build a classification model to predict if the customer has a higher probability of getting a promotion

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Background & Context

The HR team in JMD company stored data of promotion cycle last year, which consists of details of all the employees in the company working last year and also
if they got promoted or not, but every time this process gets delayed due to so many details available for each employee - it gets difficult to compare and decide.

So this time HR team wants to utilize the stored data to make a model, that will predict if a person is eligible for promotion or not.

You as a data scientist at JMD company, need to come up with a model that will help the HR team to predict if a person is eligible for promotion or not.

Objective :

1. Explore and visualize the dataset.
2. Build a classification model to predict if the customer has a higher probability of getting a promotion
3. Optimize the model using appropriate techniques
4. Generate a set of insights and recommendations that will help the company

Data Dictionary:

• employee_id: Unique ID for the employee
• department: Department of employee
• region: Region of employment (unordered)
• education: Education Level
• gender: Gender of Employee
• recruitment_channel: Channel of recruitment for employee
• no_ of_ trainings: no of other trainings completed in the previous year on soft skills, technical skills, etc.
• age: Age of Employee
• previous_ year_ rating: Employee Rating for the previous year
• length_ of_ service: Length of service in years
• awards_ won: if awards won during the previous year then 1 else 0
• avg_ training_ score: Average score in current training evaluations
• is_promoted: (Target) Recommended for promotion

 Best Practices for Notebook : 

 

• The notebook should be well-documented, with inline comments explaining the functionality of code and markdown cells containing comments on the observations and insights.
• The notebook should be run from start to finish sequentially before submission.
• It is preferable to remove all warnings and errors before submission.
• The notebook should be submitted as an HTML file (.html) and NOT as a notebook file (.ipynb)

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