This page provides detailed information about the house price prediction model used in our analysis. The model leverages historical real estate data to forecast property values based on key features like location, size, and market trends.
Key Components of the Model
- Algorithm: A hybrid approach combining linear regression 📊 and gradient boosting 🚀 for accuracy.
- Features:
- Square footage 📏
- Number of bedrooms 🛏️
- Neighborhood desirability 🏙️
- Time on market ⏳
- Training: Uses 80% of the dataset for training and 20% for validation 🧪.
Data Sources
The model is trained on a comprehensive dataset containing:
- Property listings from 2010–2023 📅
- Geographic coordinates 🌍
- Price trends visualized in time-series charts 📈
- External factors like interest rates and economic indicators 💰
Evaluation Metrics
- RMSE: 12.5% deviation from actual prices 📉
- MAE: 8.2% average error rate 📊
- R² Score: 0.94 correlation with real-world data 📈
For deeper insights into the dataset used, explore the dataset →