Assignment 3

[tanveerrouf]

PULL REQUEST TITLE: UofT-DSI | Clustering - Assignment 3

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

Implemented K-means clustering with k=3 on standardized Wine data and bootstrapped a 90% confidence interval for color intensity.

What did you learn from the changes you have made?

Data standardization is critical before clustering, and bootstrapping reveals the variability in sample statistics even when working with small datasets.

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

Considered hierarchical clustering and elbow method for optimal k selection, but assignment parameters specified k=3.

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

Column naming inconsistency initially (color_intensity vs colorintensity), resolved by checking the dataframe directly.

How were these changes tested?

Ran clustering pipeline end-to-end, verified cluster assignments, and validated bootstrap CI calculations against expected ranges.

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

N/A

Checklist

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

[PatelVishakh]

Assignment 3 Complete! Great work.

Recommended changes:

Q2) Although there is visible correlation, this would not help us differentiate between species but detecting if clusters occur in the our data can be used as an indicator of different species.
Q5)iii) Need to mention the conf interval and its interpretation.