Introduction:
In XYZ company, many
employees leave each month. The stockholders want to know facts about employees
who leave, so the recruitment will be ready and will keep enough supply for hot
jobs that employees leave, this is on one hand. On the other hand, they want to
know what rules have less lifespan more than others as well as other questions
to answer.
Data collection:
At the first analysis, the
data was stored in word files (Service certificates) of the employees that have
left the company. In the second analysis, the dataset was provided from the
company system with higher accuracy and more features.
Data Wrangling:
The wrangling process did
not take a long time for the data which was provided from the company’s system.
Conducting the analysis
and answering the questions:
From the analysis a part
of the big picture started to show up and this helped us where the problem
happens and what factors drive the employees to leave the company.
Data questions:
1- What are the most jobs that the employees leave?
2- Does education or department affect the service period?
3- Does marital status affect the service period?
In the following graph we will answer the first question:
We picked the top 10 positions that form the biggest
chunk of the employees who leave the company. We notice that most of the people
they left are in sales or customer services. In addition, most of the employees are below the supervision/management level. It is better to investigate these jobs
with other factors like education and marital status.
The second question:
Does education or department affect the service period?
We can see in the previous table,
that the average service period in the last column on the left for employees in business
units (Sales Departments) is lower than the average service of supporting
departments. However, education does not seem to have an obvious trend.
The third question:
Does marital status affect the service period?
From the previous figures 19-1
& 19-2, there is a moderate relationship between service period and marital
status.
Department with higher average
service period tend to have a higher married rate of employees and the opposite
is true, that departments with a high rate of single employees tend to have less
average service period.
Conclusions:
- There are some jobs which
require quick actions and to be ready for them with immediate candidates and
there are some jobs that employees spend a longer time in them.
- The sales department tend
to keep its employees for less time than supporting departments.
- The marital status tends to affect the service period the employees spend in the company.
References:
The whole project is available on my Github profile here, and more questions are answered.
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