Machine Learning Supervisionado para Predição ou Regressão - Sem Outlier - Excel ou Libre Office + SAS + Weka - Arquivo OK

 Machine Learning Supervisionado para Predição ou Regressão - Sem Outlier - Excel ou Libre Office + SAS + Weka - Arquivo OK 

     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

Slides elaborados na sala de aulas

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 PV Satisfac;

Cards;

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

;

proc print; run;

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

proc reg;

   model  Satisfac = Sales  Price Qu_level Claims PV NPS;

Run;

proc robustreg;

model Satisfac = Sales  Price Qu_level Claims NPS / diagnostic;

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