Aula 10/12/2020

 link: https://meet.google.com/xxr-waro-dfq

Videoaulas:

https://youtu.be/fmp9sJasXU0

https://youtu.be/Ukz9fo8CGpE

Pauta:

- Clínicas e Cursos no Verão

- Últimos slides - Parcerias 

- Analises Pesquisa de Mercado

- Carta Clinicas

- Duvidas de Exercícios

Machine Learning Não Supervicionado - Cluster Analysis

data  pessoas;

input cat $ imc corr kcal;

cards;

AT 20.475 217.4 12400

PR 25.175 10.2 10650

SE  25.575 11.7 10800

SEM 23.05 66.4 11800

;

proc cluster data=pessoas outtree = arvore method = average;

var imc corr kcal;

id cat;

run;

PROC TREE DATA = arvore;

RUN;

Tambem PCA - Biplot - Análise Discriminante Canónica

Programa SAS para MANOVA

data Q_Vida;

input cat $ imc corr kcal;

datalines;

AT 20.2 60.7 3200

AT 21.3 54.8 3100

AT 19.3 49.6 2800

AT 21.1 52.3 3300

SEM 22.4 14.9 2600

SEM 21.9 17.8 2700

SEM 23.8 18.6 3200

SEM 24.1 15.1 3300

SED  27.3 2.5 2700

SED  23.4 4.3 2300

SED  25.2 2.3 2600

SED  26.4 2.6 3200

PRO 26.2 4.1 2600

PRO 24.2 2.1 2700

PRO 25.4 1.9 2650

;

proc print;

run;

proc glm;

 class cat;

 model imc corr kcal  = cat;

  contrast " Atl e Semiat Vs Seden e Prof" cat 1 -1 -1 1;

 contrast " Professor Vs Sedentario" cat 0 1 -1 0;

 manova h=_all_ / printe printh;

run;

Arquivo em padrão Texto, 

completo, Dinheiro Falso:

(Somente copiar e colar no Programa Notes do Computador)

@RELATION banco

@ATTRIBUTE Length REAL
@ATTRIBUTE Left REAL
@ATTRIBUTE Right REAL
@ATTRIBUTE Bottom REAL
@ATTRIBUTE Top REAL
@ATTRIBUTE Diagonal REAL
@ATTRIBUTE Class {0,1}

@DATA      

214.8,131.0,131.1,9.0,9.7,141.0,0
214.6,129.7,129.7,8.1,9.5,141.7,0
214.8,129.7,129.7,8.7,9.6,142.2,0
214.8,129.7,129.6,7.5,10.4,142.0,0
215.0,129.6,129.7,10.4,7.7,141.8,0
215.7,130.8,130.5,9.0,10.1,141.4,0
215.5,129.5,129.7,7.9,9.6,141.6,0
214.5,129.6,129.2,7.2,10.7,141.7,0
214.9,129.4,129.7,8.2,11.0,141.9,0
215.2,130.4,130.3,9.2,10.0,140.7,0
215.3,130.4,130.3,7.9,11.7,141.8,0
215.1,129.5,129.6,7.7,10.5,142.2,0
215.2,130.8,129.6,7.9,10.8,141.4,0
214.7,129.7,129.7,7.7,10.9,141.7,0
215.1,129.9,129.7,7.7,10.8,141.8,0
214.5,129.8,129.8,9.3,8.5,141.6,0
214.6,129.9,130.1,8.2,9.8,141.7,0
215.0,129.9,129.7,9.0,9.0,141.9,0
215.2,129.6,129.6,7.4,11.5,141.5,0
214.7,130.2,129.9,8.6,10.0,141.9,0
215.0,129.9,129.3,8.4,10.0,141.4,0
215.6,130.5,130.0,8.1,10.3,141.6,0
215.3,130.6,130.0,8.4,10.8,141.5,0
215.7,130.2,130.0,8.7,10.0,141.6,0
215.1,129.7,129.9,7.4,10.8,141.1,0
215.3,130.4,130.4,8.0,11.0,142.3,0
215.5,130.2,130.1,8.9,9.8,142.4,0
215.1,130.3,130.3,9.8,9.5,141.9,0
215.1,130.0,130.0,7.4,10.5,141.8,0
214.8,129.7,129.3,8.3,9.0,142.0,0
215.2,130.1,129.8,7.9,10.7,141.8,0
214.8,129.7,129.7,8.6,9.1,142.3,0
215.0,130.0,129.6,7.7,10.5,140.7,0
215.6,130.4,130.1,8.4,10.3,141.0,0
215.9,130.4,130.0,8.9,10.6,141.4,0
214.6,130.2,130.2,9.4,9.7,141.8,0
215.5,130.3,130.0,8.4,9.7,141.8,0
215.3,129.9,129.4,7.9,10.0,142.0,0
215.3,130.3,130.1,8.5,9.3,142.1,0
213.9,130.3,129.0,8.1,9.7,141.3,0
214.4,129.8,129.2,8.9,9.4,142.3,0
214.8,130.1,129.6,8.8,9.9,140.9,0
214.9,129.6,129.4,9.3,9.0,141.7,0
214.9,130.4,129.7,9.0,9.8,140.9,0
214.8,129.4,129.1,8.2,10.2,141.0,0
214.3,129.5,129.4,8.3,10.2,141.8,0
214.8,129.9,129.7,8.3,10.2,141.5,0
214.8,129.9,129.7,7.3,10.9,142.0,0
214.6,129.7,129.8,7.9,10.3,141.1,0
214.5,129.0,129.6,7.8,9.8,142.0,0
214.6,129.8,129.4,7.2,10.0,141.3,0
215.3,130.6,130.0,9.5,9.7,141.1,0
214.5,130.1,130.0,7.8,10.9,140.9,0
215.4,130.2,130.2,7.6,10.9,141.6,0
214.5,129.4,129.5,7.9,10.0,141.4,0
215.2,129.7,129.4,9.2,9.4,142.0,0
215.7,130.0,129.4,9.2,10.4,141.2,0
215.0,129.6,129.4,8.8,9.0,141.1,0
215.1,130.1,129.9,7.9,11.0,141.3,0
215.1,130.0,129.8,8.2,10.3,141.4,0
215.1,129.6,129.3,8.3,9.9,141.6,0
215.3,129.7,129.4,7.5,10.5,141.5,0
215.4,129.8,129.4,8.0,10.6,141.5,0
214.5,130.0,129.5,8.0,10.8,141.4,0
215.0,130.0,129.8,8.6,10.6,141.5,0
215.2,130.6,130.0,8.8,10.6,140.8,0
214.6,129.5,129.2,7.7,10.3,141.3,0
214.8,129.7,129.3,9.1,9.5,141.5,0
215.1,129.6,129.8,8.6,9.8,141.8,0
214.9,130.2,130.2,8.0,11.2,139.6,0
213.8,129.8,129.5,8.4,11.1,140.9,0
215.2,129.9,129.5,8.2,10.3,141.4,0
215.0,129.6,130.2,8.7,10.0,141.2,0
214.4,129.9,129.6,7.5,10.5,141.8,0
215.2,129.9,129.7,7.2,10.6,142.1,0
214.1,129.6,129.3,7.6,10.7,141.7,0
214.9,129.9,130.1,8.8,10.0,141.2,0
214.6,129.8,129.4,7.4,10.6,141.0,0
215.2,130.5,129.8,7.9,10.9,140.9,0
214.6,129.9,129.4,7.9,10.0,141.8,0
215.1,129.7,129.7,8.6,10.3,140.6,0
214.9,129.8,129.6,7.5,10.3,141.0,0
215.2,129.7,129.1,9.0,9.7,141.9,0
215.2,130.1,129.9,7.9,10.8,141.3,0
215.4,130.7,130.2,9.0,11.1,141.2,0
215.1,129.9,129.6,8.9,10.2,141.5,0
215.2,129.9,129.7,8.7,9.5,141.6,0
215.0,129.6,129.2,8.4,10.2,142.1,0
214.9,130.3,129.9,7.4,11.2,141.5,0
215.0,129.9,129.7,8.0,10.5,142.0,0
214.7,129.7,129.3,8.6,9.6,141.6,0
215.4,130.0,129.9,8.5,9.7,141.4,0
214.9,129.4,129.5,8.2,9.9,141.5,0
214.5,129.5,129.3,7.4,10.7,141.5,0
214.7,129.6,129.5,8.3,10.0,142.0,0
215.6,129.9,129.9,9.0,9.5,141.7,0
215.0,130.4,130.3,9.1,10.2,141.1,0
214.4,129.7,129.5,8.0,10.3,141.2,0
215.1,130.0,129.8,9.1,10.2,141.5,0
214.7,130.0,129.4,7.8,10.0,141.2,0
214.4,130.1,130.3,9.7,11.7,139.8,1
214.9,130.5,130.2,11.0,11.5,139.5,1
214.9,130.3,130.1,8.7,11.7,140.2,1
215.0,130.4,130.6,9.9,10.9,140.3,1
214.7,130.2,130.3,11.8,10.9,139.7,1
215.0,130.2,130.2,10.6,10.7,139.9,1
215.3,130.3,130.1,9.3,12.1,140.2,1
214.8,130.1,130.4,9.8,11.5,139.9,1
215.0,130.2,129.9,10.0,11.9,139.4,1
215.2,130.6,130.8,10.4,11.2,140.3,1
215.2,130.4,130.3,8.0,11.5,139.2,1
215.1,130.5,130.3,10.6,11.5,140.1,1
215.4,130.7,131.1,9.7,11.8,140.6,1
214.9,130.4,129.9,11.4,11.0,139.9,1
215.1,130.3,130.0,10.6,10.8,139.7,1
215.5,130.4,130.0,8.2,11.2,139.2,1
214.7,130.6,130.1,11.8,10.5,139.8,1
214.7,130.4,130.1,12.1,10.4,139.9,1
214.8,130.5,130.2,11.0,11.0,140.0,1
214.4,130.2,129.9,10.1,12.0,139.2,1
214.8,130.3,130.4,10.1,12.1,139.6,1
215.1,130.6,130.3,12.3,10.2,139.6,1
215.3,130.8,131.1,11.6,10.6,140.2,1
215.1,130.7,130.4,10.5,11.2,139.7,1
214.7,130.5,130.5,9.9,10.3,140.1,1
214.9,130.0,130.3,10.2,11.4,139.6,1
215.0,130.4,130.4,9.4,11.6,140.2,1
215.5,130.7,130.3,10.2,11.8,140.0,1
215.1,130.2,130.2,10.1,11.3,140.3,1
214.5,130.2,130.6,9.8,12.1,139.9,1
214.3,130.2,130.0,10.7,10.5,139.8,1
214.5,130.2,129.8,12.3,11.2,139.2,1
214.9,130.5,130.2,10.6,11.5,139.9,1
214.6,130.2,130.4,10.5,11.8,139.7,1
214.2,130.0,130.2,11.0,11.2,139.5,1
214.8,130.1,130.1,11.9,11.1,139.5,1
214.6,129.8,130.2,10.7,11.1,139.4,1
214.9,130.7,130.3,9.3,11.2,138.3,1
214.6,130.4,130.4,11.3,10.8,139.8,1
214.5,130.5,130.2,11.8,10.2,139.6,1
214.8,130.2,130.3,10.0,11.9,139.3,1
214.7,130.0,129.4,10.2,11.0,139.2,1
214.6,130.2,130.4,11.2,10.7,139.9,1
215.0,130.5,130.4,10.6,11.1,139.9,1
214.5,129.8,129.8,11.4,10.0,139.3,1
214.9,130.6,130.4,11.9,10.5,139.8,1
215.0,130.5,130.4,11.4,10.7,139.9,1
215.3,130.6,130.3,9.3,11.3,138.1,1
214.7,130.2,130.1,10.7,11.0,139.4,1
214.9,129.9,130.0,9.9,12.3,139.4,1
214.9,130.3,129.9,11.9,10.6,139.8,1
214.6,129.9,129.7,11.9,10.1,139.0,1
214.6,129.7,129.3,10.4,11.0,139.3,1
214.5,130.1,130.1,12.1,10.3,139.4,1
214.5,130.3,130.0,11.0,11.5,139.5,1
215.1,130.0,130.3,11.6,10.5,139.7,1
214.2,129.7,129.6,10.3,11.4,139.5,1
214.4,130.1,130.0,11.3,10.7,139.2,1
214.8,130.4,130.6,12.5,10.0,139.3,1
214.6,130.6,130.1,8.1,12.1,137.9,1
215.6,130.1,129.7,7.4,12.2,138.4,1
214.9,130.5,130.1,9.9,10.2,138.1,1
214.6,130.1,130.0,11.5,10.6,139.5,1
214.7,130.1,130.2,11.6,10.9,139.1,1
214.3,130.3,130.0,11.4,10.5,139.8,1
215.1,130.3,130.6,10.3,12.0,139.7,1
216.3,130.7,130.4,10.0,10.1,138.8,1
215.6,130.4,130.1,9.6,11.2,138.6,1
214.8,129.9,129.8,9.6,12.0,139.6,1
214.9,130.0,129.9,11.4,10.9,139.7,1
213.9,130.7,130.5,8.7,11.5,137.8,1
214.2,130.6,130.4,12.0,10.2,139.6,1
214.8,130.5,130.3,11.8,10.5,139.4,1
214.8,129.6,130.0,10.4,11.6,139.2,1
214.8,130.1,130.0,11.4,10.5,139.6,1
214.9,130.4,130.2,11.9,10.7,139.0,1
214.3,130.1,130.1,11.6,10.5,139.7,1
214.5,130.4,130.0,9.9,12.0,139.6,1
214.8,130.5,130.3,10.2,12.1,139.1,1
214.5,130.2,130.4,8.2,11.8,137.8,1
215.0,130.4,130.1,11.4,10.7,139.1,1
214.8,130.6,130.6,8.0,11.4,138.7,1
215.0,130.5,130.1,11.0,11.4,139.3,1
214.6,130.5,130.4,10.1,11.4,139.3,1
214.7,130.2,130.1,10.7,11.1,139.5,1
214.7,130.4,130.0,11.5,10.7,139.4,1
214.5,130.4,130.0,8.0,12.2,138.5,1
214.8,130.0,129.7,11.4,10.6,139.2,1
214.8,129.9,130.2,9.6,11.9,139.4,1
214.6,130.3,130.2,12.7,9.1,139.2,1
215.1,130.2,129.8,10.2,12.0,139.4,1
215.4,130.5,130.6,8.8,11.0,138.6,1
214.7,130.3,130.2,10.8,11.1,139.2,1
215.0,130.5,130.3,9.6,11.0,138.5,1
214.9,130.3,130.5,11.6,10.6,139.8,1
215.0,130.4,130.3,9.9,12.1,139.6,1
215.1,130.3,129.9,10.3,11.5,139.7,1
214.8,130.3,130.4,10.6,11.1,140.0,1
214.7,130.7,130.8,11.2,11.2,139.4,1
214.3,129.9,129.9,10.2,11.5,139.6,1

Machine Learning Supervisionado para Previsão

Preço dos Imoveis

Arquivo Weka com Dados Simulados Computacionalmente - Aplificar Tamanaho da Amostra (Monte Carlo - Bootstraping - 

Jackknife etc.)

Para Weka não acusar Erro, por poucas linhas no arquivo de dados

@RELATION Precifica

@ATTRIBUTE Tam_Cas REAL

@ATTRIBUTE Tam_Terre REAL

@ATTRIBUTE Quartos REAL

@ATTRIBUTE Granito REAL

@ATTRIBUTE Banh_Refor REAL

@ATTRIBUTE Preco REAL

@DATA

3529,9191,6,0,0,205

3247,10061,5,1,1,224.9

4032,10150,5,0,1,197.9

2397,14156,4,1,0,189.9

2200,9600,4,0,1,195

3536,19994,6,1,1,325

2983,9365,5,0,1,230

3388,9626,6,0,0,214.95

3639.5,10105.5,5,1,1,211.4

3214.5,12153,5,0,1,193.9

2298.5,11878,4,1,0,192.45

2868,14797,4,0,1,260

3259.5,14679.5,6,1,1,277.5

Mayara Pardi Ikeda

21:54

8.6159 * TamanhoLote + 19842.4386 * Quartos + 24927.7258 * BanheiroReformado + 4754.3438

Robson Campos de Lima

22:00

PrecoVenda = -26.6882 * TamanhoCasa + 7.0551 * TamanhoLote + 43166.0767 * Quartos + 42292.0901 * BanheiroReformado + -21661.1208