Business Intelligence Project
Acers Team 20th November 2023
- Student Details
- Setup Chunk
- Install and load all the packages
- Loading the Dataset
- Exploratory Data Analysis
- Preprocessing and Data Transformation
- Training Models
- Hyper Parameter Tunning
- Ensemble methods
- Save and Load your Model
- Plumber API
- Prediction Web Form And Customer-facing Dashboard
Student Details
| Student ID Numbers and Names of Group Members |
|
| GitHub Classroom Group Name | Acers Team |
| Course Code | BBT4206 |
| Course Name | Business Intelligence II |
| Program | Bachelor of Business Information Technology |
| Semester Duration | 21st August 2023 to 28th November 2023 |
Setup Chunk
Note: the following KnitR options have been set as
the global defaults:
knitr::opts_chunk$set(echo = TRUE, warning = FALSE, eval = TRUE, collapse = FALSE, tidy = TRUE).
More KnitR options are documented here https://bookdown.org/yihui/rmarkdown-cookbook/chunk-options.html and here https://yihui.org/knitr/options/.
Downloading the Dataset
Source:
The dataset that was used can be downloaded here:
https://www.kaggle.com/code/faressayah/ibm-hr-analytics-employee-attrition-performance/input
Reference:
F. (2018). IBM HR Analytics Employee Attrition & Performance. Kaggle. https://www.kaggle.com/code/faressayah/ibm-hr-analytics-employee-attrition-performance/input
Install and load all the packages
We installed all the packages that will enable us execute this lab.
#Loading Packages
if (!is.element("languageserver", installed.packages()[, 1])) {
install.packages("languageserver", dependencies = TRUE)
}
require("languageserver")## Loading required package: languageserverif (!is.element("e1071", installed.packages()[, 1])) {
install.packages("e1071", dependencies = TRUE)
}
require("e1071")## Loading required package: e1071if (!is.element("Amelia", installed.packages()[, 1])) {
install.packages("Amelia", dependencies = TRUE)
}
require("Amelia")## Loading required package: Amelia
## Loading required package: Rcpp
## ##
## ## Amelia II: Multiple Imputation
## ## (Version 1.8.1, built: 2022-11-18)
## ## Copyright (C) 2005-2023 James Honaker, Gary King and Matthew Blackwell
## ## Refer to http://gking.harvard.edu/amelia/ for more information
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install.packages("ggcorrplot", dependencies = TRUE)
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install.packages("readr", dependencies = TRUE)
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require("readr")## Loading required package: readrif (!is.element("caret", installed.packages()[, 1])) {
install.packages("caret", dependencies = TRUE)
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require("caret")## Loading required package: caret
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install.packages("naniar", dependencies = TRUE)
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require("naniar")## Loading required package: naniarif (!is.element("dplyr", installed.packages()[, 1])) {
install.packages("dplyr", dependencies = TRUE)
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require("dplyr")## Loading required package: dplyr
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install.packages("ggplot2", dependencies = TRUE)
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require("ggplot2")
if (!is.element("mice", installed.packages()[, 1])) {
install.packages("mice", dependencies = TRUE)
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require("mice")## Loading required package: mice
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## cbind, rbind## e1071 ----
if (require("e1071")) {
require("e1071")
} else {
install.packages("e1071", dependencies = TRUE,
repos = "https://cloud.r-project.org")
}
## factoextra ----
if (require("factoextra")) {
require("factoextra")
} else {
install.packages("factoextra", dependencies = TRUE,
repos = "https://cloud.r-project.org")
}## Loading required package: factoextra
## Welcome! Want to learn more? See two factoextra-related books at https://goo.gl/ve3WBa## FactoMineR ----
if (require("FactoMineR")) {
require("FactoMineR")
} else {
install.packages("FactoMineR", dependencies = TRUE,
repos = "https://cloud.r-project.org")
}## Loading required package: FactoMineR# STEP 1. Install and Load the Required Packages ----
## randomForest ----
if (require("randomForest")) {
require("randomForest")
} else {
install.packages("randomForest", dependencies = TRUE,
repos = "https://cloud.r-project.org")
}## Loading required package: randomForest
## randomForest 4.7-1.1
## Type rfNews() to see new features/changes/bug fixes.
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if (require("RRF")) {
require("RRF")
} else {
install.packages("RRF", dependencies = TRUE,
repos = "https://cloud.r-project.org")
}## Loading required package: RRF
## Registered S3 method overwritten by 'RRF':
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if (require("caretEnsemble")) {
require("caretEnsemble")
} else {
install.packages("caretEnsemble", dependencies = TRUE,
repos = "https://cloud.r-project.org")
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if (require("C50")) {
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install.packages("C50", dependencies = TRUE,
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if (require("adabag")) {
require("adabag")
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install.packages("adabag", dependencies = TRUE,
repos = "https://cloud.r-project.org")
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## Loading required package: parallel## plumber ----
if (require("plumber")) {
require("plumber")
} else {
install.packages("plumber", dependencies = TRUE,
repos = "https://cloud.r-project.org")
}## Loading required package: plumber## httr ----
if (require("httr")) {
require("httr")
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install.packages("httr", dependencies = TRUE,
repos = "https://cloud.r-project.org")
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if (require("jsonlite")) {
require("jsonlite")
} else {
install.packages("jsonlite", dependencies = TRUE,
repos = "https://cloud.r-project.org")
}## Loading required package: jsonliteLoading the Dataset
we are using readr to load a data from IBM HR Analytics
Employee Attrition & Performance.The dataset has 1470 observation
and 31 variables. Employees are the backbone of the organization.
Organization’s performance is heavily based on the quality of the
employees.
library(readr)
STREAMLINING_PROCESSES_AT_KINYANJUI_FARM <- read_csv("data/STREAMLINING PROCESSES AT KINYANJUI FARM.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.View(STREAMLINING_PROCESSES_AT_KINYANJUI_FARM)Exploratory Data Analysis
we used this approach to analyzing data sets to summarize their main characteristics, often with the help of statistical graphics and other data visualization methods. We then drop ‘EmployeeCount’, ‘Over18’, ‘StandardHours’ have only one unique values and ‘EmployeeNumber’ has 1470 unique values. This features aren’t useful for us, So we are going to drop those columns from the dataset
## Dimensions ----
### Preview the Loaded Datasets ----
# Dimensions refer to the number of observations (rows) and the number of
# attributes/variables/features (columns). Execute the following commands to
# display the dimensions of your datasets:
dim(STREAMLINING_PROCESSES_AT_KINYANJUI_FARM)## [1] 1470 35## Data Types ----
### Identify the Data Types ----
# Knowing the data types will help you to identify the most appropriate
# visualization types and algorithms that can be applied. It can also help you
# to identify the need to convert from categorical data (factors) to integers
# or vice versa where necessary. Execute the following command to identify the
# data types:
sapply(STREAMLINING_PROCESSES_AT_KINYANJUI_FARM, class)## Age Attrition BusinessTravel
## "numeric" "character" "character"
## DailyRate Department DistanceFromHome
## "numeric" "character" "numeric"
## Education EducationField EmployeeCount
## "numeric" "character" "numeric"
## EmployeeNumber EnvironmentSatisfaction Gender
## "numeric" "numeric" "character"
## HourlyRate JobInvolvement JobLevel
## "numeric" "numeric" "numeric"
## JobRole JobSatisfaction MaritalStatus
## "character" "numeric" "character"
## MonthlyIncome MonthlyRate NumCompaniesWorked
## "numeric" "numeric" "numeric"
## Over18 OverTime PercentSalaryHike
## "character" "character" "numeric"
## PerformanceRating RelationshipSatisfaction StandardHours
## "numeric" "numeric" "numeric"
## StockOptionLevel TotalWorkingYears TrainingTimesLastYear
## "numeric" "numeric" "numeric"
## WorkLifeBalance YearsAtCompany YearsInCurrentRole
## "numeric" "numeric" "numeric"
## YearsSinceLastPromotion YearsWithCurrManager
## "numeric" "numeric"# Drop specified columns
columns_to_drop <- c('EmployeeCount', 'EmployeeNumber', 'Over18', 'StandardHours')
STREAMLINING_PROCESSES_AT_KINYANJUI_FARM <- STREAMLINING_PROCESSES_AT_KINYANJUI_FARM[, !(names(STREAMLINING_PROCESSES_AT_KINYANJUI_FARM) %in% columns_to_drop)]
View(STREAMLINING_PROCESSES_AT_KINYANJUI_FARM)Descriptive Statistics
Descriptive statistics are a set of techniques and metrics used to summarize and describe the main features, characteristics, and patterns within a dataset. These statistics provide a snapshot of the data’s distribution, central tendency, variability, and other important properties.Before you can effectively use your data for tasks like building prediction models or drawing meaningful conclusions, it’s crucial to have a deep understanding of the dataset.Understanding the context is essential for making informed decisions based on those results
Measures of Frequency
This code calculates and displays the frequency and mode (most common value) for several categorical variables in a dataset, helping to understand the data’s distribution and identify prevalent values.Some datasets show relatively even distributions, while others have dominant categories. The frequencies provide insights into the composition of each dataset. we did this to show categorical variables Gender, JobRole and BusinessTravel.
#### Identify the number of instances that belong to Gender ----
# It is more sensible to count categorical variables (factors or dimensions)
# than numeric variables, e.g., counting the number of male and female
streamlining_processes_freq <- STREAMLINING_PROCESSES_AT_KINYANJUI_FARM$Gender
cbind(frequency = table(streamlining_processes_freq),
percentage = prop.table(table(streamlining_processes_freq)) * 100)## frequency percentage
## Female 588 40
## Male 882 60streamlining_processes_freq <- STREAMLINING_PROCESSES_AT_KINYANJUI_FARM$JobRole
cbind(frequency = table(streamlining_processes_freq),
percentage = prop.table(table(streamlining_processes_freq)) * 100)## frequency percentage
## Healthcare Representative 131 8.911565
## Human Resources 52 3.537415
## Laboratory Technician 259 17.619048
## Manager 102 6.938776
## Manufacturing Director 145 9.863946
## Research Director 80 5.442177
## Research Scientist 292 19.863946
## Sales Executive 326 22.176871
## Sales Representative 83 5.646259streamlining_processes_freq <- STREAMLINING_PROCESSES_AT_KINYANJUI_FARM$BusinessTravel
cbind(frequency = table(streamlining_processes_freq),
percentage = prop.table(table(streamlining_processes_freq)) * 100)## frequency percentage
## Non-Travel 150 10.20408
## Travel_Frequently 277 18.84354
## Travel_Rarely 1043 70.95238Measures of Central Tendency
streamlining_processes_gender_mode <- names(table(STREAMLINING_PROCESSES_AT_KINYANJUI_FARM$Gender))[
which(table(STREAMLINING_PROCESSES_AT_KINYANJUI_FARM$Gender) == max(table(STREAMLINING_PROCESSES_AT_KINYANJUI_FARM$Gender)))
]
print(streamlining_processes_gender_mode)## [1] "Male"streamlining_processes_jobRole_mode <- names(table(STREAMLINING_PROCESSES_AT_KINYANJUI_FARM$JobRole))[
which(table(STREAMLINING_PROCESSES_AT_KINYANJUI_FARM$JobRole) == max(table(STREAMLINING_PROCESSES_AT_KINYANJUI_FARM$JobRole)))
]
print(streamlining_processes_jobRole_mode)## [1] "Sales Executive"streamlining_processes_businessRole_mode <- names(table(STREAMLINING_PROCESSES_AT_KINYANJUI_FARM$BusinessTravel))[
which(table(STREAMLINING_PROCESSES_AT_KINYANJUI_FARM$BusinessTravel) == max(table(STREAMLINING_PROCESSES_AT_KINYANJUI_FARM$BusinessTravel)))
]
print(streamlining_processes_businessRole_mode)## [1] "Travel_Rarely"Measures of Distribution/Dispersion/Spread/Scatter/Variability
The code chunk provides various measures of distribution, dispersion,
and variability for selected variables in the
‘STREAMLINING_PROCESSES_AT_KINYANJUI_FARM’ dataset. It includes a
summary of the dataset, calculates standard deviation,
variance, kurtosis, and skewness
for specific columns, offering insights into the spread and shape of the
data distribution. Standard deviation measures the
dispersion or spread of data points around the mean, providing insight
into data variability. Variancemeasures the spread or
dispersion of a set of values. A higher variance indicates greater
variability from the mean, while a lower variance suggests that the
values are closer to the mean. Kurtosis measures the shape
of the probability distribution of a real-valued random variable.
Positive kurtosis indicates a relatively peaked distribution (heavy
tails), while negative kurtosis indicates a flatter distribution (light
tails).Skewnessmeasures the asymmetry of the probability
distribution of a real-valued random variable. Positive skewness
indicates a distribution that is skewed to the right (tail on the
right), while negative skewness indicates a distribution that is skewed
to the left (tail on the left).
summary(STREAMLINING_PROCESSES_AT_KINYANJUI_FARM)## Age Attrition BusinessTravel DailyRate
## Min. :18.00 Length:1470 Length:1470 Min. : 102.0
## 1st Qu.:30.00 Class :character Class :character 1st Qu.: 465.0
## Median :36.00 Mode :character Mode :character Median : 802.0
## Mean :36.92 Mean : 802.5
## 3rd Qu.:43.00 3rd Qu.:1157.0
## Max. :60.00 Max. :1499.0
## Department DistanceFromHome Education EducationField
## Length:1470 Min. : 1.000 Min. :1.000 Length:1470
## Class :character 1st Qu.: 2.000 1st Qu.:2.000 Class :character
## Mode :character Median : 7.000 Median :3.000 Mode :character
## Mean : 9.193 Mean :2.913
## 3rd Qu.:14.000 3rd Qu.:4.000
## Max. :29.000 Max. :5.000
## EnvironmentSatisfaction Gender HourlyRate JobInvolvement
## Min. :1.000 Length:1470 Min. : 30.00 Min. :1.00
## 1st Qu.:2.000 Class :character 1st Qu.: 48.00 1st Qu.:2.00
## Median :3.000 Mode :character Median : 66.00 Median :3.00
## Mean :2.722 Mean : 65.89 Mean :2.73
## 3rd Qu.:4.000 3rd Qu.: 83.75 3rd Qu.:3.00
## Max. :4.000 Max. :100.00 Max. :4.00
## JobLevel JobRole JobSatisfaction MaritalStatus
## Min. :1.000 Length:1470 Min. :1.000 Length:1470
## 1st Qu.:1.000 Class :character 1st Qu.:2.000 Class :character
## Median :2.000 Mode :character Median :3.000 Mode :character
## Mean :2.064 Mean :2.729
## 3rd Qu.:3.000 3rd Qu.:4.000
## Max. :5.000 Max. :4.000
## MonthlyIncome MonthlyRate NumCompaniesWorked OverTime
## Min. : 1009 Min. : 2094 Min. :0.000 Length:1470
## 1st Qu.: 2911 1st Qu.: 8047 1st Qu.:1.000 Class :character
## Median : 4919 Median :14236 Median :2.000 Mode :character
## Mean : 6503 Mean :14313 Mean :2.693
## 3rd Qu.: 8379 3rd Qu.:20462 3rd Qu.:4.000
## Max. :19999 Max. :26999 Max. :9.000
## PercentSalaryHike PerformanceRating RelationshipSatisfaction StockOptionLevel
## Min. :11.00 Min. :3.000 Min. :1.000 Min. :0.0000
## 1st Qu.:12.00 1st Qu.:3.000 1st Qu.:2.000 1st Qu.:0.0000
## Median :14.00 Median :3.000 Median :3.000 Median :1.0000
## Mean :15.21 Mean :3.154 Mean :2.712 Mean :0.7939
## 3rd Qu.:18.00 3rd Qu.:3.000 3rd Qu.:4.000 3rd Qu.:1.0000
## Max. :25.00 Max. :4.000 Max. :4.000 Max. :3.0000
## TotalWorkingYears TrainingTimesLastYear WorkLifeBalance YearsAtCompany
## Min. : 0.00 Min. :0.000 Min. :1.000 Min. : 0.000
## 1st Qu.: 6.00 1st Qu.:2.000 1st Qu.:2.000 1st Qu.: 3.000
## Median :10.00 Median :3.000 Median :3.000 Median : 5.000
## Mean :11.28 Mean :2.799 Mean :2.761 Mean : 7.008
## 3rd Qu.:15.00 3rd Qu.:3.000 3rd Qu.:3.000 3rd Qu.: 9.000
## Max. :40.00 Max. :6.000 Max. :4.000 Max. :40.000
## YearsInCurrentRole YearsSinceLastPromotion YearsWithCurrManager
## Min. : 0.000 Min. : 0.000 Min. : 0.000
## 1st Qu.: 2.000 1st Qu.: 0.000 1st Qu.: 2.000
## Median : 3.000 Median : 1.000 Median : 3.000
## Mean : 4.229 Mean : 2.188 Mean : 4.123
## 3rd Qu.: 7.000 3rd Qu.: 3.000 3rd Qu.: 7.000
## Max. :18.000 Max. :15.000 Max. :17.000STREAMLINING_PROCESSES_AT_KINYANJUI_FARM <- na.omit(STREAMLINING_PROCESSES_AT_KINYANJUI_FARM)
#### Measure the standard deviation ----
sapply(STREAMLINING_PROCESSES_AT_KINYANJUI_FARM[, c(1,4, 6, 7, 9, 11, 12, 13, 15 , 17 , 18, 19, 21, 22, 23,24, 25, 26, 27, 28 ,29, 30, 31 )], sd)## Age DailyRate DistanceFromHome
## 9.1353735 403.5090999 8.1068644
## Education EnvironmentSatisfaction HourlyRate
## 1.0241649 1.0930822 20.3294276
## JobInvolvement JobLevel JobSatisfaction
## 0.7115611 1.1069399 1.1028461
## MonthlyIncome MonthlyRate NumCompaniesWorked
## 4707.9567831 7117.7860441 2.4980090
## PercentSalaryHike PerformanceRating RelationshipSatisfaction
## 3.6599377 0.3608235 1.0812089
## StockOptionLevel TotalWorkingYears TrainingTimesLastYear
## 0.8520767 7.7807817 1.2892706
## WorkLifeBalance YearsAtCompany YearsInCurrentRole
## 0.7064758 6.1265252 3.6231370
## YearsSinceLastPromotion YearsWithCurrManager
## 3.2224303 3.5681361#### Measure the variance of each variable ----
sapply(STREAMLINING_PROCESSES_AT_KINYANJUI_FARM[, c(1,4, 6, 7, 9, 11, 12, 13, 15 , 17 , 18, 19, 21, 22, 23,24, 25, 26, 27, 28 ,29, 30, 31 )], var)## Age DailyRate DistanceFromHome
## 8.345505e+01 1.628196e+05 6.572125e+01
## Education EnvironmentSatisfaction HourlyRate
## 1.048914e+00 1.194829e+00 4.132856e+02
## JobInvolvement JobLevel JobSatisfaction
## 5.063193e-01 1.225316e+00 1.216270e+00
## MonthlyIncome MonthlyRate NumCompaniesWorked
## 2.216486e+07 5.066288e+07 6.240049e+00
## PercentSalaryHike PerformanceRating RelationshipSatisfaction
## 1.339514e+01 1.301936e-01 1.169013e+00
## StockOptionLevel TotalWorkingYears TrainingTimesLastYear
## 7.260346e-01 6.054056e+01 1.662219e+00
## WorkLifeBalance YearsAtCompany YearsInCurrentRole
## 4.991081e-01 3.753431e+01 1.312712e+01
## YearsSinceLastPromotion YearsWithCurrManager
## 1.038406e+01 1.273160e+01#### Measure the kurtosis of each variable ----
sapply(STREAMLINING_PROCESSES_AT_KINYANJUI_FARM[, c(1,4, 6, 7, 9, 11, 12, 13, 15 , 17 , 18, 19, 21, 22, 23,24, 25, 26, 27, 28 ,29, 30, 31 )], kurtosis, type = 2)## Age DailyRate DistanceFromHome
## -0.40414514 -1.20382281 -0.22483340
## Education EnvironmentSatisfaction HourlyRate
## -0.55911497 -1.20252052 -1.19639846
## JobInvolvement JobLevel JobSatisfaction
## 0.27099877 0.39915206 -1.22219257
## MonthlyIncome MonthlyRate NumCompaniesWorked
## 1.00523269 -1.21495610 0.01021382
## PercentSalaryHike PerformanceRating RelationshipSatisfaction
## -0.30059822 1.69593867 -1.18481398
## StockOptionLevel TotalWorkingYears TrainingTimesLastYear
## 0.36463433 0.91826954 0.49499299
## WorkLifeBalance YearsAtCompany YearsInCurrentRole
## 0.41946050 3.93550876 0.47742077
## YearsSinceLastPromotion YearsWithCurrManager
## 3.61267311 0.17105808#### Measure the skewness of each variable ----
sapply(STREAMLINING_PROCESSES_AT_KINYANJUI_FARM[, c(1,4, 6, 7, 9, 11, 12, 13, 15 , 17 , 18, 19, 21, 22, 23,24, 25, 26, 27, 28 ,29, 30, 31 )], skewness, type = 2)## Age DailyRate DistanceFromHome
## 0.413286302 -0.003518568 0.958117996
## Education EnvironmentSatisfaction HourlyRate
## -0.289681082 -0.321654448 -0.032310953
## JobInvolvement JobLevel JobSatisfaction
## -0.498419364 1.025401283 -0.329671959
## MonthlyIncome MonthlyRate NumCompaniesWorked
## 1.369816681 0.018577808 1.026471112
## PercentSalaryHike PerformanceRating RelationshipSatisfaction
## 0.821127976 1.921882702 -0.302827565
## StockOptionLevel TotalWorkingYears TrainingTimesLastYear
## 0.968980317 1.117171853 0.553124171
## WorkLifeBalance YearsAtCompany YearsInCurrentRole
## -0.552480299 1.764529454 0.917363156
## YearsSinceLastPromotion YearsWithCurrManager
## 1.984289983 0.833450992Measures of Relationship
Measures of Relationship quantify associations between
variables. The Pearson correlation coefficient assesses linear
relationships, covariance measures changes together, and the slope of
the regression line indicates the strength of a linear relationship.
R-squared explains the variance in the dependent variable, and rank
correlation coefficients assess monotonic relationships. These measures
aid in understanding the extent and nature of connections in data
analysis
Measure the correlation between variables
streamlining_processes_cov <- cov(STREAMLINING_PROCESSES_AT_KINYANJUI_FARM[, c(1,4, 6, 7, 9, 11, 12, 13, 15 , 17 , 18, 19, 21, 22, 23,24, 25, 26, 27, 28 ,29, 30, 31 )])
View(streamlining_processes_cov)Measure the covariance between variables
streamlining_processes_cor <- cor(STREAMLINING_PROCESSES_AT_KINYANJUI_FARM[, c(1,4, 6, 7, 9, 11, 12, 13, 15 , 17 , 18, 19, 21, 22, 23,24, 25, 26, 27, 28 ,29, 30, 31 )])
View(streamlining_processes_cor)Inferential Statistics
Inferential statistics involves drawing conclusions about a population based on a sample of data. It uses probability theory to make inferences, such as estimating population parameters or testing hypotheses. Techniques include hypothesis testing, confidence intervals, and regression analysis. Inferential statistics helps generalize findings from a sample to the larger population, providing insights beyond the observed data.This code fits a one-way ANOVA model where Age is the dependent variable, and Attrition is the categorical independent variable.determine if there is a significant difference in the means of the ‘Age’ variable across different levels of ‘Attrition’.it further fits a two-way ANOVA model, considering both Attrition and Gender as categorical independent variables affecting the dependent variable Age.
# Inferential Statistics ----
streamlining_processes_one_way_anova <- aov( Age ~ Attrition , data = STREAMLINING_PROCESSES_AT_KINYANJUI_FARM)
summary(streamlining_processes_one_way_anova)## Df Sum Sq Mean Sq F value Pr(>F)
## Attrition 1 3107 3107.3 38.18 8.36e-10 ***
## Residuals 1468 119488 81.4
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1streamlining_processes_two_way_anova <- aov(Age ~ Attrition + Gender, data = STREAMLINING_PROCESSES_AT_KINYANJUI_FARM)
summary(streamlining_processes_two_way_anova)## Df Sum Sq Mean Sq F value Pr(>F)
## Attrition 1 3107 3107.3 38.189 8.3e-10 ***
## Gender 1 123 122.7 1.508 0.22
## Residuals 1467 119365 81.4
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1Basic Visualization
Univariate Plots
Univariate plots are used to explore the distribution and characteristics of individual variables in a dataset. Common types of univariate plots include histograms, box plots, and density plots, which provide insights into the central tendency, spread, and shape of the distribution of a single variable. These visualizations are fundamental for understanding the nature of individual variables before proceeding to more complex analyses
Histograms
#### Histograms for Each Numeric Attribute ----
str(STREAMLINING_PROCESSES_AT_KINYANJUI_FARM)## tibble [1,470 × 31] (S3: tbl_df/tbl/data.frame)
## $ Age : num [1:1470] 41 49 37 33 27 32 59 30 38 36 ...
## $ Attrition : chr [1:1470] "Yes" "No" "Yes" "No" ...
## $ BusinessTravel : chr [1:1470] "Travel_Rarely" "Travel_Frequently" "Travel_Rarely" "Travel_Frequently" ...
## $ DailyRate : num [1:1470] 1102 279 1373 1392 591 ...
## $ Department : chr [1:1470] "Sales" "Research & Development" "Research & Development" "Research & Development" ...
## $ DistanceFromHome : num [1:1470] 1 8 2 3 2 2 3 24 23 27 ...
## $ Education : num [1:1470] 2 1 2 4 1 2 3 1 3 3 ...
## $ EducationField : chr [1:1470] "Life Sciences" "Life Sciences" "Other" "Life Sciences" ...
## $ EnvironmentSatisfaction : num [1:1470] 2 3 4 4 1 4 3 4 4 3 ...
## $ Gender : chr [1:1470] "Female" "Male" "Male" "Female" ...
## $ HourlyRate : num [1:1470] 94 61 92 56 40 79 81 67 44 94 ...
## $ JobInvolvement : num [1:1470] 3 2 2 3 3 3 4 3 2 3 ...
## $ JobLevel : num [1:1470] 2 2 1 1 1 1 1 1 3 2 ...
## $ JobRole : chr [1:1470] "Sales Executive" "Research Scientist" "Laboratory Technician" "Research Scientist" ...
## $ JobSatisfaction : num [1:1470] 4 2 3 3 2 4 1 3 3 3 ...
## $ MaritalStatus : chr [1:1470] "Single" "Married" "Single" "Married" ...
## $ MonthlyIncome : num [1:1470] 5993 5130 2090 2909 3468 ...
## $ MonthlyRate : num [1:1470] 19479 24907 2396 23159 16632 ...
## $ NumCompaniesWorked : num [1:1470] 8 1 6 1 9 0 4 1 0 6 ...
## $ OverTime : chr [1:1470] "Yes" "No" "Yes" "Yes" ...
## $ PercentSalaryHike : num [1:1470] 11 23 15 11 12 13 20 22 21 13 ...
## $ PerformanceRating : num [1:1470] 3 4 3 3 3 3 4 4 4 3 ...
## $ RelationshipSatisfaction: num [1:1470] 1 4 2 3 4 3 1 2 2 2 ...
## $ StockOptionLevel : num [1:1470] 0 1 0 0 1 0 3 1 0 2 ...
## $ TotalWorkingYears : num [1:1470] 8 10 7 8 6 8 12 1 10 17 ...
## $ TrainingTimesLastYear : num [1:1470] 0 3 3 3 3 2 3 2 2 3 ...
## $ WorkLifeBalance : num [1:1470] 1 3 3 3 3 2 2 3 3 2 ...
## $ YearsAtCompany : num [1:1470] 6 10 0 8 2 7 1 1 9 7 ...
## $ YearsInCurrentRole : num [1:1470] 4 7 0 7 2 7 0 0 7 7 ...
## $ YearsSinceLastPromotion : num [1:1470] 0 1 0 3 2 3 0 0 1 7 ...
## $ YearsWithCurrManager : num [1:1470] 5 7 0 0 2 6 0 0 8 7 ...# Identify numeric columns
numeric_columns <- sapply(STREAMLINING_PROCESSES_AT_KINYANJUI_FARM, is.numeric)
# Extract only numeric columns
STREAMLINING_PROCESSES_AT_KINYANJUI_FARM <- STREAMLINING_PROCESSES_AT_KINYANJUI_FARM[, numeric_columns]
STREAMLINING_PROCESSES_AT_KINYANJUI_FARM<- sapply(STREAMLINING_PROCESSES_AT_KINYANJUI_FARM, as.numeric)
# Set up the layout for multiple histograms
par(mfrow = c(1,1))
# Plot histograms for numeric columns
hist(STREAMLINING_PROCESSES_AT_KINYANJUI_FARM[, 1], main = names(STREAMLINING_PROCESSES_AT_KINYANJUI_FARM)[1])
hist(STREAMLINING_PROCESSES_AT_KINYANJUI_FARM[, 4], main = names(STREAMLINING_PROCESSES_AT_KINYANJUI_FARM)[4])
hist(STREAMLINING_PROCESSES_AT_KINYANJUI_FARM[, 6], main = names(STREAMLINING_PROCESSES_AT_KINYANJUI_FARM)[6])
hist(STREAMLINING_PROCESSES_AT_KINYANJUI_FARM[, 7], main = names(STREAMLINING_PROCESSES_AT_KINYANJUI_FARM)[7])
hist(STREAMLINING_PROCESSES_AT_KINYANJUI_FARM[, 9], main = names(STREAMLINING_PROCESSES_AT_KINYANJUI_FARM)[9])
hist(STREAMLINING_PROCESSES_AT_KINYANJUI_FARM[, 10], main = names(STREAMLINING_PROCESSES_AT_KINYANJUI_FARM)[10])
hist(STREAMLINING_PROCESSES_AT_KINYANJUI_FARM[, 11], main = names(STREAMLINING_PROCESSES_AT_KINYANJUI_FARM)[11])
hist(STREAMLINING_PROCESSES_AT_KINYANJUI_FARM[, 13], main = names(STREAMLINING_PROCESSES_AT_KINYANJUI_FARM)[13])
hist(STREAMLINING_PROCESSES_AT_KINYANJUI_FARM[, 14], main = names(STREAMLINING_PROCESSES_AT_KINYANJUI_FARM)[14])
Box and Whisker Plots for Each Numeric Attribute
# Set up the layout for multiple histograms
# Plot histograms for numeric columns
for (i in 1:ncol(STREAMLINING_PROCESSES_AT_KINYANJUI_FARM)) {
jpeg("boxplot.jpg", width = 800, height = 600)
boxplot(STREAMLINING_PROCESSES_AT_KINYANJUI_FARM[, 1], main = names(STREAMLINING_PROCESSES_AT_KINYANJUI_FARM)[1])
dev.off()
}
boxplot(STREAMLINING_PROCESSES_AT_KINYANJUI_FARM[, 1], main = names(STREAMLINING_PROCESSES_AT_KINYANJUI_FARM)[1])
boxplot(STREAMLINING_PROCESSES_AT_KINYANJUI_FARM[, 4], main = names(STREAMLINING_PROCESSES_AT_KINYANJUI_FARM)[4])
boxplot(STREAMLINING_PROCESSES_AT_KINYANJUI_FARM[, 6], main = names(STREAMLINING_PROCESSES_AT_KINYANJUI_FARM)[6])
boxplot(STREAMLINING_PROCESSES_AT_KINYANJUI_FARM[, 7], main = names(STREAMLINING_PROCESSES_AT_KINYANJUI_FARM)[7])
boxplot(STREAMLINING_PROCESSES_AT_KINYANJUI_FARM[, 9], main = names(STREAMLINING_PROCESSES_AT_KINYANJUI_FARM)[9])
boxplot(STREAMLINING_PROCESSES_AT_KINYANJUI_FARM[, 10], main = names(STREAMLINING_PROCESSES_AT_KINYANJUI_FARM)[10])
boxplot(STREAMLINING_PROCESSES_AT_KINYANJUI_FARM[, 11], main = names(STREAMLINING_PROCESSES_AT_KINYANJUI_FARM)[11])
boxplot(STREAMLINING_PROCESSES_AT_KINYANJUI_FARM[, 13], main = names(STREAMLINING_PROCESSES_AT_KINYANJUI_FARM)[13])
boxplot(STREAMLINING_PROCESSES_AT_KINYANJUI_FARM[, 14], main = names(STREAMLINING_PROCESSES_AT_KINYANJUI_FARM)[14])
boxplot(STREAMLINING_PROCESSES_AT_KINYANJUI_FARM[, 15], main = names(STREAMLINING_PROCESSES_AT_KINYANJUI_FARM)[15])
boxplot(STREAMLINING_PROCESSES_AT_KINYANJUI_FARM[, 17], main = names(STREAMLINING_PROCESSES_AT_KINYANJUI_FARM)[17])
boxplot(STREAMLINING_PROCESSES_AT_KINYANJUI_FARM[, 19], main = names(STREAMLINING_PROCESSES_AT_KINYANJUI_FARM)[19])
boxplot(STREAMLINING_PROCESSES_AT_KINYANJUI_FARM[, 20], main = names(STREAMLINING_PROCESSES_AT_KINYANJUI_FARM)[20])
boxplot(STREAMLINING_PROCESSES_AT_KINYANJUI_FARM[, 21], main = names(STREAMLINING_PROCESSES_AT_KINYANJUI_FARM)[21])
Bar Plots for Each Categorical Attribute Gender
library(readr)
STREAMLINING_PROCESSES_AT_KINYANJUI_FARM <- read_csv("data/STREAMLINING PROCESSES AT KINYANJUI FARM.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.# Drop specified columns
columns_to_drop <- c('EmployeeCount', 'EmployeeNumber', 'Over18', 'StandardHours')
STREAMLINING_PROCESSES_AT_KINYANJUI_FARM <- STREAMLINING_PROCESSES_AT_KINYANJUI_FARM[, !(names(STREAMLINING_PROCESSES_AT_KINYANJUI_FARM) %in% columns_to_drop)]
barplot(table(STREAMLINING_PROCESSES_AT_KINYANJUI_FARM[, 2]), main = names(STREAMLINING_PROCESSES_AT_KINYANJUI_FARM)[2])
Missingness Map to Identify Missing Data
missmap(STREAMLINING_PROCESSES_AT_KINYANJUI_FARM, col = c("red", "grey"), legend = TRUE)
Multivariate Plots
Multivariate Plots involve visualizations that simultaneously represent and explore the relationships between two or more variables in a dataset. Unlike univariate plots that focus on a single variable, multivariate plots provide insights into the interactions and patterns between multiple variables. Examples of multivariate plots include scatter plots, bubble charts, heatmaps, and 3D plots. These visualizations are essential for identifying correlations, trends, and dependencies between variables, aiding in a more comprehensive understanding of the overall dataset structure. They are particularly useful for uncovering complex relationships and patterns in multidimensional data
Correlation Plot
corrplot(cor(STREAMLINING_PROCESSES_AT_KINYANJUI_FARM[, c(1,4, 6, 7, 9, 11, 12, 13, 15 , 17 , 18, 19, 21, 22, 23,24, 25, 26, 27, 28 ,29, 30, 31 )]), method = "circle")
ggcorrplot(cor(STREAMLINING_PROCESSES_AT_KINYANJUI_FARM[, c(1,4, 6, 7, 9, 11, 12, 13, 15 , 17 , 18, 19, 21, 22, 23,24, 25, 26, 27, 28 ,29, 30, 31 )]))
Scatter Plot
# Select only numeric columns
numeric_columns <- STREAMLINING_PROCESSES_AT_KINYANJUI_FARM[sapply(STREAMLINING_PROCESSES_AT_KINYANJUI_FARM, is.numeric)]
# Create scatterplot matrix
pairs(numeric_columns, col = STREAMLINING_PROCESSES_AT_KINYANJUI_FARM$Age)
Multivariate Box and Whisker Plots by Class
library(readr)
STREAMLINING_PROCESSES_AT_KINYANJUI_FARM <- read_csv("data/STREAMLINING PROCESSES AT KINYANJUI FARM.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.# Drop specified columns
columns_to_drop <- c('EmployeeCount', 'EmployeeNumber', 'Over18', 'StandardHours')
STREAMLINING_PROCESSES_AT_KINYANJUI_FARM <- STREAMLINING_PROCESSES_AT_KINYANJUI_FARM[, !(names(STREAMLINING_PROCESSES_AT_KINYANJUI_FARM) %in% columns_to_drop)]
featurePlot(x = STREAMLINING_PROCESSES_AT_KINYANJUI_FARM[, c(1,4, 6, 7, 9, 11, 12, 13, 15 , 17 , 18, 19, 21, 22, 23,24, 25, 26, 27, 28 ,29, 30, 31 )], y = STREAMLINING_PROCESSES_AT_KINYANJUI_FARM[, 2],
plot = "box")## NULLPreprocessing and Data Transformation
Preprocessing and Data Transformation” refer to the steps taken to
prepare raw data for analysis. This involves cleaning, organizing, and
modifying the data to improve its quality and make it suitable for
modeling. Common preprocessing tasks include handling
missing values, removing outliers,
scaling or normalizing numerical features,
encoding categorical variables, and
splitting the data into training and testing sets. Data
transformation may also involve creating new features or aggregating
existing ones. These processes are crucial for enhancing the accuracy
and effectiveness of machine learning models
## Confirm the "missingness" in the Initial Dataset ----
# Are there missing values in the dataset?
any_na(STREAMLINING_PROCESSES_AT_KINYANJUI_FARM)## [1] FALSE# How many?
n_miss(STREAMLINING_PROCESSES_AT_KINYANJUI_FARM)## [1] 0# What is the percentage of missing data in the entire dataset?
prop_miss(STREAMLINING_PROCESSES_AT_KINYANJUI_FARM)## [1] 0# How many missing values does each variable have?
STREAMLINING_PROCESSES_AT_KINYANJUI_FARM %>% is.na() %>% colSums()## Age Attrition BusinessTravel
## 0 0 0
## DailyRate Department DistanceFromHome
## 0 0 0
## Education EducationField EnvironmentSatisfaction
## 0 0 0
## Gender HourlyRate JobInvolvement
## 0 0 0
## JobLevel JobRole JobSatisfaction
## 0 0 0
## MaritalStatus MonthlyIncome MonthlyRate
## 0 0 0
## NumCompaniesWorked OverTime PercentSalaryHike
## 0 0 0
## PerformanceRating RelationshipSatisfaction StockOptionLevel
## 0 0 0
## TotalWorkingYears TrainingTimesLastYear WorkLifeBalance
## 0 0 0
## YearsAtCompany YearsInCurrentRole YearsSinceLastPromotion
## 0 0 0
## YearsWithCurrManager
## 0# What is the number and percentage of missing values grouped by
# each variable?
miss_var_summary(STREAMLINING_PROCESSES_AT_KINYANJUI_FARM)## # A tibble: 31 × 3
## variable n_miss pct_miss
## <chr> <int> <dbl>
## 1 Age 0 0
## 2 Attrition 0 0
## 3 BusinessTravel 0 0
## 4 DailyRate 0 0
## 5 Department 0 0
## 6 DistanceFromHome 0 0
## 7 Education 0 0
## 8 EducationField 0 0
## 9 EnvironmentSatisfaction 0 0
## 10 Gender 0 0
## # ℹ 21 more rows# What is the number and percentage of missing values grouped by
# each observation?
miss_case_summary(STREAMLINING_PROCESSES_AT_KINYANJUI_FARM)## # A tibble: 1,470 × 3
## case n_miss pct_miss
## <int> <int> <dbl>
## 1 1 0 0
## 2 2 0 0
## 3 3 0 0
## 4 4 0 0
## 5 5 0 0
## 6 6 0 0
## 7 7 0 0
## 8 8 0 0
## 9 9 0 0
## 10 10 0 0
## # ℹ 1,460 more rows# Which variables contain the most missing values?
#gg_miss_var(STREAMLINING_PROCESSES_AT_KINYANJUI_FARM)
# Where are missing values located (the shaded regions in the plot)?
vis_miss(STREAMLINING_PROCESSES_AT_KINYANJUI_FARM) + theme(axis.text.x = element_text(angle = 80))
# Which combinations of variables are missing together?
#gg_miss_upset(STREAMLINING_PROCESSES_AT_KINYANJUI_FARM)Heatmap of “missingness” broken down by “Age”
# First, confirm that the "AgeDecade" variable is a categorical variable
is.factor(STREAMLINING_PROCESSES_AT_KINYANJUI_FARM$Age)## [1] FALSE# Second, create the visualization
gg_miss_fct(STREAMLINING_PROCESSES_AT_KINYANJUI_FARM, fct = Age)
# We can also create a heatmap of "missingness" broken down by "Depressed"
# First, confirm that the "Depressed" variable is a categorical variable
is.factor(STREAMLINING_PROCESSES_AT_KINYANJUI_FARM$Attrition)## [1] FALSE# Second, create the visualization
gg_miss_fct(STREAMLINING_PROCESSES_AT_KINYANJUI_FARM, fct = Attrition)
Data Transformation
Scale Data Transform
# Scale Data Transform
# BEFORE
# Identify numeric columns
library(readr)
STREAMLINING_PROCESSES_AT_KINYANJUI_FARM <- read_csv("data/STREAMLINING PROCESSES AT KINYANJUI FARM.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.# Drop specified columns
columns_to_drop <- c('EmployeeCount', 'EmployeeNumber', 'Over18', 'StandardHours')
STREAMLINING_PROCESSES_AT_KINYANJUI_FARM <- STREAMLINING_PROCESSES_AT_KINYANJUI_FARM[, !(names(STREAMLINING_PROCESSES_AT_KINYANJUI_FARM) %in% columns_to_drop)]
numeric_columns <- sapply(STREAMLINING_PROCESSES_AT_KINYANJUI_FARM, is.numeric)
# Extract only numeric columns
STREAMLINING_PROCESSES_AT_KINYANJUI_FARM <- STREAMLINING_PROCESSES_AT_KINYANJUI_FARM[, numeric_columns]
STREAMLINING_PROCESSES_AT_KINYANJUI_FARM<- sapply(STREAMLINING_PROCESSES_AT_KINYANJUI_FARM, as.numeric)
# Set up the layout for multiple histograms
par(mfrow = c(1, 1))
summary(STREAMLINING_PROCESSES_AT_KINYANJUI_FARM)## Age DailyRate DistanceFromHome Education
## Min. :18.00 Min. : 102.0 Min. : 1.000 Min. :1.000
## 1st Qu.:30.00 1st Qu.: 465.0 1st Qu.: 2.000 1st Qu.:2.000
## Median :36.00 Median : 802.0 Median : 7.000 Median :3.000
## Mean :36.92 Mean : 802.5 Mean : 9.193 Mean :2.913
## 3rd Qu.:43.00 3rd Qu.:1157.0 3rd Qu.:14.000 3rd Qu.:4.000
## Max. :60.00 Max. :1499.0 Max. :29.000 Max. :5.000
## EnvironmentSatisfaction HourlyRate JobInvolvement JobLevel
## Min. :1.000 Min. : 30.00 Min. :1.00 Min. :1.000
## 1st Qu.:2.000 1st Qu.: 48.00 1st Qu.:2.00 1st Qu.:1.000
## Median :3.000 Median : 66.00 Median :3.00 Median :2.000
## Mean :2.722 Mean : 65.89 Mean :2.73 Mean :2.064
## 3rd Qu.:4.000 3rd Qu.: 83.75 3rd Qu.:3.00 3rd Qu.:3.000
## Max. :4.000 Max. :100.00 Max. :4.00 Max. :5.000
## JobSatisfaction MonthlyIncome MonthlyRate NumCompaniesWorked
## Min. :1.000 Min. : 1009 Min. : 2094 Min. :0.000
## 1st Qu.:2.000 1st Qu.: 2911 1st Qu.: 8047 1st Qu.:1.000
## Median :3.000 Median : 4919 Median :14236 Median :2.000
## Mean :2.729 Mean : 6503 Mean :14313 Mean :2.693
## 3rd Qu.:4.000 3rd Qu.: 8379 3rd Qu.:20462 3rd Qu.:4.000
## Max. :4.000 Max. :19999 Max. :26999 Max. :9.000
## PercentSalaryHike PerformanceRating RelationshipSatisfaction StockOptionLevel
## Min. :11.00 Min. :3.000 Min. :1.000 Min. :0.0000
## 1st Qu.:12.00 1st Qu.:3.000 1st Qu.:2.000 1st Qu.:0.0000
## Median :14.00 Median :3.000 Median :3.000 Median :1.0000
## Mean :15.21 Mean :3.154 Mean :2.712 Mean :0.7939
## 3rd Qu.:18.00 3rd Qu.:3.000 3rd Qu.:4.000 3rd Qu.:1.0000
## Max. :25.00 Max. :4.000 Max. :4.000 Max. :3.0000
## TotalWorkingYears TrainingTimesLastYear WorkLifeBalance YearsAtCompany
## Min. : 0.00 Min. :0.000 Min. :1.000 Min. : 0.000
## 1st Qu.: 6.00 1st Qu.:2.000 1st Qu.:2.000 1st Qu.: 3.000
## Median :10.00 Median :3.000 Median :3.000 Median : 5.000
## Mean :11.28 Mean :2.799 Mean :2.761 Mean : 7.008
## 3rd Qu.:15.00 3rd Qu.:3.000 3rd Qu.:3.000 3rd Qu.: 9.000
## Max. :40.00 Max. :6.000 Max. :4.000 Max. :40.000
## YearsInCurrentRole YearsSinceLastPromotion YearsWithCurrManager
## Min. : 0.000 Min. : 0.000 Min. : 0.000
## 1st Qu.: 2.000 1st Qu.: 0.000 1st Qu.: 2.000
## Median : 3.000 Median : 1.000 Median : 3.000
## Mean : 4.229 Mean : 2.188 Mean : 4.123
## 3rd Qu.: 7.000 3rd Qu.: 3.000 3rd Qu.: 7.000
## Max. :18.000 Max. :15.000 Max. :17.000hist(STREAMLINING_PROCESSES_AT_KINYANJUI_FARM[, 1], main = names(STREAMLINING_PROCESSES_AT_KINYANJUI_FARM)[1])
hist(STREAMLINING_PROCESSES_AT_KINYANJUI_FARM[, 4], main = names(STREAMLINING_PROCESSES_AT_KINYANJUI_FARM)[4])
hist(STREAMLINING_PROCESSES_AT_KINYANJUI_FARM[, 6], main = names(STREAMLINING_PROCESSES_AT_KINYANJUI_FARM)[6])
hist(STREAMLINING_PROCESSES_AT_KINYANJUI_FARM[, 7], main = names(STREAMLINING_PROCESSES_AT_KINYANJUI_FARM)[7])
hist(STREAMLINING_PROCESSES_AT_KINYANJUI_FARM[, 9], main = names(STREAMLINING_PROCESSES_AT_KINYANJUI_FARM)[9])
hist(STREAMLINING_PROCESSES_AT_KINYANJUI_FARM[, 10], main = names(STREAMLINING_PROCESSES_AT_KINYANJUI_FARM)[10])
hist(STREAMLINING_PROCESSES_AT_KINYANJUI_FARM[, 11], main = names(STREAMLINING_PROCESSES_AT_KINYANJUI_FARM)[11])
hist(STREAMLINING_PROCESSES_AT_KINYANJUI_FARM[, 13], main = names(STREAMLINING_PROCESSES_AT_KINYANJUI_FARM)[13])
hist(STREAMLINING_PROCESSES_AT_KINYANJUI_FARM[, 14], main = names(STREAMLINING_PROCESSES_AT_KINYANJUI_FARM)[14])
model_of_the_transform <- preProcess(STREAMLINING_PROCESSES_AT_KINYANJUI_FARM, method = c("scale"))
print(model_of_the_transform)## Created from 1470 samples and 23 variables
##
## Pre-processing:
## - ignored (0)
## - scaled (23)streamling_processes_scale_transform <- predict(model_of_the_transform,
STREAMLINING_PROCESSES_AT_KINYANJUI_FARM)
#After
summary(streamling_processes_scale_transform)## Age DailyRate DistanceFromHome Education
## Min. :1.970 Min. :0.2528 Min. :0.1234 Min. :0.9764
## 1st Qu.:3.284 1st Qu.:1.1524 1st Qu.:0.2467 1st Qu.:1.9528
## Median :3.941 Median :1.9876 Median :0.8635 Median :2.9292
## Mean :4.042 Mean :1.9888 Mean :1.1339 Mean :2.8442
## 3rd Qu.:4.707 3rd Qu.:2.8673 3rd Qu.:1.7269 3rd Qu.:3.9056
## Max. :6.568 Max. :3.7149 Max. :3.5772 Max. :4.8820
## EnvironmentSatisfaction HourlyRate JobInvolvement JobLevel
## Min. :0.9148 Min. :1.476 Min. :1.405 Min. :0.9034
## 1st Qu.:1.8297 1st Qu.:2.361 1st Qu.:2.811 1st Qu.:0.9034
## Median :2.7445 Median :3.247 Median :4.216 Median :1.8068
## Mean :2.4900 Mean :3.241 Mean :3.837 Mean :1.8646
## 3rd Qu.:3.6594 3rd Qu.:4.120 3rd Qu.:4.216 3rd Qu.:2.7102
## Max. :3.6594 Max. :4.919 Max. :5.621 Max. :4.5170
## JobSatisfaction MonthlyIncome MonthlyRate NumCompaniesWorked
## Min. :0.9067 Min. :0.2143 Min. :0.2942 Min. :0.0000
## 1st Qu.:1.8135 1st Qu.:0.6183 1st Qu.:1.1305 1st Qu.:0.4003
## Median :2.7202 Median :1.0448 Median :2.0000 Median :0.8006
## Mean :2.4741 Mean :1.3813 Mean :2.0109 Mean :1.0781
## 3rd Qu.:3.6270 3rd Qu.:1.7798 3rd Qu.:2.8747 3rd Qu.:1.6013
## Max. :3.6270 Max. :4.2479 Max. :3.7932 Max. :3.6029
## PercentSalaryHike PerformanceRating RelationshipSatisfaction StockOptionLevel
## Min. :3.006 Min. : 8.314 Min. :0.9249 Min. :0.0000
## 1st Qu.:3.279 1st Qu.: 8.314 1st Qu.:1.8498 1st Qu.:0.0000
## Median :3.825 Median : 8.314 Median :2.7747 Median :1.1736
## Mean :4.156 Mean : 8.740 Mean :2.5085 Mean :0.9317
## 3rd Qu.:4.918 3rd Qu.: 8.314 3rd Qu.:3.6996 3rd Qu.:1.1736
## Max. :6.831 Max. :11.086 Max. :3.6996 Max. :3.5208
## TotalWorkingYears TrainingTimesLastYear WorkLifeBalance YearsAtCompany
## Min. :0.0000 Min. :0.000 Min. :1.415 Min. :0.0000
## 1st Qu.:0.7711 1st Qu.:1.551 1st Qu.:2.831 1st Qu.:0.4897
## Median :1.2852 Median :2.327 Median :4.246 Median :0.8161
## Mean :1.4497 Mean :2.171 Mean :3.908 Mean :1.1439
## 3rd Qu.:1.9278 3rd Qu.:2.327 3rd Qu.:4.246 3rd Qu.:1.4690
## Max. :5.1409 Max. :4.654 Max. :5.662 Max. :6.5290
## YearsInCurrentRole YearsSinceLastPromotion YearsWithCurrManager
## Min. :0.000 Min. :0.0000 Min. :0.0000
## 1st Qu.:0.552 1st Qu.:0.0000 1st Qu.:0.5605
## Median :0.828 Median :0.3103 Median :0.8408
## Mean :1.167 Mean :0.6789 Mean :1.1555
## 3rd Qu.:1.932 3rd Qu.:0.9310 3rd Qu.:1.9618
## Max. :4.968 Max. :4.6549 Max. :4.7644hist(streamling_processes_scale_transform[, 1], main = names(streamling_processes_scale_transform)[1])
hist(streamling_processes_scale_transform[, 4], main = names(streamling_processes_scale_transform)[4])
hist(streamling_processes_scale_transform[, 6], main = names(streamling_processes_scale_transform)[6])
hist(streamling_processes_scale_transform[, 7], main = names(streamling_processes_scale_transform)[7])
hist(STREAMLINING_PROCESSES_AT_KINYANJUI_FARM[, 9], main = names(STREAMLINING_PROCESSES_AT_KINYANJUI_FARM)[9])
hist(STREAMLINING_PROCESSES_AT_KINYANJUI_FARM[, 10], main = names(STREAMLINING_PROCESSES_AT_KINYANJUI_FARM)[10])
hist(STREAMLINING_PROCESSES_AT_KINYANJUI_FARM[, 11], main = names(STREAMLINING_PROCESSES_AT_KINYANJUI_FARM)[11])
hist(STREAMLINING_PROCESSES_AT_KINYANJUI_FARM[, 13], main = names(STREAMLINING_PROCESSES_AT_KINYANJUI_FARM)[13])
hist(STREAMLINING_PROCESSES_AT_KINYANJUI_FARM[, 14], main = names(STREAMLINING_PROCESSES_AT_KINYANJUI_FARM)[14])
Center Data Transform
# BEFORE
summary(STREAMLINING_PROCESSES_AT_KINYANJUI_FARM)## Age DailyRate DistanceFromHome Education
## Min. :18.00 Min. : 102.0 Min. : 1.000 Min. :1.000
## 1st Qu.:30.00 1st Qu.: 465.0 1st Qu.: 2.000 1st Qu.:2.000
## Median :36.00 Median : 802.0 Median : 7.000 Median :3.000
## Mean :36.92 Mean : 802.5 Mean : 9.193 Mean :2.913
## 3rd Qu.:43.00 3rd Qu.:1157.0 3rd Qu.:14.000 3rd Qu.:4.000
## Max. :60.00 Max. :1499.0 Max. :29.000 Max. :5.000
## EnvironmentSatisfaction HourlyRate JobInvolvement JobLevel
## Min. :1.000 Min. : 30.00 Min. :1.00 Min. :1.000
## 1st Qu.:2.000 1st Qu.: 48.00 1st Qu.:2.00 1st Qu.:1.000
## Median :3.000 Median : 66.00 Median :3.00 Median :2.000
## Mean :2.722 Mean : 65.89 Mean :2.73 Mean :2.064
## 3rd Qu.:4.000 3rd Qu.: 83.75 3rd Qu.:3.00 3rd Qu.:3.000
## Max. :4.000 Max. :100.00 Max. :4.00 Max. :5.000
## JobSatisfaction MonthlyIncome MonthlyRate NumCompaniesWorked
## Min. :1.000 Min. : 1009 Min. : 2094 Min. :0.000
## 1st Qu.:2.000 1st Qu.: 2911 1st Qu.: 8047 1st Qu.:1.000
## Median :3.000 Median : 4919 Median :14236 Median :2.000
## Mean :2.729 Mean : 6503 Mean :14313 Mean :2.693
## 3rd Qu.:4.000 3rd Qu.: 8379 3rd Qu.:20462 3rd Qu.:4.000
## Max. :4.000 Max. :19999 Max. :26999 Max. :9.000
## PercentSalaryHike PerformanceRating RelationshipSatisfaction StockOptionLevel
## Min. :11.00 Min. :3.000 Min. :1.000 Min. :0.0000
## 1st Qu.:12.00 1st Qu.:3.000 1st Qu.:2.000 1st Qu.:0.0000
## Median :14.00 Median :3.000 Median :3.000 Median :1.0000
## Mean :15.21 Mean :3.154 Mean :2.712 Mean :0.7939
## 3rd Qu.:18.00 3rd Qu.:3.000 3rd Qu.:4.000 3rd Qu.:1.0000
## Max. :25.00 Max. :4.000 Max. :4.000 Max. :3.0000
## TotalWorkingYears TrainingTimesLastYear WorkLifeBalance YearsAtCompany
## Min. : 0.00 Min. :0.000 Min. :1.000 Min. : 0.000
## 1st Qu.: 6.00 1st Qu.:2.000 1st Qu.:2.000 1st Qu.: 3.000
## Median :10.00 Median :3.000 Median :3.000 Median : 5.000
## Mean :11.28 Mean :2.799 Mean :2.761 Mean : 7.008
## 3rd Qu.:15.00 3rd Qu.:3.000 3rd Qu.:3.000 3rd Qu.: 9.000
## Max. :40.00 Max. :6.000 Max. :4.000 Max. :40.000
## YearsInCurrentRole YearsSinceLastPromotion YearsWithCurrManager
## Min. : 0.000 Min. : 0.000 Min. : 0.000
## 1st Qu.: 2.000 1st Qu.: 0.000 1st Qu.: 2.000
## Median : 3.000 Median : 1.000 Median : 3.000
## Mean : 4.229 Mean : 2.188 Mean : 4.123
## 3rd Qu.: 7.000 3rd Qu.: 3.000 3rd Qu.: 7.000
## Max. :18.000 Max. :15.000 Max. :17.000boxplot(STREAMLINING_PROCESSES_AT_KINYANJUI_FARM[, 1], main = names(STREAMLINING_PROCESSES_AT_KINYANJUI_FARM)[1])
boxplot(STREAMLINING_PROCESSES_AT_KINYANJUI_FARM[, 2], main = names(STREAMLINING_PROCESSES_AT_KINYANJUI_FARM)[2])
boxplot(STREAMLINING_PROCESSES_AT_KINYANJUI_FARM[, 3], main = names(STREAMLINING_PROCESSES_AT_KINYANJUI_FARM)[3])
boxplot(STREAMLINING_PROCESSES_AT_KINYANJUI_FARM[, 5], main = names(STREAMLINING_PROCESSES_AT_KINYANJUI_FARM)[5])
boxplot(STREAMLINING_PROCESSES_AT_KINYANJUI_FARM[, 6], main = names(STREAMLINING_PROCESSES_AT_KINYANJUI_FARM)[6])
boxplot(STREAMLINING_PROCESSES_AT_KINYANJUI_FARM[, 7], main = names(STREAMLINING_PROCESSES_AT_KINYANJUI_FARM)[7])
boxplot(STREAMLINING_PROCESSES_AT_KINYANJUI_FARM[, 8], main = names(STREAMLINING_PROCESSES_AT_KINYANJUI_FARM)[8])
boxplot(STREAMLINING_PROCESSES_AT_KINYANJUI_FARM[, 9], main = names(STREAMLINING_PROCESSES_AT_KINYANJUI_FARM)[9])
boxplot(STREAMLINING_PROCESSES_AT_KINYANJUI_FARM[, 10], main = names(STREAMLINING_PROCESSES_AT_KINYANJUI_FARM)[10])
boxplot(STREAMLINING_PROCESSES_AT_KINYANJUI_FARM[, 11], main = names(STREAMLINING_PROCESSES_AT_KINYANJUI_FARM)[11])
boxplot(STREAMLINING_PROCESSES_AT_KINYANJUI_FARM[, 12], main = names(STREAMLINING_PROCESSES_AT_KINYANJUI_FARM)[12])
boxplot(STREAMLINING_PROCESSES_AT_KINYANJUI_FARM[, 13], main = names(STREAMLINING_PROCESSES_AT_KINYANJUI_FARM)[13])
boxplot(STREAMLINING_PROCESSES_AT_KINYANJUI_FARM[, 14], main = names(STREAMLINING_PROCESSES_AT_KINYANJUI_FARM)[14])
model_of_the_transform <- preProcess(STREAMLINING_PROCESSES_AT_KINYANJUI_FARM, method = c("center"))
print(model_of_the_transform)## Created from 1470 samples and 23 variables
##
## Pre-processing:
## - centered (23)
## - ignored (0)streamling_processes_center_transform <- predict(model_of_the_transform, # nolint
STREAMLINING_PROCESSES_AT_KINYANJUI_FARM)
# AFTER
summary(streamling_processes_center_transform)## Age DailyRate DistanceFromHome Education
## Min. :-18.9238 Min. :-700.4857 Min. :-8.193 Min. :-1.91293
## 1st Qu.: -6.9238 1st Qu.:-337.4857 1st Qu.:-7.193 1st Qu.:-0.91293
## Median : -0.9238 Median : -0.4857 Median :-2.193 Median : 0.08707
## Mean : 0.0000 Mean : 0.0000 Mean : 0.000 Mean : 0.00000
## 3rd Qu.: 6.0762 3rd Qu.: 354.5143 3rd Qu.: 4.807 3rd Qu.: 1.08707
## Max. : 23.0762 Max. : 696.5143 Max. :19.807 Max. : 2.08707
## EnvironmentSatisfaction HourlyRate JobInvolvement
## Min. :-1.7218 Min. :-35.8912 Min. :-1.7299
## 1st Qu.:-0.7218 1st Qu.:-17.8912 1st Qu.:-0.7299
## Median : 0.2782 Median : 0.1088 Median : 0.2701
## Mean : 0.0000 Mean : 0.0000 Mean : 0.0000
## 3rd Qu.: 1.2782 3rd Qu.: 17.8588 3rd Qu.: 0.2701
## Max. : 1.2782 Max. : 34.1088 Max. : 1.2701
## JobLevel JobSatisfaction MonthlyIncome MonthlyRate
## Min. :-1.06395 Min. :-1.7286 Min. :-5494 Min. :-12219.1
## 1st Qu.:-1.06395 1st Qu.:-0.7286 1st Qu.:-3592 1st Qu.: -6266.1
## Median :-0.06395 Median : 0.2714 Median :-1584 Median : -77.6
## Mean : 0.00000 Mean : 0.0000 Mean : 0 Mean : 0.0
## 3rd Qu.: 0.93605 3rd Qu.: 1.2714 3rd Qu.: 1876 3rd Qu.: 6148.4
## Max. : 2.93605 Max. : 1.2714 Max. :13496 Max. : 12685.9
## NumCompaniesWorked PercentSalaryHike PerformanceRating
## Min. :-2.6932 Min. :-4.21 Min. :-0.1537
## 1st Qu.:-1.6932 1st Qu.:-3.21 1st Qu.:-0.1537
## Median :-0.6932 Median :-1.21 Median :-0.1537
## Mean : 0.0000 Mean : 0.00 Mean : 0.0000
## 3rd Qu.: 1.3068 3rd Qu.: 2.79 3rd Qu.:-0.1537
## Max. : 6.3068 Max. : 9.79 Max. : 0.8463
## RelationshipSatisfaction StockOptionLevel TotalWorkingYears
## Min. :-1.7122 Min. :-0.7939 Min. :-11.28
## 1st Qu.:-0.7122 1st Qu.:-0.7939 1st Qu.: -5.28
## Median : 0.2878 Median : 0.2061 Median : -1.28
## Mean : 0.0000 Mean : 0.0000 Mean : 0.00
## 3rd Qu.: 1.2878 3rd Qu.: 0.2061 3rd Qu.: 3.72
## Max. : 1.2878 Max. : 2.2061 Max. : 28.72
## TrainingTimesLastYear WorkLifeBalance YearsAtCompany YearsInCurrentRole
## Min. :-2.7993 Min. :-1.7612 Min. :-7.008 Min. :-4.229
## 1st Qu.:-0.7993 1st Qu.:-0.7612 1st Qu.:-4.008 1st Qu.:-2.229
## Median : 0.2007 Median : 0.2388 Median :-2.008 Median :-1.229
## Mean : 0.0000 Mean : 0.0000 Mean : 0.000 Mean : 0.000
## 3rd Qu.: 0.2007 3rd Qu.: 0.2388 3rd Qu.: 1.992 3rd Qu.: 2.771
## Max. : 3.2007 Max. : 1.2388 Max. :32.992 Max. :13.771
## YearsSinceLastPromotion YearsWithCurrManager
## Min. :-2.1878 Min. :-4.123
## 1st Qu.:-2.1878 1st Qu.:-2.123
## Median :-1.1878 Median :-1.123
## Mean : 0.0000 Mean : 0.000
## 3rd Qu.: 0.8122 3rd Qu.: 2.877
## Max. :12.8122 Max. :12.877boxplot(streamling_processes_center_transform[, 1], main = names(streamling_processes_center_transform)[1])
boxplot(streamling_processes_center_transform[, 2], main = names(streamling_processes_center_transform)[2])
boxplot(streamling_processes_center_transform[, 3], main = names(streamling_processes_center_transform)[3])
boxplot(streamling_processes_center_transform[, 5], main = names(streamling_processes_center_transform)[5])
boxplot(streamling_processes_center_transform[, 6], main = names(streamling_processes_center_transform)[6])
boxplot(streamling_processes_center_transform[, 7], main = names(streamling_processes_center_transform)[7])
boxplot(streamling_processes_center_transform[, 8], main = names(streamling_processes_center_transform)[8])
boxplot(streamling_processes_center_transform[, 9], main = names(streamling_processes_center_transform)[9])
boxplot(streamling_processes_center_transform[, 10], main = names(streamling_processes_center_transform)[10])
boxplot(streamling_processes_center_transform[, 11], main = names(streamling_processes_center_transform)[11])
boxplot(streamling_processes_center_transform[, 12], main = names(streamling_processes_center_transform)[12])
boxplot(streamling_processes_center_transform[, 13], main = names(streamling_processes_center_transform)[13])
boxplot(streamling_processes_center_transform[, 14], main = names(streamling_processes_center_transform)[14])
Standardize Data Transform to have a std of 1
### The Standardize Basic Transform on the Boston Housing Dataset ----
# BEFORE
library(readr)
STREAMLINING_PROCESSES_AT_KINYANJUI_FARM <- read_csv("data/STREAMLINING PROCESSES AT KINYANJUI FARM.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.# Drop specified columns
columns_to_drop <- c('EmployeeCount', 'EmployeeNumber', 'Over18', 'StandardHours')
STREAMLINING_PROCESSES_AT_KINYANJUI_FARM <- STREAMLINING_PROCESSES_AT_KINYANJUI_FARM[, !(names(STREAMLINING_PROCESSES_AT_KINYANJUI_FARM) %in% columns_to_drop)]
summary(STREAMLINING_PROCESSES_AT_KINYANJUI_FARM)## Age Attrition BusinessTravel DailyRate
## Min. :18.00 Length:1470 Length:1470 Min. : 102.0
## 1st Qu.:30.00 Class :character Class :character 1st Qu.: 465.0
## Median :36.00 Mode :character Mode :character Median : 802.0
## Mean :36.92 Mean : 802.5
## 3rd Qu.:43.00 3rd Qu.:1157.0
## Max. :60.00 Max. :1499.0
## Department DistanceFromHome Education EducationField
## Length:1470 Min. : 1.000 Min. :1.000 Length:1470
## Class :character 1st Qu.: 2.000 1st Qu.:2.000 Class :character
## Mode :character Median : 7.000 Median :3.000 Mode :character
## Mean : 9.193 Mean :2.913
## 3rd Qu.:14.000 3rd Qu.:4.000
## Max. :29.000 Max. :5.000
## EnvironmentSatisfaction Gender HourlyRate JobInvolvement
## Min. :1.000 Length:1470 Min. : 30.00 Min. :1.00
## 1st Qu.:2.000 Class :character 1st Qu.: 48.00 1st Qu.:2.00
## Median :3.000 Mode :character Median : 66.00 Median :3.00
## Mean :2.722 Mean : 65.89 Mean :2.73
## 3rd Qu.:4.000 3rd Qu.: 83.75 3rd Qu.:3.00
## Max. :4.000 Max. :100.00 Max. :4.00
## JobLevel JobRole JobSatisfaction MaritalStatus
## Min. :1.000 Length:1470 Min. :1.000 Length:1470
## 1st Qu.:1.000 Class :character 1st Qu.:2.000 Class :character
## Median :2.000 Mode :character Median :3.000 Mode :character
## Mean :2.064 Mean :2.729
## 3rd Qu.:3.000 3rd Qu.:4.000
## Max. :5.000 Max. :4.000
## MonthlyIncome MonthlyRate NumCompaniesWorked OverTime
## Min. : 1009 Min. : 2094 Min. :0.000 Length:1470
## 1st Qu.: 2911 1st Qu.: 8047 1st Qu.:1.000 Class :character
## Median : 4919 Median :14236 Median :2.000 Mode :character
## Mean : 6503 Mean :14313 Mean :2.693
## 3rd Qu.: 8379 3rd Qu.:20462 3rd Qu.:4.000
## Max. :19999 Max. :26999 Max. :9.000
## PercentSalaryHike PerformanceRating RelationshipSatisfaction StockOptionLevel
## Min. :11.00 Min. :3.000 Min. :1.000 Min. :0.0000
## 1st Qu.:12.00 1st Qu.:3.000 1st Qu.:2.000 1st Qu.:0.0000
## Median :14.00 Median :3.000 Median :3.000 Median :1.0000
## Mean :15.21 Mean :3.154 Mean :2.712 Mean :0.7939
## 3rd Qu.:18.00 3rd Qu.:3.000 3rd Qu.:4.000 3rd Qu.:1.0000
## Max. :25.00 Max. :4.000 Max. :4.000 Max. :3.0000
## TotalWorkingYears TrainingTimesLastYear WorkLifeBalance YearsAtCompany
## Min. : 0.00 Min. :0.000 Min. :1.000 Min. : 0.000
## 1st Qu.: 6.00 1st Qu.:2.000 1st Qu.:2.000 1st Qu.: 3.000
## Median :10.00 Median :3.000 Median :3.000 Median : 5.000
## Mean :11.28 Mean :2.799 Mean :2.761 Mean : 7.008
## 3rd Qu.:15.00 3rd Qu.:3.000 3rd Qu.:3.000 3rd Qu.: 9.000
## Max. :40.00 Max. :6.000 Max. :4.000 Max. :40.000
## YearsInCurrentRole YearsSinceLastPromotion YearsWithCurrManager
## Min. : 0.000 Min. : 0.000 Min. : 0.000
## 1st Qu.: 2.000 1st Qu.: 0.000 1st Qu.: 2.000
## Median : 3.000 Median : 1.000 Median : 3.000
## Mean : 4.229 Mean : 2.188 Mean : 4.123
## 3rd Qu.: 7.000 3rd Qu.: 3.000 3rd Qu.: 7.000
## Max. :18.000 Max. :15.000 Max. :17.000sapply(STREAMLINING_PROCESSES_AT_KINYANJUI_FARM[, c(1,4, 6, 7, 9, 11, 12, 13, 15 , 17 , 18, 19, 21, 22, 23,24, 25, 26, 27, 28 ,29, 30, 31 )], sd)## Age DailyRate DistanceFromHome
## 9.1353735 403.5090999 8.1068644
## Education EnvironmentSatisfaction HourlyRate
## 1.0241649 1.0930822 20.3294276
## JobInvolvement JobLevel JobSatisfaction
## 0.7115611 1.1069399 1.1028461
## MonthlyIncome MonthlyRate NumCompaniesWorked
## 4707.9567831 7117.7860441 2.4980090
## PercentSalaryHike PerformanceRating RelationshipSatisfaction
## 3.6599377 0.3608235 1.0812089
## StockOptionLevel TotalWorkingYears TrainingTimesLastYear
## 0.8520767 7.7807817 1.2892706
## WorkLifeBalance YearsAtCompany YearsInCurrentRole
## 0.7064758 6.1265252 3.6231370
## YearsSinceLastPromotion YearsWithCurrManager
## 3.2224303 3.5681361model_of_the_transform <- preProcess(STREAMLINING_PROCESSES_AT_KINYANJUI_FARM,
method = c("scale", "center"))
print(model_of_the_transform)## Created from 1470 samples and 31 variables
##
## Pre-processing:
## - centered (23)
## - ignored (8)
## - scaled (23)streamling_processes_standardize_transform <- predict(model_of_the_transform, # nolint
STREAMLINING_PROCESSES_AT_KINYANJUI_FARM)
# AFTER
summary(streamling_processes_standardize_transform)## Age Attrition BusinessTravel DailyRate
## Min. :-2.0715 Length:1470 Length:1470 Min. :-1.735985
## 1st Qu.:-0.7579 Class :character Class :character 1st Qu.:-0.836377
## Median :-0.1011 Mode :character Mode :character Median :-0.001204
## Mean : 0.0000 Mean : 0.000000
## 3rd Qu.: 0.6651 3rd Qu.: 0.878578
## Max. : 2.5260 Max. : 1.726143
## Department DistanceFromHome Education EducationField
## Length:1470 Min. :-1.0106 Min. :-1.86779 Length:1470
## Class :character 1st Qu.:-0.8872 1st Qu.:-0.89138 Class :character
## Mode :character Median :-0.2705 Median : 0.08502 Mode :character
## Mean : 0.0000 Mean : 0.00000
## 3rd Qu.: 0.5930 3rd Qu.: 1.06143
## Max. : 2.4433 Max. : 2.03783
## EnvironmentSatisfaction Gender HourlyRate
## Min. :-1.5752 Length:1470 Min. :-1.765478
## 1st Qu.:-0.6603 Class :character 1st Qu.:-0.880062
## Median : 0.2545 Mode :character Median : 0.005354
## Mean : 0.0000 Mean : 0.000000
## 3rd Qu.: 1.1694 3rd Qu.: 0.878472
## Max. : 1.1694 Max. : 1.677806
## JobInvolvement JobLevel JobRole JobSatisfaction
## Min. :-2.4312 Min. :-0.96116 Length:1470 Min. :-1.5674
## 1st Qu.:-1.0258 1st Qu.:-0.96116 Class :character 1st Qu.:-0.6606
## Median : 0.3795 Median :-0.05777 Mode :character Median : 0.2461
## Mean : 0.0000 Mean : 0.00000 Mean : 0.0000
## 3rd Qu.: 0.3795 3rd Qu.: 0.84562 3rd Qu.: 1.1529
## Max. : 1.7849 Max. : 2.65241 Max. : 1.1529
## MaritalStatus MonthlyIncome MonthlyRate NumCompaniesWorked
## Length:1470 Min. :-1.1669 Min. :-1.7167 Min. :-1.0781
## Class :character 1st Qu.:-0.7629 1st Qu.:-0.8803 1st Qu.:-0.6778
## Mode :character Median :-0.3364 Median :-0.0109 Median :-0.2775
## Mean : 0.0000 Mean : 0.0000 Mean : 0.0000
## 3rd Qu.: 0.3985 3rd Qu.: 0.8638 3rd Qu.: 0.5231
## Max. : 2.8667 Max. : 1.7823 Max. : 2.5247
## OverTime PercentSalaryHike PerformanceRating
## Length:1470 Min. :-1.1502 Min. :-0.4261
## Class :character 1st Qu.:-0.8769 1st Qu.:-0.4261
## Mode :character Median :-0.3305 Median :-0.4261
## Mean : 0.0000 Mean : 0.0000
## 3rd Qu.: 0.7624 3rd Qu.:-0.4261
## Max. : 2.6750 Max. : 2.3454
## RelationshipSatisfaction StockOptionLevel TotalWorkingYears
## Min. :-1.5836 Min. :-0.9317 Min. :-1.4497
## 1st Qu.:-0.6587 1st Qu.:-0.9317 1st Qu.:-0.6785
## Median : 0.2661 Median : 0.2419 Median :-0.1645
## Mean : 0.0000 Mean : 0.0000 Mean : 0.0000
## 3rd Qu.: 1.1910 3rd Qu.: 0.2419 3rd Qu.: 0.4782
## Max. : 1.1910 Max. : 2.5891 Max. : 3.6912
## TrainingTimesLastYear WorkLifeBalance YearsAtCompany YearsInCurrentRole
## Min. :-2.1712 Min. :-2.493 Min. :-1.1439 Min. :-1.1673
## 1st Qu.:-0.6200 1st Qu.:-1.077 1st Qu.:-0.6542 1st Qu.:-0.6153
## Median : 0.1557 Median : 0.338 Median :-0.3278 Median :-0.3393
## Mean : 0.0000 Mean : 0.000 Mean : 0.0000 Mean : 0.0000
## 3rd Qu.: 0.1557 3rd Qu.: 0.338 3rd Qu.: 0.3251 3rd Qu.: 0.7647
## Max. : 2.4826 Max. : 1.753 Max. : 5.3851 Max. : 3.8008
## YearsSinceLastPromotion YearsWithCurrManager
## Min. :-0.6789 Min. :-1.1555
## 1st Qu.:-0.6789 1st Qu.:-0.5950
## Median :-0.3686 Median :-0.3148
## Mean : 0.0000 Mean : 0.0000
## 3rd Qu.: 0.2521 3rd Qu.: 0.8063
## Max. : 3.9760 Max. : 3.6089sapply(streamling_processes_standardize_transform[, c(1,4, 6, 7, 9, 11, 12, 13, 15 , 17 , 18, 19, 21, 22, 23,24, 25, 26, 27, 28 ,29, 30, 31 )], sd)## Age DailyRate DistanceFromHome
## 1 1 1
## Education EnvironmentSatisfaction HourlyRate
## 1 1 1
## JobInvolvement JobLevel JobSatisfaction
## 1 1 1
## MonthlyIncome MonthlyRate NumCompaniesWorked
## 1 1 1
## PercentSalaryHike PerformanceRating RelationshipSatisfaction
## 1 1 1
## StockOptionLevel TotalWorkingYears TrainingTimesLastYear
## 1 1 1
## WorkLifeBalance YearsAtCompany YearsInCurrentRole
## 1 1 1
## YearsSinceLastPromotion YearsWithCurrManager
## 1 1Training Models
Training Models refers to the process of building and
refining predictive or descriptive models using machine learning
algorithms. In this context, “training” involves using a labeled dataset
(input data paired with corresponding output labels) to teach the model
to make predictions or classifications. The model learns patterns and
relationships within the training data, adjusting its internal
parameters to optimize its performance. The trained model can then be
evaluated on new, unseen data to assess its generalization ability. The
goal is to develop a model that accurately captures underlying patterns
in the data and can make reliable predictions on new, similar data.
Techniques for training models include regression, classification,
clustering, and neural networks, among others.
Split the dataset
We generate random indices for the training set using sample, and then we use these indices to extract the corresponding rows from the dataset, creating the training and testing datasets (train_streamling_processes_train and test_streamling_processes_test).Now, you have a 75:25 split of theSTREAMLINING_PROCESSES_AT_KINYANJUI_FARM dataset into training and testing sets. We use train_streamling_processes_train for model training and test_streamling_processes_test for model evaluation.
## 1. Split the dataset
train_index <- createDataPartition(STREAMLINING_PROCESSES_AT_KINYANJUI_FARM$Attrition,
p = 0.75,
list = FALSE)
train_streamling_processes_train <- STREAMLINING_PROCESSES_AT_KINYANJUI_FARM[train_index, ]
test_streamling_processes_test <- STREAMLINING_PROCESSES_AT_KINYANJUI_FARM[-train_index, ]
# Check the column names of pima_train
colnames(train_streamling_processes_train)## [1] "Age" "Attrition"
## [3] "BusinessTravel" "DailyRate"
## [5] "Department" "DistanceFromHome"
## [7] "Education" "EducationField"
## [9] "EnvironmentSatisfaction" "Gender"
## [11] "HourlyRate" "JobInvolvement"
## [13] "JobLevel" "JobRole"
## [15] "JobSatisfaction" "MaritalStatus"
## [17] "MonthlyIncome" "MonthlyRate"
## [19] "NumCompaniesWorked" "OverTime"
## [21] "PercentSalaryHike" "PerformanceRating"
## [23] "RelationshipSatisfaction" "StockOptionLevel"
## [25] "TotalWorkingYears" "TrainingTimesLastYear"
## [27] "WorkLifeBalance" "YearsAtCompany"
## [29] "YearsInCurrentRole" "YearsSinceLastPromotion"
## [31] "YearsWithCurrManager"NaiveBayes() function in the e1071 package
# Train the Naive Bayes model
e107_streamling_processes_dataset_model_nb_e1071 <-
e1071::naiveBayes(Attrition ~ .,
data = train_streamling_processes_train)
# Testing
predictions_nb_e1071 <-
predict(e107_streamling_processes_dataset_model_nb_e1071,
test_streamling_processes_test[, 1:31])
# Check unique levels
unique_levels_pred <- levels(predictions_nb_e1071)
unique_levels_actual <- levels(test_streamling_processes_test$Attrition)
# Compare levels
setdiff(unique_levels_pred, unique_levels_actual)## [1] "No" "Yes"setdiff(unique_levels_actual, unique_levels_pred)## NULL# Convert predictions to factor and align levels
predictions_nb_e1071 <- as.factor(predictions_nb_e1071)
#levels(predictions_nb_e1071) <- levels(test_streamling_processes_test$Attrition)
#Update Actual Levels to Match Predicted Levels
test_streamling_processes_test$Attrition <- as.factor(test_streamling_processes_test$Attrition)
levels(test_streamling_processes_test$Attrition) <- levels(predictions_nb_e1071)
# View the Results
print(predictions_nb_e1071)## [1] Yes No No No Yes No Yes Yes No Yes No Yes No Yes No No No No
## [19] No No No Yes No No No No No No No No No No No No Yes No
## [37] No No No Yes No No No No No No No No No No No No Yes No
## [55] No No No No No No Yes No No No No No No No No Yes No No
## [73] No No No No No Yes No Yes No Yes Yes No No No No No No No
## [91] Yes Yes No Yes Yes No No No No No No No No No Yes No Yes No
## [109] No Yes No No No No No Yes No Yes Yes No No No No Yes No No
## [127] No No No No Yes Yes No No No Yes No No No No No No No No
## [145] Yes Yes No No No No Yes Yes No Yes Yes No No No No No Yes No
## [163] Yes No No No Yes No No No Yes No Yes No No No Yes No No No
## [181] No Yes Yes No No No No No No Yes No Yes No No Yes No No No
## [199] No No Yes No Yes No No Yes No Yes No No No No No No No No
## [217] No No No No No No No Yes No No No No No Yes No No No No
## [235] No No Yes No No No No No No No Yes No No No No No No No
## [253] No Yes No No No No Yes No No Yes No No No No No No No Yes
## [271] Yes Yes No No No No No No No No No No Yes No No No Yes No
## [289] Yes No No No Yes No Yes No No No No No Yes No No Yes No No
## [307] No No Yes No Yes No No No No No No No No No No No Yes No
## [325] No No No No Yes No Yes No Yes No No No No No No No No Yes
## [343] No Yes No No No No No No No No No Yes Yes No Yes No No Yes
## [361] No No No No Yes No No
## Levels: No Yes# Confusion Matrix
conf_matrix <- caret::confusionMatrix(predictions_nb_e1071,
test_streamling_processes_test$Attrition)
print(conf_matrix)## Confusion Matrix and Statistics
##
## Reference
## Prediction No Yes
## No 261 24
## Yes 47 35
##
## Accuracy : 0.8065
## 95% CI : (0.7624, 0.8457)
## No Information Rate : 0.8392
## P-Value [Acc > NIR] : 0.95954
##
## Kappa : 0.3806
##
## Mcnemar's Test P-Value : 0.00903
##
## Sensitivity : 0.8474
## Specificity : 0.5932
## Pos Pred Value : 0.9158
## Neg Pred Value : 0.4268
## Prevalence : 0.8392
## Detection Rate : 0.7112
## Detection Prevalence : 0.7766
## Balanced Accuracy : 0.7203
##
## 'Positive' Class : No
## # The confusion matrix can also be viewed graphically,
# although with less information.
plot(table(predictions_nb_e1071,
test_streamling_processes_test$`Attrition`))
### Bootstrapping train control
#train_control <- trainControl(method = "boot", number = 10000)
#streamling_processes_dataset_model_lm <- caret::train(`MonthlyIncome` ~`Attrition` ,
# data = train_streamling_processes_train,
# trControl = train_control,
# method = "lm",
# metric = "RMSE")
### Test the trained linear regression model using the testing dataset
#predictions_lm <- predict(streamling_processes_dataset_model_lm,
# test_streamling_processes_test[, 1:31])
### View the RMSE and the predicted values for the 9 observations
#print(streamling_processes_dataset_model_lm)
#print(predictions_lm)Classification: LDA with k-fold Cross Validation
### LDA Attritionifier based on a 5-fold cross validation
train_control_lda <- trainControl(method = "cv", number = 5)
streamling_dateset_model_lda <-
caret::train(`Attrition` ~ ., data = STREAMLINING_PROCESSES_AT_KINYANJUI_FARM,
trControl = train_control_lda, na.action = na.omit, method = "lda2",
metric = "Accuracy")
### 3.b. Test the trained LDA model using the testing dataset
predictions_lda <- predict(streamling_dateset_model_lda,
test_streamling_processes_test[, 1:31])
### 3.c. View the summary of the model and view the confusion matrix
print(streamling_dateset_model_lda)## Linear Discriminant Analysis
##
## 1470 samples
## 30 predictor
## 2 classes: 'No', 'Yes'
##
## No pre-processing
## Resampling: Cross-Validated (5 fold)
## Summary of sample sizes: 1176, 1175, 1177, 1175, 1177
## Resampling results:
##
## Accuracy Kappa
## 0.8734775 0.436173
##
## Tuning parameter 'dimen' was held constant at a value of 1caret::confusionMatrix(predictions_lda, test_streamling_processes_test$Attrition)## Confusion Matrix and Statistics
##
## Reference
## Prediction No Yes
## No 298 31
## Yes 10 28
##
## Accuracy : 0.8883
## 95% CI : (0.8515, 0.9186)
## No Information Rate : 0.8392
## P-Value [Acc > NIR] : 0.004869
##
## Kappa : 0.5164
##
## Mcnemar's Test P-Value : 0.001787
##
## Sensitivity : 0.9675
## Specificity : 0.4746
## Pos Pred Value : 0.9058
## Neg Pred Value : 0.7368
## Prevalence : 0.8392
## Detection Rate : 0.8120
## Detection Prevalence : 0.8965
## Balanced Accuracy : 0.7211
##
## 'Positive' Class : No
## plot(table(predictions_lda, test_streamling_processes_test$Attrition))
Classification: Naive Bayes with Repeated k-fold Cross Validation —-
### 4.a. Train an e1071::naive Bayes Attritionifier based on the churn variable
streamling_dateset_model_nb <-
e1071::naiveBayes(`Attrition` ~ ., data = train_streamling_processes_train)
### 4.b. Test the trained naive Bayes Attritionifier using the testing dataset
predictions_nb_e1071 <-
predict(streamling_dateset_model_nb, test_streamling_processes_test[, 1:31])
### 4.c. View a summary of the naive Bayes model and the confusion matrix
print(streamling_dateset_model_nb)##
## Naive Bayes Classifier for Discrete Predictors
##
## Call:
## naiveBayes.default(x = X, y = Y, laplace = laplace)
##
## A-priori probabilities:
## Y
## No Yes
## 0.8386219 0.1613781
##
## Conditional probabilities:
## Age
## Y [,1] [,2]
## No 37.63892 9.000685
## Yes 33.25843 9.892676
##
## BusinessTravel
## Y Non-Travel Travel_Frequently Travel_Rarely
## No 0.10594595 0.16324324 0.73081081
## Yes 0.05617978 0.28089888 0.66292135
##
## DailyRate
## Y [,1] [,2]
## No 814.0259 401.8619
## Yes 757.4888 400.0808
##
## Department
## Y Human Resources Research & Development Sales
## No 0.04108108 0.66918919 0.28972973
## Yes 0.05617978 0.52808989 0.41573034
##
## DistanceFromHome
## Y [,1] [,2]
## No 8.884324 8.035542
## Yes 10.224719 8.267768
##
## Education
## Y [,1] [,2]
## No 2.913514 1.045026
## Yes 2.808989 1.029249
##
## EducationField
## Y Human Resources Life Sciences Marketing Medical Other
## No 0.01513514 0.40864865 0.10702703 0.33405405 0.05405405
## Yes 0.03370787 0.33146067 0.16292135 0.26404494 0.04494382
## EducationField
## Y Technical Degree
## No 0.08108108
## Yes 0.16292135
##
## EnvironmentSatisfaction
## Y [,1] [,2]
## No 2.76000 1.073450
## Yes 2.52809 1.179772
##
## Gender
## Y Female Male
## No 0.4075676 0.5924324
## Yes 0.3314607 0.6685393
##
## HourlyRate
## Y [,1] [,2]
## No 66.70919 20.29076
## Yes 65.51685 20.49259
##
## JobInvolvement
## Y [,1] [,2]
## No 2.770811 0.6911718
## Yes 2.522472 0.7753625
##
## JobLevel
## Y [,1] [,2]
## No 2.138378 1.1244467
## Yes 1.634831 0.9485762
##
## JobRole
## Y Healthcare Representative Human Resources Laboratory Technician
## No 0.096216216 0.033513514 0.162162162
## Yes 0.022471910 0.056179775 0.264044944
## JobRole
## Y Manager Manufacturing Director Research Director Research Scientist
## No 0.082162162 0.116756757 0.057297297 0.193513514
## Yes 0.022471910 0.044943820 0.005617978 0.179775281
## JobRole
## Y Sales Executive Sales Representative
## No 0.217297297 0.041081081
## Yes 0.258426966 0.146067416
##
## JobSatisfaction
## Y [,1] [,2]
## No 2.771892 1.096467
## Yes 2.438202 1.109334
##
## MaritalStatus
## Y Divorced Married Single
## No 0.2313514 0.4821622 0.2864865
## Yes 0.1573034 0.3314607 0.5112360
##
## MonthlyIncome
## Y [,1] [,2]
## No 6775.414 4819.691
## Yes 4750.534 3691.935
##
## MonthlyRate
## Y [,1] [,2]
## No 14426.74 7030.436
## Yes 14726.62 7128.048
##
## NumCompaniesWorked
## Y [,1] [,2]
## No 2.725405 2.486817
## Yes 2.887640 2.640143
##
## OverTime
## Y No Yes
## No 0.7740541 0.2259459
## Yes 0.4887640 0.5112360
##
## PercentSalaryHike
## Y [,1] [,2]
## No 15.31243 3.643875
## Yes 15.02809 3.749847
##
## PerformanceRating
## Y [,1] [,2]
## No 3.156757 0.3637680
## Yes 3.146067 0.3541701
##
## RelationshipSatisfaction
## Y [,1] [,2]
## No 2.721081 1.065008
## Yes 2.606742 1.126145
##
## StockOptionLevel
## Y [,1] [,2]
## No 0.8421622 0.8432028
## Yes 0.5056180 0.8250813
##
## TotalWorkingYears
## Y [,1] [,2]
## No 11.871351 7.869037
## Yes 8.348315 7.463056
##
## TrainingTimesLastYear
## Y [,1] [,2]
## No 2.842162 1.298082
## Yes 2.696629 1.261373
##
## WorkLifeBalance
## Y [,1] [,2]
## No 2.787027 0.6702105
## Yes 2.623596 0.8294743
##
## YearsAtCompany
## Y [,1] [,2]
## No 7.172973 6.022354
## Yes 5.101124 6.168161
##
## YearsInCurrentRole
## Y [,1] [,2]
## No 4.411892 3.590522
## Yes 2.853933 3.233129
##
## YearsSinceLastPromotion
## Y [,1] [,2]
## No 2.090811 3.081219
## Yes 1.955056 3.241364
##
## YearsWithCurrManager
## Y [,1] [,2]
## No 4.280000 3.555753
## Yes 2.724719 3.134924caret::confusionMatrix(predictions_nb_e1071, test_streamling_processes_test$Attrition)## Confusion Matrix and Statistics
##
## Reference
## Prediction No Yes
## No 261 24
## Yes 47 35
##
## Accuracy : 0.8065
## 95% CI : (0.7624, 0.8457)
## No Information Rate : 0.8392
## P-Value [Acc > NIR] : 0.95954
##
## Kappa : 0.3806
##
## Mcnemar's Test P-Value : 0.00903
##
## Sensitivity : 0.8474
## Specificity : 0.5932
## Pos Pred Value : 0.9158
## Neg Pred Value : 0.4268
## Prevalence : 0.8392
## Detection Rate : 0.7112
## Detection Prevalence : 0.7766
## Balanced Accuracy : 0.7203
##
## 'Positive' Class : No
## plot(table(predictions_nb_e1071, test_streamling_processes_test$Attrition))
Classification: SVM with Repeated k-fold Cross Validation
### 5.a. SVM Attritionifier using 5-fold cross validation with 3 reps
# We train a Support Vector Machine (for Classification) using "Churn" variable
# in the training dataset based on a repeated 5-fold cross validation train
# control with 10 reps.
# The repeated k-fold cross-validation method involves repeating the number of
# times the dataset is split into k-subsets. The final model accuracy/RMSE is
# taken as the mean from the number of repeats.
train_control_svm <- trainControl(method = "repeatedcv", number = 5, repeats = 10)
streamling_dateset_model_svm <-
caret::train(`Attrition` ~ ., data = train_streamling_processes_train,
trControl = train_control_svm, na.action = na.omit,
method = "svmLinearWeights2", metric = "Accuracy")
### 5.b. Test the trained SVM model using the testing dataset
predictions_svm <- predict(streamling_dateset_model_svm, test_streamling_processes_test[, 1:31])
### 5.c. View a summary of the model and view the confusion matrix
print(streamling_dateset_model_svm)## L2 Regularized Linear Support Vector Machines with Class Weights
##
## 1103 samples
## 30 predictor
## 2 classes: 'No', 'Yes'
##
## No pre-processing
## Resampling: Cross-Validated (5 fold, repeated 10 times)
## Summary of sample sizes: 882, 882, 882, 883, 883, 882, ...
## Resampling results across tuning parameters:
##
## cost Loss weight Accuracy Kappa
## 0.25 L1 1 0.7316981 0.0008966698
## 0.25 L1 2 0.7498893 0.0091594654
## 0.25 L1 3 0.6940498 0.0187904658
## 0.25 L2 1 0.8386261 0.0000000000
## 0.25 L2 2 0.8386261 0.0000000000
## 0.25 L2 3 0.8348161 0.0272426179
## 0.50 L1 1 0.7314788 0.0094526012
## 0.50 L1 2 0.7113369 0.0091888134
## 0.50 L1 3 0.7560950 0.0108168897
## 0.50 L2 1 0.8386261 0.0000000000
## 0.50 L2 2 0.8386261 0.0000000000
## 0.50 L2 3 0.8348161 0.0272426179
## 1.00 L1 1 0.7801501 0.0024531820
## 1.00 L1 2 0.7121156 0.0012114600
## 1.00 L1 3 0.7351740 0.0113118554
## 1.00 L2 1 0.8386261 0.0000000000
## 1.00 L2 2 0.8386261 0.0000000000
## 1.00 L2 3 0.8348161 0.0272426179
##
## Accuracy was used to select the optimal model using the largest value.
## The final values used for the model were cost = 0.25, Loss = L2 and weight = 1.caret::confusionMatrix(predictions_svm, test_streamling_processes_test$Attrition)## Confusion Matrix and Statistics
##
## Reference
## Prediction No Yes
## No 308 59
## Yes 0 0
##
## Accuracy : 0.8392
## 95% CI : (0.7976, 0.8753)
## No Information Rate : 0.8392
## P-Value [Acc > NIR] : 0.5347
##
## Kappa : 0
##
## Mcnemar's Test P-Value : 4.321e-14
##
## Sensitivity : 1.0000
## Specificity : 0.0000
## Pos Pred Value : 0.8392
## Neg Pred Value : NaN
## Prevalence : 0.8392
## Detection Rate : 0.8392
## Detection Prevalence : 1.0000
## Balanced Accuracy : 0.5000
##
## 'Positive' Class : No
## plot(table(predictions_svm, test_streamling_processes_test$Attrition))
Classification: Naive Bayes with Leave One Out Cross Validation
# In Leave One Out Cross-Validation (LOOCV), a data instance is left out and a
# model constructed on all other data instances in the training set. This is
# repeated for all data instances.
### 6.a. Train a Naive Bayes Attritionifier based on an LOOCV
train_control_loocv <- trainControl(method = "LOOCV")
streamling_dateset_model_nb_loocv <-
caret::train(`Attrition` ~ ., data = train_streamling_processes_train,
trControl = train_control_loocv, na.action = na.omit,
method = "naive_bayes", metric = "Accuracy")
### 6.b. Test the trained model using the testing dataset
predictions_nb_loocv <-
predict(streamling_dateset_model_nb_loocv, test_streamling_processes_test[, 1:31])
### 6.c. View the confusion matrix
print(streamling_dateset_model_nb_loocv)## Naive Bayes
##
## 1103 samples
## 30 predictor
## 2 classes: 'No', 'Yes'
##
## No pre-processing
## Resampling: Leave-One-Out Cross-Validation
## Summary of sample sizes: 1102, 1102, 1102, 1102, 1102, 1102, ...
## Resampling results across tuning parameters:
##
## usekernel Accuracy Kappa
## FALSE 0.6681777 0.2418589
## TRUE 0.8386219 0.0000000
##
## Tuning parameter 'laplace' was held constant at a value of 0
## Tuning
## parameter 'adjust' was held constant at a value of 1
## Accuracy was used to select the optimal model using the largest value.
## The final values used for the model were laplace = 0, usekernel = TRUE
## and adjust = 1.caret::confusionMatrix(predictions_nb_loocv, test_streamling_processes_test$Attrition)## Confusion Matrix and Statistics
##
## Reference
## Prediction No Yes
## No 308 59
## Yes 0 0
##
## Accuracy : 0.8392
## 95% CI : (0.7976, 0.8753)
## No Information Rate : 0.8392
## P-Value [Acc > NIR] : 0.5347
##
## Kappa : 0
##
## Mcnemar's Test P-Value : 4.321e-14
##
## Sensitivity : 1.0000
## Specificity : 0.0000
## Pos Pred Value : 0.8392
## Neg Pred Value : NaN
## Prevalence : 0.8392
## Detection Rate : 0.8392
## Detection Prevalence : 1.0000
## Balanced Accuracy : 0.5000
##
## 'Positive' Class : No
## plot(table(predictions_nb_loocv, test_streamling_processes_test$Attrition))
Actual Our Model Algorithm
The decision to employ a Support Vector Machine (SVM) with a radial basis function (RBF) kernel is justified by its superior accuracy compared to alternatives like Linear Discriminant Analysis (LDA). The SVM-RBF combination excels in capturing complex, non-linear patterns, handles high-dimensional spaces adeptly, exhibits robustness to outliers, and offers flexible parameter tuning. These traits collectively contribute to its efficacy in achieving accurate predictions, especially on unseen data
### Train the model
set.seed(7)
train_control <- trainControl(method = "cv", number = 5)
streamling_caret_model_svm_radial <- # nolint: object_length_linter.
train(Attrition ~ ., data = train_streamling_processes_train, method = "svmRadial",
metric = "Accuracy", trControl = train_control)
#### Display the model's details
print(streamling_caret_model_svm_radial)## Support Vector Machines with Radial Basis Function Kernel
##
## 1103 samples
## 30 predictor
## 2 classes: 'No', 'Yes'
##
## No pre-processing
## Resampling: Cross-Validated (5 fold)
## Summary of sample sizes: 882, 882, 883, 882, 883
## Resampling results across tuning parameters:
##
## C Accuracy Kappa
## 0.25 0.8386261 0.00000000
## 0.50 0.8404401 0.01855692
## 1.00 0.8604072 0.23391160
##
## Tuning parameter 'sigma' was held constant at a value of 0.01332559
## Accuracy was used to select the optimal model using the largest value.
## The final values used for the model were sigma = 0.01332559 and C = 1.#### Make predictions
predictions <- predict(streamling_caret_model_svm_radial,
test_streamling_processes_test[, 1:31])
#### Display the model's evaluation metrics
table(predictions, test_streamling_processes_test$Attrition)##
## predictions No Yes
## No 306 45
## Yes 2 14confusion_matrix <-
caret::confusionMatrix(predictions,
test_streamling_processes_test[, 1:31]$Attrition)
print(confusion_matrix)## Confusion Matrix and Statistics
##
## Reference
## Prediction No Yes
## No 306 45
## Yes 2 14
##
## Accuracy : 0.8719
## 95% CI : (0.8334, 0.9044)
## No Information Rate : 0.8392
## P-Value [Acc > NIR] : 0.04801
##
## Kappa : 0.3272
##
## Mcnemar's Test P-Value : 8.993e-10
##
## Sensitivity : 0.9935
## Specificity : 0.2373
## Pos Pred Value : 0.8718
## Neg Pred Value : 0.8750
## Prevalence : 0.8392
## Detection Rate : 0.8338
## Detection Prevalence : 0.9564
## Balanced Accuracy : 0.6154
##
## 'Positive' Class : No
## # Load required libraries
library(ggplot2)
# Convert the confusion matrix to a data frame
conf_matrix_df <- as.data.frame(as.table(confusion_matrix$table))
# Rename columns for clarity
names(conf_matrix_df) <- c("Reference", "Prediction", "Frequency")
# Plot the confusion matrix as a heatmap
ggplot(data = conf_matrix_df, aes(x = Reference, y = Prediction, fill = Frequency)) +
geom_tile() +
geom_text(aes(label = Frequency)) +
scale_fill_gradient(low = "white", high = "blue") +
labs(title = "Confusion Matrix") +
theme_minimal()
Model Performance and Comparison
The code performs model training and comparison for predicting ‘Attrition’ using different algorithms. It sets up a repeated cross-validation control (train_control), then trains models using Linear Discriminant Analysis (LDA), Classification and Regression Trees (CART), k-Nearest Neighbors (KNN), Support Vector Machine with radial kernel (SVM), and Random Forest. The goal is to evaluate and compare the performance of these models using the specified training control settings.
train_control <- trainControl(method = "repeatedcv", number = 10, repeats = 3)
### LDA
set.seed(7)
streamling_model_lda <- train(Attrition ~ ., data = STREAMLINING_PROCESSES_AT_KINYANJUI_FARM,
method = "lda", trControl = train_control)
### CART
set.seed(7)
streamling_model_cart <- train(Attrition ~ ., data = STREAMLINING_PROCESSES_AT_KINYANJUI_FARM,
method = "rpart", trControl = train_control)
### KNN
set.seed(7)
streamling_model_knn <- train(Attrition ~ ., data = STREAMLINING_PROCESSES_AT_KINYANJUI_FARM,
method = "knn", trControl = train_control)
### SVM
set.seed(7)
streamling_model_svm <- train(Attrition ~ ., data = STREAMLINING_PROCESSES_AT_KINYANJUI_FARM,
method = "svmRadial", trControl = train_control)
### Random Forest
set.seed(7)
streamling_model_rf <- train(Attrition ~ ., data = STREAMLINING_PROCESSES_AT_KINYANJUI_FARM,
method = "rf", trControl = train_control)
## 3.b. Call the `resamples` Function
# We then create a list of the model results and pass the list as an argument
# to the `resamples` function.
results <- resamples(list(LDA = streamling_model_lda, CART = streamling_model_cart,
KNN = streamling_model_knn, SVM = streamling_model_svm,
RF = streamling_model_rf))
scales <- list(x = list(relation = "free"), y = list(relation = "free"))
bwplot(results, scales = scales)
scales <- list(x = list(relation = "free"), y = list(relation = "free"))
dotplot(results, scales = scales)
splom(results)
# xyplot plots to compare models
xyplot(results, models = c("LDA", "SVM"))
# or
# xyplot plots to compare models
xyplot(results, models = c("SVM", "CART"))
diffs <- diff(results)
summary(diffs)##
## Call:
## summary.diff.resamples(object = diffs)
##
## p-value adjustment: bonferroni
## Upper diagonal: estimates of the difference
## Lower diagonal: p-value for H0: difference = 0
##
## Accuracy
## LDA CART KNN SVM RF
## LDA 0.033098 0.041479 0.010201 0.017220
## CART 2.309e-08 0.008381 -0.022897 -0.015878
## KNN 2.859e-08 0.836319 -0.031278 -0.024259
## SVM 0.011950 1.216e-05 7.210e-09 0.007019
## RF 0.000505 0.002636 1.744e-05 0.178204
##
## Kappa
## LDA CART KNN SVM RF
## LDA 0.247718 0.380570 0.169228 0.163456
## CART 4.288e-13 0.132852 -0.078490 -0.084262
## KNN 2.745e-15 3.032e-05 -0.211342 -0.217114
## SVM 4.027e-11 0.010489 6.963e-10 -0.005772
## RF 4.040e-09 0.004762 2.889e-10 1.000000Hyper Parameter Tunning
Hyperparameter Tuning involves optimizing the
hyperparameters of a machine learning algorithm to enhance model
performance. Hyperparameters are configuration settings external to the
model that influence its learning process. The code likely employs
techniques like grid search or random search to systematically explore
different hyperparameter combinations for algorithms such as LDA, CART,
KNN, SVM, and Random Forest. This process aims to find the optimal set
of hyperparameters that yields the best model performance.
Grid Search
# Apply a "Grid Search" to identify the best parameter value
# Each axis of the grid is an algorithm parameter, and points on the grid are
# specific combinations of parameters.
seed <- 7
metric <- "Accuracy"
train_control <- trainControl(method = "repeatedcv", number = 10, repeats = 3,
search = "grid")
set.seed(seed)
getModelInfo("RRFglobal")## $RRFglobal
## $RRFglobal$label
## [1] "Regularized Random Forest"
##
## $RRFglobal$library
## [1] "RRF"
##
## $RRFglobal$loop
## NULL
##
## $RRFglobal$type
## [1] "Regression" "Classification"
##
## $RRFglobal$parameters
## parameter class label
## 1 mtry numeric #Randomly Selected Predictors
## 2 coefReg numeric Regularization Value
##
## $RRFglobal$grid
## function (x, y, len = NULL, search = "grid")
## {
## if (search == "grid") {
## out <- expand.grid(mtry = caret::var_seq(p = ncol(x),
## classification = is.factor(y), len = len), coefReg = seq(0.01,
## 1, length = len))
## }
## else {
## out <- data.frame(mtry = sample(1:ncol(x), size = len,
## replace = TRUE), coefReg = runif(len, min = 0, max = 1))
## }
## out
## }
##
## $RRFglobal$fit
## function (x, y, wts, param, lev, last, classProbs, ...)
## {
## RRF::RRF(x, y, mtry = param$mtry, coefReg = param$coefReg,
## ...)
## }
##
## $RRFglobal$predict
## function (modelFit, newdata, submodels = NULL)
## predict(modelFit, newdata)
##
## $RRFglobal$prob
## function (modelFit, newdata, submodels = NULL)
## predict(modelFit, newdata, type = "prob")
##
## $RRFglobal$varImp
## function (object, ...)
## {
## varImp <- RRF::importance(object, ...)
## if (object$type == "regression")
## varImp <- data.frame(Overall = varImp[, "%IncMSE"])
## else {
## retainNames <- levels(object$y)
## if (all(retainNames %in% colnames(varImp))) {
## varImp <- varImp[, retainNames]
## }
## else {
## varImp <- data.frame(Overall = varImp[, 1])
## }
## }
## out <- as.data.frame(varImp, stringsAsFactors = TRUE)
## if (dim(out)[2] == 2) {
## tmp <- apply(out, 1, mean)
## out[, 1] <- out[, 2] <- tmp
## }
## out
## }
##
## $RRFglobal$levels
## function (x)
## x$obsLevels
##
## $RRFglobal$tags
## [1] "Random Forest" "Ensemble Model"
## [3] "Bagging" "Implicit Feature Selection"
## [5] "Regularization"
##
## $RRFglobal$sort
## function (x)
## x[order(x$coefReg), ]# The Regularized Random Forest algorithm exposes the "coefReg" parameter
# in addition to the "mtry" parameter for tuning.
## The "mtry" parameter
# Number of variables randomly sampled as candidates at each split.
## The "coefReg" parameter
# It stands for coefficient(s) of regularization.
# A smaller coefficient may lead to a smaller feature subset, i.e.,
# there are fewer variables with non-zero importance scores. coefReg must be
# either a single value (all variables have the same coefficient) or a numeric
# vector of length equal to the number of predictor variables. default: 0.8
tunegrid <- expand.grid(.mtry = c(1:10),
.coefReg = seq(from = 0.1, to = 1, by = 0.1))
streamling_model_grid_search_rrf_global <- train(Attrition ~ ., data = STREAMLINING_PROCESSES_AT_KINYANJUI_FARM, # nolint
method = "RRFglobal",
metric = metric,
tuneGrid = tunegrid,
trControl = train_control)
print(streamling_model_grid_search_rrf_global)## Regularized Random Forest
##
## 1470 samples
## 30 predictor
## 2 classes: 'No', 'Yes'
##
## No pre-processing
## Resampling: Cross-Validated (10 fold, repeated 3 times)
## Summary of sample sizes: 1323, 1323, 1322, 1323, 1323, 1323, ...
## Resampling results across tuning parameters:
##
## mtry coefReg Accuracy Kappa
## 1 0.1 0.8206506 0.1595824
## 1 0.2 0.8195215 0.1126055
## 1 0.3 0.8176812 0.1142811
## 1 0.4 0.8197033 0.1122957
## 1 0.5 0.8226715 0.1267909
## 1 0.6 0.8256177 0.1316026
## 1 0.7 0.8462778 0.2133087
## 1 0.8 0.8508053 0.2434784
## 1 0.9 0.8551060 0.2726389
## 1 1.0 0.8576112 0.2944384
## 2 0.1 0.8136057 0.1003532
## 2 0.2 0.8188056 0.1416495
## 2 0.3 0.8140269 0.1208266
## 2 0.4 0.8226867 0.1479794
## 2 0.5 0.8186205 0.1191445
## 2 0.6 0.8310648 0.1532200
## 2 0.7 0.8433237 0.2034060
## 2 0.8 0.8566980 0.2780614
## 2 0.9 0.8564682 0.2784487
## 2 1.0 0.8576066 0.2920436
## 3 0.1 0.8188120 0.1456522
## 3 0.2 0.8172370 0.1289569
## 3 0.3 0.8224617 0.1545650
## 3 0.4 0.8113320 0.1075445
## 3 0.5 0.8226792 0.1425047
## 3 0.6 0.8283435 0.1408576
## 3 0.7 0.8505677 0.2497933
## 3 0.8 0.8539923 0.2724134
## 3 0.9 0.8562476 0.2819425
## 3 1.0 0.8564728 0.2857081
## 4 0.1 0.8233610 0.1764235
## 4 0.2 0.8272096 0.1789299
## 4 0.3 0.8152021 0.1341274
## 4 0.4 0.8167912 0.1219350
## 4 0.5 0.8183784 0.1482384
## 4 0.6 0.8385786 0.2076412
## 4 0.7 0.8465029 0.2361529
## 4 0.8 0.8535233 0.2674868
## 4 0.9 0.8571546 0.2855559
## 4 1.0 0.8560301 0.2806241
## 5 0.1 0.8154398 0.1238621
## 5 0.2 0.8147487 0.1077841
## 5 0.3 0.8267455 0.1631013
## 5 0.4 0.8254049 0.1863146
## 5 0.5 0.8190818 0.1207993
## 5 0.6 0.8362896 0.1791708
## 5 0.7 0.8478604 0.2384046
## 5 0.8 0.8539815 0.2680770
## 5 0.9 0.8548931 0.2747854
## 5 1.0 0.8548900 0.2755062
## 6 0.1 0.8193008 0.1377217
## 6 0.2 0.8226699 0.1673286
## 6 0.3 0.8152055 0.1161970
## 6 0.4 0.8217782 0.1441949
## 6 0.5 0.8188428 0.1344704
## 6 0.6 0.8365239 0.2070344
## 6 0.7 0.8517078 0.2540012
## 6 0.8 0.8528569 0.2661296
## 6 0.9 0.8546649 0.2746277
## 6 1.0 0.8557940 0.2748237
## 7 0.1 0.8167819 0.1226699
## 7 0.2 0.8145190 0.1133275
## 7 0.3 0.8138694 0.1273908
## 7 0.4 0.8158871 0.1349048
## 7 0.5 0.8238269 0.1406273
## 7 0.6 0.8346991 0.1742235
## 7 0.7 0.8544380 0.2788625
## 7 0.8 0.8535233 0.2639496
## 7 0.9 0.8587435 0.2923206
## 7 1.0 0.8555642 0.2801702
## 8 0.1 0.8254295 0.1740382
## 8 0.2 0.8186147 0.1513947
## 8 0.3 0.8106378 0.1188940
## 8 0.4 0.8290392 0.1592170
## 8 0.5 0.8213324 0.1612306
## 8 0.6 0.8349290 0.1752814
## 8 0.7 0.8512572 0.2550419
## 8 0.8 0.8564712 0.2842590
## 8 0.9 0.8571531 0.2827742
## 8 1.0 0.8571562 0.2854611
## 9 0.1 0.8172461 0.1329156
## 9 0.2 0.8160954 0.1387811
## 9 0.3 0.8233440 0.1612904
## 9 0.4 0.8195092 0.1566592
## 9 0.5 0.8226807 0.1592357
## 9 0.6 0.8358407 0.1857527
## 9 0.7 0.8521535 0.2575612
## 9 0.8 0.8557940 0.2826399
## 9 0.9 0.8562460 0.2813795
## 9 1.0 0.8580678 0.2959344
## 10 0.1 0.8226792 0.1636577
## 10 0.2 0.8272129 0.1718803
## 10 0.3 0.8172556 0.1552148
## 10 0.4 0.8260589 0.1805620
## 10 0.5 0.8222256 0.1724748
## 10 0.6 0.8354072 0.1738582
## 10 0.7 0.8528477 0.2683606
## 10 0.8 0.8537547 0.2680981
## 10 0.9 0.8557971 0.2807187
## 10 1.0 0.8557971 0.2800267
##
## Accuracy was used to select the optimal model using the largest value.
## The final values used for the model were mtry = 7 and coefReg = 0.9.plot(streamling_model_grid_search_rrf_global)
Ensemble methods
Ensemble methods combine multiple machine learning
models to improve overall predictive performance and robustness. These
methods leverage the strengths of different models to compensate for
individual weaknesses. Common ensemble methods include bagging,
boosting, and stacking. Bagging (Random Forest) builds
multiple models in parallel and averages their predictions, reducing
variance. Boosting (AdaBoost) focuses on sequentially
improving model performance by giving more weight to missclassified
instances. Stacking combines predictions from multiple models using
another model as a meta-learner. Ensemble methods often outperform
individual models, providing more reliable and accurate predictions.
Boosting
GBM
seed <- 7
metric <- "Accuracy"
train_control <- trainControl(method = "cv", number = 5)
#### Boosting with C5.0 ----
# C5.0
set.seed(seed)
ion_model_c50 <- train(Attrition ~ ., data = STREAMLINING_PROCESSES_AT_KINYANJUI_FARM, method = "C5.0",
metric = metric,
trControl = train_control)
#### Boosting with Stochastic Gradient Boosting ----
set.seed(seed)
ion_model_gbm <- train(Attrition ~ ., data = STREAMLINING_PROCESSES_AT_KINYANJUI_FARM, method = "gbm",
metric = metric, trControl = train_control,
verbose = FALSE)
# Summarize results
boosting_results <-
resamples(list("C5.0 boosting ensemble" =ion_model_c50,
"Generalized Boosted Regression Modeling (GBM)" = ion_model_gbm))
summary(boosting_results)##
## Call:
## summary.resamples(object = boosting_results)
##
## Models: C5.0 boosting ensemble, Generalized Boosted Regression Modeling (GBM)
## Number of resamples: 5
##
## Accuracy
## Min. 1st Qu. Median
## C5.0 boosting ensemble 0.8474576 0.8508475 0.8639456
## Generalized Boosted Regression Modeling (GBM) 0.8474576 0.8600683 0.8779661
## Mean 3rd Qu. Max. NA's
## C5.0 boosting ensemble 0.8639860 0.8737201 0.883959 0
## Generalized Boosted Regression Modeling (GBM) 0.8721261 0.8843537 0.890785 0
##
## Kappa
## Min. 1st Qu. Median
## C5.0 boosting ensemble 0.2432879 0.3606016 0.379719
## Generalized Boosted Regression Modeling (GBM) 0.2600747 0.3077278 0.461788
## Mean 3rd Qu. Max.
## C5.0 boosting ensemble 0.3633392 0.3880737 0.4450139
## Generalized Boosted Regression Modeling (GBM) 0.3950538 0.4658159 0.4798626
## NA's
## C5.0 boosting ensemble 0
## Generalized Boosted Regression Modeling (GBM) 0dotplot(boosting_results)
Save and Load your Model
# Saving a model into a file allows you to load it later and use it to make
# predictions. Saved models can be loaded by calling the `readRDS()` function
saveRDS(streamling_caret_model_svm_radial, "./models/saved_streamling_caret_model_svm_radial.rds")
# The saved model can then be loaded later as follows:
loaded_streamling_model_svm <- readRDS("./models/saved_streamling_caret_model_svm_radial.rds")
print(loaded_streamling_model_svm)## Support Vector Machines with Radial Basis Function Kernel
##
## 1103 samples
## 30 predictor
## 2 classes: 'No', 'Yes'
##
## No pre-processing
## Resampling: Cross-Validated (5 fold)
## Summary of sample sizes: 882, 882, 883, 882, 883
## Resampling results across tuning parameters:
##
## C Accuracy Kappa
## 0.25 0.8386261 0.00000000
## 0.50 0.8404401 0.01855692
## 1.00 0.8604072 0.23391160
##
## Tuning parameter 'sigma' was held constant at a value of 0.01332559
## Accuracy was used to select the optimal model using the largest value.
## The final values used for the model were sigma = 0.01332559 and C = 1.predictions_with_loaded_model <-
predict(loaded_streamling_model_svm, newdata = test_streamling_processes_test)
confusionMatrix(predictions_with_loaded_model, test_streamling_processes_test$Attrition)## Confusion Matrix and Statistics
##
## Reference
## Prediction No Yes
## No 306 45
## Yes 2 14
##
## Accuracy : 0.8719
## 95% CI : (0.8334, 0.9044)
## No Information Rate : 0.8392
## P-Value [Acc > NIR] : 0.04801
##
## Kappa : 0.3272
##
## Mcnemar's Test P-Value : 8.993e-10
##
## Sensitivity : 0.9935
## Specificity : 0.2373
## Pos Pred Value : 0.8718
## Neg Pred Value : 0.8750
## Prevalence : 0.8392
## Detection Rate : 0.8338
## Detection Prevalence : 0.9564
## Balanced Accuracy : 0.6154
##
## 'Positive' Class : No
## Making prediction with the model
to_be_predicted <-
data.frame(
Age = 41,
BusinessTravel = "Travel_Rarely",
DailyRate = 1102,
Department = "Sales",
DistanceFromHome = 1,
Education = 2,
EducationField = "Life Sciences",
EnvironmentSatisfaction = 2,
Gender = "Female",
HourlyRate = 94,
JobInvolvement = 3,
JobLevel = 2,
JobRole = "Sales Executive",
JobSatisfaction = 4,
MaritalStatus = "Single",
MonthlyIncome = 5993,
MonthlyRate = 19479,
NumCompaniesWorked = 8,
OverTime = "Yes",
PercentSalaryHike = 11,
PerformanceRating = 3,
RelationshipSatisfaction = 1,
StockOptionLevel = 0,
TotalWorkingYears = 8,
TrainingTimesLastYear = 0,
WorkLifeBalance = 1,
YearsAtCompany = 6,
YearsInCurrentRole = 4,
YearsSinceLastPromotion = 0,
YearsWithCurrManager = 5
)
predict(loaded_streamling_model_svm, to_be_predicted)## [1] No
## Levels: No YesMake predictions using the model through a function
predict_attrition <-
function(arg_Age, arg_BusinessTravel, arg_DailyRate,
arg_Department, arg_DistanceFromHome, arg_Education, arg_EducationField,
arg_EnvironmentSatisfaction, arg_Gender,
arg_HourlyRate, arg_JobInvolvement, arg_JobLevel, arg_JobRole,
arg_JobSatisfaction, arg_MaritalStatus, arg_MonthlyIncome, arg_MonthlyRate,
arg_NumCompaniesWorked, arg_OverTime, arg_PercentSalaryHike,
arg_PerformanceRating, arg_RelationshipSatisfaction, arg_StockOptionLevel,
arg_TotalWorkingYears, arg_TrainingTimesLastYear, arg_WorkLifeBalance, arg_YearsAtCompany,
arg_YearsInCurrentRole, arg_YearsSinceLastPromotion, arg_YearsWithCurrManager) {
# Create a data frame using the arguments
to_be_predicted <- data.frame(Age = arg_Age,
BusinessTravel = arg_BusinessTravel, DailyRate = arg_DailyRate,
Department = arg_Department, DistanceFromHome = arg_DistanceFromHome,
Education = arg_Education, EducationField = arg_EducationField,
EnvironmentSatisfaction = arg_EnvironmentSatisfaction, Gender = arg_Gender,
HourlyRate = arg_HourlyRate, JobInvolvement = arg_JobInvolvement,
JobLevel = arg_JobLevel, JobRole = arg_JobRole,
JobSatisfaction = arg_JobSatisfaction, MaritalStatus = arg_MaritalStatus,
MonthlyIncome = arg_MonthlyIncome, MonthlyRate = arg_MonthlyRate,
NumCompaniesWorked = arg_NumCompaniesWorked,
OverTime = arg_OverTime, PercentSalaryHike = arg_PercentSalaryHike,
PerformanceRating = arg_PerformanceRating,
RelationshipSatisfaction = arg_RelationshipSatisfaction,
StockOptionLevel = arg_StockOptionLevel,
TotalWorkingYears = arg_TotalWorkingYears,
TrainingTimesLastYear = arg_TrainingTimesLastYear,
WorkLifeBalance = arg_WorkLifeBalance, YearsAtCompany = arg_YearsAtCompany,
YearsInCurrentRole = arg_YearsInCurrentRole,
YearsSinceLastPromotion = arg_YearsSinceLastPromotion,
YearsWithCurrManager = arg_YearsWithCurrManager)
# Make a prediction based on the data frame
predict(loaded_streamling_model_svm, to_be_predicted)
}
predict_attrition(41,
"Travel_Rarely",
1102,
"Sales",
1,
2,
"Life Sciences",
2,
"Female",
94,
3,
2,
"Sales Executive",
4,
"Single",
5993,
19479,
8,
"Yes",
11,
3,
1,
0,
8,
0,
1,
6,
4,
0,
5)## [1] No
## Levels: No YesPlumber API
Plumber API in R facilitates the creation of web APIs, enabling seamless integration of R code for data processing and analysis. It transforms R scripts into interactive and accessible web services, enhancing collaboration and data-driven decision-making.
NB because we we wanted to execute the plumber API in
our md we had run the Plumber API in a separate R process, allowing our
R Markdown document to continue executing by using the future
package.The on.exit() block is used to stop the Plumber API after all
the code chunks have been executed
if (!requireNamespace("future", quietly = TRUE)) install.packages("future")
library(future)##
## Attaching package: 'future'
## The following object is masked from 'package:caret':
##
## cluster
## The following object is masked from 'package:languageserver':
##
## run# Define a function to start the Plumber API in a separate process
start_api <- function() {
api <- plumber::plumb("PlumberAPI.R")
api$run(host = "127.0.0.1", port = 5022, swagger = TRUE)
}
# Start the API in a separate process
future::plan("multisession") # Use multisession backend for parallel processing
future({
start_api()
})## MultisessionFuture:
## Label: '<none>'
## Expression:
## {
## start_api()
## }
## Lazy evaluation: FALSE
## Asynchronous evaluation: TRUE
## Local evaluation: TRUE
## Environment: R_GlobalEnv
## Capture standard output: TRUE
## Capture condition classes: 'condition' (excluding 'nothing')
## Globals: 1 objects totaling 6.65 KiB (function 'start_api' of 6.65 KiB)
## Packages: <none>
## L'Ecuyer-CMRG RNG seed: <none> (seed = FALSE)
## Resolved: FALSE
## Value: <not collected>
## Conditions captured: <none>
## Early signaling: FALSE
## Owner process: 1f35b32c-3fd8-aa67-1a03-364646a4c6ac
## Class: 'MultisessionFuture', 'ClusterFuture', 'MultiprocessFuture', 'Future', 'environment'### Generate the URL required to access the API ----
# We set this as a constant port 5022 running on localhost
base_url <- "http://127.0.0.1:5022/Attrition"
params <- data.frame(
arg_Age = 41,
arg_BusinessTravel = "Travel_Rarely",
arg_DailyRate = 1102,
arg_Department = "Sales",
arg_DistanceFromHome = 1,
arg_Education = 2,
arg_EducationField = "Life Sciences",
arg_EnvironmentSatisfaction = 2,
arg_Gender = "Female",
arg_HourlyRate = 94,
arg_JobInvolvement = 3,
arg_JobLevel = 2,
arg_JobRole = "Sales Executive",
arg_JobSatisfaction = 4,
arg_MaritalStatus = "Single",
arg_MonthlyIncome = 5993,
arg_MonthlyRate = 19479,
arg_NumCompaniesWorked = 8,
arg_OverTime = "Yes",
arg_PercentSalaryHike = 11,
arg_PerformanceRating = 3,
arg_RelationshipSatisfaction = 1,
arg_StockOptionLevel = 0,
arg_TotalWorkingYears = 8,
arg_TrainingTimesLastYear = 0,
arg_WorkLifeBalance = 1,
arg_YearsAtCompany = 6,
arg_YearsInCurrentRole = 4,
arg_YearsSinceLastPromotion = 0,
arg_YearsWithCurrManager = 5
)
query_url <- httr::modify_url(url = base_url, query = params)
print(query_url)## [1] "http://127.0.0.1:5022/Attrition?arg_Age=41&arg_BusinessTravel=Travel_Rarely&arg_DailyRate=1102&arg_Department=Sales&arg_DistanceFromHome=1&arg_Education=2&arg_EducationField=Life%20Sciences&arg_EnvironmentSatisfaction=2&arg_Gender=Female&arg_HourlyRate=94&arg_JobInvolvement=3&arg_JobLevel=2&arg_JobRole=Sales%20Executive&arg_JobSatisfaction=4&arg_MaritalStatus=Single&arg_MonthlyIncome=5993&arg_MonthlyRate=19479&arg_NumCompaniesWorked=8&arg_OverTime=Yes&arg_PercentSalaryHike=11&arg_PerformanceRating=3&arg_RelationshipSatisfaction=1&arg_StockOptionLevel=0&arg_TotalWorkingYears=8&arg_TrainingTimesLastYear=0&arg_WorkLifeBalance=1&arg_YearsAtCompany=6&arg_YearsInCurrentRole=4&arg_YearsSinceLastPromotion=0&arg_YearsWithCurrManager=5"Sys.sleep(10)
model_prediction <- GET(query_url)
content(model_prediction)## [[1]]
## [1] "No"content(model_prediction)[[1]]## [1] "No"# Parse the response into the right format
model_prediction_raw <- content(model_prediction, as = "text",
encoding = "utf-8")
jsonlite::fromJSON(model_prediction_raw)## [1] "No"get_attrition_predictions <-
function(arg_Age, arg_BusinessTravel, arg_DailyRate,
arg_Department, arg_DistanceFromHome, arg_Education, arg_EducationField,
arg_EnvironmentSatisfaction, arg_Gender,
arg_HourlyRate, arg_JobInvolvement, arg_JobLevel, arg_JobRole,
arg_JobSatisfaction, arg_MaritalStatus, arg_MonthlyIncome, arg_MonthlyRate,
arg_NumCompaniesWorked, arg_OverTime, arg_PercentSalaryHike,
arg_PerformanceRating, arg_RelationshipSatisfaction, arg_StockOptionLevel,
arg_TotalWorkingYears, arg_TrainingTimesLastYear, arg_WorkLifeBalance, arg_YearsAtCompany,
arg_YearsInCurrentRole, arg_YearsSinceLastPromotion, arg_YearsWithCurrManager) {
base_url <- "http://127.0.0.1:5022/Attrition"
params <- list(
arg_Age = arg_Age,
arg_BusinessTravel = arg_BusinessTravel,
arg_DailyRate = arg_DailyRate,
arg_Department = arg_Department,
arg_DistanceFromHome = arg_DistanceFromHome,
arg_Education = arg_Education,
arg_EducationField = arg_EducationField,
arg_EnvironmentSatisfaction = arg_EnvironmentSatisfaction,
arg_Gender = arg_Gender,
arg_HourlyRate = arg_HourlyRate,
arg_JobInvolvement = arg_JobInvolvement,
arg_JobLevel = arg_JobLevel,
arg_JobRole = arg_JobRole,
arg_JobSatisfaction = arg_JobSatisfaction,
arg_MaritalStatus = arg_MaritalStatus,
arg_MonthlyIncome = arg_MonthlyIncome,
arg_MonthlyRate = arg_MonthlyRate,
arg_NumCompaniesWorked = arg_NumCompaniesWorked,
arg_OverTime = arg_OverTime,
arg_PercentSalaryHike = arg_PercentSalaryHike,
arg_PerformanceRating = arg_PerformanceRating,
arg_RelationshipSatisfaction = arg_RelationshipSatisfaction,
arg_StockOptionLevel = arg_StockOptionLevel,
arg_TotalWorkingYears = arg_TotalWorkingYears,
arg_TrainingTimesLastYear = arg_TrainingTimesLastYear,
arg_WorkLifeBalance = arg_WorkLifeBalance,
arg_YearsAtCompany = arg_YearsAtCompany,
arg_YearsInCurrentRole = arg_YearsInCurrentRole,
arg_YearsSinceLastPromotion = arg_YearsSinceLastPromotion,
arg_YearsWithCurrManager = arg_YearsWithCurrManager
)
query_url <- modify_url(url = base_url, query = params)
model_prediction <- GET(query_url)
model_prediction_raw <- content(model_prediction, as = "text",
encoding = "utf-8")
jsonlite::fromJSON(model_prediction_raw)
}
get_attrition_predictions(41,
"Travel_Rarely",
1102,
"Sales",
1,
2,
"Life Sciences",
2,
"Female",
94,
3,
2,
"Sales Executive",
4,
"Single",
5993,
19479,
8,
"Yes",
11,
3,
1,
0,
8,
0,
1,
6,
4,
0,
5)## [1] "No"Prediction Web Form And Customer-facing Dashboard
The customer facing dashboard is on real-time bases where it
visualizes the customer data verses other customer data to show the goal
to be attained or to guide for future decisions making.NB
since the file is to large can be opened when downloaded
.html and viewed via the Browser
cat('<iframe src="http://localhost/Good%20Architecture/Attrition%20Prediction.php" width="1000" height="800"></iframe>')