Feature Selection, Model Selection, and Tuning (FMST) - Employee Promotion Eligibility Prediction at Likoma Company. - By David Salako.

Background and Context:

Employee Promotion means the ascension of an employee to higher ranks, this aspect of the job is what drives employees the most. The ultimate reward for dedication and loyalty towards an organization and HR team plays an important role in handling all these promotion tasks based on ratings and other attributes available.

The HR team in Likoma company stored data of promotion cycle last year, which consists of details of all the employees in the company working last year and also if they got promoted or not, but every time this process gets delayed due to so many details available for each employee - it gets difficult to compare and decide.

So this time HR team wants to utilize the stored data to make a model, that will predict if a person is eligible for promotion or not.

Problem Statement:

As a data scientist at Likoma company, I need to design a model that will help the HR team predict if an employee is eligible for promotion or not.

Objective:

To build a model to explore, visualize, predict and identify the employees who have a higher probability of getting promoted. Subsequently optimize the classification model using appropriate techniques and finally generate a set of insights and recommendations that will help the company and its human resources department.

Data Dictionary & Description:

* employee_id: Unique ID for the employee
* department: Department of employee
* region: Region of employment (unordered)
* education: Education Level
* gender: Gender of Employee
* recruitment_channel: Channel of recruitment for employee
* no_ of_ trainings: no of other trainings completed in the previous year on soft skills, technical skills, etc.
* age: Age of Employee
* previous_ year_ rating: Employee Rating for the previous year
* length_ of_ service: Length of service in years
* awards_ won: if awards won during the previous year then 1 else 0
* avg_ training_ score: Average score in current training evaluations
* is_promoted: (Target) Recommended for promotion