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austinbaggio/
toronto-housing-price-predictor

Name: toronto-housing-price-predictor
Availability: InStock

ml-intern

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Toronto Housing Price Predictor

A lightweight Ridge regression model trained to predict Toronto housing prices based on property features.

Model Details

Architecture: Ridge Regression (linear model with L2 regularization)
Alpha: 20.0
Features: 13 engineered features including beds, baths, location (lat/lon), and derived features
Target: log1p-transformed price in CAD
Dataset: 1,276 Toronto listings from Kaggle/Github housing data

Performance

Split	RMSE	MAE	R²
Train	$738,107	$343,304	0.5534
Validation	$639,453	$328,512	0.7014
Test	$505,285	$320,984	0.6645

Usage

python

import joblib
import numpy as np
import pandas as pd

model = joblib.load("toronto_housing_model.pkl")
scaler = joblib.load("toronto_housing_scaler.pkl")

# Features: Number_Beds, Number_Baths, Latitude, Longitude, Median_Family_Income,
#           lat_centered, lon_centered, dist_from_center,
#           total_rooms, bed_bath_ratio, beds_sq, baths_sq, income_per_room
X = scaler.transform([[3, 2, 43.7417, -79.3733, 97000, 0, 0, 0, 5, 1.5, 9, 4, 19400]])
log_price = model.predict(X)[0]
price = np.expm1(log_price)
print(f"Predicted price: ${price:,.0f}")

License

Open — trained on publicly available data.

Generated by ML Intern

This model repository was generated by ML Intern, an agent for machine learning research and development on the Hugging Face Hub.

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austinbaggio/toronto-housing-price-predictor