MohammedAhmed-01/Egypt_Real_Estate_Data_Analysis_2026
Domaine:
socioeconomicType de record:
dataset
Créateur:
Moh
Hôte:
A complete analytics pipeline on Egypt's real estate market — from raw CSV ingestion all the way to interactive BI dashboards.
📊 39,713 Listings
🧬 53 Raw Columns
📓 4 Notebooks
📈 4+3 Dashboard Pages
🤖 6 ML Models
🗺️ 6 Cities
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## 📑 Table of Contents
| | | | |
|:--:|:--:|:--:|:--:|
| 🎯 Overview | 📑 Deliverables | 🏗️ Architecture | 🗃️ Dataset |
| 🌳 Structure | 🔬 Notebooks | 📊 Dashboards | 📦 Outputs |
| 🚀 Setup | 🧰 Libraries | 🧠 Design Decisions | 📜 License |
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## 🎯 Project Overview
> **A complete analytics pipeline on Egypt's real estate market** — from raw CSV ingestion all the way to interactive BI dashboards.
This project deliberately **separates concerns**:
- 🧪 **The Notebooks** are the *analytical engine* — where heavy statistical modeling, Machine Learning, and Natural Language Processing live.
- 📊 **The Dashboards** are the *reporting layer* — strictly descriptive & diagnostic, powered by clean data only, so they load instantly and stay easy to read for stakeholders.
**Business questions answered:**
🏙️ Which cities and property types command the highest price per sqm?
🏊 What amenities and specs correlate with price premiums?
📈 How does listing volume and price move across months and seasons?
💬 Do description sentiment & topics relate to price? (Notebook only)
🤖 Can we predict price and classify Buy vs Rent? (Notebook only)
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## 📑 Project Deliverables & Documentation
| 📄 Artifact | 🎯 Purpose | 🔗 Access |
|:---|:---|:---:|
| **Insights Report** | Deep-dive analysis, key findings & statistical summaries | View Report |
| **Stakeholder Presentation** | Executive summary, visual story & strategic recommendations | View Presentation |
| **Business Impact & Design** | ROI analysis, design rationale & decision-making framework | View Document |
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## 🏗️ Architectural Flow
We separate **BI Reporting** (clean, fast, descriptive) from **Advanced Analytics** (notebook-based ML/NLP). The dashboards consume **only the cleaned dataset** — keeping them performant and clutter-free. ML/NLP outputs are captured as documented insights, not embedded v …