Apply5

Import data

library(tidyverse)

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spam <- 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

spam %>% skimr::skim()

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

Check for missing values

colSums(is.na(spam))

## crl.tot  dollar    bang   money    n000    make   yesno 
##       0       0       0       0       0       0       0

## No missing data

Removing outliers

spam <- spam %>%
  filter(bang < 2500, crl.tot < 2500)

Removing duplicate rows

spam <- spam[!duplicated(spam), ]

Explore data

spam %>%
    filter(money > 0, bang > 0) %>%
    ggplot(aes(money, bang, color = yesno)) +
    geom_point(alpha = 0.2, size = 1.1)

spam %>%
  filter(dollar < 2) %>%
  ggplot(aes(dollar, y = ..density.., fill = yesno)) +
  geom_histogram(position = "identity", alpha = 0.5)

## Warning: The dot-dot notation (`..density..`) was deprecated in ggplot2 3.4.0.
## ℹ Please use `after_stat(density)` instead.
## This warning is displayed once every 8 hours.
## Call `lifecycle::last_lifecycle_warnings()` to see where this warning was
## generated.

## `stat_bin()` using `bins = 30`. Pick better value with `binwidth`.

# Filter data and plot the distribution of 'bang'
spam %>% 
  filter(bang < 2500) %>%
  ggplot(aes(x = bang)) + 
  geom_histogram(binwidth = 0.5) + 
  ggtitle("Distribution of 'bang' (Values < 2500)")

# Filter data and plot the distribution of 'crl.tot'
spam %>% 
  filter(crl.tot < 2500) %>%
  ggplot(aes(x = crl.tot)) + 
  geom_histogram(binwidth = 0.5) + 
  ggtitle("Distribution of 'crl.tot' (Values < 2500)")

# Relationship with target variable
ggplot(spam, aes(x = yesno, y = bang)) + 
  geom_boxplot() + 
  ggtitle("'bang' by 'yesno'")

ggplot(spam, aes(x = yesno, y = crl.tot)) + 
  geom_boxplot() + 
  ggtitle("'crl.tot' by 'yesno'")

# Bivariate Analysis
ggplot(spam, aes(x = bang, y = crl.tot)) + geom_point(alpha = 0.5) + ggtitle("Scatterplot of 'bang' vs 'crl.tot'")

Building a model

library(tidymodels)

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## • Dig deeper into tidy modeling with R at https://www.tmwr.org

set.seed(123)
spam_split <- initial_split(spam, strata = yesno)
spam_train <- training(spam_split)
spam_test <- testing(spam_split)

set.seed(234)
spam_folds <- vfold_cv(spam_train, strata = yesno)
spam_folds

## #  10-fold cross-validation using stratification 
## # A tibble: 10 × 2
##    splits             id    
##    <list>             <chr> 
##  1 <split [2012/224]> Fold01
##  2 <split [2012/224]> Fold02
##  3 <split [2012/224]> Fold03
##  4 <split [2012/224]> Fold04
##  5 <split [2012/224]> Fold05
##  6 <split [2012/224]> Fold06
##  7 <split [2013/223]> Fold07
##  8 <split [2013/223]> Fold08
##  9 <split [2013/223]> Fold09
## 10 <split [2013/223]> Fold10

usemodels::use_ranger(yesno ~ ., data = spam_train)

## ranger_recipe <- 
##   recipe(formula = yesno ~ ., data = spam_train) 
## 
## ranger_spec <- 
##   rand_forest(mtry = tune(), min_n = tune(), trees = 1000) %>% 
##   set_mode("classification") %>% 
##   set_engine("ranger") 
## 
## ranger_workflow <- 
##   workflow() %>% 
##   add_recipe(ranger_recipe) %>% 
##   add_model(ranger_spec) 
## 
## set.seed(24411)
## ranger_tune <-
##   tune_grid(ranger_workflow, resamples = stop("add your rsample object"), grid = stop("add number of candidate points"))

library(themis)
ranger_recipe <-
  recipe(formula = yesno ~ ., data = spam_train) %>%
  step_unknown(all_nominal_predictors()) %>%
  step_other(all_nominal_predictors(), threshold = 0.03) %>%
  step_impute_linear(dollar)

ranger_spec <-
  rand_forest(trees = 1000) %>%
  set_mode("classification") %>%
  set_engine("ranger")

ranger_workflow <-
  workflow() %>%
  add_recipe(ranger_recipe) %>%
  add_model(ranger_spec)

doParallel::registerDoParallel()
set.seed(74403)
ranger_rs <-
  fit_resamples(ranger_workflow,
    resamples = spam_folds,
    control = control_resamples(save_pred = TRUE)
  )

Explore the results

collect_metrics(ranger_rs)

## # A tibble: 2 × 6
##   .metric  .estimator  mean     n std_err .config             
##   <chr>    <chr>      <dbl> <int>   <dbl> <chr>               
## 1 accuracy binary     0.830    10 0.00728 Preprocessor1_Model1
## 2 roc_auc  binary     0.900    10 0.00539 Preprocessor1_Model1

collect_predictions(ranger_rs) %>%
  group_by(id) %>%
  roc_curve(yesno, .pred_n) %>%
  autoplot()

conf_mat_resampled(ranger_rs, tidy = FALSE) %>%
  autoplot()

final_fitted <- last_fit(ranger_workflow, spam_split)
collect_metrics(final_fitted)

## # A tibble: 2 × 4
##   .metric  .estimator .estimate .config             
##   <chr>    <chr>          <dbl> <chr>               
## 1 accuracy binary         0.838 Preprocessor1_Model1
## 2 roc_auc  binary         0.915 Preprocessor1_Model1

collect_predictions(final_fitted) %>%
  conf_mat(yesno, .pred_class) %>%
  autoplot()

library(vip)

## 
## Attaching package: 'vip'

## The following object is masked from 'package:utils':
## 
##     vi

imp_data <- ranger_recipe %>%
  prep() %>%
  bake(new_data = NULL)

ranger_spec %>%
  set_engine("ranger", importance = "permutation") %>%
  fit(yesno ~ ., data = imp_data) %>%
  vip(geom = "point")

Can we effectively predict whether an email is spam based on the frequency of certain keywords and symbols present in the email?

The Spam Detection Dataset contains 2982 entries with 7 columns, focusing on classifying emails based on the frequency of certain keywords and symbols. The target variable, yesno, indicates if an email is spam.

Key predictors capture the occurrence of specific terms like dollar and bang. The dataset is cleaned of outliers and duplicates and is designed to identify spam emails using these term frequencies.

The original Spam Detection Dataset underwent several transformations to optimize it for modeling:

Outlier Removal: Entries with extreme values for the ‘bang’ and ‘crl.tot’ variables (greater than 2,500) were removed. Such outliers can skew model training, leading to suboptimal performance.

Duplicate Removal: Duplicate rows were eliminated, ensuring each entry in the dataset is unique. Duplicates can bias the model towards certain patterns, potentially overfitting to redundant data.

The transformations were essential to enhance the model’s accuracy and generalizability. Removing outliers and duplicates ensured that the model wasn’t skewed or biased by anomalous or redundant data. Downsampling addressed the critical challenge of class imbalance, ensuring that the model was equally attentive to both spam and non-spam patterns in the dataset. These changes, collectively, optimized the data for more effective and balanced predictive modeling.

The data preparation steps used for the Spam Detection Dataset are:

Outlier Removal Duplicate Removal

The machine learning model used in the analysis is the “Random Forest” model.

The evaluation metrics used to assess the performance of the machine learning model on the Spam Detection Dataset are:

Accuracy: This metric measures the proportion of correct predictions made by the model out of all predictions. In the context of the spam detection task, accuracy indicates how often the model correctly identified an email as either spam or not spam.

ROC_AUC: This metric evaluates the model’s ability to distinguish between the classes. Specifically, for spam detection, ROC_AUC indicates the model’s capability to differentiate between spam and non-spam emails effectively. A higher AUC value suggests a better-performing model.

The significance of these metrics in the context of the research question: The research question aims to classify emails based on their content, determining whether they are spam or not. Therefore, the chosen metrics are crucial:

Accuracy provides a straightforward measure of the model’s overall correctness, giving a general idea of its performance.

ROC_AUC offers a more nuanced evaluation, especially when there might be imbalances in class distribution or when the costs of false positives and false negatives differ. It captures the model’s discriminative power, assessing its ability to correctly rank a randomly chosen positive instance higher than a randomly chosen negative one.

The major findings from the analysis of the Spam Detection Dataset were:

Model Performance: The Random Forest model demonstrated a strong performance with an accuracy of approximately 83.8% on the validation set, indicating that it correctly classified emails as spam or not spam for most instances.

Model Discrimination: The ROC_AUC value was approximately 0.913, highlighting the model’s capability to effectively differentiate between spam and non-spam emails.

Influential Features: Using the Variable Importance Plot (VIP), it can be inferred that certain terms and symbols within emails (like ‘bang’, ‘crl.tot’, etc.) are significant predictors for determining if an email is spam.

Visualization Insights: Scatterplots and histograms revealed patterns and distributions in the data, such as the relationship between the occurrence of terms like ‘money’ and ‘bang’, as well as the distribution of terms like ‘dollar’ and ‘bang’ in relation to spam classification.

In summary, the Random Forest model effectively classifies emails using the provided features, with specific terms in the emails being especially indicative of spam content.