UoFT-DSI | LCR - Assignment 2

[akorade]

Filled in code cells with solutions and outputs for assignment 2, including package installation, dataset inspection, variable type explanation, data queries, and a sample plot. This provides a full walkthrough of the assignment's required steps and their results.

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

I have completed Assignment 2 by filling in all required code cells with solutions for linear regression analysis on the Auto MPG dataset.

What did you learn from the changes you have made?

Through completing this assignment, I learned:
Data relationships: Negative associations exist between vehicle characteristics (weight, horsepower, displacement) and fuel efficiency
Linear regression fundamentals: How least squares regression finds the best-fit line by minimizing squared errors
Model limitations: Linear models don't perfectly capture all relationships; residuals reveal unexplained variance
Train-test validation: Splitting data helps evaluate generalization performance on unseen data
RMSE interpretation: The calculated RMSE indicates average prediction error in the same units as the target variable (MPG)

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

NA

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

NA

How were these changes tested?

They were tested locally through visual studio code executing each block.

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

Checklist

• I can confirm that my changes are working as intended