https://github.com/inddrsingh/e-commerce_orders

ETL project, with Python for Data cleaning and MySQL for Data analysis
https://github.com/inddrsingh/e-commerce_orders

data-analysis etl-pipeline mysql python

Last synced: 2 months ago
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ETL project, with Python for Data cleaning and MySQL for Data analysis

• Host: GitHub
• URL: https://github.com/inddrsingh/e-commerce_orders
• Owner: INDDRSINGH
• Created: 2025-05-24T15:04:52.000Z (about 1 year ago)
• Default Branch: main
• Last Pushed: 2025-05-24T16:13:46.000Z (about 1 year ago)
• Last Synced: 2025-05-31T17:47:55.335Z (about 1 year ago)
• Topics: data-analysis, etl-pipeline, mysql, python
• Language: Jupyter Notebook
• Homepage:
• Size: 326 KB
• Stars: 0
• Watchers: 0
• Forks: 0
• Open Issues: 0
• Metadata Files:
  • Readme: README.md

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README

          
# E-commerce Orders Data Analysis

![DatabaseSchema](https://github.com/INDDRSINGH/E-commerce_Orders/blob/main/e-commerce%20logo.png)

## This project demonstrates an end-to-end ETL (Extract, Transform, Load) pipeline for e-commerce order data. Raw order data is cleaned and preprocessed using Python, then loaded into a MySQL database for further analysis and reporting. The workflow showcases best practices in data engineering and analytics for e-commerce businesses.

## Project Workflow

  * Extract: Load raw e-commerce order data (CSV/Excel). [here](https://github.com/INDDRSINGH/restaurant_orders_MySQL/blob/main/restaurant_orders.csv)

  * Transform: Clean, preprocess, and validate data using Python (pandas, numpy) [here](https://github.com/INDDRSINGH/E-commerce_Orders/blob/main/Orders_cleaning.ipynb)

  * Load: Insert the cleaned data into a MySQL database from Python. 

  * Analyze: Run SQL queries for business insights. [here](https://github.com/INDDRSINGH/E-commerce_Orders/blob/main/SQL_Queries.md)

## Dataset

  * Source : Kaggle

  * Content : ['Order Id', 'Order Date', 'Ship Mode', 'Segment', 'Country', 'City',

       'State', 'Postal Code', 'Region', 'Category', 'Sub Category',

       'Product Id', 'cost price', 'List Price', 'Quantity',

       'Discount Percent']

  * Size : (9994, 16)

## Programming Language

  * Python (Pandas, numpy, SQLAlchemy)  

   

## DataBase

  * MySQL