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!

Penyetelan hiperparameter

Cross-validation menggunakan sklearn

Menyiapkan parameter GridSearch

Menerapkan GridSearch

Fitur penting untuk memprediksi attrition

Menafsirkan tingkat kepentingan

Mengurutkan fitur penting

Memilih fitur penting

Kembangkan dan uji model terbaik

Pemikiran akhir

Choosing the best turnover prediction model

Employee turnover data

Di antara semua domain bisnis, SDM masih yang paling sedikit mengalami disrupsi. Namun, perkembangan terbaru dalam alat dan teknologi pengumpulan serta analisis data memungkinkan pengambilan keputusan berbasis data di semua dimensi, termasuk SDM. Kursus ini akan memberikan dasar yang kuat untuk mengelola data karyawan dan mengembangkan model prediktif untuk menganalisis perputaran karyawan.

Intermediate Python

Pelajari cara menggunakan Python untuk memproses data karyawan dan mengembangkan model prediksi.

Analitik SDM: Memprediksi Perputaran Karyawan dengan Python

Dalam kursus ini, Anda akan belajar cara menerapkan machine learning di bidang sumber daya manusia (SDM).

Menafsirkan tingkat kepentingan

Analitik SDM: Memprediksi Perputaran Karyawan dengan Python

Latihan interaktif praktis