As the COVID-19 keeps unleashing its havoc, the world continues to get pushed into the crisis of the great economic recession, more and more companies start to cut down their underperforming employees. Companies firing hundreds and thousands of Employees is a typical headline today. Cutting down employees or reducing an employee salary is a tough decision to take.

To predict Employee Attrition by the given data about his/her past history.

Numpy, Pandas, IPython, Scikit-Learn, Matplotlib, Seaborn.

I am using the data provided by IBM. This notebook belongs to a Kaggle competition https://www.kaggle.com/c/summeranalytics2020 organized by Consultacy and Analytics club of IIT, Guwahati.

The various columns are : d - an anonymous id given to an Employee

Age - Age of an Employee

Attrition - Did the Employee leave the company, 0-No, 1-Yes

BusinessTravel - Travlling frequency of an Employee

Department - Work Department

DistanceFromHome - Distance of office from home

EducationField - Field of Education

EmployeeNumber - Number of Employees in the division of a given Employee

EnvironmentSatisfaction - Work Environment Satisfaction

Gender - Gender of Employee

MartialStatus - Martial Status of an employee

MonthlyIncome - Monthly Income of Employee in USD

NumCompaniesWorked - Number of Companies in which Employee has worked before joining this Company

OverTime - Does The person work overtime

PercentSalaryHike - Average annual salary hike in percentages

StockOptionLevel - Company stocks given to an Employee

TotalWorkingYears - Total working experience of an employee

TrainingTimesLastYear - No. of trainings an employee went through last year

YearsAtCompany - Number of years worked at this company

YearsInCurrentRole - Number of years in current role

YearsSinceLastPromotion - Number of years since last promotion

YearsWithCurrManager - Number of years with the current manager

Education

1 'Below College' 2 'College' 3 'Bachelor' 4 'Master' 5 'Doctor'

EnvironmentSatisfaction

1 'Low' 2 'Medium' 3 'High' 4 'Very High'

JobInvolvement

1 'Low' 2 'Medium' 3 'High' 4 'Very High'

JobSatisfaction

1 'Low' 2 'Medium' 3 'High' 4 'Very High'

PerformanceRating

1 'Low' 2 'Good' 3 'Excellent' 4 'Outstanding'

Behaviour

1 'Good' 2 'Bad' 3 'Not Rated'

CommunicationSkill

1 'Bad' 2 'Average' 3 'Good' 4 'Better' 5 'Best'

StockOptionLevel

0 'No stocks' 1 'Less Stocks' 2 'Moderate Stocks' 3 'A lot of Stocks'