A collaborative Ideathon project exploring real-world user behaviour, sales patterns, and insights from an e-commerce dataset.
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.
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.
- 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
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.
This project is licensed under the MIT License. See the LICENSE file for details.
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.
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.
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.