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.

Tuning employee turnover classifier

Pruning the tree

Limiting the sample size

Evaluating the model

Interpreting accuracy metrics

Calculating accuracy metrics: precision

Calculating accuracy metrics: recall

Targeting both leavers and stayers

Calculating the ROC/AUC score

Class imbalance

Balancing classes

Comparison of Employee attrition models

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!

Ottimizzazione degli iperparametri

Cross-validation con sklearn

Impostare i parametri di GridSearch

Implementare GridSearch

Caratteristiche importanti per prevedere l'attrition

Interpretare l'importanza

Ordinare le feature importanti

Selezionare le feature importanti

Sviluppa e testa il modello migliore

Considerazioni finali

Choosing the best turnover prediction model

Employee turnover data

Tra tutti i settori aziendali, l’HR è ancora quello meno trasformato. Tuttavia, i recenti sviluppi negli strumenti e nelle tecnologie di raccolta e analisi dei dati consentono decisioni guidate dai dati in ogni ambito, incluso l’HR. Questo corso ti offrirà una base solida per lavorare con i dati dei dipendenti e sviluppare un modello predittivo per analizzare il turnover.

Intermediate Python

Impara a usare Python per elaborare dati dei dipendenti e sviluppare modelli predittivi con alberi decisionali.

HR Analytics: prevedere l'abbandono dei dipendenti in Python

In questo corso imparerai come usare l'apprendimento automatico nel campo delle risorse umane.

Interpretare l'importanza

HR Analytics: prevedere l'abbandono dei dipendenti in Python

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