Assignment 3

[karennh21]

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

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

Add code to display the number of many observations and features in the dataset.
Add code to apply K-means clustering to the wine dataset.
Add code to implement bootstrapping on the mean of color intensity, generating 10000 bootstrap samples, calculating the mean for each sample, and determine the 90% confidence interval.

What did you learn from the changes you have made?

How to implement bootstrapping, calculating the mean of the samples, and calculate the 90% confidence interval.

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

N/A

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

Determining the lower and upper bound of the confidence interval. I overcame the challenge by reviewing the live coding the determine the calculations.
Determining the variability of the bootstrapped means. I overcame the challenge by plotting the histogram to look at the distribution of the mean values and compared the mean color intensity, mean of the bootstrap samples, and the mean values from the lower and upper confidence interval bounds.

How were these changes tested?

By generating 10000 bootstrap samples, calculating the mean for each sample, determining the 90% confidence interval, and plotting the histogram to determine the variability.

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

Checklist

• I can confirm that my changes are working as intended

[Aditya-k-23]

Well done! Great job visualizing the bar graph. Your submission is complete!