Apply Data 5: IKEA

Goal: Build a classification model to predict the spam email (yesno). Clickhere for data.

Import data

library(tidyverse)
library(correlationfunnel)

data <- readr::read_csv('https://raw.githubusercontent.com/rfordatascience/tidytuesday/master/data/2023/2023-08-15/spam.csv')

## Rows: 4601 Columns: 7
## ── Column specification ────────────────────────────────────────────────────────
## Delimiter: ","
## chr (1): yesno
## dbl (6): crl.tot, dollar, bang, money, n000, make
## 
## ℹ Use `spec()` to retrieve the full column specification for this data.
## ℹ Specify the column types or set `show_col_types = FALSE` to quiet this message.

Clean data

skimr::skim(data)

Data summary

Name	data
Number of rows	4601
Number of columns	7
_______________________
Column type frequency:
character	1
numeric	6
________________________
Group variables	None

Variable type: character

skim_variable	n_missing	complete_rate	min	max	empty	n_unique	whitespace
yesno	0	1	1	1	0	2	0

Variable type: numeric

skim_variable	n_missing	complete_rate	mean	sd	p0	p25	p50	p75	p100	hist
crl.tot	0	1	283.29	606.35	1	35	95	266.00	15841.00	▇▁▁▁▁
dollar	0	1	0.08	0.25	0	0	0	0.05	6.00	▇▁▁▁▁
bang	0	1	0.27	0.82	0	0	0	0.32	32.48	▇▁▁▁▁
money	0	1	0.09	0.44	0	0	0	0.00	12.50	▇▁▁▁▁
n000	0	1	0.10	0.35	0	0	0	0.00	5.45	▇▁▁▁▁
make	0	1	0.10	0.31	0	0	0	0.00	4.54	▇▁▁▁▁

Issues with data

• Missing values
• Character variable
  • yesno
• Numeric variables
  • crl.tot, dollar, bang, money, n000, make
• Character variable: Convert it to numbers in the recipes steps
• Unbalanced target variable: yesno
• ID variable:

# factors_vec <- data %>% select(crl.tot, dollar, bang, money, n000, make) %>% names()

data_clean <- data
  
  # Drop zero variance data

Explore data

data_clean %>% count(yesno)

## # A tibble: 2 × 2
##   yesno     n
##   <chr> <int>
## 1 n      2788
## 2 y      1813

data_clean %>%
    ggplot(aes(yesno)) +
    geom_bar()

yesno vs. money

data_clean %>%
    ggplot(aes(yesno, money)) +
    geom_boxplot()

correlation plot

# step 1: binarize
data_binarized <- data_clean %>%
    select(-money) %>%
    binarize()

data_binarized %>% glimpse()

## Rows: 4,601
## Columns: 15
## $ `crl.tot__-Inf_35`   <dbl> 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 1, 0, 0, 0…
## $ crl.tot__35_95       <dbl> 0, 0, 0, 0, 0, 1, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0…
## $ crl.tot__95_266      <dbl> 0, 0, 0, 1, 1, 0, 1, 0, 0, 0, 0, 1, 1, 0, 1, 1, 1…
## $ crl.tot__266_Inf     <dbl> 1, 1, 1, 0, 0, 0, 0, 0, 1, 1, 0, 0, 0, 0, 0, 0, 0…
## $ `dollar__-Inf_0.052` <dbl> 1, 0, 0, 1, 1, 1, 0, 1, 0, 0, 1, 1, 1, 1, 1, 0, 1…
## $ dollar__0.052_Inf    <dbl> 0, 1, 1, 0, 0, 0, 1, 0, 1, 1, 0, 0, 0, 0, 0, 1, 0…
## $ `bang__-Inf_0.315`   <dbl> 0, 0, 1, 1, 1, 1, 1, 1, 1, 1, 0, 0, 0, 1, 0, 0, 0…
## $ bang__0.315_Inf      <dbl> 1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 1, 1, 1, 0, 1, 1, 1…
## $ n000__0              <dbl> 1, 0, 0, 1, 1, 1, 1, 1, 1, 0, 1, 1, 1, 1, 0, 1, 1…
## $ `n000__-OTHER`       <dbl> 0, 1, 1, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 1, 0, 0…
## $ make__0              <dbl> 1, 0, 0, 1, 1, 1, 1, 1, 0, 0, 1, 1, 1, 1, 1, 1, 1…
## $ make__0.1            <dbl> 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0…
## $ `make__-OTHER`       <dbl> 0, 1, 1, 0, 0, 0, 0, 0, 1, 1, 0, 0, 0, 0, 0, 0, 0…
## $ yesno__n             <dbl> 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0…
## $ yesno__y             <dbl> 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1…

# step 2: correlation
data_correlation <- data_binarized %>% 
    correlate(yesno__n)

# step 3: plot
data_correlation %>%
    correlationfunnel::plot_correlation_funnel()