{"id":28390952,"url":"https://github.com/inddrsingh/e-commerce_orders","last_synced_at":"2026-04-18T11:03:08.755Z","repository":{"id":295272886,"uuid":"989661184","full_name":"INDDRSINGH/E-commerce_Orders","owner":"INDDRSINGH","description":"ETL project, with Python for Data cleaning and MySQL for Data analysis","archived":false,"fork":false,"pushed_at":"2025-05-24T16:13:46.000Z","size":334,"stargazers_count":0,"open_issues_count":0,"forks_count":0,"subscribers_count":0,"default_branch":"main","last_synced_at":"2025-05-31T17:47:55.335Z","etag":null,"topics":["data-analysis","etl-pipeline","mysql","python"],"latest_commit_sha":null,"homepage":"","language":"Jupyter Notebook","has_issues":true,"has_wiki":null,"has_pages":null,"mirror_url":null,"source_name":null,"license":null,"status":null,"scm":"git","pull_requests_enabled":true,"icon_url":"https://github.com/INDDRSINGH.png","metadata":{"files":{"readme":"README.md","changelog":null,"contributing":null,"funding":null,"license":null,"code_of_conduct":null,"threat_model":null,"audit":null,"citation":null,"codeowners":null,"security":null,"support":null,"governance":null,"roadmap":null,"authors":null,"dei":null,"publiccode":null,"codemeta":null,"zenodo":null}},"created_at":"2025-05-24T15:04:52.000Z","updated_at":"2025-05-24T16:15:02.000Z","dependencies_parsed_at":"2025-05-24T16:33:20.339Z","dependency_job_id":"c8e2d409-69e2-429d-b557-3ca063d949c6","html_url":"https://github.com/INDDRSINGH/E-commerce_Orders","commit_stats":null,"previous_names":["inddrsingh/e-commerce_orders"],"tags_count":0,"template":false,"template_full_name":null,"purl":"pkg:github/INDDRSINGH/E-commerce_Orders","repository_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/INDDRSINGH%2FE-commerce_Orders","tags_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/INDDRSINGH%2FE-commerce_Orders/tags","releases_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/INDDRSINGH%2FE-commerce_Orders/releases","manifests_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/INDDRSINGH%2FE-commerce_Orders/manifests","owner_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/owners/INDDRSINGH","download_url":"https://codeload.github.com/INDDRSINGH/E-commerce_Orders/tar.gz/refs/heads/main","sbom_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/INDDRSINGH%2FE-commerce_Orders/sbom","host":{"name":"GitHub","url":"https://github.com","kind":"github","repositories_count":261289423,"owners_count":23136071,"icon_url":"https://github.com/github.png","version":null,"created_at":"2022-05-30T11:31:42.601Z","updated_at":"2022-07-04T15:15:14.044Z","host_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub","repositories_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories","repository_names_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repository_names","owners_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/owners"}},"keywords":["data-analysis","etl-pipeline","mysql","python"],"created_at":"2025-05-31T07:12:48.697Z","updated_at":"2025-10-09T01:11:34.732Z","avatar_url":"https://github.com/INDDRSINGH.png","language":"Jupyter Notebook","funding_links":[],"categories":[],"sub_categories":[],"readme":"# E-commerce Orders Data Analysis\n\n![DatabaseSchema](https://github.com/INDDRSINGH/E-commerce_Orders/blob/main/e-commerce%20logo.png)\n\n## 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.\n\n\n## Project Workflow\n  * Extract: Load raw e-commerce order data (CSV/Excel). [here](https://github.com/INDDRSINGH/restaurant_orders_MySQL/blob/main/restaurant_orders.csv)\n  * Transform: Clean, preprocess, and validate data using Python (pandas, numpy) [here](https://github.com/INDDRSINGH/E-commerce_Orders/blob/main/Orders_cleaning.ipynb)\n  * Load: Insert the cleaned data into a MySQL database from Python. \n  * Analyze: Run SQL queries for business insights. [here](https://github.com/INDDRSINGH/E-commerce_Orders/blob/main/SQL_Queries.md)\n\n\n## Dataset\n  * Source : Kaggle\n  * Content : ['Order Id', 'Order Date', 'Ship Mode', 'Segment', 'Country', 'City',\n       'State', 'Postal Code', 'Region', 'Category', 'Sub Category',\n       'Product Id', 'cost price', 'List Price', 'Quantity',\n       'Discount Percent']\n  * Size : (9994, 16)\n\n\n## Programming Language\n  * Python (Pandas, numpy, SQLAlchemy)  \n\n   \n## DataBase\n  * MySQL\n","project_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Finddrsingh%2Fe-commerce_orders","html_url":"https://awesome.ecosyste.ms/projects/github.com%2Finddrsingh%2Fe-commerce_orders","lists_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Finddrsingh%2Fe-commerce_orders/lists"}