ML-PROJECT-2

Title:

PREDICTING THE LIKELIHOOD OF CANDIDATE JOINING THE COMPANY OR NOT

About the dataset which i've used for this project:

This dataset was part of the recruitment process of a particular client of ScaleneWorks. ScaleneWorks supports several information technology (IT) companies in India with talent acquisition. One of the challenges they face is about 30% of the candidate who accepts the job offer, do not join the company, this leads to a huge loss of revenue and time as the companies initiate the recruitment process again to fill the workforce demand.

Feature description:

• Candidate - Unique reference number to identify candidate
• DOJ Extended - Date of joining asked by candidate or not
• Duration to accept the offer - Number of days taken by the candidate to accept the offer
• Notice period - Notice period served before candidate can join the company
• Offered band - Band offered to candidate based on experience, performance
• Percent hike expected in CTC - Percentage hike expected by the candidate
• Percent hike offered in CTC - Percentage hike offered by the company
• Percent difference CTC - Difference between expected and offered hike
• Joining Bonus - Joining bonus is given or not
• Candidate relocate actual - Candidates have to relocate or not
• Gender - Gender of the candidate
• Candidate Source - Source from which resume of the candidate was obtained
• Rex in Yrs - Relevant years of experience
• LOB - Line of business for which offer was rolled out
• Location - Company location for which offer was rolled out
• Age - Age of the candidate
• Status - Target varible wh whether the candidate joined or not

Scope & Objective:

• To find out if a model can be built to predict the likelihood of a candidate along with a column that indicates if the candidate finally joined the company or not

Business Problem Statement:

• The goal is to predict if the candidate will join or not in the company.
• The challenges they face is about 30% of the candidate who accepts the job offer, do not join the company, this leads to a huge loss of revenue and time as the companies initiate the recruitment process again to fill the workforce demand.

Analytics Tools:

Python & Machine language libraries.

Analytics Approach:

Machine Learning (Logistic Regression, KNN , SVM, Decision Tree, Random Forest )

KPIs:

Accuracy, Precision and Recall, F1-Score, ROC-AUC, Model Training time Deployment Efficiency, Cross validation.

Conclusion:

The best model is RandomForest with an Presicion score of 0.8584720861900098

Notice period affects the most whether or not candidate will join the company.

If offered hike is lower than expected than candidate is more likely to withdraw from the application

LOB and joning bonus affect the likelihood of the candidates. so keep this in mind.