The goal of this project is to predict whether an employee is likely to leave a company the following year. The dataset that this team is using is from a Human Resources department where it is made up of 3 different sources including an employee survey, a response variable attrition with general information of an employee, and the results of a manager survey. To get the final dataset that they worked on, the team merged all the data sources by employee ID and left them to evaluate 4,410 observations with 28 features.
First thing that stuck out to me was how nice the format of the project paper was, the first page seemed so official as if it was from Cornell itself. The way that the team visualized the data was done nicely so that the reader can have a better idea of what the data is portraying. The methods that they used to compare the different models that they ran on the data was useful to identify which model classified their data the best, which was the Random Forest model. To conclude, this project was done very nicely and it provided key insights into which factors will help an employee stay in a company.
The goal of this project is to predict whether an employee is likely to leave a company the following year. The dataset that this team is using is from a Human Resources department where it is made up of 3 different sources including an employee survey, a response variable attrition with general information of an employee, and the results of a manager survey. To get the final dataset that they worked on, the team merged all the data sources by employee ID and left them to evaluate 4,410 observations with 28 features.
First thing that stuck out to me was how nice the format of the project paper was, the first page seemed so official as if it was from Cornell itself. The way that the team visualized the data was done nicely so that the reader can have a better idea of what the data is portraying. The methods that they used to compare the different models that they ran on the data was useful to identify which model classified their data the best, which was the Random Forest model. To conclude, this project was done very nicely and it provided key insights into which factors will help an employee stay in a company.