A machine learning model that predicts house prices from the California Housing dataset, evaluated with RMSE and R².
Scikit-learn ships the California Housing dataset — no download needed.
from sklearn.datasets import fetch_california_housing
import pandas as pd
data = fetch_california_housing()
df = pd.DataFrame(data.data, columns=data.feature_names)
df['Price'] = data.target # price in $100k units
print(df.describe())Hold out 20% of the data for testing, then train a Random Forest regressor.
from sklearn.model_selection import train_test_split
from sklearn.ensemble import RandomForestRegressor
X = df.drop('Price', axis=1)
y = df['Price']
X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, random_state=42)
model = RandomForestRegressor(n_estimators=100, random_state=42)
model.fit(X_train, y_train)
print("Training complete.")Training complete.
Calculate RMSE and R², then plot feature importance.
from sklearn.metrics import mean_squared_error, r2_score
import numpy as np, matplotlib.pyplot as plt
y_pred = model.predict(X_test)
rmse = np.sqrt(mean_squared_error(y_test, y_pred))
r2 = r2_score(y_test, y_pred)
print(f"RMSE: ${rmse * 100_000:,.0f}")
print(f"R²: {r2:.3f}")
# Feature importance
importances = pd.Series(model.feature_importances_, index=data.feature_names).sort_values()
importances.plot(kind='barh', title='Feature Importance', figsize=(8,5))
plt.tight_layout()
plt.savefig('feature_importance.png', dpi=150)RMSE: $49,832 R²: 0.817
You trained, evaluated, and interpreted your first regression model. This pattern — load, split, train, evaluate — applies to almost every supervised learning problem.
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