The trainer class performs the Evaluation on the model. Use the sampledata.csv to train the model and output the evaluation metrics. Run using commandline arguments: Train "D:\EmployeeAttrition\Data\sampledata.csv" For this project, The used model metrics are:

1. Loss function
2. Mean Absolute Error
3. Mean Squared Error
4. RSquared
5. Root Mean Squared Error

After training the model, build a sample JSON file and save it as input.json { "durationInMonths": 0.0, "isMarried": 0, "bsDegree": 1, "msDegree": 1, "yearsExperience": 2, "ageAtHire": 29, "hasKids": 0, "withinMonthOfVesting": 0, "deskDecorations": 1, "longCommute": 1

} Run the prediction using commandline arguments on the model to find the duration the employee is gonna work for the company in months. Predict "D:\EmployeeAttrition\Data\input.json"

---------------------------------------------------------------EXAMPLE OUTPUT---------------------------------------------------------------------

train "D:\EmployeeAttrition\Data\sampledata.csv"

Loss function:331.78 Mean Absolute Error: 14.91 Mean Squared Error:331.78 RSquared:0.08 Root Mean Squared Error:18.21

predict "D:\Machine Learning Projects\EmployeeAttrition\input.json"

The Employee is predicted to work 40.33months