This project analyzes an Amazon sales dataset using Python and Microsoft Excel to extract meaningful business insights. The analysis covers sales trends, customer behavior, product performance, regional sales, and payment methods through data cleaning, aggregation, and visualization.
- Analyze monthly sales trends.
- Identify top-selling products and categories.
- Compare sales across states and cities.
- Study customer purchasing behavior.
- Evaluate payment method usage.
- Generate business insights and recommendations.
- Python
- Google Colab / Jupyter Notebook
- Pandas
- Matplotlib
- Microsoft Excel
The dataset contains order-level information, including Order ID, Order Date, Customer Details, Product Information, Category, Brand, Quantity, Unit Price, Discount, Tax, Shipping Cost, Total Amount, Payment Method, Order Status, City, State, Country, and Seller ID.
- Data loading and preprocessing
- Missing value and duplicate analysis
- Monthly sales trend analysis
- Category-wise sales analysis
- Top 10 products by sales
- Payment method distribution
- Order status analysis
- State-wise and city-wise sales
- Average Order Value (AOV)
- Business insights and recommendations
- Line Chart – Monthly Sales Trend
- Bar Chart – Category-wise Sales
- Horizontal Bar Chart – Top 10 Products
- Pie Chart – Payment Method Distribution
- State-wise Sales Comparison
- Identified the highest-performing products and categories.
- Analyzed monthly sales patterns.
- Compared regional sales performance.
- Evaluated customer payment preferences.
- Suggested strategies to improve profitability and sales.
├── amazon.xlsx
├── Business Analysis.ipynb
└── README.md
- Clone the repository.
- Open the notebook in Google Colab or Jupyter Notebook.
- Install dependencies:
pip install pandas matplotlib openpyxl- Update the dataset path if required.
- Run all notebook cells.
Naema Mohamed Rafiq
Engineering Student | Python | Data Analytics | Machine Learning