segunda-feira, 22 de abril de 2024

Machine Learning Supervisionado para Predição ou Regressão - SAS e Weka

 Machine Learning Supervisionado para Predição ou Regressão - SAS e Weka



SAS - Weka - Excel - L. Office - Conventional and Robust Data Science for SML to Prediction or Regression - 


Machine Learning Supervisionado para Predição ou Regressão - Excel ou Libre Office




Bu_Unit

Sales

Price

Qu_level

Claims

NPS

PV

Satisfac

1

65,98108

97,8022

96,77419

13,58025

98,9011

19

97,82609

2

15,8371

98,9011

98,3871

12,34568

97,8022

29

98,91304

3

8,885232

100

100

11,11111

100

21

100

4

12,46401

98,9011

95,16129

12,34568

96,7033

94

96,73913

5

80,66639

21,97802

19,35484

100

2,197802

34

21,73913

6

32,16783

23,07692

22,58065

97,53086

3,296703

64

23,91304

7

23,44714

24,17582

24,19355

96,2963

2,747253

61

25

8

89,96298

24,17582

19,35484

95,06173

2,197802

25

26,08696

9

31,4274

64,83516

56,45161

50,61728

65,93407

10

65,21739

10

11,22995

65,93407

51,6129

49,38272

71,42857

3

66,30435

11

77,45784

70,32967

53,22581

46,91358

63,73626

56

68,47826

12

23,89963

68,13187

51,6129

45,67901

61,53846

4

67,3913

13

7,40436

86,81319

80,64516

25,92593

90,10989

90

86,95652

14

0,287947

87,91209

79,03226

24,69136

85,71429

48

85,86957

15

83,42246

87,91209

77,41935

22,22222

90,10989

78

88,04348

16

100

86,81319

75,80645

25,92593

84,61538

88

84,78261

 

 

 

 

 

média=

724

 





Conventional and Robust Data Science for SML to Prediction or Regression

  Conventional and Robust Data Science for SML to Prediction or Regression 

SAS Program


Data Customer;

Input Bu_Unit  Sales  Price Qu_level Claims NPS Satisfac;

Cards;

1 65.98107775 97.8021978 96.77419355 13.58024691 98.9010989 97.82608696

2 15.83710407 98.9010989 98.38709677 12.34567901 97.8021978 98.91304348

3 8.885232415 100 100 11.11111111 100 100

4 12.46400658 98.9010989 95.16129032 12.34567901 96.7032967 96.73913043

5 80.66639243 21.97802198 19.35483871 100 2.197802198 21.73913043

6 32.16783217 23.07692308 22.58064516 97.5308642 3.296703297 23.91304348

7 23.44714109 24.17582418 24.19354839 96.2962963 2.747252747 25

8 89.9629782 24.17582418 19.35483871 95.0617284 2.197802198 26.08695652

9 31.42739613 64.83516484 56.4516129 50.61728395 65.93406593 65.2173913

10 11.22994652 65.93406593 51.61290323 49.38271605 71.42857143 66.30434783

11 77.45783628 70.32967033 53.22580645 46.91358025 63.73626374 68.47826087

12 23.89962978 68.13186813 51.61290323 45.67901235 61.53846154 67.39130435

13 7.404360346 86.81318681 80.64516129 25.92592593 90.10989011 86.95652174

14 0.287947347 87.91208791 79.03225806 24.69135802 85.71428571 85.86956522

15 83.42245989 87.91208791 77.41935484 22.22222222 90.10989011 88.04347826

16 100 86.81318681 75.80645161 25.92592593 84.61538462 84.7826087

;

proc print; run;

/* Input Bu_Unit  Sales  Price Qu_level Claims NPS Satisfac; */

proc reg;

   model  Satisfac = Sales  Price Qu_level Claims NPS;

Run;

proc robustreg;

model Satisfac = Sales  Price Qu_level Claims NPS;

Run;

Machine Learning Supervisionado para Predição ou Regressão - Excel ou Libre Office  

 MLS Causas - Efeito ou Regressão ou Predição - Weka


Download Arquivo para Weka


@RELATION Customer

@ATTRIBUTE U_Neg REAL

@ATTRIBUTE Vendas REAL

@ATTRIBUTE Preco REAL

@ATTRIBUTE Niv_Qual REAL

@ATTRIBUTE Reclama REAL

@ATTRIBUTE NPS REAL

@ATTRIBUTE Satisf REAL

@DATA

1,65.98107775,97.8021978,96.77419355,13.58024691,98.9010989,97.82608696

2,15.83710407,98.9010989,98.38709677,12.34567901,97.8021978,98.91304348

3,8.885232415,100,100,11.11111111,100,100

4,12.46400658,98.9010989,95.16129032,12.34567901,96.7032967,96.73913043

5,80.66639243,21.97802198,19.35483871,100,2.197802198,21.73913043

6,32.16783217,23.07692308,22.58064516,97.5308642,3.296703297,23.91304348

7,23.44714109,24.17582418,24.19354839,96.2962963,2.747252747,25

8,89.9629782,24.17582418,19.35483871,95.0617284,2.197802198,26.08695652

9,31.42739613,64.83516484,56.4516129,50.61728395,65.93406593,65.2173913

10,11.22994652,65.93406593,51.61290323,49.38271605,71.42857143,66.30434783

11,77.45783628,70.32967033,53.22580645,46.91358025,63.73626374,68.47826087

12,23.89962978,68.13186813,51.61290323,45.67901235,61.53846154,67.39130435

13,7.404360346,86.81318681,80.64516129,25.92592593,90.10989011,86.95652174

14,0.287947347,87.91208791,79.03225806,24.69135802,85.71428571,85.86956522

15,83.42245989,87.91208791,77.41935484,22.22222222,90.10989011,88.04347826

16,100,86.81318681,75.80645161,25.92592593,84.61538462,84.7826087







Linear Regression Model


Satisf =


      0.2481 * Preco +

      0.1063 * Niv_Qual +

     -0.4275 * Reclama +

      0.1135 * NPS +

     57.4038





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