Hr Analytics: Workforce Optimization With Machine Learning

Predicting employee turnover, performance, and promotion eligibility using Random Forest, XGBoost, and LightGBM

What you'll learn

Learn how to build employee turnover predictive model using Random Forest

Learn how to build employee performance predictive model using XGBoost

Learn how to build promotion eligibility predictive model using LightGBM

Learn how to analyze the impact of overtime work on turnover rate

Learn how to analyze the relationship between work life balance and turnover rate

Learn how to analyze the relationship between number of promotions and turnover rate

Learn how to analyze the relationship between education level and employee performance

Learn how to analyze the impact of remote work on employee performance

Learn how to identify top performers in the company

Learn the basic fundamentals of human resources analytics, technical challenges and limitations in HR analytics, and its use cases

Learn how HR predictive modeling works. This section covers data collection, preprocessing, feature selection, train test split, model selection, model training

Learn about factors that contribute to an employee's performance and turnover rate, such as job satisfaction, work life balance, compensation and benefits

Learn how to find and download HR dataset from Kaggle

Learn how to clean dataset by removing missing values and duplicates

Learn how to handle imbalanced dataset using Synthetic Minority Oversampling Technique and Adaptive Synthetic Sampling Approach

Learn how to evaluate the accuracy and performance of the model by calculating precision score, recall score, and creating confusion matrix

Requirements

No previous experience in HR analytics is required

Basic knowledge in Python and machine learning

Description

Welcome to HR Analytics: Workforce Optimization with Machine Learning course. This is a comprehensive project based course where you will learn step by step on how to build predictive models for employee retention, performance assessment, and promotion eligibility using Random Forest, XGBoost, and LightGBM. This course is a perfect combination between machine learning and HR analytics, making it an ideal opportunity to level up your data science skills while improving your technical knowledge in human resource management. The course will be mainly focusing on three major aspects, the first one is data analysis where you will explore the HR dataset from various angles, the second one is predictive modeling where you will learn how to build HR predictive models using machine learning, and the third one is to evaluate the accuracy and performance of the model. In the introduction session, you will learn the basic fundamentals of human resources analytics, such as getting to know predictive modeling use cases in human resources, getting to know more about machine learning models that will be used, and you will also learn about technical challenges and limitations in HR analytics. Then, in the next section, you will learn how the HR predictive model works. This section will cover data collection, data preprocessing, feature selection, splitting the data into training and testing sets, model selection, model training, making predictions based on training data, and model evaluation. Afterward, you will also learn about several factors that contribute to an employee's performance and turnover rate, for example like job satisfaction, work life balance, career development opportunities, working environment, benefits, and compensations. Once you have learnt all necessary knowledge about HR analytics, we will start the project. Firstly you will be guided step by step on how to set up Google Colab IDE. In addition to that, you will also learn how to find and download HR dataset from Kaggle. Once everything is ready, we will enter the first project section where you will explore the HR dataset from multiple angles, not only that, you will also visualize the data and try to identify trends or patterns in the data. In the second part, you will learn step by step on how to build employee retention predictive model, performance assessment predictive model, and promotion eligibility predictive model using Random Forest, XGBoost, and LightGBM. Meanwhile, in the third part, you will learn how to evaluate the accuracy and performance of the model using several methods like confusion matrix, precision, and recall. Lastly, at the end of the course, we will conduct testing to make sure that the HR predictive models have been fully functioning and generate accurate results.First of all, before getting into the course, we need to ask ourselves this question: why should we build HR predictive models using machine learning? Well, here is my answer. In today’s dynamic workplace, HR professionals face complex challenges in managing employee performance, retention, and talent optimization. Traditional methods often fall short in addressing these complexities. Machine learning models and big data analytics can revolutionize HR practices by providing data-driven insights and helping you to make more informed decisions. By leveraging these technologies, HR professionals can optimize employee performance by identifying factors that contribute to high productivity and high job satisfaction. In addition, it can also help you to identify top performers in your company which will enable more effective talent management and career development plans. Lastly, you can also increase retention rates by understanding and addressing the root causes of employee turnover, and formulate evidence-based company policies that drive better outcomes. Mastering these skills not only empowers HR professionals to make more strategic decisions but also opens up numerous career opportunities in the growing field of HR analytics and data science.Below are things that you can expect to learn from this course:Learn the basic fundamentals of human resources analytics, technical challenges and limitations in HR analytics, and its use casesLearn how HR predictive modeling works. This section will cover data collection, preprocessing, feature selection, train test split, model selection, model training, making prediction, and model evaluationLearn about factors that contribute to an employee's performance and turnover rate, such as job satisfaction, work life balance, career development opportunities, working environment, compensation and benefits.Learn how to find and download HR dataset from KaggleLearn how to clean dataset by removing missing values and duplicatesLearn how to analyze the relationship between number of promotions and turnover rateLearn how to analyze the relationship between work life balance and turnover rateLearn how to analyze the impact of overtime work on turnover rateLearn how to analyze the relationship between education level and employee performanceLearn how to analyze the impact of remote work on employee performanceLearn how to identify top performers in the companyLearn how to build employee turnover predictive model using Random ForestLearn how to build employee performance predictive model using XGBoostLearn how to build promotion eligibility predictive model using LightGBMLearn how to handle imbalanced dataset using Synthetic Minority Oversampling Technique and Adaptive Synthetic Sampling ApproachLearn how to evaluate the accuracy and performance of the model by calculating precision score, recall score, and creating confusion matrix

Overview

Section 1: Introduction to the Course

Lecture 1 Introduction

Lecture 2 Table of Contents

Lecture 3 Whom This Course is Intended for?

Section 2: Tools, IDE, and Datasets

Lecture 4 Tools, IDE, and Datasets

Section 3: Introduction to HR Analytics

Lecture 5 Introduction to HR Analytics

Section 4: How HR Predictive Modelling Works?

Lecture 6 How HR Predictive Modelling Works?

Section 5: Factors That Contribute to Employees Performance & Turnover Rate

Lecture 7 Factors That Contribute to Employees Performance & Turnover Rate

Section 6: Finding & Downloading HR Dataset From Kaggle

Lecture 8 Finding & Downloading HR Dataset From Kaggle

Section 7: Uploading HR Dataset to Google Colab

Lecture 9 Uploading HR Dataset to Google Colab

Section 8: Quick Overview of HR Dataset

Lecture 10 Quick Overview of HR Dataset

Section 9: Cleaning Dataset by Removing Missing Values & Duplicates

Lecture 11 Cleaning Dataset by Removing Missing Values & Duplicates

Section 10: Analyzing Relationship Between Number of Promotions & Employee Turnover Rate

Lecture 12 Analyzing Relationship Between Number of Promotions & Employee Turnover Rate

Section 11: Analyzing Relationship Between Work Life Balance & Employee Turnover Rate

Lecture 13 Analyzing Relationship Between Work Life Balance & Employee Turnover Rate

Section 12: Analyzing the Impact of Overtime Work on Employee Turnover Rate

Lecture 14 Analyzing the Impact of Overtime Work on Employee Turnover Rate

Section 13: Analyzing Relationship Between Education Level & Employee Performance

Lecture 15 Analyzing Relationship Between Education Level & Employee Performance

Section 14: Analyzing the Impact of Remote Work on Employee Performance

Lecture 16 Analyzing the Impact of Remote Work on Employee Performance

Section 15: Identifying Top Performers in the Company

Lecture 17 Identifying Top Performers in the Company

Section 16: Building Employee Turnover Predictive Model with Random Forest

Lecture 18 Building Employee Turnover Predictive Model with Random Forest Part 1

Lecture 19 Building Employee Turnover Predictive Model with Random Forest Part 2

Section 17: Building Employee Performance Predictive Model with XGBoost

Lecture 20 Building Employee Performance Predictive Model with XGBoost

Section 18: Building Promotion Eligibility Predictive Model with LightGBM

Lecture 21 Building Promotion Eligibility Predictive Model with LightGBM

Section 19: Evaluating Model Accuracy & Performance

Lecture 22 Calculating Precision Score, Recall Score, and Creating Confusion Matrix

Section 20: Conclusion & Summary

Lecture 23 Conclusion & Summary

People who are interested in gaining valuable insights from HR analytics,People who are interested in building HR predictive models using maching learning