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ISYE 6501 Week 10 Study Guide – Analytics Modeling Concepts & Practice Review

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Strengthen your understanding of Week 10 topics in ISYE 6501 (Introduction to Analytics Modeling) with this structured study and revision guide. This resource is designed to help students review key concepts, improve analytical thinking, and prepare effectively for assignments and exams. Includes focused review of data analysis techniques, predictive modeling methods, regression concepts, classification approaches, and model evaluation strategies commonly covered in Week 10 coursework. Ideal for revision, self-study, and exam preparation for Georgia Tech OMS Analytics students.

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ISYE 6501 Week 10
#ISYEꢀ 6501ꢀ Week HW
ꢀ 10ꢀ
# clear RStudio environmentꢀ
rm(list = ls())ꢀ

# import datasetꢀ
df.data <- read.table("~/Downloads/hw10-FA23/breast-cancer-
wisconsin.data.txt", header = TRUE,sep = ",", na.strings="?")ꢀ

# import column names from
http://archive.ics.uci.edu/dataset/15/breast+cancer+wisconsin+originalꢀ
colnames(df.data) <- c("ID", "Clump_Thickness", "Cell_Size", "Cell_Shape",ꢀ
"Marg_Adhesion", "Single_Epith_Cell_Size",
"Bare_Nuclei", "Bland_Chromatin", ꢀ
"Normal_Nucleoli", "Mitoses", "Class")ꢀ

# convert data to data frameꢀ
df.data$Class <- as.factor(df.data$Class)ꢀ
levels(df.data$Class) <- c(0, 1)ꢀ

# summary of the dataꢀ
summary(df.data)ꢀ

## ID Clump_Thickness Cell_Size Cell_Shape ꢀ
## Min. : 61634 Min. : 1.000 Min. : 1.000 Min. : 1.000 ꢀ
## 1st Qu.: 870258 1st Qu.: 2.000 1st Qu.: 1.000 1st Qu.: 1.000 ꢀ
## Median : 1171710 Median : 4.000 Median : 1.000 Median : 1.000 ꢀ
## Mean : 1071807 Mean : 4.417 Mean : 3.138 Mean : 3.211 ꢀ
## 3rd Qu.: 1238354 3rd Qu.: 6.000 3rd Qu.: 5.000 3rd Qu.: 5.000 ꢀ
## Max. :13454352 Max. :10.000 Max. :10.000 Max. :10.000 ꢀ
## ꢀ
## Marg_Adhesion Single_Epith_Cell_Size Bare_Nuclei Bland_Chromatin ꢀ
## Min. : 1.000 Min. : 1.000 Min. : 1.000 Min. : 1.000 ꢀ
## 1st Qu.: 1.000 1st Qu.: 2.000 1st Qu.: 1.000 1st Qu.: 2.000 ꢀ
## Median : 1.000 Median : 2.000 Median : 1.000 Median : 3.000 ꢀ
## Mean : 2.809 Mean : 3.218 Mean : 3.548 Mean : 3.438 ꢀ
## 3rd Qu.: 4.000 3rd Qu.: 4.000 3rd Qu.: 6.000 3rd Qu.: 5.000 ꢀ
## Max. :10.000 Max. :10.000 Max. :10.000 Max. :10.000 ꢀ
## NA's :16 ꢀ
## Normal_Nucleoli Mitoses Class ꢀ
## Min. : 1.00 Min. : 1.00 0:457 ꢀ
## 1st Qu.: 1.00 1st Qu.: 1.00 1:241 ꢀ
## Median : 1.00 Median : 1.00 ꢀ
## Mean : 2.87 Mean : 1.59 ꢀ
## 3rd Qu.: 4.00 3rd Qu.: 1.00 ꢀ


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## Max. :10.00 Max. :10.00 ꢀ
## ꢀ

# identify missing data in the datasetꢀ
df.data[is.na(df.data$Bare_Nuclei),]ꢀ

## ID Clump_Thickness Cell_Size Cell_Shape Marg_Adhesionꢀ
## 23 1057013 8 4 5 1ꢀ
## 40 1096800 6 6 6 9ꢀ
## 139 1183246 1 1 1 1ꢀ
## 145 1184840 1 1 3 1ꢀ
## 158 1193683 1 1 2 1ꢀ
## 164 1197510 5 1 1 1ꢀ
## 235 1241232 3 1 4 1ꢀ
## 249 169356 3 1 1 1ꢀ
## 275 432809 3 1 3 1ꢀ
## 292 563649 8 8 8 1ꢀ
## 294 606140 1 1 1 1ꢀ
## 297 61634 5 4 3 1ꢀ
## 315 704168 4 6 5 6ꢀ
## 321 733639 3 1 1 1ꢀ
## 411 1238464 1 1 1 1ꢀ
## 617 1057067 1 1 1 1ꢀ
## Single_Epith_Cell_Size Bare_Nuclei Bland_Chromatin Normal_Nucleoli
Mitosesꢀ
## 23 2 NA 7 3
1 ꢀ
## 40 6 NA 7 8
1ꢀ
## 139 1 NA 2 1
1ꢀ
## 145 2 NA 2 1
1ꢀ
## 158 3 NA 1 1
1ꢀ
## 164 2 NA 3 1
1ꢀ
## 235 2 NA 3 1
1ꢀ
## 249 2 NA 3 1
1ꢀ
## 275 2 NA 2 1
1ꢀ
## 292 2 NA 6 10
1ꢀ
## 294 2 NA 2 1
1ꢀ
## 297 2 NA 2 3
1ꢀ
## 315 7 NA 4 9



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