Employee Attrition Prediction in Apache Spark (ML) Project
What you'll learn
- In this course we will implement Spark Machine Learning Project Employee Attrition Prediction in Apache Spark using Databricks Notebook (Community server)
- Launching Apache Spark Cluster
- Process that data using a Machine Learning model (Spark ML Library)
- Hands-on learning
- Explore Apache Spark and Machine Learning on the Databricks platform.
- Real-time Use Case
- Create a Data Pipeline
- Publish the Project on Web to Impress your recruiter
- Workforce Data Analysis: Explore and preprocess large-scale HR datasets to uncover patterns and trends.
- Feature Engineering for HR: Identify and engineer key factors like job satisfaction, performance, and workload that influence employee attrition.
- Machine Learning Pipelines: Build scalable predictive models using Spark MLlib to forecast attrition risks.
- Model Optimization & Evaluation: Fine-tune your machine learning models to maximize prediction accuracy and business impact.
- Data-Driven Insights: Learn how to translate model predictions into actionable strategies for improving employee retention.
Requirements
- Apache Spark basic and Scala fundamental knowledge is required and SQL Basics along with Machine Learning basics
- Following browsers on Windows, Linux or macOS desktop:
- Google Chrome (Latest version), Firefox (Latest version), Safari (Latest version), Microsoft Edge* (Latest version)
- Internet Explorer 11* on Windows 7, 8, or 10 (with latest Windows updates applied)
- *You might see performance degradation for some features on Microsoft Edge and Internet Explorer.
- The following browsers are not supported:
- Mobile browsers.
- Beta, “preview,” or otherwise pre-release versions of desktop browsers.
Description
Spark Machine Learning Project (Employee Attrition Prediction) for beginners using Databricks Notebook (Unofficial) (Community edition Server)
In this Data science Machine Learning project, we will create Employee Attrition Prediction Project using Decision Tree Classification algorithm one of the predictive models.
Explore Apache Spark and Machine Learning on the Databricks platform.
Launching Spark Cluster
Create a Data Pipeline
Process that data using a Machine Learning model (Spark ML Library)
Hands-on learning
Real time Use Case
Publish the Project on Web to Impress your recruiter
Graphical Representation of Data using Databricks notebook.
Transform structured data using SparkSQL and DataFrames
Employee Attrition Prediction a Real time Use Case on Apache Spark
About Databricks:
Databricks lets you start writing Spark ML code instantly so you can focus on your data problems.
Are you ready to tackle one of the most pressing challenges in HR and workforce management? This project-based course will guide you through building an Employee Attrition Prediction Model using Apache Spark, equipping you with the skills to help organizations retain top talent and reduce turnover costs.
Employee attrition impacts productivity, morale, and business outcomes, making predictive insights a powerful tool for HR leaders. In this hands-on course, you’ll master big data analytics and machine learning techniques to analyze workforce data, predict attrition risks, and deliver actionable recommendations. By the end, you’ll have a real-world project in your portfolio and the confidence to use data science to drive smarter HR decisions.
What You’ll Learn:
Workforce Data Analysis: Explore and preprocess large-scale HR datasets to uncover patterns and trends.
Feature Engineering for HR: Identify and engineer key factors like job satisfaction, performance, and workload that influence employee attrition.
Machine Learning Pipelines: Build scalable predictive models using Spark MLlib to forecast attrition risks.
Model Optimization & Evaluation: Fine-tune your machine learning models to maximize prediction accuracy and business impact.
Data-Driven Insights: Learn how to translate model predictions into actionable strategies for improving employee retention.
Real-World Benefits:
Practical HR Solutions: Solve real-world business challenges by predicting and mitigating employee attrition.
Portfolio-Worthy Project: Showcase a high-impact project to demonstrate your expertise in big data and predictive analytics.
Career Growth: Position yourself as a data professional capable of delivering insights that transform organizational outcomes.
Who Should Enroll:
Data Scientists & Analysts seeking hands-on experience in predictive modeling for workforce analytics.
HR Professionals & Leaders eager to leverage data science to enhance retention strategies and optimize workforce planning.
Big Data Professionals looking to apply Apache Spark to solve human capital challenges.
Become the go-to expert in predictive workforce analytics! Enroll now to master Apache Spark, build an Employee Attrition Prediction Model, and make a real impact on organizational success.
Who this course is for:
- Beginner Apache Spark Developer, Bigdata Engineers or Developers, Software Developer, Machine Learning Engineer, Data Scientist
- Data Scientists & Analysts seeking hands-on experience in predictive modeling for workforce analytics.
- HR Professionals & Leaders eager to leverage data science to enhance retention strategies and optimize workforce planning.
- Big Data Professionals looking to apply Apache Spark to solve human capital challenges.
Instructor
I am Solution Architect with 12+ year’s of experience in Banking, Telecommunication and Financial Services industry across a diverse range of roles in Credit Card, Payments, Data Warehouse and Data Center programmes
My role as Bigdata and Cloud Architect to work as part of Bigdata team to provide Software Solution.
Responsibilities includes,
- Support all Hadoop related issues
- Benchmark existing systems, Analyse existing system challenges/bottlenecks and Propose right solutions to eliminate them based on various Big Data technologies
- Analyse and Define pros and cons of various technologies and platforms
- Define use cases, solutions and recommendations
- Define Big Data strategy
- Perform detailed analysis of business problems and technical environments
- Define pragmatic Big Data solution based on customer requirements analysis
- Define pragmatic Big Data Cluster recommendations
- Educate customers on various Big Data technologies to help them understand pros and cons of Big Data
- Data Governance
- Build Tools to improve developer productivity and implement standard practices
I am sure the knowledge in these courses can give you extra power to win in life.
All the best!!