# Load libraries
library(tidyverse)
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library(tidymodels)
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library(janitor)
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## chisq.test, fisher.test
library(readr)
# Load dataset
attrition_raw_tbl <- read_csv("../00_data/WA_Fn-UseC_-HR-Employee-Attrition.csv")
## Rows: 1470 Columns: 35
## ── Column specification ────────────────────────────────────────────────────────
## Delimiter: ","
## chr (9): Attrition, BusinessTravel, Department, EducationField, Gender, Job...
## dbl (26): Age, DailyRate, DistanceFromHome, Education, EmployeeCount, Employ...
##
## 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.
# Set seed for reproducibility
set.seed(123)
attrition_tbl <- attrition_raw_tbl %>%
mutate(Attrition = as.factor(Attrition))
glimpse(attrition_tbl)
## Rows: 1,470
## Columns: 35
## $ Age <dbl> 41, 49, 37, 33, 27, 32, 59, 30, 38, 36, 35, 2…
## $ Attrition <fct> Yes, No, Yes, No, No, No, No, No, No, No, No,…
## $ BusinessTravel <chr> "Travel_Rarely", "Travel_Frequently", "Travel…
## $ DailyRate <dbl> 1102, 279, 1373, 1392, 591, 1005, 1324, 1358,…
## $ Department <chr> "Sales", "Research & Development", "Research …
## $ DistanceFromHome <dbl> 1, 8, 2, 3, 2, 2, 3, 24, 23, 27, 16, 15, 26, …
## $ Education <dbl> 2, 1, 2, 4, 1, 2, 3, 1, 3, 3, 3, 2, 1, 2, 3, …
## $ EducationField <chr> "Life Sciences", "Life Sciences", "Other", "L…
## $ EmployeeCount <dbl> 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, …
## $ EmployeeNumber <dbl> 1, 2, 4, 5, 7, 8, 10, 11, 12, 13, 14, 15, 16,…
## $ EnvironmentSatisfaction <dbl> 2, 3, 4, 4, 1, 4, 3, 4, 4, 3, 1, 4, 1, 2, 3, …
## $ Gender <chr> "Female", "Male", "Male", "Female", "Male", "…
## $ HourlyRate <dbl> 94, 61, 92, 56, 40, 79, 81, 67, 44, 94, 84, 4…
## $ JobInvolvement <dbl> 3, 2, 2, 3, 3, 3, 4, 3, 2, 3, 4, 2, 3, 3, 2, …
## $ JobLevel <dbl> 2, 2, 1, 1, 1, 1, 1, 1, 3, 2, 1, 2, 1, 1, 1, …
## $ JobRole <chr> "Sales Executive", "Research Scientist", "Lab…
## $ JobSatisfaction <dbl> 4, 2, 3, 3, 2, 4, 1, 3, 3, 3, 2, 3, 3, 4, 3, …
## $ MaritalStatus <chr> "Single", "Married", "Single", "Married", "Ma…
## $ MonthlyIncome <dbl> 5993, 5130, 2090, 2909, 3468, 3068, 2670, 269…
## $ MonthlyRate <dbl> 19479, 24907, 2396, 23159, 16632, 11864, 9964…
## $ NumCompaniesWorked <dbl> 8, 1, 6, 1, 9, 0, 4, 1, 0, 6, 0, 0, 1, 0, 5, …
## $ Over18 <chr> "Y", "Y", "Y", "Y", "Y", "Y", "Y", "Y", "Y", …
## $ OverTime <chr> "Yes", "No", "Yes", "Yes", "No", "No", "Yes",…
## $ PercentSalaryHike <dbl> 11, 23, 15, 11, 12, 13, 20, 22, 21, 13, 13, 1…
## $ PerformanceRating <dbl> 3, 4, 3, 3, 3, 3, 4, 4, 4, 3, 3, 3, 3, 3, 3, …
## $ RelationshipSatisfaction <dbl> 1, 4, 2, 3, 4, 3, 1, 2, 2, 2, 3, 4, 4, 3, 2, …
## $ StandardHours <dbl> 80, 80, 80, 80, 80, 80, 80, 80, 80, 80, 80, 8…
## $ StockOptionLevel <dbl> 0, 1, 0, 0, 1, 0, 3, 1, 0, 2, 1, 0, 1, 1, 0, …
## $ TotalWorkingYears <dbl> 8, 10, 7, 8, 6, 8, 12, 1, 10, 17, 6, 10, 5, 3…
## $ TrainingTimesLastYear <dbl> 0, 3, 3, 3, 3, 2, 3, 2, 2, 3, 5, 3, 1, 2, 4, …
## $ WorkLifeBalance <dbl> 1, 3, 3, 3, 3, 2, 2, 3, 3, 2, 3, 3, 2, 3, 3, …
## $ YearsAtCompany <dbl> 6, 10, 0, 8, 2, 7, 1, 1, 9, 7, 5, 9, 5, 2, 4,…
## $ YearsInCurrentRole <dbl> 4, 7, 0, 7, 2, 7, 0, 0, 7, 7, 4, 5, 2, 2, 2, …
## $ YearsSinceLastPromotion <dbl> 0, 1, 0, 3, 2, 3, 0, 0, 1, 7, 0, 0, 4, 1, 0, …
## $ YearsWithCurrManager <dbl> 5, 7, 0, 0, 2, 6, 0, 0, 8, 7, 3, 8, 3, 2, 3, …
# Split the data into training and testing sets
data_split <- initial_split(attrition_tbl, prop = 0.8, strata = Attrition)
train_data <- training(data_split)
test_data <- testing(data_split)
# Create a recipe for preprocessing
attrition_recipe <- recipe(Attrition ~ ., data = train_data) %>%
# Remove columns with no predictive value
update_role(EmployeeCount, StandardHours, Over18, EmployeeNumber, new_role = "ID") %>%
step_rm(has_role("ID")) %>%
# Impute missing values (if any)
step_impute_median(all_numeric(), -all_outcomes()) %>%
step_impute_mode(all_nominal(), -all_outcomes()) %>%
# Convert categorical variables to dummy variables
step_dummy(all_nominal(), -all_outcomes()) %>%
# Normalize numeric predictors
step_normalize(all_numeric(), -all_outcomes())
# Define a logistic regression model
log_reg_model <- logistic_reg(mode = "classification") %>%
set_engine("glm")
# Create a workflow
attrition_workflow <- workflow() %>%
add_model(log_reg_model) %>%
add_recipe(attrition_recipe)
# Train the model
log_reg_fit <- fit(attrition_workflow, data = train_data)
# Predict on test set with probabilities
predictions <- predict(log_reg_fit, test_data, type = "prob") %>%
bind_cols(test_data %>% select(Attrition))
# View first few predictions
head(predictions)
## # A tibble: 6 × 3
## .pred_No .pred_Yes Attrition
## <dbl> <dbl> <fct>
## 1 0.974 0.0256 No
## 2 0.929 0.0707 No
## 3 0.932 0.0684 No
## 4 0.956 0.0440 No
## 5 0.924 0.0756 No
## 6 0.999 0.000592 No
# Generate class predictions for confusion matrix
class_preds <- predict(log_reg_fit, test_data) %>%
bind_cols(test_data %>% select(Attrition))
# Confusion matrix
conf_mat(class_preds, truth = Attrition, estimate = .pred_class)
## Truth
## Prediction No Yes
## No 235 26
## Yes 12 22
# ROC AUC
roc_auc(predictions, truth = Attrition, .pred_Yes)
## # A tibble: 1 × 3
## .metric .estimator .estimate
## <chr> <chr> <dbl>
## 1 roc_auc binary 0.112