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Courses>Programming Languages>HR Analytics: Predicting Employee Churn in Python
Data ScienceHR Analytics: Predicting Employee Churn in Python
Price:Paid
Length:4 Hours
Language:English
Content type:video
level:advanced
Updated:19 February 2024
Published:12 September 2022
Similar courses
Opportunities
Courses>Programming Languages>HR Analytics: Predicting Employee Churn in Python
HR Analytics: Predicting Employee Churn in Python
4 Hours
6897 students
Syllabus
In this chapter you will learn about the problems addressed by HR analytics, as well as will explore a sample HR dataset that will further be analyzed. You will describe and visualize some of the key variables, transform and manipulate the dataset to make it ready for analytics.
This chapter introduces one of the most popular classification techniques: the Decision Tree. You will use it to develop an algorithm that predicts employee turnover.
Here, you will learn how to evaluate a model and understand how "good" it is. You will compare different trees to choose the best among them.
In this final chapter, you will learn how to use cross-validation to avoid overfitting the training data. You will also learn how to know which features are impactful, and which are negligible. Finally, you will use these newly acquired skills to build a better performing Decision Tree!
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Careertail
Courses>Programming Languages>HR Analytics: Predicting Employee Churn in Python
Data ScienceHR Analytics: Predicting Employee Churn in Python
Price:Paid
Length:4 Hours
Language:English
Content type:video
level:advanced
Updated:19 February 2024
Published:12 September 2022
Similar courses
Opportunities
Courses>Programming Languages>HR Analytics: Predicting Employee Churn in Python
HR Analytics: Predicting Employee Churn in Python
4 Hours
6897 students
Syllabus
In this chapter you will learn about the problems addressed by HR analytics, as well as will explore a sample HR dataset that will further be analyzed. You will describe and visualize some of the key variables, transform and manipulate the dataset to make it ready for analytics.
This chapter introduces one of the most popular classification techniques: the Decision Tree. You will use it to develop an algorithm that predicts employee turnover.
Here, you will learn how to evaluate a model and understand how "good" it is. You will compare different trees to choose the best among them.
In this final chapter, you will learn how to use cross-validation to avoid overfitting the training data. You will also learn how to know which features are impactful, and which are negligible. Finally, you will use these newly acquired skills to build a better performing Decision Tree!
Similar courses
Opportunities
Make the most out of your online education
Careertail
Copyright © 2021 Careertail.
All rights reserved
Quick Links
Get StartedLog InAbout UsCourses
Company
BlogContactsPrivacy PolicyCookie PolicyTerms and Conditions
Stay up to date
Trustpilot