AI_DS_Management_China

   Outlier - Conventional and Robust Data Science for SML to Prediction or Regression    Conventional and Robust Data Science for SML to Prediction or Regression  SAS Program D ata 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 70.32...

Agenda: - Show case slides - Run PCA - Run ANOVA and Cluster - Run MLS for Prediction - If time simulate error in Weka by Outlier

   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...

  Outlier - Conventional and Robust Data Science for SML to Prediction or Regression    Conventional and Robust Data Science for SML to Prediction or Regression  SAS Program D ata 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...

     Exercício Pratico  2. Regressão Multipla em Excel ou LOffice Calc, SAS (proc reg e robustreg) e Weka. Dead Line 10/9. Exemplos de Aplicações: Análise de dados econômicos: Previsão de inflação:  Ao considerar diversos indicadores econômicos, a regressão robusta pode fornecer previsões mais precisas, mesmo na presença de choques econômicos que geram outliers. Análise de consumo:  Ao modelar o consumo em função da renda, taxas de juros e outros fatores, a regressão robusta pode identificar padrões mais robustos, mesmo na presença de consumidores com comportamentos atípicos. Bu_Unit Sales Price Qu_level Claims NPS PV Satisfac 1 65,98108 97,8022 96,77419 13,58025 98,9011 19 97,82??? 2 15,8371 98,9011 98,3871 12,34568 97,8022 29 98,91??? 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 24,1...