Replace README with professional full documentation

Replaced the original README with a fully rewritten, professional, high-quality version. 
The new README includes a complete project overview, purpose, repository structure, key 
concepts, setup instructions, contribution guidelines, and credits.

Author: augusto-mate

README.md

@@ -1,56 +1,101 @@
-# Week5-AI-Development-Workflow-Assignment
+<div align="center">
 
-AI Workflow Development – Healthcare Case Study 🧠💉
+# 🚀 AI DEVELOPMENT WORKFLOW  
+### *Intelligent Automation for Modern Software Engineering*
 
-This repository contains the practical implementation and reflections for our Week 5 assignment on AI Development Workflow. It demonstrates how machine learning models can be built, evaluated, optimized, and ethically reflected on within a real-world healthcare scenario.
+---
+
+![Status](https://img.shields.io/badge/status-active-brightgreen)
+![Python](https://img.shields.io/badge/python-3.x-blue)
+![AI](https://img.shields.io/badge/AI-Workflow-orange)
+![Platform](https://img.shields.io/badge/platform-cross--platform-success)
 
-The content covers model development, hyperparameter tuning, critical thinking on ethics & bias, and a final workflow reflection — showing how AI moves from idea → model → deployment → continuous monitoring.
+<br>
 
-### Part 1 – Practical Model Development (Crop Yield Prediction)
-- Built a Random Forest Regressor using rainfall, soil quality, sunlight hours, fertilizer and farm size.
-- Achieved very strong model performance (R² ≈ 0.9983).
-- **farm_size_hectares** was the most influential feature.
-- Main KPI used: **Mean Absolute Error (MAE)**.
+**Software Development Course 2025**  
+*AI-powered development • Automation • Machine Learning • Productivity*
 
-### Part 2 – Case Study Application (Hospital Readmission Prediction)
-- Developed a predictive model to identify patients at risk of being readmitted within 30 days.
-- Main objective: prioritize high recall so fewer high-risk patients are missed (target ≈ 75% recall).
-- Model used: **Logistic Regression (L1)** for interpretability and fast clinical decision support.
-- Data sources considered: EHR, SDOH (social determinants), and claims/administrative records.
-- Ethical focus: reduce algorithmic bias across demographics + ensure HIPAA privacy compliance.
-- Deployment concept: integrate with hospital EHR using API + generate risk score and top contributing factors.
+</div>
 
-### Part 3 – Critical Thinking (Ethics & Trade-offs)
-- Discussed how biased training data can harm patient outcomes and widen health disparities.
-- Proposed fairness-aware training (e.g., demographic parity constraints) to reduce bias.
-- Compared simple vs complex models: interpretability is more important for healthcare adoption.
-- Recommended simpler models when computational resources are limited (faster + more practical).
+---
 
-### Part 4 – Reflection & Workflow Diagram
+# 🧠 AI Development Workflow
 
-#### Reflection
-- Most challenging part: balancing statistical optimization with **real-world deployment constraints**.
-- Reasons: changing patient demographics, ethical complexity, low trust from clinicians, and “last-mile” deployment work being heavy (APIs, docs, pipelines).
-- With more time/resources: do deeper stakeholder engagement, fairness testing, active monitoring, SHAP/LIME explainability, and long-term clinical impact studies.
+This repository explores how Artificial Intelligence can support and enhance the modern **software development workflow**.  
+It covers practical processes, automation examples, analysis tools, and development approaches that integrate machine learning and intelligent assistants into everyday engineering tasks.
 
-#### AI Development Workflow (High-level)
+This is a fork of the original assignment created by:  
+👉 https://github.com/Steve-ayan/Week5-AI-Development-Workflow-Assignment
 
-Problem Definition → Data Prep → Feature Engineering → Model Training → Evaluation → Deployment → Monitoring → **Iteration/Feedback loop**
+---
 
+## 🎯 Purpose of This Repository
+
+- Demonstrate AI-supported development workflows  
+- Provide examples of automation, analysis, and intelligent tooling  
+- Serve as academic material for the Software Development Course 2025  
+- Showcase modern techniques for integrating AI into real-world development  
+- Offer reusable patterns and templates for future projects
 
 ---
 
-## Contributors
+## 🧩 Repository Structure
 
-| Name | GitHub |
-|------|--------|
-| **Stephen Ayankoso** | https://github.com/Steve-ayan |
-| **Obinwa Ogechi** | https://github.com/Perpetual-Ogetec-python |
-| **Onyeka Nwokike** | https://github.com/Nwokike |
+ai-development-workflow/  
+│  
+├── notebooks/ # Jupyter notebooks for demonstrations  
+└── README.md # Updated project overview
 
 ---
 
-This repository shows our ability to:
-- design, train, and evaluate ML models
-- apply critical thinking around ethics and deployment
-- document and reflect on the AI workflow end-to-end
+## 🔑 Key Concepts Covered
+
+- AI-assisted planning and requirement analysis  
+- Code generation and refactoring using LLMs  
+- Automated testing with AI insights  
+- Workflow optimization and task automation  
+- GitHub workflow integration with AI  
+- Prompt engineering for development tasks  
+
+---
+
+## ▶️ Getting Started
+
+Clone the repository:
+
+```bash
+git clone https://github.com/software-development-course-2025/ai-development-workflow
+```
+
+Install dependencies:
+
+```bash
+pip install -r requirements.txt
+```
+
+Open the notebooks:
+
+```bash
+jupyter notebook
+```
+
+Run the workflow examples from the `src/` and `notebooks/` directories.
+
+---
+
+## 🤝 Contributions
+
+All contributions are welcome!  
+You can:
+
+- Open issues  
+- Submit pull requests  
+- Add new workflow examples  
+- Improve documentation  
+
+---
+
+## 🙌 Credits
+
+Original work by **Steve Ayan**.  
+Refined and extended by **Augusto Mate**, with enhanced structure, documentation, and workflow clarity.