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E-Commerce Data Analysis Project Banner

A collaborative Ideathon project exploring real-world user behaviour, sales patterns, and insights from an e-commerce dataset.

Python Jupyter Git License Maintenance GitHub

Overview

This repository contains a team-based exploratory data analysis (EDA) project developed as part of our Ideathon participation.

Our goal is to:

Analyze an e-commerce dataset, uncover patterns, visualize insights, and collaboratively build a clean analytical portfolio using Git, GitHub workflows, and Jupyter Notebooks.

Each team member contributes individual analyses through feature branches and pull requests, ensuring a professional and conflict-free workflow.

Dataset Description

The dataset consists of multiple features representing real user activity on an e-commerce platform. Below are the key attributes:

Feature Description
AccessDate Date & time when the user accessed the website
DurationSeconds Total time spent on the platform
NetworkProtocol Indicates whether the user accessed via HTTP/HTTPS
IPAddress Masked IP region of the user
BytesConsumed Total data consumed during the session
Browser Browser used (Chrome, Firefox, Safari, etc.)
Age Age of the user
Gender Gender information
Country Location of the user
Membership Normal or Premium membership type
Language Website language selected
Sales Sales amount generated
Returned Whether a product return took place
ReturnedAmount Amount refunded for returns
PaymentMethod Mode of payment (UPI, Card, COD, etc.)

This structured dataset allows for meaningful segmentation, behavioural analysis, and performance insights.

Objectives

  • Clean and preprocess the dataset
  • Identify behavioural & demographic trends
  • Visualize patterns using effective plots
  • Understand the business impact of attributes (age, country, browser, etc.)
  • Perform comparative analysis across different user groups
  • Build a high-quality, structured, collaborative data-science repository
  • Showcase teamwork, analysis skills, and Git/GitHub workflow mastery

Tech Stack

python pandas matplotlib git vscode

Example Analyses Performed

This repository includes multiple analyses by different contributors, such as:

  • Browser-wise Sales Contribution
  • Country-wise Sales Performance
  • Age Group vs Purchase Behaviour
  • Membership Type vs Average Sales
  • Return Behaviour Patterns
  • Payment Method Preferences
  • Duration vs Sales Relationship

Team members may include plot images directly inside their Jupyter notebooks for clarity.

🛡 License

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

Discussions

We use GitHub Discussions to:

  • Ask questions
  • Share plot ideas
  • Seek help with Git, VS Code, or Python
  • Suggest improvements
  • Collaborate openly

Everyone is encouraged to participate.

Team

This project is collaboratively maintained by a 5-member team as part of our Ideathon initiative. Each member contributes uniquely through independent analyses and insights.

Why This Project Matters

This repository is a demonstration of:

  • Analytical thinking
  • Team collaboration
  • Coding workflows
  • Real-world data handling
  • Structured EDA practices
  • Professional GitHub project management

It showcases our ability to work like a real data analysis team while exploring meaningful e-commerce insights.