HOUSE PRICE PREDICTION (PRODUCT)
PyTorch-trained model predicts house prices accurately using deep learning for regression tasks.
Description
Real Estate Analytics
A sophisticated Deep Learning Regression Model built on the PyTorch framework. This engine goes beyond simple linear trends by modeling the complex, non-linear relationships between property features and market value.
Predictive Factors
The neural network analyzes multiple input vectors including:
- Dimension Data: Square footage, lot size, and total room counts.
- Categorical Data: Neighborhood quality, building type, and historical pricing trends.
- Condition Metrics: Overall material quality and year of construction.
Output
Provides a continuous numerical value (USD) representing the estimated market valuation based on current dataset training.
Live Deployment Sandbox
Real Estate Valuation Engine
System Ready
API Integration Guide
curl --location 'https://api.aimodelplace.com/api/v1/predict' \
--header 'Authorization: Bearer YOUR_API_KEY' \
--form 'model_slug="house-price-prediction_9"' \
--form 'MSSubClass="60"' \
--form 'MSZoning="RL"' \
--form 'LotArea="8000"'
Categorical Value Mappings:
- MSSubClass: 0-14 (5: 2-Story 1946+)
- MSZoning: 0-4 (3: Res. Low Density)
- Street: 0: Gravel, 1: Paved
- LotShape: 0-2: Irregular, 3: Regular
Successful response format (JSON):
{
"success": true,
"predicted_price": 250000
}For more detailed parameters and SDK examples, visit our Full API Documentation.
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