Employee Attrition Prediction in Apache Spark (ML) Project
Employee attrition Prediction in Apache Spark (ML) & HR Analytics Employee Attrition & Performance project for beginners
Development,Data Science and AI ML,Machine Learning
Lectures -21
Resources -2
Duration -2 hours
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Course Description
Apache 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.
Goals
What will you learn in this course:
- In this course you will implement Spark Machine Learning Project Employee Attrition Prediction in Apache Spark using Databricks Notebook (Community edition 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
Prerequisites
What are the prerequisites for this course?
- 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)
Curriculum
Check out the detailed breakdown of what’s inside the course
Introduction
1 Lectures
- Introduction 05:30 05:30
Project Begins
16 Lectures
Download Resources
3 Lectures
Instructor Details
Bigdata Engineer
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