This project focuses on analyzing e-commerce sales data to identify business trends, product performance, customer purchasing behavior, and regional sales patterns.
Using Python, Pandas, and Matplotlib, the dataset was transformed into actionable business insights through KPI tracking, sales analysis, and visual reporting.
The goal of this project is to answer key business questions such as:
- Which products generate the highest revenue?
- Which categories contribute the most to overall sales?
- Which regions perform best and worst?
- How do sales vary across different months?
- What insights can help improve business performance?
- Total Orders: 1000
- Unique Customers: 366
- Multiple Product Categories
- Multiple Sales Regions
- Order Data from 2024–2025
| Column | Description |
|---|---|
| order_id | Unique order identifier |
| order_date | Date of order |
| customer_id | Unique customer identifier |
| product | Product purchased |
| category | Product category |
| quantity | Quantity purchased |
| price | Product price |
| region | Sales region |
- Python
- Pandas
- NumPy
- Matplotlib
- Jupyter Notebook
The following KPIs were calculated:
- Total Revenue
- Total Orders
- Total Customers
- Average Order Value (AOV)
- Total Quantity Sold
- Calculated total business revenue
- Generated revenue-based KPIs
- Identified top revenue-generating products
- Top products by revenue
- Top products by quantity sold
- Product ranking analysis
- Revenue contribution by category
- Quantity sold by category
- Identification of highest-performing category
- Revenue by region
- Quantity sold by region
- Best and worst performing regions
- Monthly revenue tracking
- Seasonal sales pattern analysis
- Identification of best-performing month
Created visual reports for:
- Revenue by Category
- Revenue by Region
- Monthly Revenue Trend
- Top Products by Revenue
Electronics generated approximately 90% of total revenue, making it the primary business revenue driver.
Laptop emerged as the highest revenue-generating product.
Smartphone recorded the highest quantity sold among all products.
West Region generated the highest overall revenue and sales volume.
January recorded the highest monthly revenue, indicating strong seasonal demand.
Ecommerce_Sales_Analytics/
│
├── ecommerce_sales.csv
├── Ecommerce_Sales_Analytics.ipynb
├── ecommerce_sales_report.csv
├── category_revenue.png
├── region_revenue.png
├── monthly_sales_trend.png
├── top_products.png
└── README.md
Potential enhancements for future versions:
- Interactive Power BI Dashboard
- Customer Segmentation Analysis
- RFM Analysis
- Profit Analysis
- Sales Forecasting
- Automated Reporting
Saurav18K
Aspiring Data Analyst passionate about transforming raw data into meaningful business insights using Python, SQL, Excel, and Power BI.
This project demonstrates end-to-end data analysis skills including:
- Data Cleaning
- Data Transformation
- KPI Development
- Exploratory Data Analysis
- Business Insight Generation
- Data Visualization
- Reporting