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My Amazon Spending

Analysis of personal Amazon purchases made in 2020, utilizing a Tableau dashboard to explore and visualize various aspects of the data.

Skills Used

Challenge

Going into the project, I assumed that data acquisition would be as simple as a 1 - click download from Amazon. However, given that I requested large amount of data, it took about 2 weeks to retrieve my personal Amazon history. The second challenge was that Amazon products often have subcategories nested within other subcategories, and I found that the 'Product Category' column did not accurately categorize the products. This involved the manual categorization of each product which ensured that the data was structured and organized for effective visualization and analysis in Tableau.

Results

I must clarify that I don't use Amazon Prime Exclusively, I am sharing it with 10 other people. I did not spend over $4,500 on Amazon shopping alone, it was very much a group effort. I also interesting found it interesting to see which of my friends are using it more than I am. It is also important to preface that this was during lock down, so most people had nothing better to do than just online shop. I remember building my first personal computer at the time, and you can see that reflected in the 'Most Popular Categories' chart.

Process

Process

Process

01

Understanding Requirements

Given that I had such a large dataset, it was important to hone in on what exactly I wanted to achieve. Understanding the requirements beforehand made it easier to narrow my scope into the relevant parts of the data

02

Data Cleaning

Removal of unnecessary columns and missing values. Standardizing the report for tabular analysis

03

Exploratory Data Analysis

Created some basic charts to understand the distribution of the data and identify outliers. I also found errors in my data entry during the categorization process in some rows (i.e. Books was spelled Bookss)

04

Data Visualization

Designed the overall template in Figma to ensure the dashboard follows design principles. Calculated custom measures within Tableau to better understand spending

We conducted user interviews, surveys, and analyzed in-app analytics to understand the pain points and user needs. We also studied competitor apps and industry trends to gather insights

01

Understanding Requirements

Given that I had such a large dataset, it was important to hone in on what exactly I wanted to achieve. Understanding the requirements beforehand made it easier to narrow my scope into the relevant parts of the data

02

Data Cleaning

Removal of unnecessary columns and missing values. Standardizing the report for tabular analysis

03

Exploratory Data Analysis

Created some basic charts to understand the distribution of the data and identify outliers. I also found errors in my data entry during the categorization process in some rows (i.e. Books was spelled Bookss)

04

Data Visualization

Designed the overall template in Figma to ensure the dashboard follows design principles. Calculated custom measures within Tableau to better understand spending

We conducted user interviews, surveys, and analyzed in-app analytics to understand the pain points and user needs. We also studied competitor apps and industry trends to gather insights

01

Understanding Requirements

Given that I had such a large dataset, it was important to hone in on what exactly I wanted to achieve. Understanding the requirements beforehand made it easier to narrow my scope into the relevant parts of the data

02

Data Cleaning

Removal of unnecessary columns and missing values. Standardizing the report for tabular analysis

03

Exploratory Data Analysis

Created some basic charts to understand the distribution of the data and identify outliers. I also found errors in my data entry during the categorization process in some rows (i.e. Books was spelled Bookss)

04

Data Visualization

Designed the overall template in Figma to ensure the dashboard follows design principles. Calculated custom measures within Tableau to better understand spending

We conducted user interviews, surveys, and analyzed in-app analytics to understand the pain points and user needs. We also studied competitor apps and industry trends to gather insights

01

Understanding Requirements

Given that I had such a large dataset, it was important to hone in on what exactly I wanted to achieve. Understanding the requirements beforehand made it easier to narrow my scope into the relevant parts of the data

02

Data Cleaning

Removal of unnecessary columns and missing values. Standardizing the report for tabular analysis

03

Exploratory Data Analysis

Created some basic charts to understand the distribution of the data and identify outliers. I also found errors in my data entry during the categorization process in some rows (i.e. Books was spelled Bookss)

04

Data Visualization

Designed the overall template in Figma to ensure the dashboard follows design principles. Calculated custom measures within Tableau to better understand spending

We conducted user interviews, surveys, and analyzed in-app analytics to understand the pain points and user needs. We also studied competitor apps and industry trends to gather insights