Amazon Sales Analysis

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:

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

Techniques Used

Data extraction, cleaning, and transformation using pandas, visualization with Matplotlib/Seaborn;

Tools used:

Python, Pandas, Numpy, Matplotlib, Seaborn

Approach Used:

Exploratory Data Analysis (EDA)

Findings :

1. Absence of null values in the data.
2. 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.
3. Lithuania is the country where maximum revenue generated
4. Clothes and cosmetics are the most needed items by customers while meat is the least.
5. Outliers found in Total Cost, Total Profit and Total Revenue.

Amazon Sales Tableau dashboard :

Technologies used:

Tableau 2024.1

Link to Tableau Public:

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.