Companies are expected to be equal opportunity employers. Their recruitment and selection process is expected to provide equal opportunity irrespective of gender identity or expression, religion, color, sex, age, physical or mental disability, sexual orientation, or any other basis covered by local law. Removal of bias and providing equal opportunity to all applicants promotes access to the widest pool of talent.
XyX corporation recruits employees for fixed Job Codes every year. Basis the applications received last year for each job code profile, a ‘fitment %’ percentage was determined based on selections made. XyX followed a fair and equitable approach by personally looking at all parameters and determining the right fit. This year the number of applicants has multiplied and they are looking at an ML model to predict the ‘fitment %’ for the applications received maintaining a fair and equitable approach.
Task Build a model that calculates the ‘fitment %’ & detects the factor that influences relevancy and making sure that factor does not introduce inequality and/or bias in the’ fitment %’ by appropriate feature re-engineering.
Submit a presentation explaining how your model’s predictions will be used by business leaders to analyse and enable an equal-opportunity and bias-free recruitment process.
Data Set
Train.csv: 13645 x 22
Test.csv: 8745 x 20
Submission.csv: 8745 x 3
Columns Description
Candidate ID ID of candidate
Gender Gender of candidate
JobProfileIDApplyingFor External job posting under consideration
HighestDegree Highest educational degree received
DegreeBranch Branch/Specialization of the degree
LatestDegreeCGPA CGPA obtained for the latest degree
YearsOfExperience Number of years of relevant industry experience
GraduationYear Year of graduation
Graduating Institute Tier of Institution
Language of Communication Preferred language of communication
Age Age of employee
CurrentCTC Compensation received by current employer
ExpectedCTC Expected compensation
MartialStatus Marital status of candidate
EmpScore Score / Rating given by recommenders to the employee (out of 5)
CurrentDesignation Current designation of candidate
Fitment % % fitment of a candidate to the job profile ID basis their background and other factors [Target]
Bias influential factor Factor / Feature that introduces the most bias on the fitment %
Instructions
Download the dataset using 'Download Dataset' button
Solve the problem in your local environment using train.csv to train your model and test.csv to apply the model and generate predictions
Save the predictions in a .csv file matching the format shared in 'sample submission.csv' file
Upload the predictions .csv file under 'Upload File'
Click on 'Submit & Evaluate' to assess your model's performance
Upload the .ipynb file or notebook file along with the presentation file under 'Upload Source code'
