This project analyzes 1,000 retail sales transactions from 2023 to understand monthly revenue trends, category performance, regional differences, and discount impact using Python and pandas.
Answer key business questions:
- Which month had the highest revenue?
- Which product category drives the most sales?
- What is the average order value by region?
- How do discounts impact revenue?
| Tool | Purpose |
|---|---|
| Python | Data generation, cleaning, analysis |
| pandas | Data manipulation and aggregation |
| matplotlib | Data visualization |
| Git & GitHub | Version control and documentation |
Synthetic retail dataset with 1,000 transactions across 2023.
| Field | Description |
|---|---|
| order_id | Unique order identifier |
| date | Order date (2023) |
| region | North, South, East, West |
| category | Electronics, Clothing, Food & Beverage, Furniture, Sports |
| unit_price | Price per unit |
| quantity | Units ordered |
| discount | Discount applied (0 to 20%) |
| revenue | Final revenue after discount |
| KPI | Value |
|---|---|
| Total Revenue | $1,367,568 |
| Total Orders | 1,000 |
| Avg Order Value | $1,367 |
| Top Category | Electronics |
| Peak Month | February |
sales-data-analysis/ ├── data/ │ ├── sales_data.csv │ └── generate_data.py ├── notebooks/ │ └── sales_analysis.py ├── outputs/ │ ├── monthly_revenue.png │ ├── revenue_by_category.png │ ├── region_analysis.png │ └── discount_impact.png └── README.md
- Monthly Revenue Trend — identified peak and low months
- Revenue by Category — compared 5 product categories
- Regional Analysis — avg order value across 4 regions
- Discount Impact — effect of discounts on avg revenue
- Electronics drives 59% of total revenue due to high unit prices
- February was the peak revenue month
- North region leads with highest average order value
- Orders with no discount generate the highest average revenue
- Invest more marketing budget in Electronics during Q1
- Investigate June revenue dip for potential causes
- Review discount strategy — higher discounts reduce avg revenue
- Focus growth efforts on the West region which underperforms
git clone https://github.com/Indra0719/sales-data-analysis.git
cd sales-data-analysis
pip install pandas matplotlib
python3 data/generate_data.py
python3 notebooks/sales_analysis.py- Python data analysis with pandas
- Data visualization with matplotlib
- Feature engineering (month extraction, discount calculations)
- Business insight generation
- GitHub project documentation