Superstore Sales R-Project

Dataset Description

The dataset consists of several variables that capture different aspects of the Superstore’s operations:

•	Row ID: Unique identifier for each row in the dataset.
•	Order ID: Unique identifier for each order placed by a customer.
•	Order Date: The date on which the order was placed.
•	Ship Date: The date on which the order was shipped.
•	Ship Mode: The shipping method specified by the customer (e.g., Standard, Express).
•	Customer ID: Unique identifier for each customer.
•	Customer Name: Name of the customer who placed the order.
•	Segment: The customer segment (e.g., Consumer, Corporate, Home Office) to which the customer belongs.
•	Country: The country where the customer resides.
•	City: The city where the customer is located.
•	State: The state of residence of the customer.
•	Postal Code: The postal code of the customer’s location.
•	Region: The region where the customer is located.
•	Product ID: Unique identifier for each product.
•	Category: The category of the product ordered (e.g., Furniture, Office Supplies, Technology).
•	Sub-Category: The specific sub-category of the product ordered.
•	Product Name: The name of the product.
•	Sales: The sales revenue generated from the product.
•	Quantity: The quantity of the product ordered.
•	Discount: The discount applied to the product.
•	Profit: The profit or loss incurred from the sale of the product.

Analysis Conducted

1. Trend Analysis of Sales, Profit, and Discount

• Overall Trends: A thorough exploration of the trends in sales, profit, and discounts was conducted to understand how these key metrics evolve over time.
• State and City-Level Analysis: We analyzed these trends at the state and city levels, creating top 10 charts to identify which locations perform the best in terms of sales and profitability, and how discounts impact these outcomes.

2. Product Category and Sub-Category Analysis

• We examined which product categories and sub-categories were the most frequently purchased, providing insights into consumer preferences and helping to identify which products drive the most revenue.

3. Shipping Mode Distribution

• A pie chart was created to illustrate the distribution of different shipping modes, offering insights into customer preferences and logistics efficiency.

4. Customer Segment Analysis

• We also analyzed the distribution and count of customer segments to better understand the composition of the customer base and which segments are most lucrative.

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