HR Analytics: Workforce Optimization with Machine Learning
Predicting employee turnover, performance, and promotion eligibility using Random Forest, XGBoost, and LightGBM
4.45 (11 reviews)
1 767
students
3.5 hours
content
Aug 2024
last update
$13.99
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What you will 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
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6127915
udemy ID
15/08/2024
course created date
27/08/2024
course indexed date
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