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#!/usr/bin/env python3
# -*- coding: utf-8 -*-
import joblib
import numpy as np
from sklearn.datasets import fetch_california_housing
from sklearn.linear_model import BayesianRidge
from sklearn.model_selection import train_test_split
# Set the random seed
random_seed = 0
np.random.seed(random_seed)
# Load the dataset
dataset = fetch_california_housing()
X, y = dataset.data, dataset.target
# Split the dataset into training and testing sets
X_train, _, y_train, _ = train_test_split(X, y, test_size=0.25, random_state=random_seed)
# Create and train model
model = BayesianRidge()
model.fit(X_train, y_train)
# Save the trained model to disk
joblib.dump(model, 'bayesian_ridge.joblib')
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