chore: Update README with more project details

README.md

@@ -13,15 +13,16 @@ short_description: Extract, Load, Transform Pipeline applied to an E-Commerce
 
 ## Table of Contents
 
-1. [Description](#1-description)
-2. [Stack](#2-stack)
-3. [Entity Relationship Diagram](#3-entity-relationship-diagram)
+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. 🛍️ Description
+## 1. Project Description
 
-This dashboard presents insights from the real-world [Brazilian E-Commerce Public Dataset by Olist](https://www.kaggle.com/datasets/olistbr/brazilian-ecommerce), which includes data on over **100,000 orders** placed between **2016 and 2018** across various online marketplaces in Brazil. It also integrates data from the [Public Holiday API](https://date.nager.at/Api) to analyze sales performance during national holidays.
+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 dataset offers a detailed view of the e-commerce experience, including:
+The dashboard provides a detailed view of the e-commerce experience, including:
 
 - Order status, prices, and payment types
 - Freight and delivery performance
@@ -29,21 +30,53 @@ The dataset offers a detailed view of the e-commerce experience, including:
 - Customer reviews and satisfaction
 
 > [!IMPORTANT]  
-> Check the Dashboard deployed on Hugging Face Spaces: [E-Commerce ELT](https://huggingface.co/spaces/iBrokeTheCode/E-Commerce_ELT)
+> You can check out the deployed dashboard here: [E-Commerce ELT](https://huggingface.co/spaces/iBrokeTheCode/E-Commerce_ELT)
 
-## 2. 🧑‍💻 Stack
+![Dashboard](./public/dashboard-demo.png)
+
+## 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): A platform for hosting and sharing interactive machine learning demos and applications.
-- [Pandas](https://pandas.pydata.org/): A Python library for data manipulation and analysis.
-- [Plotly](https://plotly.com/python/): A Python library for interactive data visualization.
-- [Matplotlib](https://matplotlib.org/): A Python library for creating static, animated, and interactive visualizations.
-- [Seaborn](https://seaborn.pydata.org/): A Python library for creating statistical graphics.
-- [SQLAlchemy](https://www.sqlalchemy.org/): A Python library for interacting with databases.
-- [Requests](https://requests.readthedocs.io/en/latest/): A Python library for making HTTP requests.
-- [Ruff](https://github.com/charliermarsh/ruff): An extremely fast Python linter and code formatter, written in Rust.
-- [uv](https://github.com/astral-sh/uv): An extremely fast Python package and project manager, written in Rust.
-
-## 3. Entity Relationship Diagram
+- [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:
 
 ![ERD](./public/erd-schema.png)