Project Overview [Video]
HR Analytics Project Overview: Predicting Candidate Offer Drops
In the competitive landscape of talent acquisition, retaining a candidate after extending an offer is a significant challenge faced by many companies. Particularly for a Talent Acquisition company, every candidate who drops an offer translates to lost time, effort, and resources, which could have been better directed towards other potential candidates. Understanding the factors that influence a candidate’s decision to decline an offer can provide invaluable insights for HR teams to enhance their recruitment strategies.
Objective:
The primary objective of this project is to predict potential offer drops by candidates using Logistic Regression. By analyzing various features related to the candidate and the offer extended, we aim to build a model that can forecast if a particular candidate will join the company after accepting the offer.
Expected Outcome:
By the end of this project, we aim to have a logistic regression model that can accurately predict the likelihood of a candidate dropping an offer. Such a model will empower HR teams to take proactive measures, optimize their recruitment strategies, and ultimately reduce the number of offer drops, leading to more efficient and effective talent acquisition processes.