Predicting Employee Attrition

Problem Statement:

In recent years, attention has increasingly been paid to human resources (HR), since worker quality and skills represent a growth factor and a real competitive advantage for companies. After proving its mettle in sales and marketing, artificial intelligence is also becoming central to employee-related decisions within HR management. Organizational growth largely depends on staff retention. Losing employees frequently impacts the morale of the organization and hiring new employees is more expensive than retaining existing ones.

You are working as a data scientist with HR Department of a large insurance company focused on sales team attrition. Insurance sales teams help insurance companies generate new business by contacting potential customers and selling one or more types of insurance. The department generally sees high attrition and thus staffing becomes a crucial aspect.

To aid staffing, you are provided with the monthly information for a segment of employees for 2016 and 2017 and tasked to predict whether a current employee will be leaving the organization in the upcoming two quarters (01 Jan 2018 - 01 July 2018) or not, given:

Demographics of the employee (city, age, gender etc.)
Tenure information (joining date, Last Date)
Historical data regarding the performance of the employee (Quarterly rating, Monthly business acquired, designation, salary)

Context

Uncover the factors that lead to employee attrition and explore important questions such as ‘show me a breakdown of distance from home by job role and attrition’ or ‘compare average monthly income by education and attrition’.

Content

This dataset is about employee attrition prediction.

The data contains 19,104 instances (employees) with other features such as Age, gender, city, Date of joining, Last working date, Designation etc

Inspiration

This prediction would be useful for Hr analytics and to decrease employee attrition.