Introduction

Fari Hara men’s clothing company based in Canada will like to extend their business model to an international customer base and as such, have developed a need to create a sizing profile that will serve as a comparison reference for customers that have similar dimensions.

The profiles will be created from a series of parameters derived from the measurement datasets the company has collected over a period of 4-5years.

Tally Charts

The measurements are classified by 3 distinct groups, the shoulder size, stomach fit and the shirt cut type. I have tallied each type of classification to gain an idea of what customer builds look like overall.

The entire data set is 439 rows (Calgary & Toronto measurements combined)

Normal shoulders represent the most common shoulder size and square shoulders represent the least.

Slim cuts are also the most common cut while super slim represent the least.

Flat stomach represent the highest proportion of clients.

In doing my analysis, I have discovered that applying the averages from dataset 2 into dataset 1 would not work because the size ranges for the small, medium and large size groups are basically the same, except for the Chest sizes.

Hence, I created ranges using the chest ranges from dataset 1 and since this dataset had stomach groupings already, I used each grouping also.

I have separated the “Flat” & “Slight” stomach profiles that have chest width within 31 and 41 which is the size small range from dataset 2. The “Large” stomach measurements have no profile in this category.

I have separated the “Flat”, “Slight” & “Large” stomach profiles that have chest width within 35 and 45 which is the size medium range from dataset 2.

I have separated the “Flat”, “Slight” & “Large” stomach profiles that have chest width within 37 and 49 which is the size medium range from dataset 2.