This is a supervised machine learning model build to help the company XYZ understand what changes they should make to their workplace, in order to get most of their employees to stay thus reducing the attrition rates by analysing the datasets
A large company named XYZ, employs, at any given point of time, around 4000 employees. However, every year, around 15% of its employees leave the company and need to be replaced with the talent pool available in the job market. The management believes that this level of attrition (employees leaving, either on their own or because they got fired) is bad for the company, because of the following reasons -
1)The former employees’ projects get delayed, which makes it difficult to meet timelines, resulting in a reputation loss among consumers and partners
2)A sizeable department has to be maintained, for the purposes of recruiting new talent
3)More often than not, the new employees have to be trained for the job and/or given time to acclimatise themselves to the company Hence, the management has contracted an HR analytics firm to understand what factors they should focus on, in order to curb attrition.
In other words, they want to know what changes they should make to their workplace, in order to get most of their employees to stay. Also, they want to know which of these variables is most important and needs to be addressed right away. Since you are one of the star analysts at the firm, this project has been given to you.
You are required to model the probability of attrition using a supervised classification model. The results thus obtained will be used by the management to understand what changes they should make to their workplace, in order to get most of their employees to stay.
Tech stack includes:
Import the required libraries and the datasets and start working on the issues
Follow these 3 step process for contributions:
- Commit changes to a
git branch, making sure to sign-off those changes for the Developer Certificate of Origin. - Create a
GitHub Pull Requestfor your change, following the instructions in the pull request template. - Perform a Code Review with the project maintainers on the pull request.
