I have a dataset called attrition_raw_tbl that looks like this.
attrition_raw_tbl %>% glimpse()## Rows: 1,470
## Columns: 35
## $ Age <dbl> 41, 49, 37, 33, 27, 32, 59, 30, 38, 36, 35, 2…
## $ Attrition <chr> "Yes", "No", "Yes", "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, …The goal is to help predict attrition for employees.
Please write R code to create a predictive model that predicts the probability of attrition.
library(dplyr) library(tidyr) library(caret) library(glmnet)
attrition_data <- attrition_raw_tbl %>% # Drop irrelevant columns select(-EmployeeNumber, -EmployeeCount, -Over18, -StandardHours) %>% # Convert categorical variables to factors mutate_if(is.character, as.factor) %>% # Handle missing values if any na.omit()
set.seed(123) # for reproducibility train_index <- createDataPartition(attrition_data$Attrition, p = 0.7, list = FALSE) train_data <- attrition_data[train_index, ] test_data <- attrition_data[-train_index, ]
model <- glm(Attrition ~ ., data = train_data, family = binomial)
predictions <- predict(model, newdata = test_data, type = “response”)
confusion_matrix <- table(Actual = test_data$Attrition, Predicted = ifelse(predictions > 0.5, “Yes”, “No”)) accuracy <- sum(diag(confusion_matrix)) / sum(confusion_matrix)
print(confusion_matrix) print(paste(“Accuracy:”, accuracy))
“Please use tidymodels instead of caret and to use the h2o model instead of glmnet”
library(dplyr) library(tidymodels) library(h2o)
attrition_data <- attrition_raw_tbl %>% # Drop irrelevant columns select(-EmployeeNumber, -EmployeeCount, -Over18, -StandardHours) %>% # Convert categorical variables to factors mutate_if(is.character, as.factor) %>% # Handle missing values if any na.omit()
set.seed(123) # for reproducibility train_data <- attrition_data %>% initial_split(prop = 0.7, strata = Attrition)
train_data <- training(train_data) test_data <- testing(train_data)
h2o.init() train_data_h2o <- as.h2o(train_data) test_data_h2o <- as.h2o(test_data)
model <- h2o.glm(x = names(train_data_h2o)[-1], y = “Attrition”, training_frame = train_data_h2o, family = “binomial”)
predictions <- h2o.predict(model, newdata = test_data_h2o) predictions <- as.data.frame(predictions$predict)
confusion_matrix <- table(Actual = test_data\(Attrition, Predicted = ifelse(predictions\)predict > 0.5, “Yes”, “No”)) accuracy <- sum(diag(confusion_matrix)) / sum(confusion_matrix)
print(confusion_matrix) print(paste(“Accuracy:”, accuracy))
h2o.shutdown()
“Error”
library(dplyr) library(tidymodels) library(h2o)
attrition_data <- attrition_raw_tbl %>% # Drop irrelevant columns select(-EmployeeNumber, -EmployeeCount, -Over18, -StandardHours) %>% # Convert categorical variables to factors mutate_if(is.character, as.factor) %>% # Handle missing values if any na.omit()
set.seed(123) # for reproducibility split_data <- initial_split(attrition_data, prop = 0.7, strata = Attrition) train_data <- training(split_data) test_data <- testing(split_data)
h2o.init() train_data_h2o <- as.h2o(train_data) test_data_h2o <- as.h2o(test_data)
model <- h2o.glm(x = names(train_data_h2o)[-1], y = “Attrition”, training_frame = train_data_h2o, family = “binomial”)
predictions <- h2o.predict(model, newdata = test_data_h2o) predictions <- as.data.frame(predictions$predict)
confusion_matrix <- table(Actual = test_data\(Attrition, Predicted = ifelse(predictions\)predict > 0.5, “Yes”, “No”)) accuracy <- sum(diag(confusion_matrix)) / sum(confusion_matrix)
print(confusion_matrix) print(paste(“Accuracy:”, accuracy))
h2o.shutdown()
“Please update from h2o.glm to h2o.automl”
# Load required libraries
library(dplyr)
library(tidymodels)## ── Attaching packages ────────────────────────────────────── tidymodels 1.1.1 ──## ✔ broom 1.0.5 ✔ rsample 1.2.0
## ✔ dials 1.2.0 ✔ tune 1.1.2
## ✔ infer 1.0.6 ✔ workflows 1.1.3
## ✔ modeldata 1.3.0 ✔ workflowsets 1.0.1
## ✔ parsnip 1.1.1 ✔ yardstick 1.3.0
## ✔ recipes 1.0.8## ── 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/library(h2o)##
## ----------------------------------------------------------------------
##
## Your next step is to start H2O:
## > h2o.init()
##
## For H2O package documentation, ask for help:
## > ??h2o
##
## After starting H2O, you can use the Web UI at http://localhost:54321
## For more information visit https://docs.h2o.ai
##
## ----------------------------------------------------------------------##
## Attaching package: 'h2o'## The following objects are masked from 'package:lubridate':
##
## day, hour, month, week, year## The following objects are masked from 'package:stats':
##
## cor, sd, var## The following objects are masked from 'package:base':
##
## &&, %*%, %in%, ||, apply, as.factor, as.numeric, colnames,
## colnames<-, ifelse, is.character, is.factor, is.numeric, log,
## log10, log1p, log2, round, signif, trunc# Assuming 'attrition_raw_tbl' is your dataset
# Preprocess the data
attrition_data <- attrition_raw_tbl %>%
# Drop irrelevant columns
select(-EmployeeNumber, -EmployeeCount, -Over18, -StandardHours) %>%
# Convert categorical variables to factors
mutate_if(is.character, as.factor) %>%
# Handle missing values if any
na.omit()
# Split data into training and testing sets
set.seed(123) # for reproducibility
split_data <- initial_split(attrition_data, prop = 0.7, strata = Attrition)
train_data <- training(split_data)
test_data <- testing(split_data)
# Train an h2o AutoML model
h2o.init()## Connection successful!
##
## R is connected to the H2O cluster:
## H2O cluster uptime: 6 days 22 hours
## H2O cluster timezone: America/New_York
## H2O data parsing timezone: UTC
## H2O cluster version: 3.44.0.3
## H2O cluster version age: 4 months and 9 days
## H2O cluster name: H2O_started_from_R_Vanessa_vmr042
## H2O cluster total nodes: 1
## H2O cluster total memory: 1.15 GB
## H2O cluster total cores: 8
## H2O cluster allowed cores: 8
## H2O cluster healthy: TRUE
## H2O Connection ip: localhost
## H2O Connection port: 54321
## H2O Connection proxy: NA
## H2O Internal Security: FALSE
## R Version: R version 4.3.1 (2023-06-16)## Warning in h2o.clusterInfo():
## Your H2O cluster version is (4 months and 9 days) old. There may be a newer version available.
## Please download and install the latest version from: https://h2o-release.s3.amazonaws.com/h2o/latest_stable.htmltrain_data_h2o <- as.h2o(train_data)##
|
| | 0%
|
|======================================================================| 100%test_data_h2o <- as.h2o(test_data)##
|
| | 0%
|
|======================================================================| 100%automl <- h2o.automl(x = names(train_data_h2o)[-1],
y = "Attrition",
training_frame = train_data_h2o,
max_runtime_secs = 30) # adjust runtime as needed## Warning in .verify_dataxy(training_frame, x, y): removing response variable
## from the explanatory variables##
|
| | 0%
|
|=== | 4%
|
|========= | 13%
|
|============== | 20%
|
|==================================== | 51%
|
|========================================= | 59%
|
|================================================= | 70%
|
|======================================================= | 78%
|
|============================================================ | 86%
|
|================================================================== | 94%
|
|======================================================================| 100%# Get the best model from AutoML
best_model <- automl@leader
# Make predictions on the test set
predictions <- h2o.predict(best_model, newdata = test_data_h2o)##
|
| | 0%
|
|======================================================================| 100%predictions <- as.data.frame(predictions$predict)
# Evaluate the model
confusion_matrix <- table(Actual = test_data$Attrition, Predicted = ifelse(predictions$predict > 0.5, "Yes", "No"))## Warning in Ops.factor(predictions$predict, 0.5): '>' not meaningful for factorsaccuracy <- sum(diag(confusion_matrix)) / sum(confusion_matrix)
# Print the confusion matrix and accuracy
print(confusion_matrix)## < table of extent 2 x 0 >print(paste("Accuracy:", accuracy))## [1] "Accuracy: NaN"# Shutdown h2o
h2o.shutdown()## Are you sure you want to shutdown the H2O instance running at http://localhost:54321/ (Y/N)?