This project analyzes a retail sales dataset (Superstore) to understand sales trends, customer behavior, product performance, and shipping efficiency. The goal is to demonstrate end-to-end data analysis skills using Python, Pandas, and Matplotlib.
- Sales Performance KPIs: Total sales, total profit, total orders, average order value, and average discount.
- Monthly Sales Trend: Observe how sales fluctuate over months.
- Sales by Category and Region: Identify top-performing categories and regions.
- Top Products & Customers: Highlight products and customers contributing the most to revenue.
- Profit vs Discount Analysis: Examine how discounts impact profitability.
- Profitability by Category: Compare profit margins across product categories.
- Customer Segment Analysis: Analyze sales and profitability by customer segments.
- Shipping Performance: Evaluate average shipping time by shipping mode.
- Python
- Pandas
- Matplotlib
- Seaborn (optional)
- Jupyter Notebook
- Superstore dataset (CSV) containing columns such as
Order ID,Customer Name,Category,Sales,Profit,Order Date,Ship Date, and more.
- Top Products and Categories: Technology products generate the highest sales and profit; a small number of top products contribute disproportionately to revenue.
- Regional and Customer Insights: The West region and Consumer segment are top performers.
- Profitability and Discounts: Higher discounts often reduce profitability; Furniture has lower profit margins relative to sales.
- Top Customers: A small group of customers drives a large portion of revenue, emphasizing the importance of customer retention.
- Operational Efficiency: Same Day and First Class shipping modes deliver faster, while Standard Class takes longer.