - Categorical

Overall material and finish of the house

	Training dataset	Prediction dataset
distinct values	10	10
missing values	0	0

PavedDrive - Categorical

Paved driveway

	Training dataset	Prediction dataset
distinct values	3	3
missing values	0	0

Neighborhood - Categorical

Physical locations within Ames city limits

	Training dataset	Prediction dataset
distinct values	25	25
missing values	0	0

PoolArea - Categorical

Pool area in square feet

	Training dataset	Prediction dataset
distinct values	6	3
missing values	0	0

Condition2 - Categorical

Proximity to other various conditions

	Training dataset	Prediction dataset
distinct values	7	3
missing values	0	0

Condition1 - Categorical

Proximity to various conditions

	Training dataset	Prediction dataset
distinct values	9	8
missing values	0	0

BsmtFinType1 - Categorical

Rating of basement finished area

	Training dataset	Prediction dataset
distinct values	6	6
missing values	0	0

BsmtFinType2 - Categorical

Rating of basement finished area (if present)

	Training dataset	Prediction dataset
distinct values	6	6
missing values	0	0

BsmtExposure - Categorical

Refers to walkout or garden level walls

	Training dataset	Prediction dataset
distinct values	4	4
missing values	0	0

YearRemodAdd - Numeric

Remodel date

	Training dataset	Prediction dataset
count	1,095	365
mean	1,990	1,980
std	20.6	20.7
min	1,950	1,950
25%	1,970	1,960
50%	1,990	1,990
75%	2,000	2,000
max	2,010	2,010

RoofMatl - Categorical

Roof material

	Training dataset	Prediction dataset
distinct values	7	5
missing values	0	0

ScreenPorch - Numeric

Screen porch area in square feet

	Training dataset	Prediction dataset
count	1,095	365
mean	16.4	11
std	58	48.3
min	0	0
25%	0	0
50%	0	0
75%	0	0
max	480	396

2ndFlrSF - Numeric

Second floor square feet

	Training dataset	Prediction dataset
count	1,095	365
mean	342	362
std	435	442
min	0	0
25%	0	0
50%	0	0
75%	728	728
max	1,870	2,060

GarageArea - Numeric

Size of garage in square feet

	Training dataset	Prediction dataset
count	1,095	365
mean	475	466
std	211	222
min	0	0
25%	329	336
50%	480	466
75%	576	576
max	1,420	1,390

LandSlope - Categorical

Slope of property

	Training dataset	Prediction dataset
distinct values	3	3
missing values	0	0

HouseStyle - Categorical

Style of dwelling

	Training dataset	Prediction dataset
distinct values	8	8
missing values	0	0

3SsnPorch - Numeric

Three season porch area in square feet

	Training dataset	Prediction dataset
count	1,095	365
mean	3.72	2.48
std	31.6	21.1
min	0	0
25%	0	0
50%	0	0
75%	0	0
max	508	245

TotRmsAbvGrd - Categorical

Total rooms above grade

	Training dataset	Prediction dataset
distinct values	12	10
missing values	0	0

TotalBsmtSF - Numeric

Total square feet of basement area

	Training dataset	Prediction dataset
count	1,095	365
mean	1,070	1,030
std	453	392
min	0	0
25%	799	780
50%	996	972
75%	1,320	1,240
max	6,110	2,630

BsmtFinSF1 - Numeric

Type 1 finished square feet

	Training dataset	Prediction dataset
count	1,095	365
mean	453	416
std	465	429
min	0	0
25%	0	0
50%	399	350
75%	722	678
max	5,640	2,100

BsmtFinSF2 - Numeric

Type 2 finished square feet

	Training dataset	Prediction dataset
count	1,095	365
mean	44.7	52
std	162	159
min	0	0
25%	0	0
50%	0	0
75%	0	0
max	1,470	1,060

BldgType - Categorical

Type of dwelling

	Training dataset	Prediction dataset
distinct values	5	5
missing values	0	0

Foundation - Categorical

Type of foundation

	Training dataset	Prediction dataset
distinct values	6	5
missing values	0	0

Heating - Categorical

Type of heating

	Training dataset	Prediction dataset
distinct values	6	3
missing values	0	0

Street - Categorical

Type of road access

	Training dataset	Prediction dataset
distinct values	2	2
missing values	0	0

RoofStyle - Categorical

Type of roof

	Training dataset	Prediction dataset
distinct values	6	6
missing values	0	0

SaleType - Categorical

Type of sale

	Training dataset	Prediction dataset
distinct values	9	8
missing values	0	0

Utilities - Categorical

Type of utilities available

	Training dataset	Prediction dataset
distinct values	2	1
missing values	0	0

BsmtUnfSF - Numeric

Unfinished square feet of basement area

	Training dataset	Prediction dataset
count	1,095	365
mean	570	558
std	446	429
min	0	0
25%	224	217
50%	483	464
75%	812	796
max	2,340	2,040

WoodDeckSF - Numeric

Wood deck area in square feet

	Training dataset	Prediction dataset
count	1,095	365
mean	96	89
std	124	131
min	0	0
25%	0	0
50%	0	0
75%	168	164
max	736	857

YrSold - Categorical

Year Sold

	Training dataset	Prediction dataset
distinct values	5	5
missing values	0	0

GarageYrBlt - Numeric

Year garage was built

	Training dataset	Prediction dataset
count	1,095	365
mean	1,980	1,980
std	26.6	25.5
min	1,870	1,900
25%	1,960	1,960
50%	1,980	1,980
75%	2,000	2,000
max	2,010	2,010

Target analysis

SalePrice - Numeric

	Training dataset	Prediction dataset
count	1,095	365
mean	182,000	177,000
std	78,500	82,000
min	34,900	40,000
25%	130,000	126,000
50%	165,000	160,000
75%	215,000	205,000
max	755,000	745,000

Multivariate analysis