The dataset we have choosen is about human resources. Our aim is to answer an interesting question of a company such as “Why are our best and most experienced employees leaving prematurely?” We will try to find an answer to this question by analyzing answers of the employees to the job satisfaction survey and their work related records. The dataset is formed by the Human Resources (HR) department after conducting a survey on their employees.
We named our group as 2Yaka.
Our group’s members are:
We used Human Resources Analytics Data from kaggle. This HR data set is obtained from the results of a satisfaction survey the company has carried out on their employees in combination with other HR related records. It consists of 14999 rows and 10 columns. Each row is dedicated for a different employee. Out of 10, 8 columns are in numeric type, while the remaining 2 are in numeric values. Below you can find columns and their explanations, respectively.
1st Column: Satisfaction level
2nd Column: Last evaluation score
3rd Column: Number of projects worked on (yearly basis)
4th Column: Average monthly working hours
5th Column: Time spent in the company (in years)
6th Column: Whether they have had a work accident in the last 2 years
7th Column: Whether they have had a promotion in the last 5 years
8th Column: Departments
9th Column: Salary
10th Column: Whether the employee has left
All the data collected is from last 5 years whereas accident data belongs to past 2 years. This HR database does not take into account the employees that have been fired, transferred or hired in the past year. Our objective is to make predictions about the probabilities that employees may leave their company and what to change to increase their satisfaction levels. We will try to give insights to make best employees more loyal.