CodeAlong7
Shea Clark
2025-04-02
Goal is to predict attrition, employees who are likely to leave the company
Import Data
library(tidyverse)## ── Attaching core tidyverse packages ──────────────────────── tidyverse 2.0.0 ──
## ✔ dplyr 1.1.4 ✔ readr 2.1.5
## ✔ forcats 1.0.0 ✔ stringr 1.5.1
## ✔ ggplot2 3.5.1 ✔ tibble 3.2.1
## ✔ lubridate 1.9.4 ✔ tidyr 1.3.1
## ✔ purrr 1.0.4
## ── Conflicts ────────────────────────────────────────── tidyverse_conflicts() ──
## ✖ dplyr::filter() masks stats::filter()
## ✖ dplyr::lag() masks stats::lag()
## ℹ Use the conflicted package (<http://conflicted.r-lib.org/>) to force all conflicts to become errorslibrary(correlationfunnel)## ══ Using correlationfunnel? ════════════════════════════════════════════════════
## You might also be interested in applied data science training for business.
## </> Learn more at - www.business-science.io </>data <- 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.Explore Data
skimr::skim(data)| Name | data |
| Number of rows | 1470 |
| Number of columns | 35 |
| _______________________ | |
| Column type frequency: | |
| character | 9 |
| numeric | 26 |
| ________________________ | |
| Group variables | None |
Variable type: character
| skim_variable | n_missing | complete_rate | min | max | empty | n_unique | whitespace |
|---|---|---|---|---|---|---|---|
| Attrition | 0 | 1 | 2 | 3 | 0 | 2 | 0 |
| BusinessTravel | 0 | 1 | 10 | 17 | 0 | 3 | 0 |
| Department | 0 | 1 | 5 | 22 | 0 | 3 | 0 |
| EducationField | 0 | 1 | 5 | 16 | 0 | 6 | 0 |
| Gender | 0 | 1 | 4 | 6 | 0 | 2 | 0 |
| JobRole | 0 | 1 | 7 | 25 | 0 | 9 | 0 |
| MaritalStatus | 0 | 1 | 6 | 8 | 0 | 3 | 0 |
| Over18 | 0 | 1 | 1 | 1 | 0 | 1 | 0 |
| OverTime | 0 | 1 | 2 | 3 | 0 | 2 | 0 |
Variable type: numeric
| skim_variable | n_missing | complete_rate | mean | sd | p0 | p25 | p50 | p75 | p100 | hist |
|---|---|---|---|---|---|---|---|---|---|---|
| Age | 0 | 1 | 36.92 | 9.14 | 18 | 30.00 | 36.0 | 43.00 | 60 | ▂▇▇▃▂ |
| DailyRate | 0 | 1 | 802.49 | 403.51 | 102 | 465.00 | 802.0 | 1157.00 | 1499 | ▇▇▇▇▇ |
| DistanceFromHome | 0 | 1 | 9.19 | 8.11 | 1 | 2.00 | 7.0 | 14.00 | 29 | ▇▅▂▂▂ |
| Education | 0 | 1 | 2.91 | 1.02 | 1 | 2.00 | 3.0 | 4.00 | 5 | ▂▃▇▆▁ |
| EmployeeCount | 0 | 1 | 1.00 | 0.00 | 1 | 1.00 | 1.0 | 1.00 | 1 | ▁▁▇▁▁ |
| EmployeeNumber | 0 | 1 | 1024.87 | 602.02 | 1 | 491.25 | 1020.5 | 1555.75 | 2068 | ▇▇▇▇▇ |
| EnvironmentSatisfaction | 0 | 1 | 2.72 | 1.09 | 1 | 2.00 | 3.0 | 4.00 | 4 | ▅▅▁▇▇ |
| HourlyRate | 0 | 1 | 65.89 | 20.33 | 30 | 48.00 | 66.0 | 83.75 | 100 | ▇▇▇▇▇ |
| JobInvolvement | 0 | 1 | 2.73 | 0.71 | 1 | 2.00 | 3.0 | 3.00 | 4 | ▁▃▁▇▁ |
| JobLevel | 0 | 1 | 2.06 | 1.11 | 1 | 1.00 | 2.0 | 3.00 | 5 | ▇▇▃▂▁ |
| JobSatisfaction | 0 | 1 | 2.73 | 1.10 | 1 | 2.00 | 3.0 | 4.00 | 4 | ▅▅▁▇▇ |
| MonthlyIncome | 0 | 1 | 6502.93 | 4707.96 | 1009 | 2911.00 | 4919.0 | 8379.00 | 19999 | ▇▅▂▁▂ |
| MonthlyRate | 0 | 1 | 14313.10 | 7117.79 | 2094 | 8047.00 | 14235.5 | 20461.50 | 26999 | ▇▇▇▇▇ |
| NumCompaniesWorked | 0 | 1 | 2.69 | 2.50 | 0 | 1.00 | 2.0 | 4.00 | 9 | ▇▃▂▂▁ |
| PercentSalaryHike | 0 | 1 | 15.21 | 3.66 | 11 | 12.00 | 14.0 | 18.00 | 25 | ▇▅▃▂▁ |
| PerformanceRating | 0 | 1 | 3.15 | 0.36 | 3 | 3.00 | 3.0 | 3.00 | 4 | ▇▁▁▁▂ |
| RelationshipSatisfaction | 0 | 1 | 2.71 | 1.08 | 1 | 2.00 | 3.0 | 4.00 | 4 | ▅▅▁▇▇ |
| StandardHours | 0 | 1 | 80.00 | 0.00 | 80 | 80.00 | 80.0 | 80.00 | 80 | ▁▁▇▁▁ |
| StockOptionLevel | 0 | 1 | 0.79 | 0.85 | 0 | 0.00 | 1.0 | 1.00 | 3 | ▇▇▁▂▁ |
| TotalWorkingYears | 0 | 1 | 11.28 | 7.78 | 0 | 6.00 | 10.0 | 15.00 | 40 | ▇▇▂▁▁ |
| TrainingTimesLastYear | 0 | 1 | 2.80 | 1.29 | 0 | 2.00 | 3.0 | 3.00 | 6 | ▂▇▇▂▃ |
| WorkLifeBalance | 0 | 1 | 2.76 | 0.71 | 1 | 2.00 | 3.0 | 3.00 | 4 | ▁▃▁▇▂ |
| YearsAtCompany | 0 | 1 | 7.01 | 6.13 | 0 | 3.00 | 5.0 | 9.00 | 40 | ▇▂▁▁▁ |
| YearsInCurrentRole | 0 | 1 | 4.23 | 3.62 | 0 | 2.00 | 3.0 | 7.00 | 18 | ▇▃▂▁▁ |
| YearsSinceLastPromotion | 0 | 1 | 2.19 | 3.22 | 0 | 0.00 | 1.0 | 3.00 | 15 | ▇▁▁▁▁ |
| YearsWithCurrManager | 0 | 1 | 4.12 | 3.57 | 0 | 2.00 | 3.0 | 7.00 | 17 | ▇▂▅▁▁ |
factors_vec <- data %>% select(Education, EnvironmentSatisfaction, JobInvolvement, JobSatisfaction, PerformanceRating, RelationshipSatisfaction, WorkLifeBalance, JobLevel, StockOptionLevel) %>% names()
data_clean <- data %>%
# Address factors imported as numeric
mutate(across(all_of(factors_vec), as.factor)) %>%
# Drop zero-variance variables
select(-c(Over18, EmployeeCount, StandardHours)) %>%
# Recode Attrition
mutate(Attrition = if_else(Attrition == "Yes", "Left", Attrition))Explore Data
data_clean %>% count(Attrition)## # A tibble: 2 × 2
## Attrition n
## <chr> <int>
## 1 Left 237
## 2 No 1233data_clean %>%
ggplot(aes(Attrition)) +
geom_bar()
Attrition vs Monthly Income
data_clean %>%
ggplot(aes(Attrition, MonthlyIncome)) +
geom_boxplot()
Correlation Plot
# Step 1: binarize
data_binarized <- data_clean %>%
select(-EmployeeNumber) %>%
binarize()
data_binarized %>% glimpse()## Rows: 1,470
## Columns: 120
## $ `Age__-Inf_30` <dbl> 0, 0, 0, 0, 1, 0, 0, 1, 0, 0, 0, …
## $ Age__30_36 <dbl> 0, 0, 0, 1, 0, 1, 0, 0, 0, 1, 1, …
## $ Age__36_43 <dbl> 1, 0, 1, 0, 0, 0, 0, 0, 1, 0, 0, …
## $ Age__43_Inf <dbl> 0, 1, 0, 0, 0, 0, 1, 0, 0, 0, 0, …
## $ Attrition__Left <dbl> 1, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, …
## $ Attrition__No <dbl> 0, 1, 0, 1, 1, 1, 1, 1, 1, 1, 1, …
## $ `BusinessTravel__Non-Travel` <dbl> 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, …
## $ BusinessTravel__Travel_Frequently <dbl> 0, 1, 0, 1, 0, 1, 0, 0, 1, 0, 0, …
## $ BusinessTravel__Travel_Rarely <dbl> 1, 0, 1, 0, 1, 0, 1, 1, 0, 1, 1, …
## $ `DailyRate__-Inf_465` <dbl> 0, 1, 0, 0, 0, 0, 0, 0, 1, 0, 0, …
## $ DailyRate__465_802 <dbl> 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, …
## $ DailyRate__802_1157 <dbl> 1, 0, 0, 0, 0, 1, 0, 0, 0, 0, 1, …
## $ DailyRate__1157_Inf <dbl> 0, 0, 1, 1, 0, 0, 1, 1, 0, 1, 0, …
## $ Department__Human_Resources <dbl> 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, …
## $ `Department__Research_&_Development` <dbl> 0, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, …
## $ Department__Sales <dbl> 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, …
## $ `DistanceFromHome__-Inf_2` <dbl> 1, 0, 1, 0, 1, 1, 0, 0, 0, 0, 0, …
## $ DistanceFromHome__2_7 <dbl> 0, 0, 0, 1, 0, 0, 1, 0, 0, 0, 0, …
## $ DistanceFromHome__7_14 <dbl> 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, …
## $ DistanceFromHome__14_Inf <dbl> 0, 0, 0, 0, 0, 0, 0, 1, 1, 1, 1, …
## $ Education__1 <dbl> 0, 1, 0, 0, 1, 0, 0, 1, 0, 0, 0, …
## $ Education__2 <dbl> 1, 0, 1, 0, 0, 1, 0, 0, 0, 0, 0, …
## $ Education__3 <dbl> 0, 0, 0, 0, 0, 0, 1, 0, 1, 1, 1, …
## $ Education__4 <dbl> 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, …
## $ Education__5 <dbl> 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, …
## $ EducationField__Human_Resources <dbl> 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, …
## $ EducationField__Life_Sciences <dbl> 1, 1, 0, 1, 0, 1, 0, 1, 1, 0, 0, …
## $ EducationField__Marketing <dbl> 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, …
## $ EducationField__Medical <dbl> 0, 0, 0, 0, 1, 0, 1, 0, 0, 1, 1, …
## $ EducationField__Other <dbl> 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, …
## $ EducationField__Technical_Degree <dbl> 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, …
## $ EnvironmentSatisfaction__1 <dbl> 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 1, …
## $ EnvironmentSatisfaction__2 <dbl> 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, …
## $ EnvironmentSatisfaction__3 <dbl> 0, 1, 0, 0, 0, 0, 1, 0, 0, 1, 0, …
## $ EnvironmentSatisfaction__4 <dbl> 0, 0, 1, 1, 0, 1, 0, 1, 1, 0, 0, …
## $ Gender__Female <dbl> 1, 0, 0, 1, 0, 0, 1, 0, 0, 0, 0, …
## $ Gender__Male <dbl> 0, 1, 1, 0, 1, 1, 0, 1, 1, 1, 1, …
## $ `HourlyRate__-Inf_48` <dbl> 0, 0, 0, 0, 1, 0, 0, 0, 1, 0, 0, …
## $ HourlyRate__48_66 <dbl> 0, 1, 0, 1, 0, 0, 0, 0, 0, 0, 0, …
## $ HourlyRate__66_83.75 <dbl> 0, 0, 0, 0, 0, 1, 1, 1, 0, 0, 0, …
## $ HourlyRate__83.75_Inf <dbl> 1, 0, 1, 0, 0, 0, 0, 0, 0, 1, 1, …
## $ JobInvolvement__1 <dbl> 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, …
## $ JobInvolvement__2 <dbl> 0, 1, 1, 0, 0, 0, 0, 0, 1, 0, 0, …
## $ JobInvolvement__3 <dbl> 1, 0, 0, 1, 1, 1, 0, 1, 0, 1, 0, …
## $ JobInvolvement__4 <dbl> 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 1, …
## $ JobLevel__1 <dbl> 0, 0, 1, 1, 1, 1, 1, 1, 0, 0, 1, …
## $ JobLevel__2 <dbl> 1, 1, 0, 0, 0, 0, 0, 0, 0, 1, 0, …
## $ JobLevel__3 <dbl> 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, …
## $ JobLevel__4 <dbl> 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, …
## $ JobLevel__5 <dbl> 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, …
## $ JobRole__Healthcare_Representative <dbl> 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, …
## $ JobRole__Human_Resources <dbl> 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, …
## $ JobRole__Laboratory_Technician <dbl> 0, 0, 1, 0, 1, 1, 1, 1, 0, 0, 1, …
## $ JobRole__Manager <dbl> 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, …
## $ JobRole__Manufacturing_Director <dbl> 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, …
## $ JobRole__Research_Director <dbl> 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, …
## $ JobRole__Research_Scientist <dbl> 0, 1, 0, 1, 0, 0, 0, 0, 0, 0, 0, …
## $ JobRole__Sales_Executive <dbl> 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, …
## $ JobRole__Sales_Representative <dbl> 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, …
## $ JobSatisfaction__1 <dbl> 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, …
## $ JobSatisfaction__2 <dbl> 0, 1, 0, 0, 1, 0, 0, 0, 0, 0, 1, …
## $ JobSatisfaction__3 <dbl> 0, 0, 1, 1, 0, 0, 0, 1, 1, 1, 0, …
## $ JobSatisfaction__4 <dbl> 1, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, …
## $ MaritalStatus__Divorced <dbl> 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, …
## $ MaritalStatus__Married <dbl> 0, 1, 0, 1, 1, 0, 1, 0, 0, 1, 1, …
## $ MaritalStatus__Single <dbl> 1, 0, 1, 0, 0, 1, 0, 0, 1, 0, 0, …
## $ `MonthlyIncome__-Inf_2911` <dbl> 0, 0, 1, 1, 0, 0, 1, 1, 0, 0, 1, …
## $ MonthlyIncome__2911_4919 <dbl> 0, 0, 0, 0, 1, 1, 0, 0, 0, 0, 0, …
## $ MonthlyIncome__4919_8379 <dbl> 1, 1, 0, 0, 0, 0, 0, 0, 0, 1, 0, …
## $ MonthlyIncome__8379_Inf <dbl> 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, …
## $ `MonthlyRate__-Inf_8047` <dbl> 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, …
## $ MonthlyRate__8047_14235.5 <dbl> 0, 0, 0, 0, 0, 1, 1, 1, 1, 0, 0, …
## $ MonthlyRate__14235.5_20461.5 <dbl> 1, 0, 0, 0, 1, 0, 0, 0, 0, 1, 1, …
## $ MonthlyRate__20461.5_Inf <dbl> 0, 1, 0, 1, 0, 0, 0, 0, 0, 0, 0, …
## $ `NumCompaniesWorked__-Inf_1` <dbl> 0, 1, 0, 1, 0, 1, 0, 1, 1, 0, 1, …
## $ NumCompaniesWorked__1_2 <dbl> 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, …
## $ NumCompaniesWorked__2_4 <dbl> 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, …
## $ NumCompaniesWorked__4_Inf <dbl> 1, 0, 1, 0, 1, 0, 0, 0, 0, 1, 0, …
## $ OverTime__No <dbl> 0, 1, 0, 0, 1, 1, 0, 1, 1, 1, 1, …
## $ OverTime__Yes <dbl> 1, 0, 1, 1, 0, 0, 1, 0, 0, 0, 0, …
## $ `PercentSalaryHike__-Inf_12` <dbl> 1, 0, 0, 1, 1, 0, 0, 0, 0, 0, 0, …
## $ PercentSalaryHike__12_14 <dbl> 0, 0, 0, 0, 0, 1, 0, 0, 0, 1, 1, …
## $ PercentSalaryHike__14_18 <dbl> 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, …
## $ PercentSalaryHike__18_Inf <dbl> 0, 1, 0, 0, 0, 0, 1, 1, 1, 0, 0, …
## $ PerformanceRating__3 <dbl> 1, 0, 1, 1, 1, 1, 0, 0, 0, 1, 1, …
## $ PerformanceRating__4 <dbl> 0, 1, 0, 0, 0, 0, 1, 1, 1, 0, 0, …
## $ RelationshipSatisfaction__1 <dbl> 1, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, …
## $ RelationshipSatisfaction__2 <dbl> 0, 0, 1, 0, 0, 0, 0, 1, 1, 1, 0, …
## $ RelationshipSatisfaction__3 <dbl> 0, 0, 0, 1, 0, 1, 0, 0, 0, 0, 1, …
## $ RelationshipSatisfaction__4 <dbl> 0, 1, 0, 0, 1, 0, 0, 0, 0, 0, 0, …
## $ StockOptionLevel__0 <dbl> 1, 0, 1, 1, 0, 1, 0, 0, 1, 0, 0, …
## $ StockOptionLevel__1 <dbl> 0, 1, 0, 0, 1, 0, 0, 1, 0, 0, 1, …
## $ StockOptionLevel__2 <dbl> 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, …
## $ StockOptionLevel__3 <dbl> 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, …
## $ `TotalWorkingYears__-Inf_6` <dbl> 0, 0, 0, 0, 1, 0, 0, 1, 0, 0, 1, …
## $ TotalWorkingYears__6_10 <dbl> 1, 1, 1, 1, 0, 1, 0, 0, 1, 0, 0, …
## $ TotalWorkingYears__10_15 <dbl> 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, …
## $ TotalWorkingYears__15_Inf <dbl> 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, …
## $ `TrainingTimesLastYear__-Inf_2` <dbl> 1, 0, 0, 0, 0, 1, 0, 1, 1, 0, 0, …
## $ TrainingTimesLastYear__2_3 <dbl> 0, 1, 1, 1, 1, 0, 1, 0, 0, 1, 0, …
## $ TrainingTimesLastYear__3_Inf <dbl> 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, …
## $ WorkLifeBalance__1 <dbl> 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, …
## $ WorkLifeBalance__2 <dbl> 0, 0, 0, 0, 0, 1, 1, 0, 0, 1, 0, …
## $ WorkLifeBalance__3 <dbl> 0, 1, 1, 1, 1, 0, 0, 1, 1, 0, 1, …
## $ WorkLifeBalance__4 <dbl> 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, …
## $ `YearsAtCompany__-Inf_3` <dbl> 0, 0, 1, 0, 1, 0, 1, 1, 0, 0, 0, …
## $ YearsAtCompany__3_5 <dbl> 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, …
## $ YearsAtCompany__5_9 <dbl> 1, 0, 0, 1, 0, 1, 0, 0, 1, 1, 0, …
## $ YearsAtCompany__9_Inf <dbl> 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, …
## $ `YearsInCurrentRole__-Inf_2` <dbl> 0, 0, 1, 0, 1, 0, 1, 1, 0, 0, 0, …
## $ YearsInCurrentRole__2_3 <dbl> 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, …
## $ YearsInCurrentRole__3_7 <dbl> 1, 1, 0, 1, 0, 1, 0, 0, 1, 1, 1, …
## $ YearsInCurrentRole__7_Inf <dbl> 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, …
## $ `YearsSinceLastPromotion__-Inf_1` <dbl> 1, 1, 1, 0, 0, 0, 1, 1, 1, 0, 1, …
## $ YearsSinceLastPromotion__1_3 <dbl> 0, 0, 0, 1, 1, 1, 0, 0, 0, 0, 0, …
## $ YearsSinceLastPromotion__3_Inf <dbl> 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, …
## $ `YearsWithCurrManager__-Inf_2` <dbl> 0, 0, 1, 1, 1, 0, 1, 1, 0, 0, 0, …
## $ YearsWithCurrManager__2_3 <dbl> 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, …
## $ YearsWithCurrManager__3_7 <dbl> 1, 1, 0, 0, 0, 1, 0, 0, 0, 1, 0, …
## $ YearsWithCurrManager__7_Inf <dbl> 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, …# Step 2: correlation
data_correlation <- data_binarized %>%
correlate(Attrition__Left)
data_correlation## # A tibble: 120 × 3
## feature bin correlation
## <fct> <chr> <dbl>
## 1 Attrition Left 1
## 2 Attrition No -1
## 3 OverTime No -0.246
## 4 OverTime Yes 0.246
## 5 JobLevel 1 0.213
## 6 MonthlyIncome -Inf_2911 0.207
## 7 StockOptionLevel 0 0.195
## 8 YearsAtCompany -Inf_3 0.183
## 9 MaritalStatus Single 0.175
## 10 TotalWorkingYears -Inf_6 0.169
## # ℹ 110 more rows# Step 3: Plot
data_correlation %>%
correlationfunnel::plot_correlation_funnel()## Warning: ggrepel: 73 unlabeled data points (too many overlaps). Consider
## increasing max.overlaps
## Model Building
Split Data
library(tidymodels)## ── Attaching packages ────────────────────────────────────── tidymodels 1.2.0 ──## ✔ broom 1.0.7 ✔ rsample 1.2.1
## ✔ dials 1.4.0 ✔ tune 1.2.1
## ✔ infer 1.0.7 ✔ workflows 1.1.4
## ✔ modeldata 1.4.0 ✔ workflowsets 1.1.0
## ✔ parsnip 1.3.0 ✔ yardstick 1.3.2
## ✔ recipes 1.1.1## ── Conflicts ───────────────────────────────────────── tidymodels_conflicts() ──
## ✖ scales::discard() masks purrr::discard()
## ✖ dplyr::filter() masks stats::filter()
## ✖ recipes::fixed() masks stringr::fixed()
## ✖ dplyr::lag() masks stats::lag()
## ✖ yardstick::spec() masks readr::spec()
## ✖ recipes::step() masks stats::step()
## • Learn how to get started at https://www.tidymodels.org/start/set.seed(1234)
data <- data_clean %>% sample_n(100)
data_split <- initial_split(data_clean, strata = Attrition)
data_train <- training(data_split)
data_test <- testing(data_split)
data_cv <- rsample::vfold_cv(data_train, strata = Attrition)
data_cv## # 10-fold cross-validation using stratification
## # A tibble: 10 × 2
## splits id
## <list> <chr>
## 1 <split [990/111]> Fold01
## 2 <split [990/111]> Fold02
## 3 <split [990/111]> Fold03
## 4 <split [990/111]> Fold04
## 5 <split [991/110]> Fold05
## 6 <split [991/110]> Fold06
## 7 <split [991/110]> Fold07
## 8 <split [992/109]> Fold08
## 9 <split [992/109]> Fold09
## 10 <split [992/109]> Fold10library(themis)## Warning: package 'themis' was built under R version 4.4.3xgboost_recipe <- recipes::recipe(Attrition ~ ., data = data_train) %>%
update_role(EmployeeNumber, new_role = "ID") %>%
step_dummy(all_nominal_predictors()) %>%
step_YeoJohnson(DistanceFromHome, MonthlyIncome, NumCompaniesWorked,
PercentSalaryHike, TotalWorkingYears, starts_with("Years")) %>%
step_normalize(all_numeric_predictors()) %>%
step_pca(all_numeric_predictors(), threshold = .99)%>%
step_smote(Attrition)
xgboost_recipe %>% prep() %>% juice() %>% glimpse()## Rows: 1,848
## Columns: 55
## $ EmployeeNumber <dbl> 19, 27, 31, 33, 42, 47, 55, 58, 64, 65, 90, 118, 137, 1…
## $ Attrition <fct> Left, Left, Left, Left, Left, Left, Left, Left, Left, L…
## $ PC01 <dbl> -2.5408074, -1.1373048, -1.6165450, -0.7146510, -1.9523…
## $ PC02 <dbl> -1.0750269, 1.8861792, -1.0954849, -1.0907458, 2.180957…
## $ PC03 <dbl> -0.43971157, 1.32357749, -0.95149664, -2.41103759, 2.24…
## $ PC04 <dbl> -1.34414052, -0.96050860, -1.56475884, 0.24168915, 0.18…
## $ PC05 <dbl> -2.0002222, -2.5297942, 0.7492029, -2.1753056, 2.687550…
## $ PC06 <dbl> -0.01695946, 0.31653822, -0.92773875, 1.40466526, 1.396…
## $ PC07 <dbl> -0.36271712, 0.16217554, -0.47505146, 0.75583638, -0.04…
## $ PC08 <dbl> 2.23583978, -0.87866085, 0.26925166, -2.39633041, 0.473…
## $ PC09 <dbl> 0.64670653, -0.04543999, -1.66535729, 0.20726907, -0.14…
## $ PC10 <dbl> 0.78925511, 1.34308972, -0.30122077, 0.07338142, 0.8990…
## $ PC11 <dbl> -0.876412194, -3.247754656, -0.006523447, -2.395912600,…
## $ PC12 <dbl> 0.74295506, -0.02192118, -0.91219547, 1.06797897, -0.84…
## $ PC13 <dbl> 1.55811726, 2.17286307, 0.80868236, 1.46329352, -0.7973…
## $ PC14 <dbl> 0.14255426, -1.15507431, 1.45972495, 1.36343587, -0.887…
## $ PC15 <dbl> -0.51038097, -2.17807250, 0.40425699, -1.41373786, -2.0…
## $ PC16 <dbl> -1.48860724, -0.51209227, 0.34268316, 0.53585198, 1.335…
## $ PC17 <dbl> -1.00279800, 1.21081549, -0.38836101, -0.10545648, 1.28…
## $ PC18 <dbl> -0.79053703, -0.07181857, 0.34748319, 1.05026066, -0.25…
## $ PC19 <dbl> -0.64810826, 0.16461037, 0.40342647, 0.30939573, -1.709…
## $ PC20 <dbl> -1.68992645, 0.62045310, 0.58469091, 0.67257274, 1.3170…
## $ PC21 <dbl> -0.84775520, -1.06666492, 1.86717611, 0.80000089, -0.45…
## $ PC22 <dbl> 0.56939618, -0.61420691, 0.43486881, 0.69950105, -3.159…
## $ PC23 <dbl> 0.47817791, 0.86316036, -0.42580172, -0.53319176, -0.49…
## $ PC24 <dbl> 0.49808821, -0.17973295, -0.84614627, -1.00624090, -0.0…
## $ PC25 <dbl> 0.78620071, 0.20493504, 0.09117069, -0.18327167, -2.509…
## $ PC26 <dbl> -0.127908095, 1.299112284, -0.095477728, 0.724729929, -…
## $ PC27 <dbl> 0.62601740, -0.98396158, -0.60288799, 0.52831676, -2.11…
## $ PC28 <dbl> -0.6806088, 0.4130443, -1.5616478, -0.5402558, 0.597098…
## $ PC29 <dbl> 0.24233079, -0.07014478, 0.92811689, -0.41890605, 1.514…
## $ PC30 <dbl> -0.43537978, 0.46354129, 0.94194097, -1.14419845, -0.82…
## $ PC31 <dbl> 1.2513049, -0.7568263, 0.1233751, 1.8231411, 0.4716849,…
## $ PC32 <dbl> -0.83558512, 0.59227146, -0.24156444, 1.16704391, 0.353…
## $ PC33 <dbl> -0.4477988, -0.7770123, -0.4835247, -0.7985294, -0.2408…
## $ PC34 <dbl> -0.1030229, -1.5638596, 0.4217827, 0.5491512, -1.430093…
## $ PC35 <dbl> -0.87421858, 0.37285651, 0.54985090, 0.80555081, -0.193…
## $ PC36 <dbl> 2.13676028, 1.75303488, -1.30700219, 1.19592865, 1.6970…
## $ PC37 <dbl> -0.59557223, 1.26862302, 0.92549208, -0.74904142, -0.70…
## $ PC38 <dbl> 0.76338383, -0.60478939, -0.53455128, 0.71756491, -1.16…
## $ PC39 <dbl> -1.19548022, -2.00890111, -0.47083477, -0.56457379, -2.…
## $ PC40 <dbl> 0.04868816, -0.65311107, 0.17374232, 0.30621479, -0.435…
## $ PC41 <dbl> 0.96241115, 1.19578777, -0.36893444, -1.18588089, 0.678…
## $ PC42 <dbl> 0.95907329, 0.49094719, -0.07078001, 0.19491976, -1.185…
## $ PC43 <dbl> -0.89127353, -0.80372168, -0.15338440, 0.08430098, 0.07…
## $ PC44 <dbl> -0.30105375, -0.31402824, -0.54386701, -0.38452616, 1.1…
## $ PC45 <dbl> -0.5067550766, 0.6858554310, 0.6060915011, 0.3147418547…
## $ PC46 <dbl> -0.174188501, 1.132482844, 0.914117550, 1.340247553, 0.…
## $ PC47 <dbl> -0.14292336, -0.37286358, -0.33491796, -0.13965105, -0.…
## $ PC48 <dbl> 0.50964670, 0.07342574, 0.09677421, 0.47488762, 0.05715…
## $ PC49 <dbl> 1.047428e-02, -1.880170e-01, 9.256232e-02, 4.470833e-01…
## $ PC50 <dbl> 0.15706359, -0.16317621, -0.18612135, 0.03997567, 0.490…
## $ PC51 <dbl> 0.31598906, -0.04902101, -0.21210018, -0.48368012, -0.9…
## $ PC52 <dbl> -0.23026881, -0.26587024, 0.87425856, 0.92635176, -0.51…
## $ PC53 <dbl> 0.071992418, -0.152065915, 0.296425363, 0.321754122, 0.…Specify Model
xgboost_spec <-
boost_tree(trees = tune(), tree_depth = tune()) %>%
set_mode("classification") %>%
set_engine("xgboost")
xgboost_workflow <-
workflow() %>%
add_recipe(xgboost_recipe) %>%
add_model(xgboost_spec) Tune Hyperparameters
tree_grid <- grid_regular(cost_complexity(),
tree_depth(),
levels = 5)
doParallel::registerDoParallel()
set.seed(65447)
xgboost_tune <-
tune_grid(xgboost_workflow,
resamples = data_cv,
grid = 5,
control = control_grid(save_pred = TRUE))Model Evaluation
Identify Optimal Values for Hyperparameters
collect_metrics(xgboost_tune)## # A tibble: 15 × 8
## trees tree_depth .metric .estimator mean n std_err .config
## <int> <int> <chr> <chr> <dbl> <int> <dbl> <chr>
## 1 1759 3 accuracy binary 0.859 10 0.00850 Preprocessor1_Mo…
## 2 1759 3 brier_class binary 0.119 10 0.00815 Preprocessor1_Mo…
## 3 1759 3 roc_auc binary 0.779 10 0.0213 Preprocessor1_Mo…
## 4 798 6 accuracy binary 0.847 10 0.00994 Preprocessor1_Mo…
## 5 798 6 brier_class binary 0.125 10 0.00798 Preprocessor1_Mo…
## 6 798 6 roc_auc binary 0.773 10 0.0216 Preprocessor1_Mo…
## 7 219 8 accuracy binary 0.856 10 0.00732 Preprocessor1_Mo…
## 8 219 8 brier_class binary 0.120 10 0.00559 Preprocessor1_Mo…
## 9 219 8 roc_auc binary 0.765 10 0.0212 Preprocessor1_Mo…
## 10 890 11 accuracy binary 0.852 10 0.00811 Preprocessor1_Mo…
## 11 890 11 brier_class binary 0.122 10 0.00673 Preprocessor1_Mo…
## 12 890 11 roc_auc binary 0.765 10 0.0216 Preprocessor1_Mo…
## 13 1246 13 accuracy binary 0.844 10 0.00888 Preprocessor1_Mo…
## 14 1246 13 brier_class binary 0.124 10 0.00659 Preprocessor1_Mo…
## 15 1246 13 roc_auc binary 0.762 10 0.0278 Preprocessor1_Mo…collect_predictions(xgboost_tune) %>%
group_by(id) %>%
roc_curve(Attrition, .pred_Left) %>%
autoplot()
Fit the model for the last time
xgboost_last <- xgboost_workflow %>%
finalize_workflow(select_best(xgboost_tune, metric = "accuracy")) %>%
last_fit(data_split)
collect_metrics(xgboost_last) ## # A tibble: 3 × 4
## .metric .estimator .estimate .config
## <chr> <chr> <dbl> <chr>
## 1 accuracy binary 0.835 Preprocessor1_Model1
## 2 roc_auc binary 0.775 Preprocessor1_Model1
## 3 brier_class binary 0.138 Preprocessor1_Model1collect_predictions(xgboost_last) %>%
yardstick::conf_mat(Attrition, .pred_class) %>%
autoplot()
Variable Importance
library(vip)## Warning: package 'vip' was built under R version 4.4.3##
## Attaching package: 'vip'## The following object is masked from 'package:utils':
##
## vixgboost_last %>%
workflows::extract_fit_engine() %>%
vip()
Conclusion
The previous model had accuracy 0f 0.851 and AUC of 0.753
Feature transformation: normalized numeric data. It resulted in a slight improvement with accuracy of 0.859 and AUC of .770
Feature transformation: YeoJohnson transformation. No improvement.
Feature selection: PCA didnt make an improvement