Assignment 3

[alf-99]

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

I completed Assignment 3 on clustering and bootstrapping with the Wine dataset. The main changes were:
-Loading and exploring the wine chemical composition data.
-Creating scatter plots to visualize feature relationships.
-Standardizing the data for K-means clustering.
-Applying K-means with 3 clusters and labeling the data.
-Implementing bootstrapping to calculate a confidence interval for color intensity.

What did you learn from the changes you have made?

This assignment helped me understand:
-How to prepare data for clustering algorithms (especially scaling).
-The importance of standardization when using distance-based methods like K-means.
-How bootstrapping works to estimate confidence intervals without needing more data.
-How to interpret cluster patterns in multidimensional data.
-The elbow method for choosing optimal cluster numbers.

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

For the clustering part, I considered trying different values of k (not just 3) and using the elbow method to pick the best one. For bootstrapping, I thought about comparing different confidence levels (95% vs 90%) to see how the interval width changes.

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

The main challenge was the K-means warning about memory leaks on Windows. The sklearn documentation mentioned this is a known issue with MKL on Windows. I decided to proceed since it doesn't affect the results, just a warning. Also, creating all those scatter plots (78 of them!) was computationally heavy but helped visualize the patterns.

How were these changes tested?

-Verified the dataset loaded correctly (178 wines, 13 features).
-Checked that standardization gave mean=0, std=1 for all features.
-Confirmed clustering assigned all points to one of 3 clusters.
-Validated bootstrap results by checking the original mean (5.058) fell within the 90% CI (4.78 to 5.35).
-Ran all code cells sequentially to ensure no errors.

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

N/A - This is for Assignment 3 submission.

Checklist

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

[Aditya-k-23]

Well written code and complete submission. Good job!