HR analytics

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. SceleneWorks wants 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. 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 The model output should be presented in a user-friendly dashboard format. The model should prioritize accuracy to ensure the highest likelihood of predicting candidates who will join the company. The model output should include charts and graphs for easy interpretation. Excel The model will use a supervised machine learning approach. The dashboard should highlight the probability of acceptance for each candidate. The dashboard should be built as an Excel spreadsheet. The model should use a random forest algorithm for predicting the likelihood of candidates joining the company. Advanced customization options will be available for all dashboard visuals. The dashboard visuals should include advanced customization options for detailed analysis.