## Context:
An MNC has thousands of employees spread out across the globe. The company believes in hiring the best talent available and retaining them for as long as possible. A huge amount of resources is spent on retaining existing employees through various initiatives. The Head of People Operations wants to bring down the cost of retaining employees. For this, he proposes limiting the incentives to only those employees who are at risk of attrition. As a recently hired Data Scientist, you have been asked to identify patterns in characteristics of employees who leave the organization. Also, you have to use this information to predict if an employee is at risk of attrition. This information will be used to target them with incentives.

## Objective :

* To identify the different factors that drive attrition
* To make a model to predict if an employee will attrite or not


## Dataset :

The data contains demographic details, work-related metrics and attrition flag.

* **EmployeeNumber** - Employee Identifier
* **Attrition** - Did the employee attrite?
* **Age** - Age of the employee
* **BusinessTravel** - Travel commitments for the job
* **DailyRate** - Data description not available**
* **Department** - Employee Department
* **DistanceFromHome** - Distance from work to home (in km)
* **Education** - 1-Below College, 2-College, 3-Bachelor, 4-Master,5-Doctor
* **EducationField** - Field of Education
* **EnvironmentSatisfaction** - 1-Low, 2-Medium, 3-High, 4-Very High
* **Gender** - Employee's gender
* **HourlyRate** - Data description not available**
* **JobInvolvement** - 1-Low, 2-Medium, 3-High, 4-Very High
* **JobLevel** - Level of job (1 to 5)
* **JobRole** - Job Roles
* **JobSatisfaction** - 1-Low, 2-Medium, 3-High, 4-Very High
* **MaritalStatus** - Marital Status
* **MonthlyIncome** - Monthly Salary
* **MonthlyRate** - Data description not available**
* **NumCompaniesWorked** - Number of companies worked at
* **Over18** - Over 18 years of age?
* **OverTime** - Overtime?
* **PercentSalaryHike** - The percentage increase in salary last year
* **PerformanceRating** - 1-Low, 2-Good, 3-Excellent, 4-Outstanding
* **RelationshipSatisfaction** - 1-Low, 2-Medium, 3-High, 4-Very High
* **StandardHours** - Standard Hours
* **StockOptionLevel** - Stock Option Level
* **TotalWorkingYears** - Total years worked
* **TrainingTimesLastYear** - Number of training attended last year
* **WorkLifeBalance** - 1-Low, 2-Good, 3-Excellent, 4-Outstanding
* **YearsAtCompany** - Years at Company
* **YearsInCurrentRole** - Years in the current role
* **YearsSinceLastPromotion** - Years since the last promotion
* **YearsWithCurrManager** - Years with the current manager

**In the real world, you will not find definitions for some of your variables. It is a part of the analysis to figure out what they might mean.**