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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

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