Exercício 1 - Dinâmica Explicando Toda a Analise - Vídeo em Site e Nesta Postagem
Assunto: Exercício 1 Pratico - Analise em Weka - Resolvido pelo Gabriel como se fosse um aluno
Data: 5/10/2022
https://youtu.be/aiDUg5I6vn8
Exercício 1 - Dinâmica Explicando Toda a Analise - Vídeo em Site e Nesta Postagem
Assunto: Exercício 1 Pratico - Analise em Weka - Resolvido pelo Gabriel como se fosse um aluno
Data: 5/10/2022
https://youtu.be/aiDUg5I6vn8
Exercícios Teóricos
Enviar por Favor para o E-mail da Disciplina:
gestao.estat.cert@gmail.com
O SAS (linguagem de computação) e o Weka (principal programa para ensino de IA do mundo).
Exercício Teórico 1 - Elaborar 10 slides sobre o Sistema SAS, importância para sua futura profissão no mercado de trabalho.
Dead Line = 26/10/2022
Exercício Teórico 2 - Slides (12 slides, fonte do título maximo32, e do corpo do slide máximo 28) Importância da Ciência de Dados, Inteligência Artificial, Machine Learning, Deep Learning, Data Mining, Big Data e Text Mining para sua futura profissão no mercado de trabalho. Ou seja para aumentar empregabilidade e competitividade. Foco em habilidades de mercado que nos levarão para a situação mercadológica denominada BOS ==> Hipercompetitividade - Hiperinovação e Inovação Disruptiva.
Dead Line = 26/10/2022
Exercício Teórico 3 - Elaborar 8 slides sobre Inteligência Artificial e Robotica Aplicada para o mercado de trabalho de sua futura profissão.
Dead Line = 26/10/2022
Exercício Teórico 4 - Slides (12 slides, fonte do título maximo32, e do corpo do slide máximo 28). Importância da Certificação Internacional da Qualidade e dos Sistemas de Gestão mais importantes do Mundo. Ou seja para aumentar empregabilidade e competitividade. Foco em habilidades de mercado que nos levarão para a situação mercadológica denominada BOS ==> Hipercompetitividade - Hiperinovação e Inovação Disruptiva. Asistir a 5 videos e > 100 slides que estão nas ponstgens de Insights para Seminarios.
Dead Line = 26/10/2022
Exercícios Práticos
Enviar por Favor para o E-mail da Disciplina:
gestao.estat.cert@gmail.com
Coloque seu nome o número do exercício e se se trata de exercício pratico ou teórico no assunto do e-mail
O SAS (linguagem de computação, principal habilidade de mercado para os tomadores de decisão em empresas do Brasil, versão gratuita excelente) e o Weka (principal programa para ensino de IA do mundo).
Exercício 1 Pratico:
Invente um exemplo (se inventar, envie uma copia para gasarrie@usp,br) para aplicar ML Supervisionado para Classificação ou troque os sinais de interrogação pelos últimos dígitos do seu numero USP no arquivo do Dinheiro Falsificado, Dead Line: 12/10/2022 . Rode uma rede neural com 1-2-3 camadas de neurônios:
@RELATION banco
@ATTRIBUTE Length REAL
@ATTRIBUTE Left REAL
@ATTRIBUTE Right REAL
@ATTRIBUTE Bottom REAL
@ATTRIBUTE Top REAL
@ATTRIBUTE Diagonal REAL
@ATTRIBUTE Class {FALSE,TRUE}
@DATA
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215,130,129.8,8.6,10.6,141.5,FALSE
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214.6,130.5,130.4,10.1,11.4,139.3,TRUE
214.7,130.2,130.1,10.7,11.1,139.5,TRUE
214.7,130.4,130,11.5,10.7,139.4,TRUE
214.5,130.4,130,8,12.2,138.5,TRUE
214.8,130,129.7,11.4,10.6,139.2,TRUE
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214.7,130.3,130.2,10.8,11.1,139.2,TRUE
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214.9,130.3,130.5,11.6,10.6,139.8,TRUE
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215.1,130.3,129.9,10.3,11.5,139.7,TRUE
214.8,130.3,130.4,10.6,11.1,140,TRUE
214.7,130.7,130.8,11.2,11.2,139.4,TRUE
214.3,129.9,129.9,10.2,11.5,139.6,TRUE
Exercício 2 Pratico:
Invente um exemplo (se inventar, envie uma copia para gasarrie@usp,br) para aplicar ML Supervisionado para Predição ou Causas e Efeito ou Regressão ou crie uma variável preditora aleatória para pós-venda (de 0 a 100), no arquivo da Satisfação do Cliente, rode no SAS e no Weka, dead line ou due date: 26/10:
Dados:
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 Customer
@ATTRIBUTE U_Neg REAL
@ATTRIBUTE Vendas REAL
@ATTRIBUTE Preco REAL
@ATTRIBUTE Niv_Qual REAL
@ATTRIBUTE Reclama REAL
@ATTRIBUTE NPS REAL
@ATTRIBUTE P_Vend REAL
@ATTRIBUTE Satisf REAL
@DATA
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;