Assignment 2

[Yutong2002]

What changes are you trying to make? (e.g. Adding or removing code, refactoring existing code, adding reports)

Implemented a full multivariable linear regression workflow using the Auto MPG dataset. Added train–test split , model fitting, coefficient extraction, and RMSPE evaluation.

What did you learn from the changes you have made?

• How to apply training testing_split in regression context.
• How to interpret model coefficients and extract them programmatically.
• How to compute RMSPE and understand percentage-based model error.

Was there another approach you were thinking about making? If so, what approach(es) were you thinking of?

Considered standardizing predictors before fitting; decided against it because standardization is not required for linear regression to work and the assignment did not require scaling.

Were there any challenges? If so, what issue(s) did you face? How did you overcome it?

Encountered the error builtin_function_or_method object has no attribute columns due to overwriting a DataFrame variable name. Fixed by referencing the correct predictor list X_train.columns.

How were these changes tested?

All code was tested on Python using Jupyter Notebook.

A reference to a related issue in your repository (if applicable)

None

Checklist

• [ x] I can confirm that my changes are working as intended

[PatelVishakh]


Assignment 2: Incomplete, Needs some changes.

Required changes:
Q2) I) Also Should explain what Positive, Negative and No association imply about the relationship with the response i.e. when model year increases on average the MPG goes down.
Q3) I) Although your analysis is not wrong, the intension of the assignment is to remove categorical variables ie origin, name.
Q4) This is the formula for Root mean square PERCENTAGE error, rather than Root mean square PREDICTION ERROR.

[Yutong2002]

Assignment 2: Incomplete, Needs some changes.
 Required changes: Q2) I) Also Should explain what Positive, Negative and No association imply about the relationship with the response i.e. when model year increases on average the MPG goes down. Q3) I) Although your analysis is not wrong, the intension of the assignment is to remove categorical variables ie origin, name. Q4) This is the formula for Root mean square PERCENTAGE error, rather than Root mean square PREDICTION ERROR.

Hi Patel, I modified the errors based on your comments. Please let me know if the current version is improved. Thank you!

[PatelVishakh]

Assignment 2 Complete. Good Changes