Free Download Hr Analytics Workforce Optimization With Machine Learning
Published 8/2024
MP4 | Video: h264, 1920x1080 | Audio: AAC, 44.1 KHz
Language: English | Size: 1.87 GB | Duration: 3h 33m
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
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