(SANDBOX) LPA Final Project

Experiment setup and predictive modeling for AnCo

Purpose: This notebook [Explain why we're doing this]

Data source: https://www.kaggle.com/akbaldawa/hr-analytics?select=Train.csvVB

Actually is from this challenge, which we will use for our test model verification: https://datahack.analyticsvidhya.com/contest/wns-analytics-hackathon-2018-1/

Section 1: Load data and packages

#Read data df_train <- read.csv("Train.csv")

#Install Libraries (to be extended) #install.packages("tidyverse") library(tidyverse) library(jtools) library(stargazer) library(sjPlot) #Set plot size options(repr.plot.width=6, repr.plot.height=5)

Section 2: Prepare data

2.1 Clean and Transform variables

As a first step, we will need to "prepare" the data for our analysis, by transforming data types into appropriate ones

#let's have an initial look head(df_train)

# Several variables are stored as characters (<chr>) but will actually act as factors # (department, region, education, gender, recruitment_channel, previous_year_rating should all be factors) # we should have a look at the observations, and then convert them into factors df_train %>% count(department) df_train %>% count(region) df_train %>% count(education) df_train %>% count(gender) df_train %>% count(recruitment_channel) df_train %>% count(previous_year_rating)

#seems like there are som NA's in the "previous year rating" column. These are likely to be new employees #let's see how many there are table(is.na(df_train$previous_year_rating))

#only 8% of observations lack this data #intuitively we probably are not promoting people within their first year #it makes sense to delete these observations df_train <- filter(df_train, previous_year_rating >= 1)

#make sure the NA rows are now deleted table(is.na(df_train$previous_year_rating))

#similarly, 2409 have no education on file. let's remove these as well and make sure the rows are deleted df_train <- filter(df_train, education != "") df_train <- filter(df_train, is.na(education)==FALSE)

#now we can transform them into factors #department df_train$department <- parse_factor(df_train$department, levels=NULL, ordered=FALSE) class(df_train$department)

#region df_train$region <- parse_factor(df_train$region, levels=NULL, ordered=FALSE) class(df_train$region)

#gender df_train$gender <- parse_factor(df_train$gender, levels=NULL, ordered=FALSE) class(df_train$gender)

#recruitment channel df_train$recruitment_channel <- parse_factor(df_train$recruitment_channel, levels=NULL, ordered=FALSE) class(df_train$recruitment_channel)

#education (we will sort) ed_levels <- c("Below Secondary","Bachelor's","Master's & above") df_train$education <- parse_factor(df_train$education, levels=ed_levels, ordered=TRUE) class(df_train$education)

#previous_year_rating (we will sort) - different formula as the previous works for characters only rating_levels <- c("1","2","3","4","5") df_train$previous_year_rating <- as.factor(df_train$previous_year_rating) df_train$previous_year_rating <- factor(df_train$previous_year_rating, levels=rating_levels, ordered = TRUE) class(df_train$previous_year_rating)

#few more transformations needed #employee_id should be character and not int, as the numbers do not represent values; df_train$employee_id <- as.character(df_train$employee_id) #is_promoted,awards_won,KPIs_met should be booleans, as they are binary options df_train$is_promoted <- as.logical(df_train$is_promoted) df_train$awards_won. <- as.logical(df_train$awards_won.) df_train$KPIs_met..80. <- as.logical(df_train$KPIs_met..80.)

Section 3: Visualize and understand data

TBD

Section 4: Running the models

4.1 "Intuitive" model

In the first attempt, we will use our business understanding and intuition to pick variables that we believe have influenced promotion results.

Given that we are predicting "is_promoted", a binary variable, we will use logistic regression

#our initial hypothesis is that promotion decisions could be linked to previous ratings, meeting KPIs, average training scores, and education levels #let's test a model with these predictors model_berten = glm(is_promoted ~ education + previous_year_rating + KPIs_met..80. + avg_training_score, data = df_train)

summary(model_berten)

4.1 Step-wise model

The second model will utilize a stepwise method to automatically select variables. In this example we will go from the full model (that includes all variables) to a more selective one

model_cem_step1 = glm(is_promoted ~ . - employee_id, data = df_train) model_cem_step2 = step(model_cem_step1, direction = "backward", trace = FALSE) summary(model_cem_step2)

4.2 Random Forest

Lastly, let's also try running a random forest algorithm

# we will need some new libraries if we want to try machine learning algorithms install.packages("randomForest") library(rpart.plot) library(lubridate) library(randomForest)

#first we need to turn the outcome variable into a factor df_train$is_promoted = as.factor(df_train$is_promoted)

#now we can fit the random forest model (m=4 as rule of thumb of m= variables/3) model_rf = randomForest(is_promoted ~ department + region + education + gender + recruitment_channel + no_of_trainings + age + previous_year_rating + length_of_service + KPIs_met..80. + awards_won. + avg_training_score, data = df_train, importance = TRUE, mtry = 4)

model_rf

#now we can check the importance assigned by the algorithm to each variable varImpPlot(model_rf, type=1)