I have some experience with data visualization tools like Looker Studio by Google and Power BI by Microsoft. Tableau caught my attention as a tool that could offer valuable insights. Initially, I intended to work with birthday data and correlate Zodiac Signs with participation, but I found that managing this data was quite challenging. Consequently, I opted for an alternative approach – analyzing my clothing items to distinguish between thrifted and fast fashion pieces.

I focused solely on my everyday wear, which includes shirts, pants, jackets, sweaters, and T-shirts. I categorized each item into one of four categories: Fast Fashion, Thrift Shop, Hand Me Down, or Small Business. Additionally, I assigned secondary attributes to the clothing items. To facilitate this analysis, I created a spreadsheet and conducted a qualitative assessment of my wardrobe, which I later imported into Tableau for visualization.

While working in Tableau, one of the challenges I encountered was ensuring that the graphs had consistent colors to establish connections between them. Additionally, some of the terminology within Tableau made it a bit difficult to navigate the platform.

Nonetheless, I persevered and experimented with various charts and graphs, ultimately selecting the Bubble Graph for a comprehensive closet breakdown. This project served as an exploratory data analysis, as I had preconceived assumptions about my wardrobe composition. However, I sought to validate these assumptions and uncover any intriguing findings that could influence my future shopping habits.

In summary, my exploration in Tableau was not only aimed at confirming my initial beliefs about my closet but also at discovering new insights that may shape my future fashion choices.

Here is the visualization of My Closet Breakdown from Tableau.