Spaces:
Sleeping
Sleeping
| title: E-Commerce ELT | |
| emoji: 🍃 | |
| colorFrom: indigo | |
| colorTo: purple | |
| sdk: docker | |
| pinned: true | |
| license: mit | |
| short_description: Extract, Load, Transform Pipeline applied to an E-Commerce | |
| # 📦 E-Commerce ELT Pipeline | |
| ## Table of Contents | |
| 1. [Project Description](#1-project-description) | |
| 2. [Methodology & Key Features](#2-methodology--key-features) | |
| 3. [Technology Stack](#3-technology-stack) | |
| 4. [Dataset](#4-dataset) | |
| ## 1. Project Description | |
| This project showcases an Extract, Load, and Transform (ELT) pipeline applied to a real-world e-commerce dataset. The primary goal is to extract valuable business insights from transactional data and present them through an interactive dashboard. The pipeline integrates data from the **Brazilian E-Commerce Public Dataset by Olist**, which contains over **100,000 orders** from 2016 to 2018, and also incorporates data from the Public Holiday API to analyze sales performance during national holidays. | |
| The dashboard provides a detailed view of the e-commerce experience, including: | |
| - Order status, prices, and payment types | |
| - Freight and delivery performance | |
| - Customer locations and product categories | |
| - Customer reviews and satisfaction | |
| > [!IMPORTANT] | |
| > | |
| > - Check out the deployed app here: 👉️ [E-Commerce ELT](https://huggingface.co/spaces/iBrokeTheCode/E-Commerce_ELT) 👈️ | |
| > - Check out the Jupyter Notebook for a detailed walkthrough of the project here: 👉️ [Jupyter Notebook](https://huggingface.co/spaces/iBrokeTheCode/E-Commerce_ELT/blob/main/tutorial_app.ipynb) 👈️ | |
|  | |
| ## 2. Methodology & Key Features | |
| The ELT pipeline extracts raw data, loads it into a structured format, and then transforms it to generate key metrics and visualizations. The analysis is presented using an interactive dashboard built with Marimo, a Python library. | |
| ### Key Features: | |
| - **Data Integration**: Combines e-commerce order data with public holiday information to analyze temporal sales patterns. | |
| - **Data Transformation**: Cleans and prepares raw data for analysis, enabling the calculation of key performance indicators (KPIs). | |
| - **Interactive Dashboard**: Provides a dynamic and user-friendly interface for exploring business insights. | |
| ## 3. Technology Stack | |
| This project was built using the following technologies and libraries: | |
| **Dashboard & Hosting:** | |
| - [Marimo](https://github.com/marimo-team/marimo): A Python library for building interactive dashboards. | |
| - [Hugging Face Spaces](https://huggingface.co/docs/hub/spaces-config-reference): Used for hosting and sharing the interactive dashboard. | |
| **Data Analysis & Visualization:** | |
| - [Pandas](https://pandas.pydata.org/): For data manipulation and analysis. | |
| - [Plotly](https://plotly.com/python/): For creating interactive data visualizations. | |
| - [Matplotlib](https://matplotlib.org/): For creating static visualizations. | |
| - [Seaborn](https://seaborn.pydata.org/): For creating statistical graphics. | |
| **Data Handling & Utilities:** | |
| - [SQLAlchemy](https://www.sqlalchemy.org/): For interacting with databases. | |
| - [Requests](https://requests.readthedocs.io/en/latest/): For making HTTP requests to external APIs. | |
| **Development Tools:** | |
| - [Ruff](https://github.com/charliermarsh/ruff): A fast Python linter and code formatter. | |
| - [uv](https://github.com/astral-sh/uv): A fast Python package installer and resolver. | |
| ## 4. Dataset | |
| This project utilizes the **Brazilian E-Commerce Public Dataset by Olist** from Kaggle, a public dataset containing details on over 100,000 orders. The data spans from 2016 to 2018 and includes a wide range of transactional information. | |
| - **Source**: [Kaggle Dataset](https://www.kaggle.com/datasets/olistbr/brazilian-ecommerce) | |
| - **Additional Data**: The project also integrates data from the [Public Holiday API](https://date.nager.at/Api). | |
| Here is the ERD diagram for the database schema: | |
|  | |