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employee_recomender_system's Introduction

Employee_promotion_recommender

Problem statement:

A machine learning model to determine whether an employee is due for promotion based on the HR metrics

Objective:

The company has hired you to help out in identifying the eligible candidates at a specific checkpoint, so as to help them expedite the whole promotion process. The company has provided multiple data points around the staff’s past and present performance, together with some demographics. Predict whether a candidate within the promotion pipeline should be promoted or not.

Description

  • optimization of each of the the above models to Identify the best model, and used the best model to determine which features are most impactful in influencing the prediction

Solution 1: XGBoost

Solution 2:LightGBM

Solution 3:CatBoost

Data Features

Dataset

The dataset has the following features:

Column: Description

employee_id: Unique ID for 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: number of other trainings completed in previous year

age: Age of Employee

previous_year_rating: Employee Rating for the previous year

length_of_service: Length of service in years

KPIs_met >80%: if KPIs >80% then 1 else 0

awards_won?: if awards won during previous year then 1 else 0

avg_training_score: Average score in current training evaluations

is_promoted (Target): Recommended for promotion

Context

A Kenyan company, Simba Deliveries, has 9 departments across the company. The company HR department usually has a hard task identifying the right staff to be considered for various promotions within the company. The promotions are usually only for the manager position and below. The current process they are following is:

  1. Identify the set of staff liable for promotion based on past performance or recommendations
  2. Chosen staff are taken through separate trainings programs and evaluations based on the skills that are required
  3. In light of different factors such as program and training performance, KPI completion (only employees with KPIs > 80% are considered) and such, the chosen staff are considered for promotion.

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