Machine LearningGrundlagen

🐍 Python Code-Beispiel

# 1. Bibliotheken importieren
import pandas as pd
from sklearn.model_selection import train_test_split
from sklearn.linear_model import LinearRegression
from sklearn.metrics import mean_squared_error, r2_score

# 2. Daten laden
data = pd.read_csv('house_prices.csv')

# 3. Features und Target definieren
X = data[['size', 'bedrooms', 'age', 'location_score']]
y = data['price']

# 4. Train/Test Split
X_train, X_test, y_train, y_test = train_test_split(
    X, y, test_size=0.2, random_state=42
)

# 5. Modell trainieren
model = LinearRegression()
model.fit(X_train, y_train)

# 6. Vorhersagen machen
y_pred = model.predict(X_test)

# 7. Modell evaluieren
rmse = mean_squared_error(y_test, y_pred, squared=False)
r2 = r2_score(y_test, y_pred)

print(f"RMSE: {rmse:.2f}")
print(f"R² Score: {r2:.3f}")