This HR Analytics project attempted to predict the attrition rate of workers based on several features for each employee. Their project was looking to predict a boolean variable for attrition, encoded as -1 or 1. Their initial feature analysis was comprehensive and they did a good job finding the correlation with variables and identifying useful techniques to handle over and underfitting. Even though their project only attempted to predict attrition, it would've been interesting to also see if they could build a model that predicted how long an employee would remain in the company as well. The errors in the model seem to be pretty acceptable, but it is not clear how the test set was created and how certain they were that the model was not overfitting to the test set.
Their recommendations and conclusions are comprehensive. They take into account all the variables and their final product would be quite useful for their client. With some more testing, I would personally recommend this model to their client. It would have also been nice to see al little more interpretation on why the initially chose the models they did, and how they think each regularizer affected the accuracy of each model. Overall, a great project with a very useful premise and good application.
This HR Analytics project attempted to predict the attrition rate of workers based on several features for each employee. Their project was looking to predict a boolean variable for attrition, encoded as -1 or 1. Their initial feature analysis was comprehensive and they did a good job finding the correlation with variables and identifying useful techniques to handle over and underfitting. Even though their project only attempted to predict attrition, it would've been interesting to also see if they could build a model that predicted how long an employee would remain in the company as well. The errors in the model seem to be pretty acceptable, but it is not clear how the test set was created and how certain they were that the model was not overfitting to the test set. Their recommendations and conclusions are comprehensive. They take into account all the variables and their final product would be quite useful for their client. With some more testing, I would personally recommend this model to their client. It would have also been nice to see al little more interpretation on why the initially chose the models they did, and how they think each regularizer affected the accuracy of each model. Overall, a great project with a very useful premise and good application.