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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.