Employee attrition analysis means analyzing the behaviour of the employees who left your organization and comparing them with the current employees in your organization. It helps in finding which employee may leave soon. So, if you want to learn how to analyze employee attrition, this article is for you. In this article, I will take you through the task of employee attrition analysis using Python.
Employee Attrition Analysis
Employee attrition analysis is a type of behavioural analysis where we study the behaviour and characteristics of the employees who left the organization and compare their characteristics with the current employees to find the employees who may leave the organization soon.
A high rate of attrition of employees can be expensive for any company in terms of recruitment and training costs, loss of productivity and morale reduction of employees. By identifying the causes of attrition, a company can take measures to reduce the attrition of employees and maintain precious employees.
For the task of employee attrition analysis, we need to have a dataset of employees with their attrition status and features about the career of employees in a specific company. I found an ideal dataset for this task. You can download the dataset from here.
In the section below, I will take you through the task of employee attrition analysis using the Python programming language.
Employee Attrition Analysis using Python
I will start this task by importing the necessary Python libraries and the dataset:
import pandas as pd
import numpy as np
import seaborn as sns
import matplotlib.pyplot as plt
import plotly.express as px
import plotly.graph_objects as go
import plotly.io as pio
pio.templates.default = "plotly_white"
data = pd.read_csv("WA_Fn-UseC_-HR-Employee-Attrition.csv")
print(data.head())Age Attrition BusinessTravel DailyRate Department \ 0 41 Yes Travel_Rarely 1102 Sales 1 49 No Travel_Frequently 279 Research & Development 2 37 Yes Travel_Rarely 1373 Research & Development 3 33 No Travel_Frequently 1392 Research & Development 4 27 No Travel_Rarely 591 Research & Development DistanceFromHome Education EducationField EmployeeCount EmployeeNumber \ 0 1 2 Life Sciences 1 1 1 8 1 Life Sciences 1 2 2 2 2 Other 1 4 3 3 4 Life Sciences 1 5 4 2 1 Medical 1 7 ... RelationshipSatisfaction StandardHours StockOptionLevel \ 0 ... 1 80 0 1 ... 4 80 1 2 ... 2 80 0 3 ... 3 80 0 4 ... 4 80 1 TotalWorkingYears TrainingTimesLastYear WorkLifeBalance YearsAtCompany \ 0 8 0 1 6 1 10 3 3 10 2 7 3 3 0 3 8 3 3 8 4 6 3 3 2 YearsInCurrentRole YearsSinceLastPromotion YearsWithCurrManager 0 4 0 5 1 7 1 7 2 0 0 0 3 7 3 0 4 2 2 2 [5 rows x 35 columns]
Let’s have a look at whether this dataset contains any missing values or not:
print(data.isnull().sum())
Age 0 Attrition 0 BusinessTravel 0 DailyRate 0 Department 0 DistanceFromHome 0 Education 0 EducationField 0 EmployeeCount 0 EmployeeNumber 0 EnvironmentSatisfaction 0 Gender 0 HourlyRate 0 JobInvolvement 0 JobLevel 0 JobRole 0 JobSatisfaction 0 MaritalStatus 0 MonthlyIncome 0 MonthlyRate 0 NumCompaniesWorked 0 Over18 0 OverTime 0 PercentSalaryHike 0 PerformanceRating 0 RelationshipSatisfaction 0 StandardHours 0 StockOptionLevel 0 TotalWorkingYears 0 TrainingTimesLastYear 0 WorkLifeBalance 0 YearsAtCompany 0 YearsInCurrentRole 0 YearsSinceLastPromotion 0 YearsWithCurrManager 0 dtype: int64
Now let’s have a look at the distribution of the age in the dataset:
sns.displot(data['Age'], kde=True)
plt.title('Distribution of Age')
plt.show()
Let’s have a look at the percentage of attrition by department:
# Filter the data to show only "Yes" values in the "Attrition" column
attrition_data = data[data['Attrition'] == 'Yes']
# Calculate the count of attrition by department
attrition_by = attrition_data.groupby(['Department']).size().reset_index(name='Count')
# Create a donut chart
fig = go.Figure(data=[go.Pie(
labels=attrition_by['Department'],
values=attrition_by['Count'],
hole=0.4,
marker=dict(colors=['#3CAEA3', '#F6D55C']),
textposition='inside'
)])
# Update the layout
fig.update_layout(title='Attrition by Department', font=dict(size=16), legend=dict(
orientation="h", yanchor="bottom", y=1.02, xanchor="right", x=1
))
# Show the chart
fig.show()
We can see that the Research & Development department has a high attrition rate. Now let’s have a look at the percentage of attrition by education field:
attrition_by = attrition_data.groupby(['EducationField']).size().reset_index(name='Count')
# Create a donut chart
fig = go.Figure(data=[go.Pie(
labels=attrition_by['EducationField'],
values=attrition_by['Count'],
hole=0.4,
marker=dict(colors=['#3CAEA3', '#F6D55C']),
textposition='inside'
)])
# Update the layout
fig.update_layout(title='Attrition by Educational Field', font=dict(size=16), legend=dict(
orientation="h", yanchor="bottom", y=1.02, xanchor="right", x=1
))
# Show the chart
fig.show()
We can see that the employees with Life Sciences as an education field have a high attrition rate. Now let’s have a look at the percentage of attrition by number of years at the company:
attrition_by = attrition_data.groupby(['YearsAtCompany']).size().reset_index(name='Count')
# Create a donut chart
fig = go.Figure(data=[go.Pie(
labels=attrition_by['YearsAtCompany'],
values=attrition_by['Count'],
hole=0.4,
marker=dict(colors=['#3CAEA3', '#F6D55C']),
textposition='inside'
)])
# Update the layout
fig.update_layout(title='Attrition by Years at Company', font=dict(size=16), legend=dict(
orientation="h", yanchor="bottom", y=1.02, xanchor="right", x=1
))
# Show the chart
fig.show()
We can see that most of the employees leave the organization after completing a year. Now let’s have a look at the percentage of attrition by the number of years since the last promotion:
attrition_by = attrition_data.groupby(['YearsSinceLastPromotion']).size().reset_index(name='Count')
# Create a donut chart
fig = go.Figure(data=[go.Pie(
labels=attrition_by['YearsSinceLastPromotion'],
values=attrition_by['Count'],
hole=0.4,
marker=dict(colors=['#3CAEA3', '#F6D55C']),
textposition='inside'
)])
# Update the layout
fig.update_layout(title='Attrition by Years Since Last Promotion', font=dict(size=16), legend=dict(
orientation="h", yanchor="bottom", y=1.02, xanchor="right", x=1
))
# Show the chart
fig.show()
We can see that the employees who don’t get promotions leave the organization more compared to the employees who got promotions. Now let’s have a look at the percentage of attrition by gender:
attrition_by = attrition_data.groupby(['Gender']).size().reset_index(name='Count')
# Create a donut chart
fig = go.Figure(data=[go.Pie(
labels=attrition_by['Gender'],
values=attrition_by['Count'],
hole=0.4,
marker=dict(colors=['#3CAEA3', '#F6D55C']),
textposition='inside'
)])
# Update the layout
fig.update_layout(title='Attrition by Gender', font=dict(size=16), legend=dict(
orientation="h", yanchor="bottom", y=1.02, xanchor="right", x=1
))
# Show the chart
fig.show()
Men have a high attrition rate compared to women. Now let’s have a look at the attrition by analyzing the relationship between monthly income and the age of the employees:
fig = px.scatter(data, x="Age", y="MonthlyIncome", color="Attrition", trendline="ols") fig.update_layout(title="Age vs. Monthly Income by Attrition") fig.show()
We can see that as the age of the person increases, monthly income increases. We can also see a high rate of attrition among the employees with low monthly incomes.
So this is how we can analyze employee attrition. You can explore many more features in the dataset in the same way.
Employee Attrition Prediction Model
Now let’s prepare a Machine Learning model for employee attrition prediction. This dataset has a lot of features having categorical values. I will convert those categorical variables into numerical:
from sklearn.preprocessing import LabelEncoder le = LabelEncoder() data['Attrition'] = le.fit_transform(data['Attrition']) data['BusinessTravel'] = le.fit_transform(data['BusinessTravel']) data['Department'] = le.fit_transform(data['Department']) data['EducationField'] = le.fit_transform(data['EducationField']) data['Gender'] = le.fit_transform(data['Gender']) data['JobRole'] = le.fit_transform(data['JobRole']) data['MaritalStatus'] = le.fit_transform(data['MaritalStatus']) data['Over18'] = le.fit_transform(data['Over18']) data['OverTime'] = le.fit_transform(data['OverTime'])
Now let’s have a look at the correlation:
correlation = data.corr() print(correlation["Attrition"].sort_values(ascending=False))
Attrition 1.000000 OverTime 0.246118 MaritalStatus 0.162070 DistanceFromHome 0.077924 JobRole 0.067151 Department 0.063991 NumCompaniesWorked 0.043494 Gender 0.029453 EducationField 0.026846 MonthlyRate 0.015170 PerformanceRating 0.002889 BusinessTravel 0.000074 HourlyRate -0.006846 EmployeeNumber -0.010577 PercentSalaryHike -0.013478 Education -0.031373 YearsSinceLastPromotion -0.033019 RelationshipSatisfaction -0.045872 DailyRate -0.056652 TrainingTimesLastYear -0.059478 WorkLifeBalance -0.063939 EnvironmentSatisfaction -0.103369 JobSatisfaction -0.103481 JobInvolvement -0.130016 YearsAtCompany -0.134392 StockOptionLevel -0.137145 YearsWithCurrManager -0.156199 Age -0.159205 MonthlyIncome -0.159840 YearsInCurrentRole -0.160545 JobLevel -0.169105 TotalWorkingYears -0.171063 EmployeeCount NaN Over18 NaN StandardHours NaN Name: Attrition, dtype: float64
I will add a new feature to this data known as the satisfaction score:
data['SatisfactionScore'] = data['EnvironmentSatisfaction'] + data['JobSatisfaction'] + data['RelationshipSatisfaction']
Now let’s split the data into training and test sets:
from sklearn.model_selection import train_test_split from sklearn.metrics import accuracy_score # Split the data into training and testing sets X = data.drop(['Attrition'], axis=1) y = data['Attrition'] xtrain, xtest, ytrain, ytest = train_test_split(X, y, test_size=0.3, random_state=42)
Now here’s how we can train an employee attrition prediction model:
from sklearn.ensemble import RandomForestClassifier
model = RandomForestClassifier()
model.fit(xtrain, ytrain)
# Evaluate the model's performance
ypred = model.predict(xtest)
accuracy = accuracy_score(ytest, ypred)
print("Accuracy:", accuracy)Accuracy: 0.8662131519274376
Summary
Employee attrition analysis is a kind of behavioural analysis where we study the behaviour and characteristics of the employees who left the organization and compare their characteristics with the current employees to find the employees who may leave the organization soon. I hope you liked this article on Employee Attrition Prediction using Python. Feel free to ask valuable questions in the comments section below.
