DO
DoNguyenAnhTuan/Fuzzy-House-Pricing
House price prediction using fuzzy logic to handle uncertainty in real estate variables. Implemented in Jupyter Notebook.
# 🏡 Fuzzy House Pricing
This project applies **Fuzzy Logic** to predict house prices based on uncertain or imprecise input variables such as location, area, and condition.
Fuzzy logic allows for reasoning with vague concepts like "large", "medium", or "near center" — making it well-suited for complex, real-world decision-making like real estate valuation.
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## 💡 Why Fuzzy Logic?
- Traditional models rely on precise input (e.g., 120m² = $150,000)
- Fuzzy logic works with **linguistic terms** (e.g., "medium area", "good condition")
- Human-like reasoning mimics expert appraisers
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## 🧠 Model Components
- **Inputs**:
- Distance to city center (km)
- House area (m²)
- Condition score (0–10)
- **Output**:
- Predicted house price (in currency unit)
- **Tech stack**:
- Python, NumPy, scikit-fuzzy
- Visualizations with Matplotlib
- Interactive development via Jupyter Notebook
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## 📊 Fuzzy Membership Functions
| Variable | Linguistic Terms |
|----------------|--------------------------------|
| Area | Small, Medium, Large |
| Distance | Near, Moderate, Far |
| Condition | Poor, Average, Good |
| Price (Output) | Cheap, Moderate, Expensive |
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## 🚀 Run the Notebook
1. Clone the repo:
```bash
git clone https://github.com/DoNguyenAnhTuan/Fuzzy-House-Pricing.git
cd Fuzzy-House-Pricing-
Install dependencies:
pip install -r requirements.txt
-
Launch the notebook:
jupyter notebook
📎 Sample Output
- Input: 85m², 5km from center, condition = 7
- Output: Moderately expensive
📬 Author
Do Nguyen Anh Tuan
📍 MSc in IT @ Lac Hong University
🔗 Portfolio Website
🐙 GitHub