The goal is to help predict attrition for employees. Please write R code to create a predictive model that predicts the probability of attrition.
Series of with errors Prompt 1:
I have a dataset called attrition_raw_tbl that looks like this.
Prompt 2:
Load required libraries library(dplyr) library(tidyr) library(caret) library(glmnet)
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 train_index <- createDataPartition(attrition_data$Attrition, p = 0.7, list = FALSE) train_data <- attrition_data[train_index, ] test_data <- attrition_data[-train_index, ]
Train a logistic regression model
model <- glm(Attrition ~ ., data = train_data, family = binomial)
Make predictions on the test set
Evaluate the model
confusion_matrix <- table(Actual = test_data$Attrition, Predicted = ifelse(predictions > 0.5, “Yes”, “No”)) accuracy <- sum(diag(confusion_matrix)) / sum(confusion_matrix) predictions <- predict(model, newdata = test_data, type = “response”)
Print the confusion matrix and accuracy
print(confusion_matrix) print(paste(“Accuracy:”, accuracy))
Prompt 3: “Please use tidymodels instead of caret and to use the h2o model instead of glmnet”
Load required libraries
library(dplyr) library(tidymodels) library(h2o)
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 train_data <- attrition_data %>% initial_split(prop = 0.7, strata = Attrition)
train_data <- training(train_data) test_data <- testing(train_data)
Train an h2o logistic regression model 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”)
Make predictions on the test set
predictions <- h2o.predict(model, newdata = test_data_h2o) predictions <- as.data.frame(predictions$predict)
Evaluate the model
confusion_matrix <- table(Actual = test_dataAttrition,Predicted=ifelse(predictions predict > 0.5, “Yes”, “No”)) accuracy <- sum(diag(confusion_matrix)) / sum(confusion_matrix)
Print the confusion matrix and accuracy
print(confusion_matrix) print(paste(“Accuracy:”, accuracy))
Shutdown h2o
h2o.shutdown()
Prompt 4:
“Error”
Load required libraries
Assuming ‘attrition_raw_tbl’ is your dataset
Preprocess the data
attrition_data <- attrition_raw_tbl %>% # Drop irrelevant columns select(-EmployeeNumber, -EmployeeCount, -Over18, -StandardHours) %>% # Convert categorical variables to factors mutate_if(is.character, as.factor) %>% # Handle missing values if any na.omit() library(dplyr) library(tidymodels) library(h2o)
Split data into training and testing sets
set.seed(123) # for reproducibility split_data <- initial_split(attrition_data, prop = 0.7, strata = Attrition) train_data <- training(split_data) test_data <- testing(split_data)
Train an h2o logistic regression model
h2o.init() train_data_h2o <- as.h2o(train_data) test_data_h2o <- as.h2o(test_data)
model <- h2o.glm(x = names(train_data_h2o)[-1], y = “Attrition”, training_frame = train_data_h2o, family = “binomial”)
Make predictions on the test set
predictions <- h2o.predict(model, newdata = test_data_h2o) predictions <- as.data.frame(predictions$predict)
Evaluate the model
confusion_matrix <- table(Actual = test_dataAttrition,Predicted=ifelse(predictions predict > 0.5, “Yes”, “No”)) accuracy <- sum(diag(confusion_matrix)) / sum(confusion_matrix)
Print the confusion matrix and accuracy
print(confusion_matrix) print(paste(“Accuracy:”, accuracy))
Shutdown h2o h2o.shutdown()
Prompt 5: “Please update from h2o.glm to h2o.automl”
Final Code
# Load required libraries
library(tidyverse)## ── Attaching core tidyverse packages ──────────────────────── tidyverse 2.0.0 ──
## ✔ dplyr 1.1.3 ✔ readr 2.1.4
## ✔ forcats 1.0.0 ✔ stringr 1.5.0
## ✔ ggplot2 3.4.3 ✔ tibble 3.2.1
## ✔ lubridate 1.9.2 ✔ tidyr 1.3.0
## ✔ purrr 1.0.2
## ── 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(tidymodels)## ── Attaching packages ────────────────────────────────────── tidymodels 1.1.1 ──
## ✔ broom 1.0.5 ✔ rsample 1.2.0
## ✔ dials 1.2.0 ✔ tune 1.1.2
## ✔ infer 1.0.5 ✔ workflows 1.1.3
## ✔ modeldata 1.2.0 ✔ workflowsets 1.0.1
## ✔ parsnip 1.1.1 ✔ yardstick 1.2.0
## ✔ 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)## Warning: package 'h2o' was built under R version 4.3.3##
## ----------------------------------------------------------------------
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## Your next step is to start H2O:
## > h2o.init()
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## For H2O package documentation, ask for help:
## > ??h2o
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## After starting H2O, you can use the Web UI at http://localhost:54321
## For more information visit https://docs.h2o.ai
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## Attaching package: 'h2o'
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## The following objects are masked from 'package:lubridate':
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## day, hour, month, week, year
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## The following objects are masked from 'package:stats':
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## The following objects are masked from 'package:base':
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## %*%, %in%, &&, ||, apply, as.factor, as.numeric, colnames,
## colnames<-, ifelse, is.character, is.factor, is.numeric, log,
## log10, log1p, log2, round, signif, truncattrition_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.# Assuming you have your data loaded as 'attrition_raw_tbl'
# Step 1: Split the data into training and testing sets
set.seed(123) # for reproducibility
split <- initial_split(attrition_raw_tbl, prop = 0.8, strata = "Attrition")
train_data <- training(split)
test_data <- testing(split)
# Step 2: Preprocess the data using tidymodels
# For simplicity, let's use recipe to preprocess the data
attrition_recipe <- recipe(Attrition ~ ., data = train_data) %>%
step_rm(Over18, EmployeeCount, StandardHours) %>% # Remove columns with zero variance
step_dummy(all_nominal(), one_hot = TRUE) %>%
step_center(all_numeric(), -all_outcomes()) %>%
step_scale(all_numeric(), -all_outcomes())
# Fit the recipe
attrition_recipe_prep <- prep(attrition_recipe, training = train_data)
# Apply the recipe to the data
train_data_processed <- bake(attrition_recipe_prep, new_data = train_data)
test_data_processed <- bake(attrition_recipe_prep, new_data = test_data)
# Step 3: Train a model using h2o.automl
h2o.init()## Connection successful!
##
## R is connected to the H2O cluster:
## H2O cluster uptime: 4 hours 3 minutes
## 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 18 days
## H2O cluster name: H2O_started_from_R_OPend_eji420
## H2O cluster total nodes: 1
## H2O cluster total memory: 3.18 GB
## H2O cluster total cores: 12
## H2O cluster allowed cores: 12
## 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 ucrt)## Warning in h2o.clusterInfo():
## Your H2O cluster version is (4 months and 18 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.html# Convert data to H2OFrame
train_h2o <- as.h2o(train_data_processed)##
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|======================================================================| 100%# Define predictors and response variable
predictors <- setdiff(names(train_data_processed), "Attrition_Yes")
response <- "Attrition_Yes"
# Train AutoML model
automl <- h2o.automl(
x = predictors,
y = response,
training_frame = train_h2o,
max_runtime_secs = 30, # Maximum runtime in seconds
seed = 123
)##
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## 16:01:19.425: AutoML: XGBoost is not available; skipping it.
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|======================================================================| 100%# Step 4: Evaluate the best model from AutoML
predictions <- h2o.predict(automl@leader, newdata = test_h2o)$predict##
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|======================================================================| 100%# Step 5: Assess model performance using h2o.performance
performance <- h2o.performance(automl@leader, newdata = test_h2o)
print(performance)## H2ORegressionMetrics: gbm
##
## MSE: 2.722912e-13
## RMSE: 5.218153e-07
## MAE: 2.651004e-07
## RMSLE: 1.917963e-07
## Mean Residual Deviance : 2.722912e-13# Optionally, you can also inspect the best model
summary(automl@leader)## Model Details:
## ==============
##
## H2ORegressionModel: gbm
## Model Key: GBM_grid_1_AutoML_8_20240509_160119_model_1
## Model Summary:
## number_of_trees number_of_internal_trees model_size_in_bytes min_depth
## 1 155 155 13057 1
## max_depth mean_depth min_leaves max_leaves mean_leaves
## 1 1 1.00000 2 2 2.00000
##
## H2ORegressionMetrics: gbm
## ** Reported on training data. **
##
## MSE: 2.405081e-13
## RMSE: 4.904163e-07
## MAE: 2.917838e-07
## RMSLE: 2.427209e-07
## Mean Residual Deviance : 2.405081e-13
##
##
##
## H2ORegressionMetrics: gbm
## ** Reported on cross-validation data. **
## ** 5-fold cross-validation on training data (Metrics computed for combined holdout predictions) **
##
## MSE: 3.842066e-13
## RMSE: 6.19844e-07
## MAE: 3.858446e-07
## RMSLE: 3.586859e-07
## Mean Residual Deviance : 3.842066e-13
##
##
## Cross-Validation Metrics Summary:
## mean sd cv_1_valid cv_2_valid cv_3_valid
## mae 0.000000 0.000000 0.000000 0.000000 0.000000
## mean_residual_deviance 0.000000 0.000000 0.000000 0.000000 0.000000
## mse 0.000000 0.000000 0.000000 0.000000 0.000000
## r2 1.000000 0.000000 1.000000 1.000000 1.000000
## residual_deviance 0.000000 0.000000 0.000000 0.000000 0.000000
## rmse 0.000001 0.000000 0.000001 0.000001 0.000001
## rmsle 0.000000 0.000000 0.000000 0.000000 0.000000
## cv_4_valid cv_5_valid
## mae 0.000000 0.000000
## mean_residual_deviance 0.000000 0.000000
## mse 0.000000 0.000000
## r2 1.000000 1.000000
## residual_deviance 0.000000 0.000000
## rmse 0.000001 0.000001
## rmsle 0.000000 0.000000
##
## Scoring History:
## timestamp duration number_of_trees training_rmse training_mae
## 1 2024-05-09 16:01:35 1.085 sec 0 0.99957 0.73447
## 2 2024-05-09 16:01:35 1.086 sec 5 0.59024 0.43370
## 3 2024-05-09 16:01:35 1.104 sec 10 0.34853 0.25610
## 4 2024-05-09 16:01:35 1.113 sec 15 0.20580 0.15122
## 5 2024-05-09 16:01:35 1.125 sec 20 0.12153 0.08929
## training_deviance
## 1 0.99915
## 2 0.34838
## 3 0.12147
## 4 0.04236
## 5 0.01477
##
## ---
## timestamp duration number_of_trees training_rmse training_mae
## 27 2024-05-09 16:01:35 1.365 sec 130 0.00000 0.00000
## 28 2024-05-09 16:01:35 1.383 sec 135 0.00000 0.00000
## 29 2024-05-09 16:01:35 1.385 sec 140 0.00000 0.00000
## 30 2024-05-09 16:01:35 1.413 sec 145 0.00000 0.00000
## 31 2024-05-09 16:01:35 1.430 sec 150 0.00000 0.00000
## 32 2024-05-09 16:01:35 1.433 sec 155 0.00000 0.00000
## training_deviance
## 27 0.00000
## 28 0.00000
## 29 0.00000
## 30 0.00000
## 31 0.00000
## 32 0.00000
##
## Variable Importances: (Extract with `h2o.varimp`)
## =================================================
##
## Variable Importances:
## variable relative_importance scaled_importance percentage
## 1 Attrition_No 6178.946289 1.000000 1.000000
## 2 Age 0.000000 0.000000 0.000000
## 3 DailyRate 0.000000 0.000000 0.000000
## 4 DistanceFromHome 0.000000 0.000000 0.000000
## 5 Education 0.000000 0.000000 0.000000
##
## ---
## variable relative_importance scaled_importance
## 48 JobRole_Sales.Representative 0.000000 0.000000
## 49 MaritalStatus_Divorced 0.000000 0.000000
## 50 MaritalStatus_Married 0.000000 0.000000
## 51 MaritalStatus_Single 0.000000 0.000000
## 52 OverTime_No 0.000000 0.000000
## 53 OverTime_Yes 0.000000 0.000000
## percentage
## 48 0.000000
## 49 0.000000
## 50 0.000000
## 51 0.000000
## 52 0.000000
## 53 0.000000# Shut down H2O
h2o.shutdown()## Are you sure you want to shutdown the H2O instance running at http://localhost:54321/ (Y/N)?