An elegant machine learning application for real-time Boston housing price predictions
✨ Live Demo : https://boston-houseprice-prediction-g32w.onrender.com/
Precision forecasting meets beautiful design
graph LR
A[User Input] --> B[Flask API]
B --> C[Trained Model]
C --> D[Price Prediction]
D --> E[Visual Result]
style A fill:#ff6b6b
style E fill:#51cf66
This application transforms 13 housing features into accurate price estimates using a sophisticated Linear Regression model. Built with precision and designed with elegance, it bridges data science with practical application.
# Core Prediction Flow
Input Features → Data Validation → Model Inference → Result Formatting → Beautiful Output| Layer | Technology | Purpose |
|---|---|---|
| Frontend | HTML5, CSS3, JavaScript | User Interface & Experience |
| Backend | Python Flask, REST API | Server Logic & Routing |
| ML Engine | Scikit-learn, Linear Regression | Price Prediction |
| Data | Pandas, NumPy | Data Processing |
| Deployment | Render | Production Hosting |
| Version Control | Git, GitHub | Code Management |
Boston_HousePrice_Prediction-/
├── 📊 house_price_analysis.ipynb # Complete EDA & Model Training
├── 🤖 house_price_analysis.pkl # Serialized Trained Model
├── 🚀 app.py # Flask Application Entry Point
├── 📋 requirements.txt # Dependency Management
├── 🎨 templates/
│ └── index.html # Interactive Web Interface
└── 📈 BostonHousing.csv # Training Dataset (506 samples)
# Clone repository
git clone https://github.com/24f2004698/Boston_HousePrice_Prediction-.git
# Navigate to project
cd Boston_HousePrice_Prediction-
# Create virtual environment
python -m venv venv
# Activate environment
source venv/bin/activate # Windows: venv\Scripts\activatepip install -r requirements.txtpython app.py🔗 Access at: http://localhost:5000
- Access the application via Live Demo or localhost
- Input the 13 Boston housing features:
- Location Factors: CRIM, ZN, INDUS
- Property Details: CHAS, NOX, RM
- Community Metrics: AGE, DIS, RAD, TAX
- Education & Demographics: PTRATIO, B, LSTAT
- Click the Predict button
- View your instant price estimate with visual feedback
The Linear Regression model was trained on the BostonHousing.csv dataset, achieving optimal performance through:
- Feature Selection: 13 most impactful housing metrics
- Data Preprocessing: Normalized and cleaned inputs
- Validation: Rigorous testing for reliability
- Persistence: Model saved for immediate inference
Status: ✅ Active
