E Commerce Sales Analysis

Abstract

1. Introduction

E-commerce Sales Insights Dataset and Power BI Dashboard Welcome to the E-commerce Sales Insights repository! Here, we present a comprehensive dataset and an interactive Power BI Dashboard that shed light on the dynamics of e-commerce sales and consumer behavior. Whether you're an analyst, a marketer, or a business strategist, this resource will provide you with valuable insights into the world of online retail.

Our meticulously curated dataset encompasses a wealth of e-commerce sales and order details, offering a panoramic view of transactions, products, and customer preferences. With records spanning a significant timeframe, this dataset provides a robust foundation for exploring sales trends, understanding consumer choices, and deriving actionable insights that can drive business growth.

Whether you're seeking to uncover seasonal trends, understand customer preferences, or optimize marketing efforts, our E-commerce Sales Insights Dashboard is your compass in the data-driven world of e-commerce.

2. Objective

E-commerce Sales Insights Dataset and Power BI Dashboard

This project demonstrates how modern tools can be applied to a real-world Data Science problem.

4. Technology Stack

5. System Requirements

General requirements for this technology stack — check the README for exact versions.

• See the project README for exact requirements
• Git (to clone the repository)

6. Installation & Setup

git clone https://github.com/learnwithnaofil/E-commerce-Sales-Analysis.git
cd E-commerce-Sales-Analysis

Full setup instructions are in the project README.

7. Future Enhancements

Suggested extensions you can add to make this your own project.

• Turn the analysis into an interactive dashboard
• Automate data refresh with a scheduled job
• Add a predictive model on top of the analysis

8. Viva / Review Questions

Common questions examiners ask for projects in this domain.

1. What is the source of the dataset and how was missing data handled?
2. Which exploratory analysis steps revealed the most useful insight?
3. Why were these particular charts chosen to present the data?
4. Which statistical or ML technique supports the conclusions?
5. How could the analysis be automated or refreshed with new data?

9. Source Code & License

This project is developed by learnwithnaofil and published on GitHub under the GNU General Public License v3.0. Please follow the license terms and credit the original author when you use or modify this code.