Worker attrition Prediction in Apache Spark (ML) & HR Analytics Worker Attrition & Efficiency mission for newbies
What you’ll study
On this course we’ll implement Spark Machine Studying Challenge Worker Attrition Prediction in Apache Spark utilizing Databricks Pocket book (Group server)
Launching Apache Spark Cluster
Course of that information utilizing a Machine Studying mannequin (Spark ML Library)
Palms-on studying
Discover Apache Spark and Machine Studying on the Databricks platform.
Actual-time Use Case
Create a Knowledge Pipeline
Publish the Challenge on Internet to Impress your recruiter
Workforce Knowledge Evaluation: Discover and preprocess large-scale HR datasets to uncover patterns and traits.
Function Engineering for HR: Determine and engineer key components like job satisfaction, efficiency, and workload that affect worker attrition.
Machine Studying Pipelines: Construct scalable predictive fashions utilizing Spark MLlib to forecast attrition dangers.
Mannequin Optimization & Analysis: Effective-tune your machine studying fashions to maximise prediction accuracy and enterprise affect.
Knowledge-Pushed Insights: Discover ways to translate mannequin predictions into actionable methods for bettering worker retention.
English
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