akmcmasters's repositories
Attrition_Prediction
Employee turnover (attrition) is a major cost to an organization, and predicting turnover is at the forefront of needs of Human Resources (HR) in many organizations. Until now the mainstream approach has been to use logistic regression or survival curves to model employee attrition. However, with advancements in machine learning (ML), we can now get both better predictive performance and better explanations of what critical features are linked to employee attrition. In this post, we’ll use two cutting edge techniques.
HR-Analytics
Human Resources Analytics: Why are our best and most experienced employees leaving prematurely?
HR-Analytics-1
Analyzing the HR Criteria of a Company and how they promote their Employees and keep Balance between them using Data Analytics, Data Visualizations, and Machine Learning Models for Classification Purposes.
Human-Resources
Code for automating reporting/analysis
Human-Resources-Analytics-1
Reducing Employee Attrition
Human-Resources-Analytics-Prediction
EDA and predicting if an individual may leave the Company or NOT.
Management-Analytics
Data science for management
predicting-employee-attrition-Soton-ECS
A repo for Human Resources Analytics by using data mining
testing
Testing repo for linkage to Rstudio
Wharton-People-Analytics
College classification using K-means clustering to optimize recruiting effort