# Advanced demo: Load California Housing, clean, engineer, regress, evaluate, visualize
import pandas as pd
import numpy as np
from sklearn.model_selection import train_test_split
from sklearn.linear_model import LinearRegression
from sklearn.metrics import mean_squared_error, r2_score
from sklearn.datasets import fetch_california_housing
import matplotlib.pyplot as plt

def advanced_housing_demo():
    # Load and clean
    housing = fetch_california_housing()
    df = pd.DataFrame(housing.data, columns=housing.feature_names)
    df['Target'] = housing.target
    df = df.fillna(df.mean(numeric_only=True))  # Handle missing values
    # Feature engineering
    # Avoid division by zero
    df['RoomsPerHouse'] = df['AveRooms'] / (df['HouseAge'] + 1e-5)
    df['PopDensity'] = df['Population'] / (df['AveOccup'] + 1e-5)
    # Split
    X = df.drop('Target', axis=1)
    y = df['Target']
    X_train, X_test, y_train, y_test 
        = train_test_split(X, y, test_size=0.25, random_state=42)
    # Train and predict
    model = LinearRegression()
    model.fit(X_train, y_train)
    y_pred = model.predict(X_test)
    # Evaluate
    mse = mean_squared_error(y_test, y_pred)
    r2 = r2_score(y_test, y_pred)
    print(f"Mean Squared Error: {mse:.2f}")
    print(f"R² Score: {r2:.2f}")
    # Visualize
    plt.scatter(y_test, y_pred, c='blue', alpha=0.5)
    plt.xlabel('True Values')
    plt.ylabel('Predictions')
    plt.title('California Housing Regression')
    plt.annotate('Best Fit', xy=(y_test.mean(), y_pred.mean()), 
        xytext=(y_test.mean() + 0.5, y_pred.mean() + 0.5), 
        arrowprops=dict(facecolor='black', shrink=0.05))
    plt.show()

advanced_housing_demo()