[100% Off] Employee Attrition Prediction In Apache Spark (Ml) Project
Employee attrition Prediction in Apache Spark (ML) & HR Analytics Employee Attrition & Performance project for beginners
What you’ll learn
- Understand the business challenge of employee attrition and how predictive analytics can help.
- Set up and work with Apache Spark environments (Databricks free account + Spark cluster).
- Use notebooks (Databricks/Zeppelin) for developing Spark ML projects.
- Load
- explore
- and preprocess HR employee datasets using Spark DataFrames.
- Perform feature engineering with categorical and numerical variables.
- Build and configure a Spark ML classification pipeline to predict employee attrition.
- Train machine learning models such as Logistic Regression and Decision Trees in Spark MLlib.
- Evaluate models using Accuracy
- Precision
- Optimize pipelines and improve predictions for real-world readiness.
- Apply the same Spark ML workflow to solve other HR and business analytics projects.
Requirements
- Basic programming knowledge (Python
- Scala
- or general coding experience).
- Fundamental understanding of Machine Learning concepts (helpful but not mandatory — we’ll cover the essentials).
- No prior Spark or Databricks experience needed — we’ll set everything up step by step.
- A modern laptop/PC with internet access (Databricks provides free cloud clusters).
- Willingness to learn by doing — this is a project-based
- hands-on course.
Description
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.
