A prototype of a real estate search engine. We leverage machine learning techniques to provide user with a better experience and to help them make good decision. #WeAreAnts (http://weareants.fr/)

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IMMOVIZ
BORDEAUX
Emeline Gaulard - Du Phan1

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WHO ARE WE
EMELINE GAULARD
BACKEND DEVELOPER
EPITECH ’18
DU PHAN
DATA SCIENTIST
ENSC ‘17
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WE
ARE
ANTS
WHERE DO WE WORK
Prototyping Data Science Internet of Things Fun
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Immoviz ?
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Elastic Search
Python
Search
Machine Learning
ipython notebook
pandas/numpy
seaborn/folium
scikit-learn
hyperopt
Backend
NodeJs
Express
CasperJS
PostgreSQL
Slack bot (node-slackr)
TOOLBOX

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Before
INFRASTRUCTURE

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Now
INFRASTRUCTURE
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OUTLINE
SCRAPPERS
ELASTIC SEARCH
DUPLICATE AGGREGATION
PRICE PREDICTION
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Scrappers
CasperJS
PostgreSQL
Elastic SearchReal estate sites
(seloger, leboncoin, bienici, sudouest,…)
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Scrappers
Simple to use
Lightweight
Debugging is non-trivial

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Indexing
Mapping
Analyzing
Querying
Elastic Search
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Analyzer example

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Query example

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Text analysis
Price comparison
Elastic Search
Duplicate aggregation
ID comparison
Price comparison
PostgreSQL
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Duplicate aggregation
ID comparison
Price comparison
PostgreSQL
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Text analysis
Price comparison
Elastic Search
Duplicate aggregation
ID comparison
Price comparison
PostgreSQL
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Error analysis
Cross-validate for testing error
Locate sensitive zone
Visualize error
…
MACHINE LEARNING WORKFLOW
Data Cleaning
Check input format
Split data and hide holdout
Drop/impute null values
Filter outlier
…
Feature Engineering
Extract features
Scale/normalize data
Test contextual data
…
Data Modeling
Cross-validate for model selection
Optimize hyper-parameters
…
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If a data set has
affected any step in
the learning process,
its ability to assess the
outcome has been
compromised.
Data snooping

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k-fold Cross Validation

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Error analysis
Cross-validate for testing error
Locate sensitive zone
Visualize error
…
MACHINE LEARNING WORKFLOW
Data Cleaning
Check input format
Drop/impute null values
Filter outlier
Split data and hide holdout
…
Feature Engineering
Extract features
Scale/normalize data
Test contextual data
…
Data Modeling
Cross-validate for model selection
Optimize hyper-parameters
…
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Source: Professor Yaser Abu-Mostafa, Caltech

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If you torture the data
long enough, it will
confess.
Data snooping

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Some key numbers
60 000 adverts, including 20 432 selling ads
12 839 unique selling ads with 61 features
10 883 selling ads remaining with 52 features after filtering
8 months of data

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Data Cleaning & EDA
Data Modeling
20%
Error Analysis
Allocation of time
10%
20%
Feature Engineering 50%

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Location features
Contextual data (Open
Moulinette)
Imputing Room features
Removing contextual
outliers
Improving ES queries
Feature engineering - what work ?
Time series features
NLP on text data
Dimensionality reduction
Numerical values
transforming/scaling
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Linear Model Tree-based model Average Ensemble
method
Metamodel
Ensemble method
Data Modeling: what algorithms to use ?

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This is how you win ML
competitions: you
take other peoples’
work and ensemble
them together.”
Vitaly Kuznetsov - NIPS2014

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Meta-model ensemble method: explanation

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Kaggle Homesite winner
Source: Homesite Quote Conversion, Winners' Write-Up, 1st Place: KazAnova

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Error analysis: visualization is key

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Error analysis: visualization is key

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Result
Linear Regression
Lasso
Random Forest
Gradient Boosting
Average Ensemble Method
Metamodel Ensemble Method
0 6,5 13 19,5 26
10-fold CV mean error (%)
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Result
12.3% 13.1%
CV mean error Holdout mean error
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8.8% 9.3%
CV median error Holdout median error

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Feature importance

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How to improve the model
More data
Improve ES queries (sector, type, … )
Leverage time series data
More data

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How to improve the model

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Metrics
Recommendation System
User Experience
Speed
What’s next ?

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Conclusion
Better data beats cleverer algorithm
System monitoring is vital
There needs to be a coherent data flow
between backend and ML engine

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Thank you for your attention.
Any questions ?