Designing machines for Data Mining

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A4Everyone

Data analytics challenges in Retail

Data & Analytics

DESIGNING MACHINES FOR DATA MINING
Alexander Efremov
Challenges in Retail Industry
http://www.a4everyone.com

 Data Science, Data
 Data Mining Workflows – Specifics
 Analytics for Retail
Agenda
http://www.a4everyone.com

Data?
Data Science?
http://www.a4everyone.com

Data Mining Workflows – Specifics
 Use Data
 Use Business Logic
 Reduce/Remove human intervention
http://www.a4everyone.com

Analytics for Retail
 Why A4Retail?
Demand forecast, Storage planning, Price Optimization, etc.
 Retail System – Spec.
Dynamic, Time-Varying, Non-Linear, MIMO system
 Retail Data – Spec.
Missings, Outliers, Uninformative vars, Seasonality, Trend, etc.
http://www.a4everyone.com

……
Analytics for Retail
………
Retail
System
product1:
price, disc., adv., disp...
sale1
salen
…
weather
social events
other
productn:
price, disc., adv., disp...
http://www.a4everyone.com
…

Analytics for Retail
Data Prep Modelling Validation
Business knowledgeData
no yes
Sales
Forecast
& Stats
http://www.a4everyone.com

Analytics for Retail
Data Prep
Cleaning
- Miss, Outs, LowVar
Preprocessing
- Vars decomp. /detrend,
deseas./
- Transform
- Standardization
- Remove multicol., etc.
Modelling & Validation
Multistage modelling
SWR - Model dev
Model val
Sales forecast & Stats
Model destandardization
Detransformation
Add trend & seas
http://www.a4everyone.com

Analytics for Retail
 Wrong Interconnections
http://www.a4everyone.com

Analytics for Retail
 Multistage Modelling
Modelling 2
Modelling 1
Fact. Set 1
Product
Modelling 3
Fact. Set 2
Category
Fact. Set 3
Rest
y
e1
e2
Depend.
http://www.a4everyone.com

Analytics for Retail
A4Retail – Results
www.a4everyone.com
alexander.efremov@a4everyone.com

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Designing machines for Data Mining

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Designing machines for Data Mining

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