classification classification Catherine 12/18/2021 knitr::opts_chunk$set(echo = TRUE,cache = TRUE,warning = FALSE,message = FALSE,dpi = 180,fig.width = 8,fig.height = 5) library(dplyr) library(ggplot2) library(magrittr) library(rmarkdown) library(C50) library(pROC) Fit Decision Tree and Logistic Regression to predict affairs (Attribute if_affair is the dependent/target variable). library(readr) affairs affairs% mutate(if_affair=ifelse(if_affair=="no",0,1)) summary(affairs) ## age yearsmarried religiousness rating ## Min. :17.50 Min. : 0.125 Min. :1.000 Min. :1.

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Answered Same Day Dec 17, 2021

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Mohd answered on Dec 18 2021

117 Votes

classification
classification
Catherine
12/18/2021
knitr::opts_chunk$set(echo = TRUE,cache = TRUE,warning = FALSE,message = FALSE,dpi = 180,fig.width = 8,fig.height = 5)
li
ary(dplyr)
li
ary(ggplot2)
li
ary(magrittr)
li
ary(rmarkdown)
li
ary(C50)
li
ary(pROC)
Fit Decision Tree and Logistic Regression to predict affairs (Attribute if_affair is the dependent/target variable).
li
ary(readr)
affairs <- read_csv("New folder (2)/affairs.csv")
affairs$if_affai
-factor(affairs$if_affair)
str(affairs)
## spec_tbl_df [601 x 5] (S3: spec_tbl_df/tbl_df/tbl/data.frame)
## $ age : num [1:601] 37 27 32 57 22 32 22 57 32 22 ...
## $ yearsma
ied : num [1:601] 10 4 15 15 0.75 1.5 0.75 15 15 1.5 ...
## $ religiousness: num [1:601] 3 4 1 5 2 2 2 2 4 4 ...
## $ rating : num [1:601] 4 4 4 5 3 5 3 4 2 5 ...
## $ if_affair : Factor w/ 2 levels "no","yes": 1 1 1 1 1 1 1 1 1 1 ...
## - attr(*, "spec")=
## .. cols(
## .. age = col_double(),
## .. yearsma
ied = col_double(),
## .. religiousness = col_double(),
## .. rating = col_double(),
## .. if_affair = col_character()
## .. )
## - attr(*, "problems")=affairs<-affairs%>%
mutate(if_affair=ifelse(if_affair=="no",0,1))
summary(affairs)
## age yearsma
ied religiousness rating
## Min. :17.50 Min. : 0.125 Min. :1.000 Min. :1.000
## 1st Qu.:27.00 1st Qu.: 4.000 1st Qu.:2.000 1st Qu.:3.000
## Median :32.00 Median : 7.000 Median :3.000 Median :4.000
## Mean :32.49 Mean : 8.178 Mean :3.116 Mean :3.932
## 3rd Qu.:37.00 3rd Qu.:15.000 3rd Qu.:4.000 3rd Qu.:5.000
## Max. :57.00 Max. :15.000 Max. :5.000 Max. :5.000
## if_affair
## Min. :0.0000
## 1st Qu.:0.0000
## Median :0.0000
## Mean :0.2496
## 3rd Qu.:0.0000
## Max. :1.0000
affairs$if_affai
-factor(affairs$if_affair)
set.seed(333)
size <- floor(0.8 * nrow(affairs))

### randomly decide which ones for training
training_index <- sample(nrow(affairs), size = size, replace = FALSE)

train <- affairs[training_index,]
...

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