Superstore Data Analysis

Overview

This project focuses on analyzing the Superstore dataset, which contains information about sales, profit, and other attributes of a retail business. The primary objective is to gain insights into sales trends, identify key performance metrics, and provide actionable recommendations for improving business performance.

Project Structure

• data/: Contains the Superstore dataset files.
• scripts/: Python scripts for data analysis and visualization.
• notebooks/: Jupyter notebooks for exploratory data analysis (EDA).
• results/: Output files such as charts, graphs, and analysis reports.
• README.md: This file.

Dataset

The dataset used in this analysis is the Superstore dataset, which includes the following columns:

• Row ID: Unique identifier for each order.
• Order ID: Unique identifier for each order.
• Order Date: The date when the order was placed.
• Ship Date: The date when the order was shipped.
• Ship Mode: The shipping method used for the order.
• Customer ID: Unique identifier for each customer.
• Customer Name: Name of the customer.
• Segment: The segment to which the customer belongs (e.g., Consumer, Corporate, Home Office).
• Country: Country where the customer is located.
• City: City where the customer is located.
• State: State where the customer is located.
• Postal Code: Postal code of the customer's address.
• Region: The region where the customer is located.
• Product ID: Unique identifier for each product.
• Category: The category of the product (e.g., Furniture, Office Supplies, Technology).
• Sub-Category: The sub-category of the product.
• Product Name: Name of the product.
• Sales: The total sales amount for the order.
• Quantity: The quantity of products sold.
• Discount: Discount applied to the order.
• Profit: The profit earned from the order.

Installation

1. Clone the repository:

   git clone https://github.com/koke3/superstore_dataanlysis.git

2. Navigate to the project directory:

   cd superstore_dataanlysis

3.Install the required packages:

 pip install -r requirements.txt

Results:

The analysis will yield insights such as:

Trends in sales over time. Identification of top-selling products and categories. Customer segments contributing most to profits. Recommendations for improving sales and profitability. Contributing Contributions are welcome! Please feel free to submit a pull request or open an issue.

License

This project is licensed under the MIT License - see the LICENSE file for details.