Dr. basavaraj patil predictive analytics challenges_opportunities_original ieg2012_jan

http://informationexcellence.wordpress.com 2012 Jan Session

Dr.S.Basavaraj PatilPrincipal Consultant & Founder,Predictive ResearchProfessor & Head, CSE,BTL Institute of Technology Bangalore

Predictive Analytics Challenges and Opportunities

Predictive Research


Agenda Part-I Background: Relevance to Current Trends Predictive Analytics Predictive Research – Bird’s eye view Challenges in Different Sectors Mapping: Business Problems to Analytical

Problems Part-II Two Case Studies

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Background: Relevance to Current Trends

• Rapidly changing Industrial Scenario– IBM,Accenture,Genpact,Capgemini

• Businesses extracting knowledge from their data– Eagerly looking at Researchers/

professionals for solutions• There is great need to fill up the gap

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Predictive Analytics

• Evolving field• Exciting Opportunities• Use of data mining, artificial

Intelligence, statistics• Next level of

Datawharehousing/Business Intelligence…is Data Mining & Predictive Analytics

• MBA’s, Statisticians dominant

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Predictive Research– Bird’s eye view

Predictive Analytics Practice

Retail /Customer / Marketing Analytics

Banking/Finance/Insurance

Pharma/Chemo-informatics/Bio-tech Analytics

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Challenges in Pharma/ Chemoinformatics /Biotechnology Sector• Virtual Screening in Drug Discovery• Genomics sequencing• Patient Data Analysis and Mining• Clinical trial data analysis• Bangalore based companies

– PharmARC Analytics– Polyclonebioservices– ArisGlobal– Many more

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Challenges in Telecom Sector• Calls Reduction Vs. High expectation of

Customers• Response Modeling• Call time reduction• Threat of Churn• Declining ARPU(average Revenue per user)• Companies

– ABIBA Systems– Mu-Sigma– Many more

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Challenges in Finance Sector• Quant Finance Analytics• Credit Risk Analytics• Core Banking, Data Mining• Customer Segmentation• Companies

– All Banks– Mu-Sigma– Amba Research– Many more

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Challenges in Retail Sector Retail Analytics

– Sales Forecasting– Cause and Effect Analysis, Sensitivity Analysis

Market Basket Analytics– Increasing Promotional effectiveness– Create best combination purchase offers– Improve effectiveness of store displays

Companies– Manthan Systems– Marketetics, Customercentria– Many more

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• I would like to see “ what happens tomorrow to my business (Revenue, Profits, Expenditure, etc.,)?”

- Prediction Problem

• I would like to know “Is there anyway I can minimize my expenditure still keep effect normal? ”

- Optimization Problem

• Can anyone help me to know “ If I increase, decrease, promotion activities what happens?”

- What if analysis

• Oh! How to make a list of people who are very loyal to my business among the thousands of customers

- Clustering Problem

• Is there anyway “I can find out when to put More Money to maintain sufficient Stock (so that I will not say “No” to my loyal customers) and when to reduce?”

- Decision Support System (Analysis of Patterns)

Mapping Business Problems to Analytical Problems

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Part-II: Case Studies

Two cases1. Retail Analytics

2. Financial Analytics

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Sample Business Case: Problem Definition Given the data values of m1, m2,

m3,….mN and ‘Root Value ’(Rv) for ‘y’ days find the solution strategy to identify or predict the value of ‘Rv’ for y+1,y+2…., days ? (e.g., Rv can be Sales per quarter)

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Analysis of Problem: Tabular RepresentationScore Card

TIME

BF1 BF2 BF3 RVALUE

KPI1 KPI2 KPI3 KPI4 KPI5

M1 M2 M3 M4 M5 M6 M7 M8 M9 M10 M11

01 0.1 0.5 0.7 1.4 2.1 3 6.4 2.1 5.1 4.0 6 202 0.2 0.4 0.8 1.5 2.2 4 6.5 2.2 5.2 4.1 7 103 0.3 0.4 0.9 1.5 2.2 3 7.0 2.2 5.1 4.2 8 2.. … … … … … … …. …. …. …. … ..364. ?365 ?

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Solution Strategies -I

• The variable ‘Root value (Rv) ’ is considered for several days (say 270 days ) and ‘Rv ’ for next 7/90 days is predicted value solely on the basis of past values of ‘Rv’

• Prediction Techniques– Statistical Techniques : Regression based methods– Artificial Intelligence : Neural Network

– Feedforward Neural Network with gradient descent backpropagation

– Radial Basis Neural Network– Probabilistic Neural Network

Building the Predictive System based on past values of Root Value

Predictive System

Root Value

database

Past 7 day’s data

Learning

Future Value

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The variable ‘Rv’ is dependent on M1, M2,…M n

Step 1: First predict the future values of M1, M2,..Mn for (y+1, y+2, …days)

Step 2: Based on the values of M1, M2, ….Mn and Rv for ‘y’ days build the Predictive System for which inputs are M1,M2,…Mn and the output is ‘Root’

Step 3: Feed the predicted values of M1,M2,..Mn to the predictive system built in Step 2 and find the ‘Rv’

Solution Strategies -II

...

M1,M2,M3,…Mn

&Rv

M1predictive System I

Mnpredictive System I

Lear-ning

RvPredictive

System (C S-II)

M2predictive System I

M1 M2 Mn

Future Value

Learning

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•The variable ‘Rv’ depends on all variables, M1,M2, M3…M n •The Variable M1, M2, M3,…M n , may be interdependent,

–i.e. The future value of M1 may depend on past values of M2, M3, M4 and future value of M3 may depend on M5, M7, M8, etc.,

–Thus it may be more accurate to predict the future value of M1,. Based on all past M2, M3, M4, M5,..except M1.•STEPS TO BUILD–Step 1: Build the predictive models for each M, with all different M’s as inputs

–Step 2: Build one more predictive system to predict the Rv using all past values of M’s and Rv.

–Step 3: Feed the predicted values of M’s in Step1 to the system built in Step 2 to find the future values of ‘Rv’.

Solution Strategies -III

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M1,M2,M3,..Mn

M1,M2,M3,..Mn

M1,M2,M3,..Mn

Solution Strategy III : Implementation

M1,M2,M3,…Mn

Rv

M1predictive

System (CS –II)

Mnpredictive

System

Build

R valuePredictive

System

M2predictive

System

.

.

.

Future Value

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System Development and Testing • Example data sets are generated• System I is built using Neural Network Techniques• System is Experimented and tested with unseen data• Prediction Accuracy is found out• Conclusions are drawn

Experimentation with System I• Previous Data Set of RvValues are taken

– 214 values (428)

• Created a Neural Network which accepts last seven values and presents the next value– 2,2,2,2,4,6,6 ---Mon, Tue,Wed,Thur, Fri, Sat, Data

• Neural Network built has 7 inputs, 1 outputPredictive Research


•Different Neural Network Models are experimented and chosen based on the following parameters

•Prediction Rate = Number of correctly predicted instances/Total Number of instances tested

•Prediction Accuracy= Number of correctly predicted instances/Total Number of instances (Training + Testing)

Measures of Predictive Model

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Results of the System

A. Input to the System

Rv versus Time graph of input data Predictive Research


B. Output of the System

Rv versus Time graph of output dataPredictive Research


C. Error in Prediction

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Case Study: Quant Finance Stock Market Analytics Prediction, Portfolio Management Risk Analytics Trading Strategies

- Pair trading- case study- Short_Idea_Long_Reliance.pdf- Take_Profit_Short_Idea_Long_Reliance

.pdf

HFT Strategies- TradeAlgos /Robos- shttp://www.youtube.com/watch?featu

re=player_detailpage&v=FGu4S-7PN5U

- http://www.youtube.com/watch?feature=player_detailpage&v=aiZGnrjjOF8

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Business Partnerships and Talent Hunt (parallely MS c.Engg & PhD(CSE) ) Predictive Research is looking for Business Partnerships Predictive Research is also on look out for Right Talent Career is also associated with earning advanced degrees from

recognized universities Contact details

- [email protected] - [email protected] Phone: 91 9886404008

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http://informationexcellence.wordpress.com 2012 Jan SessionPredictive Research

Dr. basavaraj patil predictive analytics challenges_opportunities_original ieg2012_jan

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