https://github.com/muhammadusman-khan/e-commerce-store-eda
Exploratory Data Analysis on E-commerce store data to uncover insights about sales trends, customer behavior, and product performance using Python libraries like Pandas, NumPy, and Matplotlib/Seaborn.
https://github.com/muhammadusman-khan/e-commerce-store-eda
data-analysis data-science data-visualization e-commerce eda exploratory-data-analysis jupyter-notebook matplotlib numpy pandas python seaborn
Last synced: 3 months ago
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Exploratory Data Analysis on E-commerce store data to uncover insights about sales trends, customer behavior, and product performance using Python libraries like Pandas, NumPy, and Matplotlib/Seaborn.
- Host: GitHub
- URL: https://github.com/muhammadusman-khan/e-commerce-store-eda
- Owner: MuhammadUsman-Khan
- Created: 2025-10-05T11:02:04.000Z (10 months ago)
- Default Branch: main
- Last Pushed: 2025-10-05T11:14:55.000Z (10 months ago)
- Last Synced: 2025-10-05T13:07:41.043Z (10 months ago)
- Topics: data-analysis, data-science, data-visualization, e-commerce, eda, exploratory-data-analysis, jupyter-notebook, matplotlib, numpy, pandas, python, seaborn
- Language: Jupyter Notebook
- Homepage:
- Size: 461 KB
- Stars: 0
- Watchers: 0
- Forks: 0
- Open Issues: 0
-
Metadata Files:
- Readme: README.md
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README
# E-Commerce Store EDA
## Description
This repository contains a complete **Exploratory Data Analysis (EDA)** of an E-commerce store dataset. The analysis aims to uncover meaningful insights into customer behavior, sales trends, and product performance. Using Python's popular data analysis and visualization libraries, this project demonstrates a systematic approach to exploring and understanding e-commerce data.
---
## Features
- Data cleaning and preprocessing
- Handling missing values and duplicates
- Summary statistics of key features
- Visualizations to identify trends and patterns:
- Sales trends over time
- Product category performance
- Customer purchase behavior
- Correlation analysis to understand feature relationships
---
## Technologies Used
- **Python**
- **Pandas** – Data manipulation and analysis
- **NumPy** – Numerical operations
- **Matplotlib & Seaborn** – Data visualization
- **Jupyter Notebook** – Interactive coding and visualization
---
## How to Run
1. Clone the repository:
```bash
git clone https://github.com/MuhammadUsman-Khan/E-Commerce-Store-EDA.git
```
2. Navigate to the project folder:
```bash
cd E-Commerce-Store-EDA
```
3. Open the notebook in Jupyter:
```bash
jupyter notebook E_Commerce_Store_EDA.ipynb
```
4. Run the cells sequentially to explore the analysis and visualizations.
## Dataset
The project uses an E-commerce store dataset (CSV format). Ensure the dataset is in the same directory as the notebook or update the path in the notebook accordingly.
## Project Outcome
- Identified top-performing products and categories
- Analyzed monthly/seasonal sales trends
- Gained insights into customer purchasing patterns
- Generated actionable visualizations for business decisions
## Contributing
Feel free to fork this repository, add improvements, or create additional visualizations. Contributions are welcome!