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Employee_retention_machine_learning

Machine learning analysis to identify employees at risk of leaving a company

CONTEXT Retaining top employees represents a major challenge for companies, especially when unemployment is low and companies compete with each other for talent. The goal of this analysis is to identify employees who are most likely to leave in the future, and understand some of the main factors driving churn.

DATASET The dataset represents fictitious/fake data on terminations. For each of 10 years it show employees that are active and those that terminated. The dataset comes from Kaggle: https://www.kaggle.com/HRAnalyticRepository/employee-attrition-data/home

The intent is to see if individual terminations can be predicted from the data provided.

The task is to predict the status of active or terminated.

Content The data contains

employee id, employee record, date ( year of data), birth date, hire date, termination date, age, length of service, city, department, job title, store number, gender, termination reason, termination type, status year, status, business unit

These might be typical types of data in hris.

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Machine learning analysis to identify employees at risk of leaving a company

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