The dataset comprises with Amazon Sales Data details of 12 different products in 76 countries to help optimize product profitability by answering several key questions related to the sales data.
Objective of this project is to find key metrics and factors and then show meaningful relationships between them and to create visualizations for better understanding trends and patterns
Data extraction, cleaning, and transformation using pandas, visualization with Matplotlib/Seaborn;
Python, Pandas, Numpy, Matplotlib, Seaborn
Exploratory Data Analysis (EDA)
- Absence of null values in the data.
- Most sales occurrences happened in the country 'The Gambia' so, most received orders are from Sub-Saharan Africa. Also maximum revenue generated and yielded maximum profitability.
- Lithuania is the country where maximum revenue generated
- Clothes and cosmetics are the most needed items by customers while meat is the least.
- Outliers found in Total Cost, Total Profit and Total Revenue.
Tableau 2024.1
https://public.tableau.com/app/profile/sai.m.s/vizzes
Here, bar charts, area chart and map and pie chart are used to perform data visualization to show trends and insights. We can see how the sales data varies by countries, regions and item-types.