simple-house-price-linear-regression

Simple House Price Linear Regression

A simple Linear Regression model built with scikit-learn that predicts house price based on house size.

• Input: House size in square feet
• Output: Predicted house price in lakhs
• Model: sklearn.linear_model.LinearRegression

This model uses a small demonstration dataset and is intended for learning purposes only.

Installation

pip install scikit-learn joblib huggingface_hub

Usage

Option 1: Load directly from Hugging Face

from huggingface_hub import hf_hub_download
import joblib

model_path = hf_hub_download(
    repo_id="iron04/simple-house-price-linear-regression",
    filename="model.joblib",
)

model = joblib.load(model_path)

prediction = model.predict([[1500]])

print(f"Predicted price: {prediction[0]:.2f} lakh")

Option 2: Download model.joblib manually

Download model.joblib from this repository and place it in your project.

import joblib

model = joblib.load("model.joblib")

prediction = model.predict([[1500]])

print(f"Predicted price: {prediction[0]:.2f} lakh")

Input

The model expects house size in square feet as a 2D array:

model.predict([[1000]])
model.predict([[1500]])
model.predict([[2000]])

Multiple predictions are also supported:

model.predict([
    [1000],
    [1500],
    [2000],
])

Output

The model returns predicted prices in lakhs.

Example:

1000 sq ft → 40.00 lakh
1500 sq ft → 60.00 lakh
2000 sq ft → 80.00 lakh

Limitations

This is a small educational model trained on artificial data. It should not be used for real-world house-price valuation or financial decisions.