mahaqj/ml_assignment_3

ML Assignment 3 – Maha Qaiser

Dataset: California Housing from sklearn.datasets

File Description

• best_model.joblib: Mini-Batch Linear Regression model
• scaler.joblib: StandardScaler object to preprocess input features
• inference.py: Script to load the model + scaler and run predictions with user input

Model Overview

• Model Type: Mini-Batch Linear Regression
• Features Used:
  • Avg. Rooms
  • Avg. Bedrooms
  • Population
  • Household
  • Median Income
  • Latitude
  • Longitude
  • Housing Median Age
• Regularization: L2 (Ridge)
• Early Stopping: Applied during training

How to Run Inference

1. Clone the repository or download the files:

git clone https://huggingface.co/mahaqj/ml_assignment_3
cd ml_assignment_3

2. Install dependencies:

pip install joblib numpy scikit-learn huggingface_hub

3. Run the script:

python inference.py

You’ll be prompted to enter the following features:

• Avg. Rooms
• Avg. Bedrooms
• Population
• Household
• Median Income
• Latitude
• Longitude
• Housing Median Age

The model will return the predicted housing value.

Loading the Model in Python

import joblib
import requests
from io import BytesIO

# urls to both model and scaler
model_url = "https://huggingface.co/mahaqj/ml_assignment_3/resolve/main/best_model.joblib"
scaler_url = "https://huggingface.co/mahaqj/ml_assignment_3/resolve/main/scaler.joblib"

# download and load model
model_bytes = BytesIO(requests.get(model_url).content)
model = joblib.load(model_bytes)

# download and load scaler
scaler_bytes = BytesIO(requests.get(scaler_url).content)
scaler = joblib.load(scaler_bytes)