Employee Attrition Prediction in Apache Spark (ML) Project

About Employee Attrition Prediction in Apache Spark (ML) Project

Employee attrition is one of the biggest challenges organizations face today. Companies invest heavily in hiring and training employees, but when employees leave unexpectedly, it creates financial loss and operational challenges. Predicting employee attrition using data-driven approaches helps organizations take proactive measures to retain talent. In this

hands-on project-based course

, you will learn how to build a complete

Employee Attrition Prediction system

using

Apache Spark and Spark MLlib

. This course is designed for

data engineers, data scientists, and ML enthusiasts

who want to gain real-world experience with Spark Machine Learning by solving a

business-critical HR analytics problem

. We will begin with

Apache Spark basics

— setting up the environment, provisioning a cluster, and working with notebooks in both

Zeppelin and Databricks

. You will learn how to explore, clean, and transform HR datasets with

Spark DataFrames

. Then, we’ll dive deep into

feature engineering, model training, and evaluation

using Spark MLlib. By the end of this course, you will not only have built a

fully working attrition prediction model

but also understand how to apply

Spark ML workflows

to other real-world business scenarios. This is a

practical, project-driven course

— no boring theory, just step-by-step implementation with real datasets, clear explanations, and guidance to help you become confident in applying Spark MLlib for predictive analytics.

Key highlights of the course

:

Understand the

business problem of employee attrition

and why it matters.

Learn to

set up Apache Spark locally and on Databricks

(free account).

Work with

Spark DataFrames

for data manipulation.

Explore and understand the

HR dataset

used for attrition analysis.

Perform

data preprocessing

and handle categorical variables.

Build

feature vectors

using

StringIndexer

and

VectorAssembler

.

Train a

classification model

in Spark MLlib to predict employee attrition.

Evaluate the model with

classification metrics

like Accuracy, Precision, Recall, and F1-score.

Optimize your ML pipeline and improve prediction performance.

Deploy and interpret results for

business decision-making

.

Gain experience with both

on-premise Zeppelin

and

cloud-based Databricks

workflows. Whether you are a

student, professional, or aspiring data engineer/scientist

, this course will equip you with the

skills and hands-on practice

you need to work on

real Spark ML projects

.