https://github.com/mahmoudwal27/e-commerce-data-analysis
A collection of data analysis and visualization projects focused on ecommerce datasets. Using Python in Google Colab for analysis and Excel for exploration, these projects uncover key insights and trends, showcasing expertise in data manipulation and visualization to inform business decisions.
https://github.com/mahmoudwal27/e-commerce-data-analysis
analytics data-analysis data-analysis-python data-set google-cloud python
Last synced: 3 months ago
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A collection of data analysis and visualization projects focused on ecommerce datasets. Using Python in Google Colab for analysis and Excel for exploration, these projects uncover key insights and trends, showcasing expertise in data manipulation and visualization to inform business decisions.
- Host: GitHub
- URL: https://github.com/mahmoudwal27/e-commerce-data-analysis
- Owner: mahmoudwal27
- Created: 2025-01-16T17:11:47.000Z (4 months ago)
- Default Branch: main
- Last Pushed: 2025-02-25T17:24:22.000Z (3 months ago)
- Last Synced: 2025-02-25T17:36:45.751Z (3 months ago)
- Topics: analytics, data-analysis, data-analysis-python, data-set, google-cloud, python
- Language: Jupyter Notebook
- Homepage:
- Size: 13.9 MB
- Stars: 0
- Watchers: 1
- Forks: 0
- Open Issues: 0
-
Metadata Files:
- Readme: README.md
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README
# E-Commerce Data Analysis
This project analyzes an **E-Commerce dataset** using **Python (Pandas, Plotly)** to perform **data cleaning, exploratory analysis, and visualization**.
## 📌 Project Workflow
### 1️⃣ Data Import
- Loaded dataset from **Excel** using `pandas`.
### 2️⃣ Exploratory Data Analysis (EDA)
- Displayed dataset structure (`.info()`, `.describe()`).
- Checked data types and statistics.
### 3️⃣ Data Cleaning
- Handled **missing values** and **duplicates**.
- Filtered necessary columns for analysis.
### 4️⃣ Data Analysis & Insights
- **Product & Category Trends** – Identified top-selling products.
- **Sales Analysis** – Analyzed revenue by **location**.
- **Customer Ratings** – Categorized ratings into groups.
- **Order Trends** – Explored order frequency across categories.
### 5️⃣ Data Visualization
- **Pivot tables** for category insights.
- **Pie charts** for product distribution (using **Plotly**).
## 🛠 Tools & Technologies Used
- **Python** (Pandas, NumPy, Plotly)
- **Google Colab**
- **Excel** (Dataset Storage)
## 📊 Key Insights
✅ Which product categories generate the most revenue?
✅ How do customer ratings vary across different products?
✅ Which locations drive the highest sales?
This project provides valuable insights into **customer behavior and product performance** in an e-commerce business! 🚀
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Let me know if you need further refinements! 😊