CAU–USP-administration-gabriel

File to Download: Weka file Customer Satisfaction   @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...

  Slides First Class of the day - SML for Prediction SML for Prediction - Slides Second Class - Management Systems Management Systems

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 53.22580645 46.91358025 63.73626374 68.47826087 12 23.89962978 68.13186813 51.61290323 45.67901235 61....

  UML - Cluster Analysis - ANOVA and MANOVA Program to do ANOVA data People; /* BMI:  body mass index      Movm: Movement (Km)      KCal : Kilocalories      ATL: Athletes      SEMI: Semi-athletes      SEDE: S edentary      PROF: P rofessor */ input  Categ $ BMI Movm  Kcal ; cards; ATL 20.2 60.7 3200 ATL 21.3 54.8 3100 ATL 19.3 49.6 2800 ATL 21.1 52.3 3300 SEMI 22.4 14.9 2600 SEMI 21.9 17.8 2700 SEMI 23.8 18.6 3200 SEMI 24.1 15.1 3300 SEDE  27.3 2.5 2700 SEDE 23.4 4.3 2300 SEDE  25.2 2.3 2600 SEDE  26.4 2.6 3200 PROF 26.2 4.1 2600 PROF 24.2 2.1 2700 PROF 25.4 1.9 2650 ; Proc ANOVA;      Class Categ;       Model  BMI Movm  Kcal = Categ;      Means Categ / Duncan Lines; Run; Arithmetic Averages of Categories Categ BMI Movm Kcal AT 20.5 54.4 3100 PR 25.3 2.7 2650 SE 25.6 2.9 2700 SEM 23.1 16.6 2950 Obtaining ...

  SAS Program for  Unsupervised Machine Learning - PCA - Biplot Data PCA_Bip; Input Region $ Food_D Surv_R Econ_Ap Culture; cards; Midwest 20.1 46.5 89.5 44 North 97.2 49.1 5.3 59.25 North_E 44.75 85.2 51.75 44.25 South 20.1 54.5 50.75 85 South_E 20.3 51.5 88.2 63.4 ; Title " PCA - Biplot --> ML Not Supervised"; proc prinqual data=PCA_Bip plots=(MDPref);     transform identity(Food_D Surv_R Econ_Ap Culture);     id Region;     ods select MDPrefPlot; run; Original Databanc - Sample of Farms em Each Region SAS Program to Test Differentiation by Predictor Variables Data Public; input Region $ Food_Div Surv_Ris Econ_App Culture; Cards;  North 84 56 46 64 North 95 39 62 54 North 90 52 61 62 North 80 40 47 57 South 63 53 61 94 South 59 52 42 82 South 63 56 47 81 South 40 57 53 83 Nort_Eas 46 85 50 58 Nort_Eas 52 82 35 42 Nort_Eas 43 93 57 39 Nort_Eas 38 80 65 38 Midwest 45 38 93 37 Midwest 42 56 93 45 Midwest 46 56 91 52 Midwest 6...

Slides from 08/13/2022 Standard pdf file for download PDF file