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Employee attrition Prediction in Apache Spark (ML) & HR Analytics Employee Attrition & Performance project for beginners
Language: English
Instructors: Bigdata Engineer
Why this course?
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:
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.
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