Superstore Data Analysis

Project Overview

This project focuses on analyzing the Superstore dataset to uncover insights related to sales, profit, and customer behavior. The analysis aims to identify key factors that drive performance and provide actionable recommendations for business improvements.

Table of Contents

1. Introduction
2. Dataset
3. Installation
4. Usage
5. Analysis
6. Results
7. Conclusion
8. Contributing

Introduction

The sales analysis on Superstore dataset is a comprehensive study that aims to analyze the sales performance of a fictional retail company called "Superstore". The dataset used in this analysis contains information about sales, profits, shipment modes, product categories, and geographical locations.

Dataset

The dataset used in this project:

• Source: Kaggle Superstore Dataset . (Also provided the CSV file here)
• Description: A dataset containing sales data for a Superstore, including information on sales, profit, quantity, and customer demographics.
• Columns: Ship mode, Segmnet, Country, City, State, Postal Code, Region, Category, Sub-Category, Sales, Quantity, Discount and Profit.

Installation

To run the analysis locally, follow these steps:

1. Clone the repository:

   git clone https://github.com/Rakshitha-ks/Superstore-Data-Analysis.git

2. Navigate to the project directory:

   cd Superstore-Data-Analysis

Usage

1. Open the Jupyter notebook:

   jupyter notebook Superstore Data Analysis.ipynb

2. Run the cells in the notebook to perform the analysis.

Analysis

By performing Exploratory Data Analysis (EDA) , we aim to uncover trends, patterns, and insights that can be used to improve business operations and decision-making.

Key aspects of this project includes:

• Data Exploration
• Data Cleaning
• Data Analysis
• Data Visualization
• Insights and Recommendations

Results

Conclusion

Exploratory Data Analysis (EDA):

• Investigated sub-categories, sales, and profit patterns.
• Identified best-performing categories and products.
• Explored customer segment profitability and shipping preferences.
• Analyzed profitability across geographical regions.

Contributing

If you would like to contribute to this project, please follow these steps:

1. Fork the repository.
2. Create a new branch:

   git checkout -b feature-branch

3. Make your changes and commit them:

   git commit -m "Add new feature"

4. Push to the branch:

   git push origin feature-branch

5. Open a pull request.