Prova da LCE 105 em 29/11

   Prova da LCE 105 em 29/11

Aplique analise de regressão e Machine Learning Supervisionado para Predição no exemplo abaixo (Exercício Pratico numero 2). Se não tiver tempo de aplicar os três programas (Excel, Weka e SAS) com dois deles já é suficiente.

A prova vale 20% da nota final.

 Simule PV (Pós-venda) na faixa de 40 a 100% e calcule a média aritmética.

Envie o resultado, arquivo Excel, Weka e SAS para o e-mail de exercícios colocando no assunto, Prova 1 e seu nome:

economia.usp10@gmail.com

 

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

Arquivo de Dados para o Weka

@RELATION Cliente

@ATTRIBUTE Vendas REAL

@ATTRIBUTE Preco REAL

@ATTRIBUTE Niv_Qual REAL

@ATTRIBUTE Reclama REAL

@ATTRIBUTE NPS REAL

@ATTRIBUTE P_Vend REAL

@ATTRIBUTE Satisf REAL

@DATA

65.98108,97.8022,96.77419,13.58025,98.9011,65,97.82609

15.8371,98.9011,98.3871,12.34568,97.8022,84,98.91304

8.885232,100,100,11.11111,100,46,100

12.46401,98.9011,95.16129,12.34568,96.7033,98,96.73913

80.66639,21.97802,19.35484,100,2.197802,75,21.73913

32.16783,23.07692,22.58065,97.53086,3.296703,97,23.91304

23.44714,24.17582,24.19355,96.2963,2.747253,77,25

89.96298,24.17582,19.35484,95.06173,2.197802,49,26.08696

31.4274,64.83516,56.45161,50.61728,65.93407,99,65.21739

11.22995,65.93407,51.6129,49.38272,71.42857,52,66.30435

77.45784,70.32967,53.22581,46.91358,63.73626,61,68.47826

23.89963,68.13187,51.6129,45.67901,61.53846,48,67.3913

7.40436,86.81319,80.64516,25.92593,90.10989,45,86.95652

0.287947,87.91209,79.03226,24.69136,85.71429,75,85.86957

83.42246,87.91209,77.41935,22.22222,90.10989,60,88.04348

100,86.81319,75.80645,25.92593,84.61538,66,84.78261

Programa SAS - Ciência de Dados Robusta para Machine Learnic Superv. para Casusas & Efeito

 Data Customer;

Input Bu_Unit  Sales  Price Qu_level Claims NPS P_Vend Satisfac;

Cards;

DADOS

;

proc print; 

run;

proc reg;

   model  Satisfac = Sales  Price Qu_level Claims NPS P_Vend ;

Run;

proc robustreg;

model Satisfac = Sales  Price Qu_level Claims NPS P_Vend ;

Run;