When will I have access to the lectures and assignments?

To access the course materials, assignments and to earn a Certificate, you will need to purchase the Certificate experience when you enroll in a course. You can try a Free Trial instead, or apply for Financial Aid. The course may offer 'Full Course, No Certificate' instead. This option lets you see all course materials, submit required assessments, and get a final grade. This also means that you will not be able to purchase a Certificate experience.

What will I get if I purchase the Certificate?

When you purchase a Certificate you get access to all course materials, including graded assignments. Upon completing the course, your electronic Certificate will be added to your Accomplishments page - from there, you can print your Certificate or add it to your LinkedIn profile.

Is financial aid available?

Yes. In select learning programs, you can apply for financial aid or a scholarship if you can’t afford the enrollment fee. If fin aid or scholarship is available for your learning program selection, you’ll find a link to apply on the description page.

R: Design & Evaluate Random Forests for Attrition

Économisez sur les compétences qui vous font briller avec 40 % de réduction sur 3 mois de Coursera Plus. Économisez maintenant

Ce cours n'est pas disponible en Français (France)

Nous sommes actuellement en train de le traduire dans plus de langues. Consultez les langues disponibles.

R: Design & Evaluate Random Forests for Attrition

Instructeur : EDUCBA

Inclus avec

2 modules

Obtenez un aperçu d'un sujet et apprenez les principes fondamentaux.

4 heures à compléter

Planning flexible

Apprenez à votre propre rythme

2 modules

Obtenez un aperçu d'un sujet et apprenez les principes fondamentaux.

4 heures à compléter

Planning flexible

Apprenez à votre propre rythme

Ce que vous apprendrez

Build and tune Random Forest models in R for real-world HR attrition datasets.
Apply preprocessing and variable selection for accurate employee attrition modeling.
Evaluate and validate model performance using metrics and optimization strategies.

Compétences que vous acquerrez

Catégorie : Data Preprocessing
Catégorie : Workforce Management
Catégorie : Model Training
Catégorie : Model Optimization
Catégorie : Model Evaluation
Catégorie : People Analytics

Outils que vous découvrirez

Catégorie : Classification Algorithms

Détails à connaître

Certificat partageable

Ajouter à votre profil LinkedIn

Évaluations

6 devoirs

Enseigné en Anglais

Découvrez comment les employés des entreprises prestigieuses maîtrisent des compétences recherchées

En savoir plus sur Coursera pour les affaires

logos de Petrobras, TATA, Danone, Capgemini, P&G et L'Oreal

Il y a 2 modules dans ce cours

This course guides learners through the structured development of predictive models using Random Forest techniques in R, specifically applied to employee attrition data. The course is divided into two comprehensive modules. The first module introduces the foundational concepts of classification and Random Forest algorithms, guiding learners to explain, identify, and prepare relevant variables. Learners also perform essential preprocessing tasks to shape the dataset for analysis.

In the second module, students construct, tune, and evaluate Random Forest models using real-world HR data. Through practical lessons, participants will apply parameter optimization techniques, analyze model performance using appropriate metrics, and justify their modeling choices using validation strategies. By the end of the course, learners will have the capability to build robust, interpretable machine learning models for workforce analytics and make informed data-driven decisions regarding employee retention.

Détails du module

This module introduces learners to the fundamentals of employee attrition prediction using Random Forest algorithms in R. It begins with an overview of the business problem, explores the machine learning methodology behind Random Forest, and establishes a strong conceptual framework. Learners will also examine the structure and significance of the dataset, understand variable types and transformations, and perform essential pre-modeling tasks such as data cleaning and encoding. By the end of this module, learners will be able to prepare data and understand Random Forest fundamentals essential for building predictive models.

Inclus

7 vidéos3 devoirs

7 vidéosTotal 54 minutes

Introduction to Employee Attrition Prediction Using Random Forest3 minutes
Random Forest Overview8 minutes
Random Forest Overview Continue11 minutes
Variable Explanation12 minutes
Variable Explanation Continue7 minutes
Pre Modelling Steps8 minutes
Pre Modelling Steps Continue6 minutes

3 devoirsTotal 50 minutes

Foundations of Employee Attrition Prediction30 minutes
Understanding the Problem and Technique10 minutes
Exploring and Preparing the Data10 minutes

This module focuses on implementing, tuning, and validating Random Forest models for employee attrition prediction. Learners will begin by developing a predictive model using cleaned and preprocessed data. They will then explore techniques to optimize model performance, including parameter tuning and validation methods. Emphasis is placed on understanding how hyperparameters influence model behavior and ensuring robust evaluation using appropriate metrics. By the end of the module, learners will be able to build, fine-tune, and validate a Random Forest model that generalizes well to unseen data.