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.

Menyetel pengklasifikasi turnover karyawan

Memangkas pohon

Membatasi ukuran sampel

Mengevaluasi model

Menafsirkan metrik akurasi

Menghitung metrik akurasi: precision

Menghitung metrik akurasi: recall

Menargetkan karyawan yang keluar dan yang tetap

Menghitung skor ROC/AUC

Ketidakseimbangan kelas

Menyeimbangkan kelas

Perbandingan model attrition karyawan

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

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 metrik akurasi

Analitik SDM: Memprediksi Perputaran Karyawan dengan Python

Latihan interaktif praktis