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.

Introduction and overview

Finding categorical variables

Observing categoricals

Transforming categorical variables

Encoding categories

Getting dummies

Dummy trap

Descriptive statistics

Correlations in the employee data

Percentage of employees who churn

Introduction to HR 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.

Splitting the data

Separating Target and Features

Spliting employee data

Introduction to Decision Tree classification

Computing Gini index

Splitting the tree

Predicting employee churn using decision trees

Fitting the tree to employee data

Checking the accuracy of prediction

Interpretation of the decision tree

Exporting the tree

Interpretation of results

Predicting 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.

De churn-classifier voor medewerkers finetunen

De boom snoeien

De steekproefgrootte beperken

Het model evalueren

Nauwkeurigheidsmaten interpreteren

Nauwkeurigheidsmaat berekenen: precision

Nauwkeurigheidsmaten berekenen: recall

Richten op zowel vertrekkers als blijvers

De ROC/AUC-score berekenen

Klassenonevenwicht

Klassen balanceren

Vergelijking van modellen voor personeelsverloop

Evaluating the turnover prediction model

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!

Hyperparameter tuning

Cross-validation using sklearn

Setting up GridSearch parameters

Implementing GridSearch

Important features for predicting attrition

Interpreting importance

Sorting important features

Selecting important features

Develop and test the best model

Final thoughts

Choosing the best turnover prediction model

Employee turnover data

Van alle bedrijfsdomeinen is HR nog het minst ontwricht. Met de nieuwste ontwikkelingen in tools en technologieën voor gegevensverzameling en -analyse kun je echter in alle onderdelen, dus ook in HR, datagedreven beslissingen nemen. Deze cursus geeft je een stevige basis om met werknemersgegevens te werken en een voorspellend model te ontwikkelen om personeelsverloop te analyseren.

Intermediate Python

Leer hoe je Python gebruikt voor personeelsdata en een voorspellend model voor personeelsverloop.

HR-analytics: verloop van medewerkers voorspellen in Python

In deze cursus leer je hoe je machine learning kunt gebruiken in HR.

Assistant Professor of Data Science American University of Armenia

Targeting both leavers and stayers

AUC score

Let's practice!

Evaluating the turnover prediction model - Richten op zowel vertrekkers als blijvers

Richten op zowel vertrekkers als blijvers

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