Store Sales & Profit Analysis

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Overview

With growing demands and cut-throat competitions in the market, a Superstore Giant is seeking your knowledge in understanding what works best for them. They would like to understand which products, regions, categories and customer segments they should target or avoid.

You can even take this a step further and try and build a Regression model to predict Sales or Profit.

Go crazy with the dataset, but also make sure to provide some business insights to improve.

The given dataset includes features such as the following:

• Row ID: Unique ID for each row.
• Order ID: Unique Order ID for each Customer.
• Order Date: Order Date of the product.
• Ship Date: Shipping Date of the Product.
• Ship Mode: Shipping Mode specified by the Customer.
• Customer ID: Unique ID to identify each Customer.
• Customer Name: Name of the Customer.
• Segment: The segment where the Customer belongs.
• Country: Country of residence of the Customer.
• City: City of residence of of the Customer.
• State: State of residence of the Customer.
• Postal Code: Postal Code of every Customer.
• Region: Region where the Customer belong.
• Product ID: Unique ID of the Product.
• Category: Category of the product ordered.
• Sub-Category: Sub-Category of the product ordered.
• Product Name: Name of the Product
• Sales: Sales of the Product.
• Quantity: Quantity of the Product.
• Discount: Discount provided.
• Profit: Profit/Loss incurred.

Objectives

Store sales and profit analysis help businesses identify areas for improvement and make data-driven decisions to optimize their operations, pricing, marketing, and inventory management strategies to drive revenue and growth