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srivardhan/
california-housing-price-prediction

Name: california-housing-price-prediction
Availability: InStock

joblib

ml-intern

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California Housing Price Prediction Model 🏠

A Gradient Boosting Regressor trained on the California Housing dataset from scikit-learn to predict median house values.

Model Performance

Model	R² Score	RMSE	MAE
Gradient Boosting ✅	0.8397	0.4583 ($45,829)	0.3030 ($30,298)
Random Forest	0.8004	0.5115 ($51,147)	0.3332 ($33,322)
Linear Regression	0.5758	0.7456 ($74,558)	0.5332 ($53,320)

Cross-validation (5-fold) R²: 0.6768 ± 0.0606

Dataset

Source: sklearn.datasets.fetch_california_housing
Samples: 20,640
Features: 8 numerical features
Target: Median house value (in $100,000s)
Train/Test Split: 80/20

Features

Feature	Description
MedInc	Median income in block group
HouseAge	Median house age in block group
AveRooms	Average number of rooms per household
AveBedrms	Average number of bedrooms per household
Population	Block group population
AveOccup	Average number of household members
Latitude	Block group latitude
Longitude	Block group longitude

Feature Importance

MedInc (53.5%) — Median income is the strongest predictor
AveOccup (13.5%) — Average occupancy
Longitude (11.1%) — Geographic location (east-west)
Latitude (10.5%) — Geographic location (north-south)
HouseAge (4.5%)
AveRooms (3.4%)
AveBedrms (1.8%)
Population (1.7%)

Usage

python

import joblib
import numpy as np
from huggingface_hub import hf_hub_download

# Download model
model_path = hf_hub_download(
    repo_id="srivardhan/california-housing-price-prediction",
    filename="california_housing_model.joblib"
)
model = joblib.load(model_path)

# Predict - input features: [MedInc, HouseAge, AveRooms, AveBedrms, Population, AveOccup, Latitude, Longitude]
sample = np.array([[8.3252, 41.0, 6.984127, 1.023810, 322.0, 2.555556, 37.88, -122.23]])
prediction = model.predict(sample)
print(f"Predicted house value: ${prediction[0] * 100000:,.0f}")

Model Details

Algorithm: Gradient Boosting Regressor
Hyperparameters:
- n_estimators: 300
- max_depth: 5
- learning_rate: 0.1
- subsample: 0.8
Framework: scikit-learn

Files

california_housing_model.joblib — Trained model
scaler.joblib — StandardScaler (for Linear Regression usage)
model_metadata.json — Full metrics and configuration
training_script.py — Complete training script

License

MIT

Generated by ML Intern

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Source code: https://github.com/huggingface/ml-intern