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Click to edit Master text styles [Confidential]
Predictive Modeling and Analytics…The Rest of the Story
Jeffrey Strickland, Ph.D.
January 2015
Times Series Models/Forecasting Models.
• A Time Series (TS) model is a statistical
model based on time series data.
• time-series data reflects seasonality.
• TS uses “smoothing” techniques to
account for things like seasonality in
predicting or forecasting what may
happen in the near future.
Regression Models
• These models are based on data.
• An example is a logistic regression models
used in propensity modeling
• Other types include:
• Linear Regression
• Generalized Linear Models (GLM)
• Robust Regression
• These model depend on data without many
anomalies—they do not learn from the data.
8/18/2015 Copyright © 2014 Jeffrey Strickland 3
Statistical Models
• The first two examples, Time Series
and Regression models, are
statistical models.
• However, I list it separately because
many do not realize that statistical
models are mathematical models,
based on mathematical statistics.
• Things like means and standard
deviations are statistical moments,
derived from mathematical
moment generating functions.
• Every statistic in Statistics is based
on a mathematical function.
8/18/2015 Copyright © 2014 Jeffrey Strickland 4
Machine Leaning Models
• These include:
• auto neural networks (ANN),
• support vector machines,
• classification trees,
• random forests, etc.
• These are based on data, but
unlike statistical models, they
“learn” from the data.
8/18/2015 Copyright © 2014 Jeffrey Strickland 5
Physical Models
• These models are based on
physical phenomena
• They include 6-DoF (Degrees of
Freedom):
• flight models
• space flight models
• missile models
• Combat Attrition models are
based on physical properties of
munitions and equipment
8/18/2015 Copyright © 2014 Jeffrey Strickland 6
Mathematical Models
• These are usually restricted to
continuous time models based on
differential equations or estimated
using difference equations.
• They are often used to model very
precise processes like the dynamics
solid fuel rockets
• Can approximate physical
phenomena in the absence of
actual data, like attrition
coefficients approximation or
direct fire effects in combat
models.
8/18/2015 Copyright © 2014 Jeffrey Strickland 7
Propensity Models
• Life Insurance
• Auto Insurance
• Homeowner’s Insurance
• Mortgage
• Re-Financing
• Credit cards
• Personal Loans
• Investments
• Satellite TV
• Cable
• Netflix
• Online banking
• Online money management
• Voting
• Disease
8/18/2015 Copyright © 2014 Jeffrey Strickland 8
• Propensity to buy
• Propensity to use
• Propensity to engage
• Propensity to contract
• Etc.
Propensity models - Statistical
• One dependent variable
• Target variable/Response is binary
• Yes or no
• 1 or 0
• One to many independent variables
• Parametric
• Log-linear functional assumption
8/18/2015 Copyright © 2014 Jeffrey Strickland 9
Logistic regression
Propensity to buy a new mortgage
• Target Data
• Homeowner data by county
• Compiled monthly
• Ported to SAS/SPSS/etc. database
• Independent variables
• 1 to 20 customer databases
• Up to 2000 variable
• Up to 20 million records
8/18/2015 Copyright © 2014 Jeffrey Strickland 10
• Homebuyer model
• Buy a new home within next 3 months
• Acquisition window = 3 months
• Preprocessing period = 3 months
• Profile window = 1 year
Model Performance
8/18/2015 Copyright © 2014 Jeffrey Strickland 11
1.00
1.50
2.00
2.50
3.00
3.50
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20
Cumulative Lift
Model Lift Actual Lift
0
0.1
0.2
0.3
0.4
0.5
0.6
0.7
0.8
0.9
1
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20
Cumulative % Captured Response
Random Model Actual
Economic Evaluation
• 5-Year NPV = $1,500.00 (approximate)
• Cost per direct mail = $0.45
• Evaluated by pentile
• About 9 million customers
8/18/2015 Copyright © 2014 Jeffrey Strickland 12
Assumptions
Economic Evaluation
Cost of
Mail $ 0.45 Direct Mail Net Profit with Model Net Profit without Model
NVP $ 1,500.00
Total
Mailed
Acq Cost per Acq
Incremental
Revenue due
to Model
Total Mail
Cost with No
Model
Total Mail
Cost with
Model PTI Net Profit
Cum Net
Profit with
Model PTI Net Profit
Cum Net Profit
No Model
Net Profit
Difference
Cum Net
Profit
Difference
Mailed
Pop
Resp
Rate
Cum
Resp
Rate
No
Model
With
Model
No
Model
With
Model
5% 2.97% 2.97% 150000 1437 4457 $ 46.96 $ 15.14 $ 5,408,837 $ 67,500.00 $ 1,992.71 $ 7,930,990 $ 7,928,998 $ 7,928,998 $ 2,574,361 $ 2,506,861 $ 2,506,861 $ 5,422,136 $ 5,422,136
10% 2.17% 5.14% 150000 1437 3255 $ 46.96 $ 20.74 $ 3,254,726 $ 67,500.00 $ 1,496.75 $ 5,957,071 $ 5,955,574 $13,884,572 $ 2,574,361 $ 2,506,861 $ 5,013,723 $ 3,448,712 $ 8,870,849
15% 1.58% 6.72% 150000 1437 2363 $ 46.96 $ 28.56 $ 1,658,568 $ 67,500.00 $ 1,110.86 $ 4,421,203 $ 4,420,092 $18,304,665 $ 2,574,361 $ 2,506,861 $ 7,520,585 $ 1,913,230 $10,784,079
20% 1.35% 8.07% 150000 1437 2031 $ 46.96 $ 33.24 $ 1,062,658 $ 67,500.00 $ 906.44 $ 3,607,637 $ 3,606,731 $21,911,396 $ 2,574,361 $ 2,506,861 $ 10,027,447 $ 1,099,869 $11,883,949
25% 1.25% 9.32% 150000 1437 1872 $ 46.96 $ 36.06 $ 778,410 $ 67,500.00 $ 803.56 $ 3,198,152 $ 3,197,349 $25,108,745 $ 2,574,361 $ 2,506,861 $ 12,534,309 $ 690,4878 $12,574,436
30% 1.00% 10.31% 150000 1437 1494 $ 46.96 $ 45.19 $ 101,127 $ 67,500.00 $ 730.12 $ 2,905,897 $ 2,905,167 $28,013,913 $ 2,574,361 $ 2,506,861 $ 15,041,171 $ 398,305 $12,972,741
35% 0.99% 11.30% 150000 1437 1481 $ 46.96 $ 45.58 $ 78,195 $ 67,500.00 $ 676.64 $ 2,693,014 $ 2,692,337 $30,706,250 $ 2,574,361 $ 2,506,861 $ 17,548,033 $ 185,475 $13,158,217
40% 0.90% 12.20% 150000 1437 1349 $ 46.96 $ 50.05 $ (158,801) $ 67,500.00 $ 626.62 $ 2,493,949 $ 2,493,322 $33,199,573 $ 2,574,361 $ 2,506,861 $ 20,054,894 $ (13,539) $13,144,678
45% 0.87% 13.07% 150000 1437 1307 $ 46.96 $ 51.65 $ (233,710) $ 67,500.00 $ 598.28 $ 2,381,142 $ 2,380,544 $35,580,117 $ 2,574,361 $ 2,506,861 $ 22,561,756 $ (126,317) $13,018,360
50% 0.81% 13.89% 150000 1437 1222 $ 46.96 $ 55.22 $ (384,977) $ 67,500.00 $ 554.53 $ 2,207,035 $ 2,206,481 $37,786,598 $ 2,574,36 $ 2,506,861 $ 25,068,618 $ (300,380) $12,717,980
55% 0.77% 14.65% 150000 1437 1150 $ 46.96 $ 58.69 $ (514,643) $ 67,500.00 $ 513.98 $ 2,045,626 $ 2,045,112 $39,831,711 $ 2,574,361 $ 2,506,861 $ 27,575,480 $ (461,748) $12,256,231
60% 0.66% 15.32% 150000 1437 992 $ 46.96 $ 68.06 $ (798,125) $ 67,500.00 $ 483.64 $ 1,924,892 $ 1,924,408 $41,756,120 $ 2,574,361 $ 2,506,861 $ 30,082,342 $ (582,453) $11,673,778
65% 0.70% 16.01% 150000 1437 1045 $ 46.96 $ 64.59 $ (702,743) $ 67,500.00 $ 453.90 $ 1,806,534 $ 1,806,080 $43,562,201 $ 2,574,361 $ 2,506,861 $ 32,589,204 $ (700,781) $10,972,996
70% 0.64% 16.65% 150000 1437 963 $ 46.96 $ 70.09 $ (849,502) $ 67,500.00 $ 420.14 $ 1,672,147 $ 1,671,727 $45,233,928 $ 2,574,361 $ 2,506,861 $ 35,096,066 $ (835,134) $10,137,862
75% 0.63% 17.29% 150000 1437 949 $ 46.96 $ 71.15 $ (875,309) $ 67,500.00 $ 386.76 $ 1,539,307 $ 1,538,920 $46,772,849 $ 2,574,361 $ 2,506,861 $ 37,602,928 $ (967,940) $ 9,169,921
80% 0.48% 17.76% 150000 1437 716 $ 46.96 $ 94.33 $(1,292,708) $ 67,500.00 $ 349.63 $ 1,391,542 $ 1,391,193 $48,164,042 $ 2,574,361 $ 2,506,861 $ 40,109,789 $(1,115,668) $ 8,054,252
85% 0.48% 18.25% 150000 1437 724 $ 46.96 $ 93.27 $(1,278,218) $ 67,500.00 $ 312.11 $ 1,242,186 $ 1,241,873 $ 49,405,916 $ 2,574,361 $ 2,506,861 $ 42,616,651 $(1,264,987) $ 6,789,264
90% 0.41% 18.65% 150000 1437 611 $ 46.96 $110.47 $(1,479,972) $ 67,500.00 $ 272.76 $ 1,085,577 $ 1,085,305 $50,491,221 $ 2,574,361 $ 2,506,861 $ 45,123,513 $(1,421,556) $ 5,367,707
95% 0.31% 18.96% 150000 1437 463 $ 46.96 $145.73 $(1,744,789) $ 67,500.00 $ 224.93 $ 895,234. $ 895,009 $51,386,230 $ 2,574,361 $ 2,506,861 $ 47,630,375 $(1,611,852) $ 3,755,855
100% 0.20% 19.17% 150000 1437 304 $ 46.96 $221.68 $(2,029,022) $ 67,500.00 $ 154.29 $ 614,094. $ 613,939 $52,000,170 $ 2,574,361 $ 2,506,861 $ 50,137,237 $(1,892,922) $ 1,862,933
8/18/2015 13Copyright © 2014 Jeffrey Strickland
Mail to top 35% or 1st through 7th Pentile
Economic Evaluation
8/18/2015 Copyright © 2014 Jeffrey Strickland 14
$-
$50.00
$100.00
$150.00
$200.00
$250.00
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20
Cost per Acquisition
No Model With Model
$-
$1,000,000.00
$2,000,000.00
$3,000,000.00
$4,000,000.00
$5,000,000.00
$6,000,000.00
$7,000,000.00
$8,000,000.00
$9,000,000.00
0 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20
Net Profit
Net Profit Net Profit
Economic Evaluation
8/18/2015 Copyright © 2014 Jeffrey Strickland 15
$(4,000,000.00)
$(2,000,000.00)
$-
$2,000,000.00
$4,000,000.00
$6,000,000.00
$8,000,000.00
$10,000,000.00
$12,000,000.00
$14,000,000.00
0 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20
Net Profit with Model
Net Profit Difference Cum Net Profit Difference
$-
$10,000,000.00
$20,000,000.00
$30,000,000.00
$40,000,000.00
$50,000,000.00
$60,000,000.00
0 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20
Cumulative Net Profit
Cum Net Profit with Model Cum Net Profit No Model
Predictive Modeling and Analytics
This book is about predictive modeling. Yet, each chapter could easily be
handled by an entire volume of its own. So one might think of this as a
survey of predictive models, both statistical and machine learning. We
define A predictive model as a statistical model or machine learning model
used to predict future behavior based on past behavior. In order to use this
book, the reader should have a basic understanding of statistics (statistical
inference, models, tests, etc.)-this is an advanced book. Every chapter
culminates in an example using R. R is a free software environment for
statistical computing and graphics. It compiles and runs on a wide variety
of Unix platforms, Windows and MacOs. The book is organized so that
statistical models are presented first (hopefully in a logical order), followed
by machine learning models, and then applications: uplift modeling and
time series. One could use this as a textbook with problem solving in R
(there are no "by-hand" exercises).
8/18/2015 Copyright © 2014 Jeffrey Strickland 16
Click to edit Master text styles [Confidential]
• http://www.amazon.com/Jeffrey-Strickland/e/B00IQ69QZK/
• http://www.lulu.com/spotlight/strickland_jeffrey
8/18/2015 Copyright © 2014 Jeffrey Strickland 17

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predictive models

  • 1. Click to edit Master text styles [Confidential] Predictive Modeling and Analytics…The Rest of the Story Jeffrey Strickland, Ph.D. January 2015
  • 2. Times Series Models/Forecasting Models. • A Time Series (TS) model is a statistical model based on time series data. • time-series data reflects seasonality. • TS uses “smoothing” techniques to account for things like seasonality in predicting or forecasting what may happen in the near future.
  • 3. Regression Models • These models are based on data. • An example is a logistic regression models used in propensity modeling • Other types include: • Linear Regression • Generalized Linear Models (GLM) • Robust Regression • These model depend on data without many anomalies—they do not learn from the data. 8/18/2015 Copyright © 2014 Jeffrey Strickland 3
  • 4. Statistical Models • The first two examples, Time Series and Regression models, are statistical models. • However, I list it separately because many do not realize that statistical models are mathematical models, based on mathematical statistics. • Things like means and standard deviations are statistical moments, derived from mathematical moment generating functions. • Every statistic in Statistics is based on a mathematical function. 8/18/2015 Copyright © 2014 Jeffrey Strickland 4
  • 5. Machine Leaning Models • These include: • auto neural networks (ANN), • support vector machines, • classification trees, • random forests, etc. • These are based on data, but unlike statistical models, they “learn” from the data. 8/18/2015 Copyright © 2014 Jeffrey Strickland 5
  • 6. Physical Models • These models are based on physical phenomena • They include 6-DoF (Degrees of Freedom): • flight models • space flight models • missile models • Combat Attrition models are based on physical properties of munitions and equipment 8/18/2015 Copyright © 2014 Jeffrey Strickland 6
  • 7. Mathematical Models • These are usually restricted to continuous time models based on differential equations or estimated using difference equations. • They are often used to model very precise processes like the dynamics solid fuel rockets • Can approximate physical phenomena in the absence of actual data, like attrition coefficients approximation or direct fire effects in combat models. 8/18/2015 Copyright © 2014 Jeffrey Strickland 7
  • 8. Propensity Models • Life Insurance • Auto Insurance • Homeowner’s Insurance • Mortgage • Re-Financing • Credit cards • Personal Loans • Investments • Satellite TV • Cable • Netflix • Online banking • Online money management • Voting • Disease 8/18/2015 Copyright © 2014 Jeffrey Strickland 8 • Propensity to buy • Propensity to use • Propensity to engage • Propensity to contract • Etc.
  • 9. Propensity models - Statistical • One dependent variable • Target variable/Response is binary • Yes or no • 1 or 0 • One to many independent variables • Parametric • Log-linear functional assumption 8/18/2015 Copyright © 2014 Jeffrey Strickland 9 Logistic regression
  • 10. Propensity to buy a new mortgage • Target Data • Homeowner data by county • Compiled monthly • Ported to SAS/SPSS/etc. database • Independent variables • 1 to 20 customer databases • Up to 2000 variable • Up to 20 million records 8/18/2015 Copyright © 2014 Jeffrey Strickland 10 • Homebuyer model • Buy a new home within next 3 months • Acquisition window = 3 months • Preprocessing period = 3 months • Profile window = 1 year
  • 11. Model Performance 8/18/2015 Copyright © 2014 Jeffrey Strickland 11 1.00 1.50 2.00 2.50 3.00 3.50 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 Cumulative Lift Model Lift Actual Lift 0 0.1 0.2 0.3 0.4 0.5 0.6 0.7 0.8 0.9 1 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 Cumulative % Captured Response Random Model Actual
  • 12. Economic Evaluation • 5-Year NPV = $1,500.00 (approximate) • Cost per direct mail = $0.45 • Evaluated by pentile • About 9 million customers 8/18/2015 Copyright © 2014 Jeffrey Strickland 12 Assumptions
  • 13. Economic Evaluation Cost of Mail $ 0.45 Direct Mail Net Profit with Model Net Profit without Model NVP $ 1,500.00 Total Mailed Acq Cost per Acq Incremental Revenue due to Model Total Mail Cost with No Model Total Mail Cost with Model PTI Net Profit Cum Net Profit with Model PTI Net Profit Cum Net Profit No Model Net Profit Difference Cum Net Profit Difference Mailed Pop Resp Rate Cum Resp Rate No Model With Model No Model With Model 5% 2.97% 2.97% 150000 1437 4457 $ 46.96 $ 15.14 $ 5,408,837 $ 67,500.00 $ 1,992.71 $ 7,930,990 $ 7,928,998 $ 7,928,998 $ 2,574,361 $ 2,506,861 $ 2,506,861 $ 5,422,136 $ 5,422,136 10% 2.17% 5.14% 150000 1437 3255 $ 46.96 $ 20.74 $ 3,254,726 $ 67,500.00 $ 1,496.75 $ 5,957,071 $ 5,955,574 $13,884,572 $ 2,574,361 $ 2,506,861 $ 5,013,723 $ 3,448,712 $ 8,870,849 15% 1.58% 6.72% 150000 1437 2363 $ 46.96 $ 28.56 $ 1,658,568 $ 67,500.00 $ 1,110.86 $ 4,421,203 $ 4,420,092 $18,304,665 $ 2,574,361 $ 2,506,861 $ 7,520,585 $ 1,913,230 $10,784,079 20% 1.35% 8.07% 150000 1437 2031 $ 46.96 $ 33.24 $ 1,062,658 $ 67,500.00 $ 906.44 $ 3,607,637 $ 3,606,731 $21,911,396 $ 2,574,361 $ 2,506,861 $ 10,027,447 $ 1,099,869 $11,883,949 25% 1.25% 9.32% 150000 1437 1872 $ 46.96 $ 36.06 $ 778,410 $ 67,500.00 $ 803.56 $ 3,198,152 $ 3,197,349 $25,108,745 $ 2,574,361 $ 2,506,861 $ 12,534,309 $ 690,4878 $12,574,436 30% 1.00% 10.31% 150000 1437 1494 $ 46.96 $ 45.19 $ 101,127 $ 67,500.00 $ 730.12 $ 2,905,897 $ 2,905,167 $28,013,913 $ 2,574,361 $ 2,506,861 $ 15,041,171 $ 398,305 $12,972,741 35% 0.99% 11.30% 150000 1437 1481 $ 46.96 $ 45.58 $ 78,195 $ 67,500.00 $ 676.64 $ 2,693,014 $ 2,692,337 $30,706,250 $ 2,574,361 $ 2,506,861 $ 17,548,033 $ 185,475 $13,158,217 40% 0.90% 12.20% 150000 1437 1349 $ 46.96 $ 50.05 $ (158,801) $ 67,500.00 $ 626.62 $ 2,493,949 $ 2,493,322 $33,199,573 $ 2,574,361 $ 2,506,861 $ 20,054,894 $ (13,539) $13,144,678 45% 0.87% 13.07% 150000 1437 1307 $ 46.96 $ 51.65 $ (233,710) $ 67,500.00 $ 598.28 $ 2,381,142 $ 2,380,544 $35,580,117 $ 2,574,361 $ 2,506,861 $ 22,561,756 $ (126,317) $13,018,360 50% 0.81% 13.89% 150000 1437 1222 $ 46.96 $ 55.22 $ (384,977) $ 67,500.00 $ 554.53 $ 2,207,035 $ 2,206,481 $37,786,598 $ 2,574,36 $ 2,506,861 $ 25,068,618 $ (300,380) $12,717,980 55% 0.77% 14.65% 150000 1437 1150 $ 46.96 $ 58.69 $ (514,643) $ 67,500.00 $ 513.98 $ 2,045,626 $ 2,045,112 $39,831,711 $ 2,574,361 $ 2,506,861 $ 27,575,480 $ (461,748) $12,256,231 60% 0.66% 15.32% 150000 1437 992 $ 46.96 $ 68.06 $ (798,125) $ 67,500.00 $ 483.64 $ 1,924,892 $ 1,924,408 $41,756,120 $ 2,574,361 $ 2,506,861 $ 30,082,342 $ (582,453) $11,673,778 65% 0.70% 16.01% 150000 1437 1045 $ 46.96 $ 64.59 $ (702,743) $ 67,500.00 $ 453.90 $ 1,806,534 $ 1,806,080 $43,562,201 $ 2,574,361 $ 2,506,861 $ 32,589,204 $ (700,781) $10,972,996 70% 0.64% 16.65% 150000 1437 963 $ 46.96 $ 70.09 $ (849,502) $ 67,500.00 $ 420.14 $ 1,672,147 $ 1,671,727 $45,233,928 $ 2,574,361 $ 2,506,861 $ 35,096,066 $ (835,134) $10,137,862 75% 0.63% 17.29% 150000 1437 949 $ 46.96 $ 71.15 $ (875,309) $ 67,500.00 $ 386.76 $ 1,539,307 $ 1,538,920 $46,772,849 $ 2,574,361 $ 2,506,861 $ 37,602,928 $ (967,940) $ 9,169,921 80% 0.48% 17.76% 150000 1437 716 $ 46.96 $ 94.33 $(1,292,708) $ 67,500.00 $ 349.63 $ 1,391,542 $ 1,391,193 $48,164,042 $ 2,574,361 $ 2,506,861 $ 40,109,789 $(1,115,668) $ 8,054,252 85% 0.48% 18.25% 150000 1437 724 $ 46.96 $ 93.27 $(1,278,218) $ 67,500.00 $ 312.11 $ 1,242,186 $ 1,241,873 $ 49,405,916 $ 2,574,361 $ 2,506,861 $ 42,616,651 $(1,264,987) $ 6,789,264 90% 0.41% 18.65% 150000 1437 611 $ 46.96 $110.47 $(1,479,972) $ 67,500.00 $ 272.76 $ 1,085,577 $ 1,085,305 $50,491,221 $ 2,574,361 $ 2,506,861 $ 45,123,513 $(1,421,556) $ 5,367,707 95% 0.31% 18.96% 150000 1437 463 $ 46.96 $145.73 $(1,744,789) $ 67,500.00 $ 224.93 $ 895,234. $ 895,009 $51,386,230 $ 2,574,361 $ 2,506,861 $ 47,630,375 $(1,611,852) $ 3,755,855 100% 0.20% 19.17% 150000 1437 304 $ 46.96 $221.68 $(2,029,022) $ 67,500.00 $ 154.29 $ 614,094. $ 613,939 $52,000,170 $ 2,574,361 $ 2,506,861 $ 50,137,237 $(1,892,922) $ 1,862,933 8/18/2015 13Copyright © 2014 Jeffrey Strickland Mail to top 35% or 1st through 7th Pentile
  • 14. Economic Evaluation 8/18/2015 Copyright © 2014 Jeffrey Strickland 14 $- $50.00 $100.00 $150.00 $200.00 $250.00 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 Cost per Acquisition No Model With Model $- $1,000,000.00 $2,000,000.00 $3,000,000.00 $4,000,000.00 $5,000,000.00 $6,000,000.00 $7,000,000.00 $8,000,000.00 $9,000,000.00 0 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 Net Profit Net Profit Net Profit
  • 15. Economic Evaluation 8/18/2015 Copyright © 2014 Jeffrey Strickland 15 $(4,000,000.00) $(2,000,000.00) $- $2,000,000.00 $4,000,000.00 $6,000,000.00 $8,000,000.00 $10,000,000.00 $12,000,000.00 $14,000,000.00 0 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 Net Profit with Model Net Profit Difference Cum Net Profit Difference $- $10,000,000.00 $20,000,000.00 $30,000,000.00 $40,000,000.00 $50,000,000.00 $60,000,000.00 0 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 Cumulative Net Profit Cum Net Profit with Model Cum Net Profit No Model
  • 16. Predictive Modeling and Analytics This book is about predictive modeling. Yet, each chapter could easily be handled by an entire volume of its own. So one might think of this as a survey of predictive models, both statistical and machine learning. We define A predictive model as a statistical model or machine learning model used to predict future behavior based on past behavior. In order to use this book, the reader should have a basic understanding of statistics (statistical inference, models, tests, etc.)-this is an advanced book. Every chapter culminates in an example using R. R is a free software environment for statistical computing and graphics. It compiles and runs on a wide variety of Unix platforms, Windows and MacOs. The book is organized so that statistical models are presented first (hopefully in a logical order), followed by machine learning models, and then applications: uplift modeling and time series. One could use this as a textbook with problem solving in R (there are no "by-hand" exercises). 8/18/2015 Copyright © 2014 Jeffrey Strickland 16
  • 17. Click to edit Master text styles [Confidential] • http://www.amazon.com/Jeffrey-Strickland/e/B00IQ69QZK/ • http://www.lulu.com/spotlight/strickland_jeffrey 8/18/2015 Copyright © 2014 Jeffrey Strickland 17