This project analyzes Amazon sales data to uncover trends, patterns, and insights that can drive strategic decision-making. Using Python’s data analysis libraries, we explore sales performance across regions, countries, sales channels, and product categories, while also identifying profit drivers and seasonal trends.
The analysis answers key business questions, such as:
- Which regions generate the highest total sales revenue?
- What are the average unit price and unit cost per item type?
- Which country yields the highest total profit?
- How does the sales channel affect order priority distribution?
- What is the average order processing time per sales channel?
- Which item types have the highest and lowest total sales?
- Are there seasonal sales patterns?
- What’s the correlation between unit price and total profit?
- Python
- Pandas – Data manipulation
- NumPy – Numerical computations
- Matplotlib & Seaborn – Data visualization
- Jupyter Notebook – Interactive analysis
The dataset contains sales transactions from Amazon, including:
- Order Date, Ship Date
- Region, Country
- Sales Channel
- Item Type
- Units Sold, Unit Price, Unit Cost
- Total Revenue, Total Cost, Total Profit
- Order Priority
- Top Regions by Sales Revenue – Identified high-performing regions contributing to most sales.
- Profitability by Country – Highlighted the most profitable markets.
- Sales Channel Impact – Compared performance between Online and Offline sales.
- Seasonality Trends – Detected periods of peak sales activity.
- Order Fulfillment Efficiency – Measured processing time differences between channels.
- Clone the repository:
git clone https://github.com/your-username/amazon-sales-data-analysis.git