Predictive analytics The next generation

of MaaS

Rok Okorn, Ektimo d.o.o.

Agenda

About predictive analytics

Supervised, unsupervised or reinforcement

Methods

Examples of usage

What is data science?

Data science reveals previously unknown cause and effect relationships and possibly forecasts future events by a systematic analysis (of large amounts) of data.

Objective: usage of data for improved business cases.

Better decisions

Higher effiecency

Cost optimization

Improved experience

New products

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New levels of analytics with a data science

Complexity

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Descriptive analytics

Diagnostic analytics

Predictive analytics

Prescriptive analytics

What happened?

Why it happened?

What will happen?

Which decision leads to the best outcome?

Survey results: Big data use cases 2015, BARC:

39%

31%

8%

10%

EU companies perform big data projects.

companies implemented predictive analytics.

increase in income due to big data projects..

mean cost reduction due to big data projects.

In the center of data science is artificial intelligence

These algorithms enable computers to:

• learn from past data without explicit programming

• improve with new data.

• effectively recognize patterns in complex data from a variety of sources.

Supervised, unsupervised or reinforcement?

Example: object recognition •Supervised learning:

Learn by examples as to what object it is in terms of structure, color, shape, etc. So that after several iterations it learns to define an object.

•Unsupervised learning: There is no desired output that is provided, therefore categorization is done so that the algorithm differentiates correctly between bikes, cars, houses or people (clustering of data).

•Reinforcement learning: The predictions are continuously updated, unlike in the previous types. For example, when a robot sees an object: first classify it and then go around it and classify it again on new observed parameters. Alternatively, when the robot learns that some object is dangerous, it will avoid it, next time

Usable tools

Typical process

Obtain the data

Data preparation

Feature creation

Validation Modelling Application

Identification

Enrichment

Import

Integration

Cleaning

Exploration

Transformation

Normalization

Categorization

Statistical

Business

Splitting

Subsetting

Learning

Optimization

Implementation

Monitoring

Visualization

UI

Feedback

Data preparation

DA

Transactions Products

Demographics

Campains

Calls E-mail

Mobile media

External data

What we knew about person A up to some date T?

What happened 1 month after T?

Buys new product

Integration and

transformation of data

Features (predictors)

Response

30 1.2 1 4.5 1 1 0.9

Person A’s digital print

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Modelling H

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Learning set

Test set

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Predictive model

Learning/training

Model validation

Input (features)

Output (prediction)

67%

The model predicts a purchase for person X

with 67% probability

Ensembles – diversification at the level of models

Predictive models

Input Prediction

Final prediction based on some function of the

individual models, e.g. mean

Instead of one single model we train multiple different models.

65% ??

90%

10%

55%

Some useful algorithms

Regressions: linear, logistic, poisson, lasso

SVM: linear, kernel, hard/soft margin

Clustering: k-means, kNN, hierarchical

Decision trees: decision tree, randomForest

Deep learning: Boltzman machines, autoencoders, recurrent networks

Ensemble methods: AdaBoost, VotingClassifier

Variouos use cases

• Demand forecasting

• Loyalty programs

• Dynamic pricing

• Recommendation systems

• Optization of asortment

• Credit scoring

• Claims prediction

• Fraud detection

• Predictive lead scoring

• Targeting

• Optimization

• Susceptibility to the purchase

• Personalization

• Churn prediction

• Customer lifetime value prediction

• Routing optimization

Self-driving

cars

Predictive maintenance

Optimization of supply

Usage of PA in mobility

What elementary problems need to be solved?

• Basic infrastructure

• Data gathering

• What are the KPIs?

Predictive analytics tasks:

• Predict (stochastic) demand and supply

• Predict defects, malfunctions or failures

• Recognize objects on paths and deal with them

• etc.

Examples of predictive analytics capabilities

Image recognition – problem formulation

•What is it?

Handwriting, CAPTCHAs; discriminating humans from

computers

•Where is it?

Detecting objects regions in images

•How is it constructed?

Determining how a group of something is related (e.g. math

symbols) or determining some structure of objects

Given a database of objects and an image

determine what, if any of the objects are

present in the image.

Image recognition – solution I source: Bernd Heisele,Visual Object Recognition with Supervised Learning

Image recognition – solution II source: https://s3.amazonaws.com/datarobotblog/images/deepLearningIntro/013.png

Image recognition – mobility usage

• Obstacle detection

• Terrain reconstruction

• Convoying

• Collision detection

• Road recognition

Demand prediction - problem formulation Different forecasts for different types of products:

• Nondurable consumer goods

• vanish after a single act of consumption

• depends upon price of the commodity and the related goods and population

and characteristics

• Durable consumer goods

• can be consumed a number of times or repeatedly used

• depends upon social status, level of money income, taste and fashion, the

provision of allied services and their cost, sensitive to price changes

• Capital goods

• used for further production

• depends on the specific markets they serve and the end uses for which they are

bought, consumption per unit of each end-use product

• New-products

• new to the consumers

• depends on type (evolution, substitute), same group products demand

Given current

and past data,

predict the

demand of a

given product.

Demand prediction – solution I

Classical time series approach

• Seasonality

• Trend

• ARIMA, GARCH

Demand prediction – solution II

Machine learning methods source: Application of machine learning techniques for supply chain demand forecasting Original Research Article,

European Journal of Operational Research, Volume 184, Issue 3, 1 February 2008

Demand prediction– mobility usage

• Predicting demand in a specific location

• Adding new infrastructure elements (stations, cars)

• Dynamic pricing

• Power demand

Predictive maintenance - problem formulation

Can you tell

me, when to

perform

maintenance?

Three types of maintenance:

• emergency; when failure occurs

• preventive; regularly on time, cleaning cycle of x weeks

• predictive; when it is needed

Predictive maintenance is condition based using advanced

technology and instrumentation

Assumes installed indicators; read and reported by operators or

sensors

•What symptoms indicate the pending failure under review?

•How can the symptom be detected?

•Which methods of detection might be useful?

•How long is the anticipated failure development period?

•What does this suggest about inspection intervals?

Predictive maintenance – solution I source: Architecture diagram: Solution Template for predictive maintenance

Predictive maintenance – solution II source: http://revolution-computing.typepad.com/.a/6a010534b1db25970b01bb08cba61c970d-800wi

Predictive maintenance – mobility usage

• Safety, motor breakdowns

• Infrastructure faults

• Electric component failures

• Battery performance / failure

Beware: Issues

• Methods gathering and labeling data, problem

formulation

• Image recognition range of viewing conditions, 2D vs. 3D, point

of view, size of known image pool

• Demand prediction seasonality, special events, weather,

location, only sales data (instead of demand)

• Preventive maintenance immediate critical faults, sensor placements

Thank you for your attention!

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Q&A