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%%{init: {'theme':'dark', 'themeVariables': { 'primaryColor':'#2E86AB','secondaryColor':'#F77F00','tertiaryColor':'#06A77D','lineColor':'#2E86AB','fontSize':'18px'}}}%%
graph LR
A[🏠 PROPERTY<br/>DATA] --> B[📊 FEATURE<br/>EXTRACTION]
B --> C[🔍 DATA<br/>ANALYSIS]
C --> D[🤖 XGBOOST<br/>MODEL]
D --> E[💰 PRICE<br/>PREDICTION]
E --> F[📈 VALUATION<br/>REPORT]
style A fill:#2E86AB,stroke:#fff,stroke-width:4px,color:#fff
style B fill:#F77F00,stroke:#fff,stroke-width:4px,color:#fff
style C fill:#06A77D,stroke:#fff,stroke-width:4px,color:#fff
style D fill:#E63946,stroke:#fff,stroke-width:4px,color:#fff
style E fill:#2E86AB,stroke:#fff,stroke-width:4px,color:#fff
style F fill:#F77F00,stroke:#fff,stroke-width:4px,color:#fff
|
CRIME RATE Per Capita |
LAND ZONE Residential % |
INDUSTRY Business Acres |
RIVER Bounds Charles |
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AIR QUALITY NOx Concentration |
ROOMS Average Count |
AGE Built Before 1940 |
DISTANCE Employment Centers |
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HIGHWAY Accessibility |
TAX RATE Property Tax |
EDUCATION Student-Teacher |
DEMOGRAPHICS Population Stats |
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LOWER STATUS Population % Lower Status |
|||
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✅ Starter Homes 35% of Market |
🏘️ Family Homes 45% of Market |
⭐ Premium Locations 20% of Market |
# 📥 Clone Repository
git clone https://github.com/yourusername/house-price-prediction-xgboost-ml.git
# 📂 Navigate to Directory
cd house-price-prediction-xgboost-ml
# 💊 Install Dependencies
pip install -r requirements.txt
# 🏠 Run Prediction System
python "House Price Prediction.py"✅ READY TO PREDICT PROPERTY VALUES!
# 🏡 Import House Price Predictor
from xgboost import XGBRegressor
import pandas as pd
# 📊 Load Model
model = XGBRegressor()
model.load_model('house_price_model.json')
# 🏠 Property Features
property_data = {
'crim': 0.00632, # Crime rate
'zn': 18.0, # Residential land zoned
'indus': 2.31, # Non-retail business acres
'chas': 0, # Charles River (0 = No, 1 = Yes)
'nox': 0.538, # Nitric oxides concentration
'rm': 6.575, # Average number of rooms
'age': 65.2, # Proportion of units built before 1940
'dis': 4.0900, # Distance to employment centers
'rad': 1, # Accessibility to highways
'tax': 296, # Property tax rate
'ptratio': 15.3, # Pupil-teacher ratio
'b': 396.90, # Proportion of demographic
'lstat': 4.98 # Lower status of population
}
# 💰 Predict House Price
price = model.predict([list(property_data.values())])
print(f"🏠 Estimated House Price: ${price[0]*1000:.2f}")Output:
🏠 Estimated House Price: $285,650.00
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Best Real Estate AI PropTech Summit 2025 |
Innovation Award ML Competition 2024 |
Top Predictor Kaggle Challenge |
Community Choice GitHub 2024 |
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Property Photos Computer Vision Interior Quality Assessment |
Location Intelligence Neighborhood Analysis Market Trends |
iOS & Android Real-time Valuation AR Property View |
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Data Protection |
Secure API |
Anonymous Data |
Real Estate Laws |
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Market Analysis Property Valuation |
Code Improvements Feature Development |
Model Optimization Algorithm Research |
ML Projects Learning Resources |
📖 Read CONTRIBUTING.md for Guidelines
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User Guide |
API Docs |
Model Papers |
Deployment |
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⭐ Star Repo |
🍴 Fork Project |
📢 Share It |
🐛 Report Issues |
☕ Sponsor |
╔══════════════════════════════════════════════════════════╗
║ ⚠️ REAL ESTATE DISCLAIMER ║
╠══════════════════════════════════════════════════════════╣
║ ║
║ 🏠 FOR EDUCATIONAL & RESEARCH PURPOSES ONLY ║
║ ❌ NOT PROFESSIONAL PROPERTY APPRAISAL ║
║ ❌ NOT FINANCIAL OR INVESTMENT ADVICE ║
║ 🏢 CONSULT LICENSED REALTORS FOR ACTUAL VALUATIONS ║
║ ║
║ 📊 Model predictions are estimates based on ║
║ historical data and may not reflect current ║
║ market conditions or unique property features ║
║ ║
╚══════════════════════════════════════════════════════════╝
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UCI Repository Boston Housing Dataset |
XGBoost Team ML Framework |
Kaggle Community Data Science Support |
Open Source Python Libraries |
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R² Score |
Avg Error (MAE) |
Prediction Speed |
Properties Analyzed |
XGBOOST PERFORMANCE
LINEAR REGRESSION BASELINE
RANDOM FOREST COMPARISON
NEURAL NETWORK