HR analytics is revolutionising the way human resources departments operate, leading to higher efficiency and better results overall. Human resources have been using analytics for years. However, the collection, processing and analysis of data have been largely manual, and given the nature of human resources dynamics and HR KPIs, the approach has been constraining HR. Therefore, it is surprising that HR departments woke up to the utility of machine learning so late in the game.
You work for Alphabet Inc. which is a large Multinational Corporation. The company has 9 broad verticals across the organisation. One of the problems the company is facing is around identifying the right people for promotion (only for the manager position and below) and prepare them in time. Currently, the process that HR is following is as follows:
- They first identify a set of employees based on recommendations/ past performance.
- Selected employees go through the separate training and evaluation program for each vertical.
- These programs are based on the required skill of each vertical. At the end of the program, based on various factors such as training performance, KPI completion (only employees with KPIs completed greater than 60% are considered) etc., the employee gets a promotion.
For the above-mentioned process, the final promotions are only announced after the evaluation and this leads to delay in transition to their new roles. Hence, the company needs your help in identifying the eligible candidates at a particular checkpoint so that they can expedite the entire promotion cycle. They have provided multiple attributes around Employee's past and current performance along with demographics. Now, The task is to predict whether a potential promotee at a checkpoint in the test set will be promoted or not after the evaluation process. For your review process, you will be required to compare the effectiveness of your solution with some emerging technologies such as Neural Networks.
Dataset URL: https://bit.ly/2ODZvLCHRDataset Glossary URL: https://bit.ly/2Wz3sWcGlossary
Project Source: https://bit.ly/2CFzoRX