📊 Amazon Sales Data Analysis

📌 Project Overview

This project focuses on analyzing Amazon sales data to extract meaningful insights related to sales performance, product demand, and profitability. Using Python and data analysis libraries, the project transforms raw business data into valuable insights for better decision-making.

🎯 Objectives

• Analyze overall sales, quantity, and profit
• Identify top-selling products and categories
• Analyze profit distribution and loss-making products
• Study customer purchasing behavior
• Understand relationships between sales, quantity, and profit
• Perform data cleaning and preprocessing
• Visualize trends using charts and graphs

📂 Dataset Description

The dataset includes the following attributes:

• Order ID
• Order Date & Ship Date
• Customer Email
• Geography (Country, City, State)
• Product Category & Product Name
• Sales, Quantity, Profit

🛠️ Technologies Used

• Python
• Pandas
• NumPy
• Matplotlib
• Seaborn

🔧 Data Preprocessing

• Handled missing values
• Removed null records
• Split geography into Country, City, and State
• Converted date columns for time-based analysis

📊 Key Analysis & Case Studies

• Total Sales, Quantity, and Profit calculation
• Top 10 highest-selling products
• Top categories generating highest profit
• State-wise sales and profit analysis
• Monthly and yearly sales trends
• Customer order frequency analysis
• Identification of loss-making products
• Average Order Value (AOV) calculation
• Repeat customer analysis
• Category-wise quantity analysis
• Profit Margin (%) calculation
• Most profitable month identification
• Shipping time analysis
• Correlation between shipping time and profit

📈 Key Insights

• Total Sales: 725,338
• Total Quantity Sold: 12,255
• Total Profit: 108,375
• Average Order Value: 450.8
• Average Shipping Time: ~3.93 days
• Shipping time has slight negative impact on profit

💡 Conclusion

This project demonstrates how Python can be used for real-world data analysis. It highlights the importance of data-driven decision-making by identifying sales trends, profitable products, and customer behavior patterns. The insights gained can help businesses improve strategies and increase profitability.

👩‍💻 Author

Komal Kushwaha Aspiring Data Analyst | Python | SQL | Power BI |Machine Learning

⭐ Support

If you like this project, give it a ⭐ on GitHub!