The project seeks to classify employers as likely to leave the company based on their employee survey and department reviews. The data comes from one company’s employee surveys, employee demographics, and the results of a manager survey evaluation. The project will help the company identify which features are most important in determining if an employee will stay, so they know what to improve on in the company.
Likes:
-- I think balancing the data set was a good idea to make sure that the model trains on both employees that leave and those that stay. I liked the discussion about how you picked between oversampling and undersampling because it showed that you thought about the consequences of each choice.
-- I like your graphs in the exploratory analysis because it shows the distribution of many features overlayed together, showing how they may affect each other and gives an idea of the importance of different features.
-- I liked that you interpreted each model. I think that this shows the merits of each model and shows that you thought about more than minimizing error when picking a model.
Suggestions:
-- I do not agree that employees that do not answer a question would have given a bad rating. Many people do not rate things because they do not care enough to answer and provide good feedback. I think an external source would have helped me believe this.
-- Labels on the correlation chart would have helped me understand the correlation between features and the value of the correlation.
-- I think that a fairness metric, like parity, could have been useful in your report because it would show the current biases in the model.
The project seeks to classify employers as likely to leave the company based on their employee survey and department reviews. The data comes from one company’s employee surveys, employee demographics, and the results of a manager survey evaluation. The project will help the company identify which features are most important in determining if an employee will stay, so they know what to improve on in the company.
Likes: -- I think balancing the data set was a good idea to make sure that the model trains on both employees that leave and those that stay. I liked the discussion about how you picked between oversampling and undersampling because it showed that you thought about the consequences of each choice. -- I like your graphs in the exploratory analysis because it shows the distribution of many features overlayed together, showing how they may affect each other and gives an idea of the importance of different features. -- I liked that you interpreted each model. I think that this shows the merits of each model and shows that you thought about more than minimizing error when picking a model.
Suggestions: -- I do not agree that employees that do not answer a question would have given a bad rating. Many people do not rate things because they do not care enough to answer and provide good feedback. I think an external source would have helped me believe this. -- Labels on the correlation chart would have helped me understand the correlation between features and the value of the correlation. -- I think that a fairness metric, like parity, could have been useful in your report because it would show the current biases in the model.